Print ISSN 2277-5412 Online ISSN 2322-0430 Indian Journal of Economics and Development

Society of Economics and Development www.soed.in Indian Journal of Economics and Development Volume 11 (4) 2015

©Society of Economics and Development

Printed and Published by Dr. Parminder Kaur on the behalf of the Society of Economics and Development

Email: [email protected] Website: www.soed.in Journal is available on www.indianjournals.com

Printed at PRINTVIZION 1766/1, Street No. 2 Maharaj Nagar Ludhiana-141004 Phone: 0161-2442233 Email: [email protected] Indian Journal of Economics and Development (Journal of the Society of Economics and Development)

Volume 11 October-December, 2015 No. 4 Contents

Research Articles Demand and supply analysis of livestock products in Andaman and Nicobar Islands 801 Ajmer Singh Optimization of farming systems on tribal farms in Uttarakhand 815 Shalini Raghav and Sanjay Kumar Srivastava Marketing of potato in Jalandhar district of Punjab 823 Amritpal Kaur and M.S. Sidhu Sugarcane production scenario in India with particular reference to Punjab 833 A.K. Brar and P. Kataria Trend analysis in market arrivals and prices of moth bean in Rajasthan 843 Subhita Kumawat and I.P. Singh Spatial price transmission in groundnut markets of Rajasthan 851 Richard Kwasi Bannor and Madhu Sharma Researching the relationship between financial and real sectors in India 861 Bhanu Pratap Singh and Alok Kumar Mishra An economic analysis of soybean cultivation in Hoshangabad district of Madhya Pradesh 869 Punit Kumar Agarwal and O.P. Singh Sustainable development through farming system approach: A study of tribal region in Central 877 Gujarat Mahammadhusen K., S. Jadav and V. B. Darji Economic analysis of milk production in Parbhani district of Marathwada region of 887 Maharashtra-A study of small scale farms Ravi Shrey and S. H. Kamble Income, saving, investment, and consumption pattern of farm households in Karnal district of 895 Haryana Jatinder K. Bhatia, Dalip Bishnoi, R.K. Khatkar, J.C. Karwasra and V.K. Singh Impact analysis of joint forest management programme on rural household income in 901 Uttarakhand Bishwa Bhaskar Choudhary and S.K. Srivastava Impact of MNREGA on household income employment generation, labour scarcity and 907 migration: A study in Dahod district of Gujarat Macwan J.D and Zala Y.C. Pairs trading in financial stock futures: An empirical investigation in Indian stock markets 915 Navdeep Aggarwal and Mohit Gupta Prospects of agritourism in Bikaner district of Rajasthan 923 Aditi Mathur, Surjeet Singh Dhaka and Urmila Research Notes Economics of vegetable production in Manipur 933 L. Priscilla and S.P. Singh Contract farming -An efficient marketing method of Ailanthus excelsa 939 A.Rohini, S. Selvanayaki and M. Padma Selvi Growth and instability of wheat production in Rajasthan 945 Meera and Hemant Sharma Use of e-health information: A case study 951 Dhiraj Kumar and Sonia Bansal Effect of contract farming on production and price of barley in the state of Rajasthan 961 Sita Ram and R.C. Kumawat Is MNREGA affecting availability, wages and cost of labour in Indian agriculture? Discerning 967 quantitative evidences Pushpa, Punit Kumar, Agarwal, Bulbul G. Nagrale and B.S. Chandel Abstracts of Dissertations/Thesis 975 Contents: Volume 11 (1, 2 and 3): 2015 979 List of Referees: Volume 11, 2015 985 Declaration Form IV (See Rule 8) 986 Society of Economics and Development Objectives 1. To promote awareness on the issues relating to economic development. 2. To promote better social and ethical values to promote development. 3. To promote economic prosperity and serve as a tool to create the consciousness for development. 4. To conduct research and publish reports on economic issues. 5. To organize seminars, symposia, workshops to discuss the economic problems. 6. To offer consultancy, liaison and services as a facilitator. Executive Committee Founder President Dr. S.S. Chhina, Former Dean, Faculty of Agriculture, Khasla College, Amritsar

President Dr. M.S. Toor, Professor of Economics, PAU, Ludhiana

Vice Presidents Dr. D.K. Grover, Director, AERC, PAU, Ludhiana Dr. A.K. Chauhan, Principal Scientist (Dairy Economics), NDRI, Karnal Dr. Simran K. Sidhu, Professor of Sociology, PAU, Ludhiana Dr. Pratibha Goyal, Professor of Business Management, PAU, Ludhiana Dr. Narinder Pal Singh, District Extension Specialist (FM), FASS (PAU), Amritsar

General Secretary Dr. Parminder Kaur, Professor of Economics, PAU, Ludhiana

Finance Secretary Dr. Mini Goyal, Professor of Economics, PAU, Ludhiana

Joint Secretary Mr. Taptej Singh, Research Fellow, Technology Marketing and IPR Cell, PAU, Ludhiana

Members Dr. Gian Singh, Professor of Economics, Punjabi University, Patiala Dr. Deepak Shah, Professor, Gokhale Institute of Politics and Economics, Deccan Gymkhana, Pune Dr. S.S. Burak, Professor, Maharana Pratap University of Agriculture and Technology, Udaipur Dr. Ranjit Kumar, Professor, International Crops Research Institute for Semi-Arid Tropics, Hyderabad Dr. Varinder Kumar, Professor, CSK HP Krishi Vishvavidyalaya, Palampur Dr. Prabhjot Kaur, Profeeosr of Extension Education, PAU, Ludhiana Dr. Seema Sharma, Professor, PAU, Ludhiana Dr. J.M. Singh, Senior Agricultural Economist, PAU, Ludhiana Dr. M. Javed, Associate Professor of Statistics, PAU, Ludhiana Dr. Sukhmani Virk, Assistant Professor, PAU, Ludhiana Dr. Arjinder Kaur, Professor of Economics, PAU, Ludhiana Dr. Jatinder Sachdeva, Assistant Economist, PAU, Ludhiana Dr. H.S. Kingra, Farm Economist, PAU, Ludhiana Ms. Amanpreet Kaur, Research Scholar, PAU, Ludhiana Ms. Sadika Beri, Research Scholar, PAU, Ludhiana Subscription Rates Particular Academics Students Institutional Annual Life Retired Annual Life Annual Indian (`) 600.00 4000.00 300.00 300.00 2000.00 2000.00 Developed Countries ($) 25.00 250.00 - - - 200.00 Developing Countries ($) 10.00 100.00 - - - 100.00 Membership should be paid by demand draft drawn in favour of Society of Economics and Development payable at Ludhiana and be sent to the General Secretary, Society of Economics and Development, Department of Economics and Sociology, Punjab Agricultural University, Ludhiana-141004 (Punjab). Alternately, the membership fee can be deposited in Saving Bank Account No. 29380100009412 (IFSC: BARB0PAULUD), Bank of Baroda, Punjab Agricultural University, Ludhiana. Indian J Econ Dev DOI: 10.5958/2322-0430.2015.00088.8 Volume 11 No. 4 (2015): 801-814 Research Article

DEMAND AND SUPPLY ANALYSIS OF LIVESTOCK PRODUCTS IN ANDAMAN AND NICOBAR ISLANDS

Ajmer Singh*

ABSTRACT

Livestock sector, sustains the growth of regional economy. Adequate policy measures need to be established for appreciable growth in productivity and sustainability in the regional and farm level productions and in the processing sectors. Migration of people, tourism development, increase in population, globalisation, urbanisation, increase in the economic status of people coupled with the adequate measures from governments, the demand for livestock products is on the steady increase. The effect of price and income on the demand of livestock products and other parameters on their supply is analysed; the predictions are made for the next two decades and the gaps; thereof are indicated.

Key words: A&N islands, chicken, demand, egg, milk, mutton, supply JEL Classification: C21, C51, C82, Q11, Q12, R15

INTRODUCTION refugees, migrants from states of Tamilnadu, The Andaman and Nicobar (A&N) Islands, Kerala, Andhra Pradesh, Bihar, Chhatisgarh, strategically located between 92-940 E etc. defence, and government staff members longitude and 6-140 N latitude in Bay of Bengal, and the tribes of the land. are facing problems of unemployment, non Having limited scope of agriculture in the availability of land for agriculture and industrial islands, livestock and poultry gained development, rising population, tourists’ flow appreciable improvement and emerged as the etc. The total geographical area of A&N Islands major source of self-employment and is 8249 sq km and total population in these subsidiary income. Livestock sector helps in Islands is 3.80 lakhs (Census, 2011), which alleviating poverty and acts as contributor to increased at a compound growth rate of 2.36 savings and investments (Birthal et al., 2002). percent/annum during the last three decades. There is a paradigm shift in the food At present the population of A & N islands consumption pattern towards livestock is composed of settlers from mainland India products (Gandhi et al., 2010). This structural under various government schemes, families shift is taking place in the dietary pattern for of freedom fighters, Sri Lankan and Bangladeshi the last two decades. Due to aesthetic taste and budgetary allocation shift is taking place from the cereals based food to fruits, *Principal Scientist (Agri. Economics), Dairy Economics, Statistics and Management Division, vegetables and livestock products (Kumar et National Dairy Research Institute, Karnal-132001 al., 2011). (India) This shift would continue to intensify Email: [email protected] further with the increase in per capita income

801 (economic growth), globalisation, rapid was taken from North & middle Andaman (25), urbanisation, taste and preferences (Millar et Neil Island (15), Havelock Island (10), South al., 2008). Andaman (50) and Port Blair city (20) by simple With the rise in per capita income, demand random method. for livestock products is expected to rise faster. An equal number of non-producers were It has been observed that during the recent sampled from these different places. In past, there is a decline in the cereal addition to that, 57 tourists were interviewed consumption while the consumption of fruits, to analyse their spending profile. The data vegetables and animal products are on the were taken twice a year to record variables for increase and demand for the livestock products the dry as well as rainy season. An utmost will double by the year 2020 (Parthasarathy et care was taken to include both producers and al., 2004). non-producers from rural as well as urban areas Considering the demand and supply of in the same proportions and also to include food commodities in these islands and farmers from different size groups. especially keeping in view the changing food Parameters habits and tourists flow, the study of potential The information on parameters viz. per of livestock sector is essential for the capita consumption (monthly) of food and development of these islands. non-food articles and expenditure (`), prices, In order to formulate an effective policy sale of livestock products (agencies, quantities, for the growth and development of livestock prices ), livestock inventory, families’ profile, sector, it is crucial to know the demand and income status and production, consumption, supply situation of various livestock products. prices and purchase of livestock products What would be the growth rates of demand during different seasons etc were collected and supply and difference between the two by personal interview method and secondary during the next two decades? What factors sources were utilized for income, milch animals’ are relevant to demand and supply and what and human population growth rates etc. is the impact at these factors on future growth Demand Analysis of production and consumption? In this The demand has been a function of income backdrop, the specific objectives are: (expenditure), own prices and prices of other (i) to work out production and consumption products comprising food basket of consumers levels of various livestock products in (Dastagiri, 2004). Andaman group of islands, Demand system comprising consumption (ii) to study effects of price, income and other of selected livestock products and other foods variables on demand and supply, and has been estimated. The actual amount of (iii)to make projections for demand and consumption worked out for the current year supply of selected livestock products was taken as level of demand of a particular towards 2021 and 2031. livestock product. Double log model was The knowledge emanating from demand employed to estimate the complete system of and supply analysis would help not only food demand equations as: management but also proper resource N allocation in livestock sector. Log Yi a i bilogM cijlogPj  dilogD THE DATA AND METHODOLOGY j1 The study is based on the data from cross- i, j =1, 2, ………, N sectional survey of livestock farmers and non- where th producers of Andaman group of islands. Yi = quantity of consumption of the i Sampling commodity,

A sample size of 120 respondents (farmer) Pjs = commodities prices,

802 M = total income and producers to changes in the livestock food

Cij = price coefficients, prices and feed prices. D = Dummy variable (to capture the Lagged values of the variables are regional variation). important explanatory variables in most bi and di are the coefficients for the structural economic relationships because economic and dummy variables, respectively. The behaviour in any one period is, to a great estimated coefficients gave elasticities in the extent, determined by the past experience and double log specifications. the past pattern in behaviour. A sustained economic growth and steady The Model increase of at least six percent per annum in Yt = boxt + b1xt-1 + b2xt-2 + b3f + mt households’ income is expected and has been where used in the estimation function. This is Yt = Quantity of production of milk, expected to substantially boost the demand mutton, beef, chicken, egg in current for livestock products. period (t)

The projections for 2021 and 2031 were Xt = Price of current period (t) made using the simple growth rate model based xt-1 = One lag price on estimated expenditure elasticities, growth xt-2 = Two lag price in population and per capita income growth f = Feed prices of milk, mutton, pork, rates. The demand projections are limited to chicken, egg the households’ consumption only. m = Random variable The demand for direct consumption of each of these products is projected as per 1 Elasticity = ep = bi pi model: i Y Demand forecasting model Supply Projection t The projection of production as well as Dt = d0 * Nt (1 + y * e) where domestic consumption for future requires knowledge of the future values of the Dt = Household demand for a commodity in year t, exogenous variables (Kumar et al., 2011). The projection for local production do = Per capita demand of the commodity in the base year, (supply) of these products towards 2021 and y = Growth in per capita income (6%) 2031 were made using simple growth rate model e = Income elasticity of demand for the based on elasticities, livestock population commodity and growth rates, nominal price growth rates and productivity growth rates where as current Nt = Projected population in year t. Supply analysis level of supply (2011) was estimated based on The quantity produced of a livestock food the sample used herein. is hypothesized to be a function of its own The model t price, prices of inputs used in the production St = So  Nt (1+Pg  Ps) process, state of technology and governmental where policy variables. It is observed that there is a St = Production of commodity in the year t, lagged response to changes in prices which is So = Per animal production of the commodity assumed to be the result of biological and in the base year, technical factors. (Dastagiri, 2004 and Philip Pg = Growth in nominal prices (8%) and Rutger, 2007) Ps = Price elasticity of supply for the In this study, linear regression model was commodity and considered to determine the current as well as Nt = Projected livestock supply population lagged response of the livestock food in the year t.

803 RESULTS AND DISCUSSION the occurrence of tsunami scared the visiting Population Dynamics of the Andaman and tourists and it was only 32,381 in 2005, but Nicobar Islands later on, the trend was appreciable and the In 1981 A & N Islands had a population of visiting population went up to 195,396 in 2010. 1,88,741 which increased by 48.7 percent Apart from the domestic tourists (from during the next decade and became 2,80,661 in mainland India), the foreign visits have been 1991. In the next decade (1991 to 2001), the from Israel (43 percent), British (15 percent), growth rate decreased to 26.9 percent and Americans (07 percent), and Italians (06 during 2001-2011, it decreased further to 6.68 percent). The tourists being non-vegetarian percent which is unbelievably low in indicated that the food consumptions would comparison with the past trend and all India mainly be relied upon the livestock products growth rate. This has not been observed (Reddy, 2007). So the predicted demand of the anywhere else (Dhigra, 2005). The population livestock products would also depend on the control measures, earthquakes (Tsunami, 2004) estimated tourists’ population. Accordingly, and low fertile lands have collectively annual compound growth rates were calculated contributed to the slowing down of the using semi log linear function and it is found population growth rates (Sieh, 2005, Reddy, that the estimated arrival of tourists in the year 2007 and Rajendran et al., 2003). The annual 2021 can be 420,678 and this figure may exactly compound growth rate for the population of double in every decade in future (Table 2). these islands has been at 2.36 percent. Based From the projections, it is evident that the on that, it is estimated that population of these visiting tourists population and the local islands will increase to 479758 in 2021 and population will be same in magnitude in the 605794 in 2031 (Table 1). Table 2: Estimation of tourist arrivals to Table 1: Estimation of local population of Andaman and Nicobar Islands Andaman and Nicobar Islands Year Number Year Population 1991 34490 1981 188741 1992 38252 1991 280661 1993 36771 2001 356152 1994 54535 2011 379944 1995 68339 2021E 479758 1996 73754 2031E 605794 1997 78082 Growth rate (%) 2.36 1998 79647 E: Estimate 1999 83483 2000 86066 2001 91115 Ultimately, understanding the nature of 2002 95336 population dynamics and thus the nature of 2003 98180 current population dynamics and the expected 2004 109582 future trend in the population would provide 2005 32389 2006 127625 insight to formulate and design the appropriate 2007 146990 schemes for livestock sector. 2008 136951 Tourists’ flow in Andaman & Nicobar Islands 2009 155737 The trends in tourist arrival and their 2010 195396 seasonality is a complex phenomenon to 2011E 209504 E understand (Butler, 2001). In Andaman and 2021 420678 2031E 844709 Nicobar, there had been tenfold increases from Growth rate (%) 7.22 1980 till 2004 in the tourists arrival. However, E: Estimate

804 year 2021, while in the year 2031, the visiting observed that out of the cash expenditure of population will nearly be the double than that `10,464 per month, the spending on livestock of the local population. The incoming tourists and high value products constitutes 47 will have enough economic influence for all percent. The food production portfolio must sorts of foods including livestock products. be synchronized with changing pattern/pace To ensure food and nutritional security to the of consumption. The priorities of investment locals, two options are available; to produce decision in food production must be drawn food commodities locally and/ or to import food keeping in view the expected scenarios of food from the neighbouring states. Besides, the demand especially the livestock products. tourism industry has to meet out its needs of Price Behaviour of Livestock Products livestock products. From 1990 onwards with the liberalisation Socio-economic Profile of the Respondents of Indian economy, there is increased private Income and Expenditure Pattern participation (Rosegrant et al., 2009). The farmers of Andaman were earning Consequently, Indian agricultural commodities `16674 per month in total, out of which have played a pivotal role in global market in maximum share was received from service recent years. The seasonality played a sector (52.98 percent) followed by private jobs significant role in production as well as including self employment (30.49 percent) and consumption. Meat is generally consumed pensions (7.57 percent). Agriculture was more during winter than summer season to keep contributing only 5.35 percent to the families’ the body more energetic (Gandhi and Mani, monthly income. Looking to overall picture of 1995). In these Islands, the price behaviour of A&N islands, the income status of farm livestock products showed no significant families of Andaman was weaker than other difference in price between seasons except sections (Table 3). mutton and chicken. The households’ consumption requirements and tourists arrival Table 3: Monthly expenditure pattern in during dry season (from November to April) Andaman and Nicobar Islands slightly influence the prices of mutton (0.67 Particulars Percent percent) and chicken ( 3.5 percent ). The yearly Non-food items 30.16 movement in case of prices of milk and mutton Livestock products 22.76 Hi-value foods 23.29 was 20-22 percent. In case of chicken, it was Other food items 23.79 26-30 percent. Fish prices fluctuate largely at 23-33 percent but maximum fluctuation was in Farming was being supported by the case of pork prices, which were at 20-90 service sector as farming was adopted as percent. In case of egg prices, no clear trend supplementary activity by the government was observed. The production as well as prices employees (Datt and Ravallion, 1998). It is of livestock products rise during dry season.

Table 4: Prevailing prices of livestock products over season (`kg-1)

Product Py Py-1 Py-2 (Current price) (One year lag price) (Two year lag price) Rainy Dry Rainy Dry Rainy Dry Milk 33 33 27 27 23 23 Mutton 317 319 262 262 217 217 Chicken 151 153 119 124 92 96 Eggs (`piece-1) 5.5 5.5 5.9 5.9 4.6 4.6 Fish 74 74 60 60 45 45 Pork 250 240 150 150 125 125

805 This may be due to the influence of tourists’ at home (Kumar et al., 2009). The meat is demand for the livestock products. The consumed more because of taste and seasonal variationin the prices of the livestock availability (Ali, 2007). products are presented in Table 4. Overall, famers in Andaman were Production and Consumption of Livestock consuming 74 g/cap/d of milk against the Products required amount of 170 g/cap/d. The results On an average, every household in revealed that the urban consumers were Andaman & Nicobar Islands possesses one consuming more (879 g/cap/d) than the rural to three cattle/buffalo and/or sheep/goat and/ counterparts (61g/cap/d) implying that the or pig in their backyard (Chand et al., 2013). education and income level of the people Milk production was recorded at 33.5 litre per makes them consume adequate levels of milk month in a given farm family. Out of this, they (Birthal et al., 2002). The scenario of meat was consumed only 10.5 litre and the rest was sold opposite to that of milk consumption. The out locally. They consumed lower than the consumption of meat is nearly the twice in requirements as per the ICMR guidelines magnitude to that of requirement. Meat is (ICMR, 1999). In the case of meat, they consumed more in rural areas (54 g/cap/d consume twice the magnitude of their against 28 g/cap/ d in urban areas) implying requirements. The production and that they are not aware of obesity, heart consumption of fish was lower than the ailments and so on which the urban population recommended level (ICMR guidelines) while is aware of by virtue of their appropriate the consumption of egg was slightly above education and experience (Thornton, 2010). production and slightly lesser than the More importantly, availability in the rural areas requirements (Table 5). was the major reason for higher consumption From the ancient times, the milk has been of meat as the farmers in rural areas keep meat procured locally in all parts of India and the animals in their backyards; after self same trend is observed in Andaman Islands. consumption, whatever is left out, is sold off The consumers living in rural areas and in to urban centres. Fish and eggs were also urban centres encourage the producers to consumed less than the dietary requirement. produce milk for sale and earn money to From Table 5, it is evident that the consumption support their livelihoods. Such a trend restricts of livestock products is more among the non- the consumption of adequate amounts of milk producers than producers. This has serious

Table 5: Consumption and requirement of livestock products (g/cap/d) Particulars Milk Meat Fish Eggs (No.) Requirement 170 21 82 0.49 Consumption Rural Producers 102 42 87 0.46 Non-producers 5 65 89 0.28 All 61 54 41 0.43 Urban Producers 52 48 41 0.49 Non-producers 250 75 67 1.25 All 87 28 80 0.71 Over all Producers 78 45 65 0.47 Non-producers 100 69 72 0.67 All 74 41 60 0.69

806 implications as far as development of livestock Chicken and egg sales were about 35 and sector and nutritional security of farming 37 percent respectively while the fish industry community is concerned. Similar observations needs a kick-off for business yet. (Srivastava have been expressed by Ali (2007), Birthal et and Ambast, 2009). al. (2002), Chand et al. (2013), and Gandhi and Demand Analysis Zhou (2010). Income (expenditure) elasticities Marketed Surplus of Livestock Products The values obtained for expenditure Milk is sold up to 86 percent of its (income) elasticities are presented in Table 7. production in Andaman group of islands. The All the livestock products were found to be proportion of mutton production has been income elastic. The results are commensurate found to be 96 percent going for sale. In case with the observed trend of income elasticities of chicken, it was 36 percent of production of livestock products in the mainland India (Table 6). The data regarding the sale of pork (Dastagiri, 2004). The significant elasticities from livestock could not be obtained in were observed in case of milk, mutton, chicken Andaman; there are many socio-cultural and other foods. The income elasticities of egg, aspects, constraints and religious impediments fish and pork were not significant. Egg and that impede the growth of the pork industry in fish are being taken as part of staple diet in India and particularly in Andaman (Gandhi and Andaman. So, less variability was observed in Mani, 1995). The proportion of sale of these their consumption which could be the reason products was higher among rural farmers than of elasticity to be non-significant. The pork their counterparts in urban areas except milk was consumed by a small number of where sell-off was higher in urban areas. respondents who were in high income group

Table 6: Production and Sale of livestock products in Andaman (kg/family/month) Particulatrs Production Sell off Marketed Surplus (%) Milk Rural 25.59 19.86 77.61 Urban 41.67 37.92 91.00 All 33.52 28.77 85.82 Mutton Rural 2.65 2.55 96.22 Urban 3.82 3.53 92.37 All 3.23 3.09 95.55 Chicken Rural 2.62 1.05 40.21 Urban 1.74 0.50 28.80 All 2.18 0.78 35.74 Fish Rural 2.65 0.54 20.41 Urban 0.44 - - All 1.56 0.27 17.54 Pork Rural 0.14 - - Urban - - - All 0.07 - - Eggs (no.) Rural 69.59 33.78 48.54 Urban 45.83 8.33 18.18 All 57.88 21.23 36.69

807 Table 7: Income elasticity at mean level of consumption of livestock products Commodity b-value t-value Rural Urban All Rural Urban All Milk 1.207** 1.002* 1.140** 2.57 2.09 2.50 Mutton 0.05NS 1.239** 0.140* 0.27 2.13 2.11 Chicken 1.793*** 1.098NS 1.201** 3.56 0.98 2.79 Eggs 0.253NS -0.561NS -0.357NS 0.32 -0.62 -0.32 Fish 0.217* 0.0762NS 0.128NS 2.00 0.18 0.36 Pork 0.670NS 0.315NS 0.437NS 0.40 0.62 0.72 Other foods 0.260** 0.128* 0.258** 2.54 2.01 2.58 Degrees of freedom 65 R2 0.71 ***, ** and * significant at 1, 5 and 10 percent level NS: Non-significant and very less variability was found in its more in rural areas than in urban areas. A perusal consumption by the families. The negative of income elasticities indicate that an increase coefficient for egg imply that an increase in in income of consumers in rural areas will lead income leads to decrease in eggs consumption. to more consumption of milk, chicken and other Obvious, as a result of rise in income, poor foods. In urban areas, the income growth will consumers tend to shift towards chicken and create more demand for milk, chicken, mutton other hi-value foods. In case of other products, and other foods. This has implications for the demand was income elastic but to a lesser setting up production and processing centres extent. The high income elasticity implies that of these products in different locations. consumption of all these products except eggs, Price elasticities increase more than the proportionate increase The estimates of own and cross price in the families’ income. elasticities are presented in Table 8 for rural The income elasticities for rural and urban situation. All own price elasticities were found groups were varying for all products except to be negative which is commensurate with eggs and fish. Elasticity for milk and chicken the findings of other studies and based on the holds importance in urban as well as in rural fact that livestock foods are normal foods areas. It implies that these commodities are having nutritional value (Millar and taken up sufficiently in urban areas. Similar Photakoun, 2008). In rural areas, the demand view has been expressed by workers in other for milk, mutton and pork was found to be parts of the world (Vandamme et al., 2010, highly elastic to their prices. Sansoucy, 1995, Rao et al., 2004 and Millar High price elasticities also imply high and Photakoun, 2008). The other foods are instability in consumption. The cross price taken up more with the increase in their elasticities supported the view that the most incomes in rural as well as in urban areas. The livestock products are substitutes in nature, coefficient for other food is higher for rural while own price elasticities suggest that areas. The reason may be that in rural areas, consumers are highly price responsive the residents are consuming only the basic (Dastagiri, 2004). Most of the cross price foods and they are not able to stretch their elasticities were positive except a few budgets on luxury food items. The low level of exceptions. Most significant of them were in income in rural areas does not allow people to case of milk-mutton, milk-chicken, mutton- spend much on ‘other foods’ which include chicken, mutton-fish and pork-chicken. In case luxury foods and staple foods both. So their of egg-fish, the cross-price elasticity was tendency to expend more on other foods in negative which implies that an increase in price response to increase in income has been found of eggs will lead to decrease in consumption

808 Table 8: Own and cross price elasticities of livestock products Product Price Milk Muton Chicken Egg Fish Pork Rural Milk -1.6260*** 3.8967** 1.0590* 0.5634NS 0.3442NS 0.279NS (-3.0268) (2.7265 ) (2.0599) (1.9191) (1.2933) (0.956) Mutton 1.6190*** -3.8967** 1.0590* 0.5634NS 0.3442NS 0.652NS (3.0178) (2.7265) (2.0599) (1.9191) (1.2933) (0.746) Chicken 0.6346NS 10.9060*** -1.5284NS 0.6595NS -0.6083NS 1.563NS (0.6305) (4.0728) (-1.5867) (1.1989) (-.2200) (1.265) Egg 1.8074NS 2.7839NS 1.9310NS -0.1182NS -1.1553NS 0.652NS (-1.0263) (0.5942) (1.1459) (-0.1228) (-1.3243) (0.563) Fish -1.7304NS 3.3047* 0.5104NS -1.6505NS -0.2829NS 0.637NS (-0.9564) (2.1941) (0.9421) (-5.3346) (-1.0088) (0.369) Pork 1.030NS 1.30NS 1.69* 0.692NS 1.367NS -1.350* (0.457) (0.654) (2.561) (0.563) (0.341) (-2042) Degrees of freedom 65 R2 0.71 Urban Milk -1.573*** 1.279* 0.675NS -0.139NS 0.137NS 0.191NS (-2.715) (1.623) (1.601) (-1.025) (0.954) (0.965) Mutton 2.345* -3.109** 0.731** -0.431NS- 0.291NS 0.327NS (1.576) (-2.564) (1.570) (0.796) (1.568 (0.741) Chicken 0.372NS 4.796*** -0.786NS -2.71NS -0.713NS 0.901NS (0.596) (3.906) (-0.961) (1.265) (-1.489) (1.352) Egg 1.650NS 1.431** 0.623NS -1.131* 0.931NS 0.517NS (1.013) (1.803) (0.784) (-1.409) (-1.256) (0.512) Fish -0.721NS 1.210NS 0.431NS 1.134NS -0.319NS 1.237NS (-0.648) (1.401) (0.714) (-1.35) (-1.024) (0.365) Pork 0.932NS 2.310* 0.231NS 0.213NS 0.320NS -0.701* (0.357) (1.332) (0.684) (0.357) (0.571) (-1.537) Degrees of freedom 48 R2 0.62 ***, ** and * indicates 1, 5 and 10 percent level of significance NS: Non-significant Figures in parentheses are t-values of fish. It may happen because both the eggs negative in urban areas as well. In urban areas, and fish form the same product group for low the demand for milk, mutton, egg, and pork income consumers. was found to be significantly elastic to their Thus both income and price changes prices. Most of the cross price elasticities were affected the demand of all these livestock positive except a few. Most significant of them products. The study conducted based on were in case of milk-mutton, mutton-chicken, consumers’ expenditure data from NSSO 50th egg-mutton and pork-mutton. The positive round by NCAP found that all the own price coefficient of elasticity between egg and fish elasticities were negative except for beef. The could not be observed in urban areas of milk, eggs and beef in rural areas and milk and Andaman which was the case in rural areas. eggs in urban areas showed the demand to be Thus both income and price changes affected highly price elastic. the demand of all these livestock products in For urban situation, the estimates of own rural as well as urban areas of Andaman. Similar and cross price elasticities are presented in views have also been expressed by (Dastagiri, Table 8. All own price elasticities were found 2004, Vandamme et al., 2010, Thornton, 2010

809 and Kumar et al., 2009) than all India average (GOI, 2013).Thus the Demand Projection demand analysis states that the demand on The demand for milk was estimated to be the livestock product will increase gradually 40289 tons/yr which will increase at about two except eggs. percent per annum. Same trend was observed Estimates of Supply Response Model for mutton and fish for which present level of The estimated supply response function consumption has been estimated at 1724 tons/ incorporated the price lags of 1-2 years. yr and 9208 tons/yr respectively. In case of Elasticities were worked out based on the

chicken, the present level of demand of 4967 coefficients (bis) and prices. The value of the tons/year would grow even faster at seven R2 was ranging from 0.58 to 0.74 in the supply percent in a year. The pork demand of 211 tons/ response function of milk, mutton, chicken and yr is expected to grow at 301 tons in 2011 at eggs. This suggests that own price and feed the rate of almost four percent per year. Egg prices had played a significant role in demand showed a declining trend. The present enhancing the production of livestock demand of 92 million eggs would fall to 82 products in Andaman Islands. million in the year 2021 and to 72 million in The elasticities of linear regression model 2031 (Table 9). are presented in Table 10. The coefficient of The reason for this can be attributed to price in current year was positive in case of all negative income elasticity of egg demand. The the products. For mutton, it was highly phenomenon of decreasing demand of eggs is significant statistically. The lagged price of one peculiar to A & N islands because, already, the year was positive only in case of milk where as egg consumption in these islands is greater two lag price was positive in case of chicken and eggs otherwise it was negative. Feed price Table 9: Demand projections of livestock was positive in case of milk. For rest of the products products, feed price coefficient was negative. (tonnes year-1) Out of 12 price coefficients, 7 were positive

Commodity D0 2011 Dt 2021 Dt 2031 and 5 were negative. The price elasticity of Milk 40289 48186 57630 these products stimulates their production Mutton 1724 2062 2466 (Dastagiri, 2004). Some variations observed Chicken 4967 8739 15374 Eggs (million No.) 92 82 72 might have been due to very small size of local Fish 9208 10937 12990 market for these products in the islands and Pork 211 301 429 more influence of tourists and floating

Table 10: Estimates of supply response model (Linear Regression model) Variable Mutton Chicken Egg Milk Constant -6489.92NS -57093.50NS -2310593.00NS -4514.92NS (-4.522) (-4.65) (-11.54) (-17.64) *** NS *** NS Price t0 1.21 1.75 2.36 0.43 (2.42) (1.13) (3.00) (0.65) NS NS * NS Price t-1 -0.95 -0.50 -3.31 0.35 (-0.66) (0.61) (-1.39) (0.20) NS NS NS NS Price t-2 -0.24 1.73 1.54 -0.48 (-0.21) (0.56) (0.85) (-0.36) Feed price -0.07NS -0.50NS -0.53*** 0.04NS (-0.61) (-0.54) (-2.29) (1.28) R2 0.58NS 0.74NS 0.69NS 0.66NS ***, ** and * indicates 1, 5 and 10 % level of significance NS: Non-significant Figures in parentheses are t-values

810 population in effecting the demand- supply between the observed figures of current equation of these products. supply estimates and government records The lagged response of price change was requires more in-depth analysis of supply of generally negative as expected except in case these products and this also speaks for re- of milk where second lag was negative. It examination of records and estimation indicates that market forces play strongly as procedures of the A&N administration. far as milk production is concerned (Dastagiri, Demand Supply Gap 2004). Prices of feed used for the production Based on the projected production and of these products were impacting the supply consumption trends for livestock products of these products negatively as is clear from asestimated in the previous section, the gap coefficient of feed prices.It was more between production and consumption levels prominent in case of eggs. The R2 value livestock products for 2011, 2021 and 2031 are suggests that own prices and feed prices play shown in Table 12. The baseline scenario in a significant role in enhancing the production the year 2011 revealed that the actual of livestock products in Andaman. So, we can production of milk falls short of demand and synthesize that current prices affect supply there is a gap of 22134 tonnes. The trend is positively where as lagged prices affect the likely to continue in future and the gap will be supply negatively. Also, feed prices play a 32158 tonnes in the year 2031. Same is the case significant role in enhancing the production with mutton where the present gap of 804 of livestock products in Andaman. tonnes, which will go up to 1030 tonnes in Supply Projection 20121 and up to 1307 tons in 2031. The futuristic supply projections for 2021 and 2031 with linear price model are presented Table 12: Gap between demand and supply in Table 11. The projections show that present of Livestock products supply level of milk (18155 tonnes per year) (tonnes year-1) would go up to 21314 tonnes in 2021 and 25472 Commodity G0 2011 Gt 2021 Gt 2031 tonnes in 2031 at nearly two percent per year. Milk -22134 -26872 -32158 Even faster growth was expected in the case Mutton -804 -1030 -1307 of chicken and eggs. Chicken supply would Chicken -441 33 2627 almost double in next decade which is 4526 Eggs (Million No.) 31 114 250 tons at current level at around 9.4 percent per year where as egg supply will increase at In the case of chicken, there is a gap of 441 around 6.0 percent in a year. tonnes between supply and demand in the Local production of mutton will increase current year but supply takes over the demand at 1.2 percent in a year which will fall short in 2021 and there will be surplus production of than its demand growth would grow at almost chicken at the tune of 2627 tonnes in 2031. 1.9 percent. These figures concur with the The scenario in case of chicken and eggs is government records (GOI 2013). Any mismatch typical to the A & N islands where the high price elasticity is effecting the supply to a larger extent and consumption level is already Table 11: Supply projection of Livestock more than the requirement. So, the situation of products chicken supply taking over its demand in 2021 -1 (tonnes year ) has significant policy implications. The Commodity S 2011 S 2021 S 2031 0 t t scenario calls for processing facilities for Milk 18155 21314 25472 Mutton 920 1032 1159 chicken and its exports promotion. Chicken 4526 8772 18001 The islands administration has to dovetail Eggs (million no) 123 196 322 its planning tool to create state of the art

811 chicken processing, storage, transportation CONCLUSIONS AND POLICY and marketing infrastructure that would IMPLICATIONS provide gainful employment to the youth and The results provide the critical source of income generation. Similar perspectives on various parameters that are arrangements are to be made to export eggs operative in the market of livestock products from the islands. Eggs are produced more than in the Andaman and Nicobar Islands of India. their actual consumption. Same trend has been From the analysis, it was found that the tourist anticipated in future as well. The current population would grow faster than local surplus of 31 million eggs will reach up to 114 population and it would be almost same in million in 2021 and further up to 250 million in magnitude as that of local population in the 2031. The results conclude that the shift among year 2021, while in the year 2031, the visiting livestock products is inevitable as per trend in population would be nearly double than that Andamans. of the local population. These results indicate that in 2021, Rural population were found to consume Andaman and Nicobar will be deficit in case of lesser quantities of meat and egg than the milk and mutton but it would become surplus urban, especially in the livestock producing in chicken and eggs as the projected growth population and the same trend is observed for rates of local supply are more than the demand. non-producers also. The demand for milk, The expected production growth rates for chicken and eggs is more elastic in the rural these products which are taken as proxy for households than urban households except for supply and the same for their consumption mutton which has higher expenditure elasticity (proxy for demand) are given in Table 13. more in urban areas. It implies that increase in per capita income of rural population would lead to acceleration in demand for livestock Table 13: Growth rates of demand and products. supply of livestock products Product Demand Supply Consumer expenditure on livestock Milk 1.96 1.74 products is on the rise and households’ Mutton 1.92 1.22 monthly expenditure on these products along Chicken 7.59 9.38 with other hi-value foods was around 47 per Eggs 1.09 5.93 cent. Hardly any market existed for beef and buffalo meat. Further, the expenditure Limitations of the study elasticities of livestock products were higher The demand-supply projections as than thoseof other foods. This implies that attempted in this study are to be taken as there is a shift in the consumption behavior indicative trends at Andaman level only. The towards livestock products. The food data for such an exercise was collected production portfolio at farmers’ field level need primarily from the consumers and producers to be kept in alignment with emergent of livestock products during a span of one consumption pattern. This would be possible year in two seasons. This study has not taken only by diversification in agriculture. in to account the habits, tastes and preferences Thus, a favourable pricing policy in the of consumers because it involves long time. livestock sector is warranted that would help Trend based elasticities are more important to farmers to increase investments in the livestock compute then cross section data. The trend of sector is warranted. On the other hand, feed consumption, tastes and preferences and and fodder supply has to be increased. behaviour is not captured. As more and more The demand-supply gap have indicated data is available for such a study, further fine- that in 2021, the demand of chicken would be tuning of future projection is possible. almost double in 2021 and will be four times in

812 2031, the supply of which will increase faster Food and Agriculture Organisation of United and will surpass the demand in 2021. It implies Nations, 2003b. FAO statistical databases. that in Andaman, planning emphasis should www.apps.fao.org. shift from poultry to dairying and goatery. G.O.I. 2006. Livestock ownership across operational Land holding classes in India, 2002- Still, the livestock sector is not yet in 2003. NSS 59 th Round, Report No. 493 (59/ developed stage in these islands, the 18.1/1), Government of India, New Delhi. improvement and advancement may be seen Gandhi, V.P. and Mani, G. 1995. Are livestock only once the investment is made from products rising in importance? A study of the government and private sectors particularly growth and behaviour of their consumption in in processing and value-addition. India. Indian Journal of Agricultural Economics. This could be possible only when policy 50 (3): 283-293. measures are translated in to action at ground Gandhi, V.P. and Zhou, Z.Y. 2010. Rising demand level for improving access to institutional for livestock products in India: Nature, patterns and implications. Australasian Agribusiness credit and pumping capital for developing Review. 18: 103-135. processing and storage facilities in addition Government of India. 2013. Basic animal to equipping producers with improved Husbandry statistics. Department of Animal technologies. Husbandry, Dairying and Fisheries, Ministry REFERENCES of Agriculture, New Delhi, India. Ali, J., 2007. Livestock sector development and ICMR. 1999. Dietary guidelines for Indians-A implications for rural poverty alleviation in manual. National Institute of Nutrition. India. Research for Rural Development. 19 (2): Hyderabad, India. 1-15. Kumar, B.G., Sendhil, R., Venkatesh, P., Raja, R., Bansil, P.S. 1999. Demand for foodgrains by 2020 Jayakumar, V., and Jeyakumar, S. 2009. Socio- AD. Observer Research Foundation, New Delhi. economic impact assessment of livelihood Birthal, P.S., Joshi, P.K., and Kumar, A. 2002. security in agriculture, animal husbandry and Assessment of research priorities for livestock aquaculture on the tsunami-hit lands of sector in India. Policy Paper #15, National Andaman. Agricultural Economics Research Centre for Agricultural Economics and Policy Review. 22: 483-494. Research (ICAR), New Delhi. Kumar, P., Kumar, A., Parappurathu, S., and Raju, Butler, R.W. 2001. Seasonality in tourism: Issues S.S. 2011. Estimation of demand elasticity for and implications. In: Tom, B. and Svend, L. good commodities in India. Agricultural (eds.) Seasonality in tourism, Elsevier, Oxford: Economics Research Review. 24: 1-14. 5-23. Mehta, R. and Nambiar, R.G. 2007. The poultry C.A.R.I. 2013. Development of island fisheries: industry in India. Paper delivered at the FAO Policy Paper. Central Agricultural Research Conference on Poultry in the 21st Century. 5-7 Institute, Port Blair, A&N Islands, India. November, Bangkok. Chand, S., Kumar, N., and Roy, D.S. 2013. Millar, J. and Photakoun, V. 2008. Livestock Livestock production challenges and strategies development and poverty alleviation: for tropical agro ecosystem, Andaman and revolution or evolution for upland livelihoods Nicobar Islands, India. Basic Research Journal in Lao PDR? International Journal of of Agricultural Science and Review. 2: 195-201. Agriculture Sustainability. 6: 89-102. Dastagiri, M.B., 2004. Demand and supply Philip, H.F. and Rutger, V.O. 2007. On the projections for livestock products in India. econometrics of the Koyck Model. Econometric Policy paper # 21, NCAP, New Delhi, India. Institute, Erasmus, Rotterdam, Netherlands. Datt, G. and Ravallion, M. 1998. Farm Rajendran, C.P., Earnest, A., Rajendran, K., Das, productivity and rural poverty in India. Journal R.D., and Sreekumari, K. 2003. The 13 of Development Studies. 34 (4): 62-85. September 2002, North Andaman (Diglipur) Dhigra, K. 2005. The Andaman and Nicobar earthquake: An analysis in the context of Islands in the 20th Century: A Gazetteer. Oxford regional seismicity. Current Science. 84: 919- University Press. New Delhi. 924.

813 Rao, P.P., Birthal, P.S., Kar, D., Wickramaratne, Singh, S., Singh. D.R., Singh, L.B., Chand, S., and S.H.G., and Shrestha, H.R., 2004. Increasing Roy, S.D. 2013. Indigenous vegetables for food livestock productivity in mixed crop–livestock and nutritional security in Andaman and systems in South Asia. ICRISAT (International Nicobar Islands, India. International Journal Crops Research Institute for the Semi-Arid of Agriculture and Food Science Technology. 4: Tropics), Patancheru, India. 503-512. Reddy, S., 2007. Mega tourism in Andaman and Srivastava, R.C. and Ambast, S.K. 2009. Water Nicobar Islands: Some concerns. Journal of policy for Andaman and Nicobar Islands: A Human Ecology. 21: 231-239. scientific perspective. CARI, Port Blair: 18 Rosegrant, M.W., Fernandez, M., Sinha, A., Alder, Thornton, P.K., 2010. Livestock production: J., Ahammad, H., de Fraiture, C., Eickhout, B., recent trends, future prospects. Philosophical Fonseca, J., Huang, J., and Koyama, O. 2009. Transactions of the Royal Society B. 365: 2853- Looking into the future for agriculture and 2867. agricultural knowledge science and technology. Vandamme, M., D’Haese, M., Speelman, S., and In: McIntyre, B.D., Herren, H.R., Wakhungu, D’Haese, L. 2010. Livestock against risk and J., and Watson, R.T. (eds.). Agriculture at vulnerability: Multi-functionality of livestock crossroads. Island Press, Washington D.C., keeping in Burundi. In: Swanepoel, F.J.C., USA. Stroebel, A, and Moyo, S. (eds.) The role of Sekhasaria, P. 2008. Troubled Islands: Writings livestock in developing communities: on the indigenous people and the environment Enhancing multi functionality. CTA, of the Andaman and Nicobar Islands. Wageningen, Netherlands. Kalpavriksha. Mumbai. Sieh, K., 2005. Aceh-Andaman earthquake what happened and what’s next? Nature. 434: 573- Received: Septemver 21, 2015 574. Accepted: October 20, 2015

The author gracefully, acknowledges the help rendered by Ms. Seema Ramani, Lecturer, Belfast Metropolitan College, U.K. in developing the original material and also the comments/ suggestions of learned unknown referee are duly acknowledged.

814 Indian J Econ Dev DOI:10.5958/2322-0430.2015.00089.X Volume 11 No. 4 (2015): 815-822 Research Article

OPTIMIZATION OF FARMING SYSTEMS ON TRIBAL FARMS IN UTTARAKHAND

Shalini Raghav* and Sanjay Kumar Srivastava**

ABSTRACT

The linear programming technique was used to examine the potentiality to increase the farm income. Across the farming systems, potential to increase Return Over Variable Cost (ROVC) over existing plan was highest in Farming System (FS)-IV (Livestock) followed by FS-I (Crop+Livestock) and FS-II (Crop). Whereas in the case of FS-III (Crop+Livestock+Orchard), potential to increase the income was only 0.27 percent. Major policy implications emerged from the study were more attention is required towards the improvement of orchard and livestock rearing. Potato cultivation should be encouraged in the study area.

Keywords: Farm income, farming system, optimization, tribal farms. JEL Classification: C61, C83, O13, O53, Q12

INTRODUCTION intensity of 158 per cent, which is significantly Agriculture including crop and animal higher than the national average of 129 per husbandry, fisheries, forestry and agro cent (State Planning Commission, Government processing provides the underpinnings of our of Uttarakhand, 2011-12). The land holdings food and livelihood security. It provides are small and scattered; the average land significant support for economic growth and holding was around 0.68 hectare (that too is social transformation of the country (Raghav divided into many patches) in the hills and and Srivastava, 2014a). Uttarakhand is 1.77 ha in the plains. As a result, it does not primarily an agricultural state. Agriculture provide sufficient income levels to the people sector of Uttarakhand is the most significant (Uttarakhand State Perspective Strategic Plan sector which provides employment to about 2009-27). 70 per cent of state’s population and The state’s population dependent on contributes 17 per cent to the state’s gross agriculture and allied activities also constitute domestic product (Uttarakhand State tribal people. The population of scheduled Perspective Strategic Plan 2009-27). The total tribes in Uttarakhand is 0.29 million which area under agriculture in the state is 0.75 million constitutes 2.89 per cent of the state’s total hectare which accounts to only 13.3 per cent population and 0.28 per cent of India’s total of the total reported area with the cropping scheduled tribes population. The decadal growth in tribal population in the state is 14 percent from 2001-2011 (Demographic Status *Department of Agricultural Economics, Institute of Agricultural Sciences, Banaras Hindu University of Scheduled Tribe Population of India). The Varanasi-221005 **G.B.Pant University of tribes constitute weakest section of state’s and Agriculture and Technology Pantnagar-263145. India’s population from the ecological, Email: [email protected] economical and educational angles (Raghav

815 and Srivastava, 2014b). The tribal farms are specification of methods are described in characterized by low productivity of crop and Section-IIand III, respectively. Section-IV livestock, poor income, unemployment, small presents main results of the study. Last section and fragmented land holdings etc. Therefore, summarizes the concluding remarks. to have sustainable livelihood security and THE DATA improve the standard of living, the farm families Uttarakhand consists of thirteen districts need to generate additional income in a and spreads over plains, terrain, sub sustainable manner from the available farm mountainous and alpine zones (Raghav and resources (Kumar et al., 2006). Srivasatava, 2013). Out of these, Udham Singh Land being the most important natural Nagar district was selected purposively for the resource deserves the top priority in the study present study as this district has the highest of agricultural development of a country or a population of tribes in the state. Udham Singh region (Srivastava, 2011). Besides, availability Nagar district has seven development blocks of land, water has also become the most limiting namely Jaspur, Kashipur, Bajpur, Gadarpur, factors in farming for increasing the levels of Rudrapur, Sitarganj, and Khatima. Tribal income and employment (Kumar and Jain, population in these blocks varies from about 2002). 0.005 per cent to 30.26 per cent of their total Moreover, nature of agriculture also plays population. Khatima block which has the an important role in the growth and highest tribal population among the seven development. As agriculture is subject to a development blocks was selected purposively high degree of risks and uncertainty. It provides for the present study. There are total 89 villages seasonal, irregular, and uncertain farm income in Khatima block of Udham Singh Nagar district and employment to the farmers. Therefore, to (District Statistical Bulletin, Udham Singh enhance the risk bearing ability of the farm Nagar, 2006). Out of these, seventy villages families, diversification of the enterprise mix have more than fifty families of tribal people on their farms is essential. Livestock, poultry, therein. Thus, from these seventy villages, fishery, bee keeping, etc. are found to be three villages namely Banusi, Jhankat, and potential farm activities for the diversification. Bigrabagh were selected randomly by using Besides, providing useful products, they help random number table. The tribal farmers in the secure net cash income which cannot be selected villages were grouped according to achieved through crop production alone. farming systems. Thereafter, a joint list of Diversification also provides employment for farming systems was prepared in descending some of the family members and can serve as a order on the basis of number of farmers useful outlet for crop by products which would following the farming system in all the selected otherwise go waste. Therefore, one has to look three villages. Then, farming systems (FS) for alternatives to increase the farm income practiced by up to 91.06 per cent of tribes in and employment opportunities on farm selected villages on cumulative basis were through sustainable farming system by considered as major farming systems. The optimizing the use of farm resources. Therefore, farming systems selected were Crop + the main aim of this study was to identify the Livestock (FS-I), only Crop (FS-II), Crop + different farming systems practiced by the Livestock + Orchard (FS-III) and only livestock tribal farmers and potential to increase their (FS-IV). After selection of the farming systems farm income through major farming systems a list of farmers was prepared according to practiced by them. adoption of the farming system. Then 15 The study is organized into different farmers were selected randomly from each sections. Section-I presents the brief identified farming systems and detail is introduction about the study. Data and presented in Table 1. Thus, the study based

816 Table 1: List of farming systems practiced on tribal farms in the study area Farming systems No. of tribal Percent to Cumulative total of No. of selected farmers total percent tribal farmers Crop + Livestock 190 43.58 43.58 15 Crop 117 26.83 70.41 15 Crop + Livestock + Orchard 70 16.06 86.47 15 Livestock 20 4.59 91.06 15 Crop + Livestock + Poultry 17 3.89 94.95 0 Crop + Orchard 16 3.67 98.62 0 Crop+Livestock+Poultry+Orchard 4 0.92 99.54 0 Poultry + Livestock 1 0.23 99.77 0 Crop + Poultry 1 0.23 100.00 0 Total 436 100 60 on the information generated from sample of separately. The returns over variable cost Cj 60 tribal farm families (Raghav and Srivastava, calculated for different crop, livestock and 2010). orchard activities were put in C-vector in the Analytical Framework models prepared for different farming systems Linear programming technique was used activities. to estimate the potential to increase the farm RESULTS AND DISCUSSION income through different farming systems. The The linear programming technique was mathematical formulation of the linear used to identify the optimum enterprise programming model used is given below (for combination and to calculate Return over detail, see, Raghav and Srivastava, 2012): Variable Cost (ROVC) from optimum plan over existing plan. This technique aims to examine n the potential to increase the farm income Max Zij  Cif Xif subject to J1 through different farming systems. Optimum n farm plans were formulated for the major farming Aijf Xif  bif j1 systems (FS) such as Crop+Livestock (FS-I), X Crop (FS-II ), Crop+Livestock+Orchard (FS- if 0 III) and Livestock (FS-IV). where, Optimum Enterprise Combination in FS-I Z = Return over variable cost (`) from the Optimum enterprise combination obtained fth farming system. from optimization of farming systems using linear programming models are discussed Cjf = Total return over variable cost per unit of jth activity in fth farming system. below. The perusal of Table 2 shows that in th th existing plan, paddy and wheat occupied Xjf = Level of j activity in f farming system. almost same and largest proportion of the total th th cropped area which was replaced by potato bif = Supply level of i resources in f farming system. and sorghum in the optimum plan. Due to th higher returns over variable cost from potato Aijf = Requirement of i resource per unit of jth activity in fth farming system. in existing situation the optimum plan f = farming systems (f = 1, 2…….4) suggested to increase the area under potato i = resources (i = 1, 2……..m) and sorghum crops on tribal farms. j = activity (j = 1, 2………n) Before the optimization process, lentil, The objective function of the linear gram, pea, and berseem occupied very less programming model was set to maximize return acreage of gross cropped area but in optimum over variable cost for different farming systems plan these crops were disappeared. In livestock

817 Table 2: Production plans in crop plus total return over variable cost in existing as livestock farming system (FS-I) well as optimum plan. The highest increase in (`ha-1) return over variable cost over existing situation Particulars Existing Plan Optimum Plan was observed in potato crop followed by ha Percent ha Percent ROVC 57196 - 102364 - sorghum. Neither paddy nor wheat provides Crops (ha) the highest return in optimum plan as it was Paddy 1.32 46.73 0.54 19.31 observed in existing plan. The increase in Wheat 1.28 45.45 0.22 7.87 returns over variable cost under optimum plan Lentil 0.04 1.28 0 0 Gram 0.012 0.43 0 0 for cow and buffalo was two and three times Mustard 0.032 1.14 0 0 more than that in existing plan. Pea 0.02 0.71 0 0 Optimum Enterprise Combination in FS-II Potato 0.008 0.28 1.18 42.20 The results presented in Table 4 exhibited Sorghum 0.092 3.27 0.86 30.62 the optimum enterprise combination for FS-II. Berseem 0.02 0.71 0 0 Total cropped area 2.82 100.00 2.80 100.00 In the existing plan, paddy comprises half of Livestock (No.) the total cropped area followed by wheat (42.93 Cow 1 - 2 - per cent). The rest of the crop activities cover Buffalo 1 - 3 - Table 4: Production plans in crop farming enterprises, both activities cow and buffaloes system (FS-II) (ha) increased by 1 and 2 numbers, respectively. Particulars Existing plan Optimum plan The return over variable cost for FS-I increased Ha-1 Percent Ha-1 Percent by `46960.08 (78.97 per cent) under optimum ROVC (`farm-1) 57391 66636 plan over the existing plan. Crop (ha) ROVC in Optimum Farm Plan under FS-I Paddy 1.58 50.00 1.06 40.31 Wheat 1.36 42.93 0.23 8.85 The results presented in Table 3 provide Lentil 0.052 1.64 0 0 the detail of return over variable cost for Gram 0.04 1.26 0 0 optimum plan over existing situation for Mustard 0.048 1.52 0 0 various components of crop+livestock farming Pea 0.032 1.01 0 0 Potato 0.052 1.64 1.33 50.84 system. The results revealed that the returns Total cropped area 3.168 100.00 2.62 100.00 from crop activities covered the largest part of

Table 3: ROVC from different enterprises in optimum plan under FS-I (`) Particulars Existing plan Optimum plan (ha-1) (farm-1) (farm-1) (ha-1) Crops Paddy 21194.23 27891.60 11444.88 8174.91 Wheat 17601.30 22529.67 3872.29 2765.92 Lentil 17129.73 616.67 0.00 0.00 Gram 9339.18 112.07 0.00 0.00 Mustard 22041.58 705.33 0.00 0.00 Pea 9020.50 180.41 0.00 0.00 Potato 61667.50 493.34 72767.65 51976.89 Sorghum 5060.23 456.54 4247.80 3034.14 Berseem 25946.00 518.92 0.00 0.00 Livestock (`.animal-1) Cow 1043.32 1043.32 2086.64 2086.64 Buffalo 2648.26 2648.26 7944.78 7944.78 Total 192691.83 57196.12 102364.04 75983.28

818 very small share of total cropped area. In return over variable cost was highest in this optimum plan, 50.84 and 40.31 per cent of gross farming system followed by paddy and wheat cropped area was covered by potato and crop. The return over variable cost under paddy crops, respectively. Lentil, gram, optimum plan from potato, paddy and wheat mustard and pea which together occupied 0.56 was `45382.01, `16234.23, and `5020.13, per cent of the total cropped area in the existing respectively. While lentil, gram, mustard and plan were disappeared from the optimum plan. pea crops which together provide `1839.49 to The return obtained from this farming system the total return over variable cost of the increased by `9245.55 (16.11 per cent) under farming system in existing plan are disappeared optimum plan over existing plan. from the optimum plan. ROVC in Optimum Farm Plan under FS-II Optimum Enterprise Combination in FS-III The ROVC in optimum plan under FS-II on The perusal of Table 6 show an optimum tribal farms is presented in Table 5. In the enterprise combination for the FS-III. In the optimum plan, the share of potato crop in total existing plan, paddy occupied highest (44.49

Table 5: ROVC in from crop enterprises in optimum plan under FS-II (`) Crops Existing plan Optimum plan Ha-1 Farm-1 Farm-1 Ha-1 Paddy 15373.33 24351.34 16234.23 10248.88 Wheat 21638.48 29428.32 5020.13 3169.28 Lentil 15667.3 809.50 0.00 0 Gram 12974.75 518.99 0.00 0 Mustard 5145.83 247.00 0.00 0 Pea 1320 264.00 0.00 0 Potato 34070.58 1771.67 45382.01 28650.26 Total 106190.27 57390.82 66636.37 42068.41

Table 6: Production plans in crop + livestock +orchard farming system (FS-III) (ha) Particulars Existing Plan Optimum Plan Ha-1 Percent Ha-1 Percent ROVC (`farm-1) 88639 88878 Crops Paddy 2.232 44.49 0.32 6.87 Wheat 2.04 40.67 0.26 5.74 Lentil 0.05 1.04 0.00 0 Gram 0.08 1.59 0 0 Mustard 0.052 1.04 0 0 Pea 0.06 1.19 0 0 Potato 0.06 1.19 0.16 3.57 Sorghum 0.132 2.63 0.77 16.69 Berseem 0.02 0.39 1.47 32.00 Orchard Mango 0.29 5.74 1.62 35.13 Total cropped area 5.02 100.00 4.6 100.00 Livestock Cow 1 - 2 - Buffalo 1 - 2 -

819 per cent) share of total cropped area followed enterprise, ROVC from cow and buffalo by wheat. In this plan minimum area comes increased two times over existing plan under under fodder crop (0.39). However, in optimum optimum plan. Due to higher increase in the plan, sorghum and berseem appeared with per area of mango over existing plan under cent share of 16.69 and 32 of the total cropped optimum plan, return over variable cost from area, respectively whereas paddy and wheat mango was higher. covers only 0.32 and 0.26 hectare of total Optimum Plan Under FS-IV cropped area. While, potato crop not covers The production plans for FS-IV is given in significant area under optimum plan as Table 8 . In existing plan, number of each animal compared to other farming systems. In existing that is, cow, buffalo and goat was one each. plan, area under orchard is 0.29 hectare which The number of cow and buffalo was increased is increased by 35.13 percent under optimum to two, which has doubled in the optimum plan plan. The number of cow and buffalo was one while number of goat remains the same in both in existing plan but by the process of existing as well as optimum plan. optimization, both cow and buffalo, just The return under optimum plan appeared doubled in the optimum plan. The returns of more than 93 per cent as compared to the the farming system increased only by `239.32 existing one. (0.27 percent) under optimum plan over existing plan. Table 8: Production plans in livestock of ROVC in Optimum Farm Plan in FS-III FS-IV The detail of returns over variable cost for (`farm-1) optimum plan over existing situation for crop Particulars Existing plan Optimum plan + livestock + orchard farming system is Return over variable cost 6138.11 11902.56 prsented in Table 7 . In the existing plan, paddy Livestock (No.) Cow 1 2 and wheat contributed highest returns over Buffalo 1 2 variable cost but in optimum plan, fodder crop Goat 1 1 appeared as more beneficial. In livestock Farming system (FS)

Table 7: ROVC from different enterprises in optimum plan under FS-III (`) Particulars Existing plan Optimum plan Ha-1 Farm-1 Ha-1 Farm-1 Crops Paddy 19309.55 43098.89 6101.82 2581.14 Wheat 14522.18 29625.25 3833.85 1621.76 Lentil 14804.23 769.82 0.00 0.00 Gram 12997.88 1039.83 0.00 0.00 Mustard 15545.78 808.38 0.00 0.00 Pea 12441.68 746.50 0.00 0.00 Potato 30027.68 1801.66 4924.54 2083.14 Sorghum 7204.70 951.02 5533.21 2340.61 Berseem 27900.5 558.01 41069.54 17372.9 Orchard Mango 8592.6 2474.67 13885.64 47553.56 Livestock Cow 6006.13 6006.13 12012.26 12012.26 Buffalo 758.62 758.62 1517.24 1517.24 Total 170111.52 88638.78 88878.10 87082.62

820 ROVC in FS-IV FS-III income can be increased only by 0.27 The detail of ROVC for optimum farm plans per cent. Therefore, it can be concluded that in FS-IV is provided in Table 9. In this farming potato proved to be the highest profit giving system, return over variable cost from cow and crop on tribal farms. Hence, potato cultivation buffalo under optimum plan increased two may be encouraged in the study area. Though, times over existing plan while it remains same its cold storage requirements for the crop for the goat. produce need to be created. The study suggests that the farm income of tribal farmers Table 9: Return over variable cost from can also be increased by adopting orchard livestock enterprises in optimum plan under FS-IV enterprise. Therefore, it is possible to increase (`animal-1) the farm income of tribal farmers through Particulars Existing plan Existing plan Optimum plan reallocation of existing farm resources Licestock optimally under all the farming systems. Cow 2771.26 2771.26 5542.52 REFERENCES Buffalo 3263.19 3263.19 6256.38 Anonymous. 2006. District statistical bulletin. Goat 103.66 103.66 103.66 Udham Singh Nagar, Uttarakhand 2006. Total 6138.11 6138.11 11902.56 Anonymous 2012. Demographic status of scheduled tribal population. Retrieved on August 3, 2015 from http://www.tribal.nic.in. CONCLUSIONS Kumar, S., Jain, D.K., and Singh, R. 2006. Increasing The main aim of this paper was to measure income and employment through sustainable the potential to increase the farm income of farming systems in water scarce region of Uttar tribal farmers through existing resource base Pradesh. Agricultural Economics Research of the farming systems. Optimum farm plans Review. 19 (1): 145-157. was formulated under major farming systems Kumar, S. and Jain, D.K. 2002. Interactions and over existing situation. Among all optimum changes in farming systems in semi-arid parts plans, crop activities provides the highest of India: Some issues in sustainability. return in FS-I, FS-II and FS-III. In FS-I (crop + Agricultural Economics Research Review. 15 (2): 217-230. livestock) and FS-II (crop), optimum plan Raghav, S. and Srivastava, S.K. 2010. Economics of suggests to increase the area under potato on farming systems on tribal farms in Udham Singh tribal farms. There was significant increase in Nagar district of Uttarakhand. M.Sc. Thesis. the area under mango orchard for the FS-III Govind Ballabh Pant University of Agriculture (Crop + livestock + orchard) while none of the and Technology, Pantnaga. cereal crop or cash crop covered significant Raghav, S. and Srivastava, S.K. 2012. Farming area under optimum plan. Optimum plan systems on tribal farms: Economics and suggests to increase the number of cow and optimization. Lambert Academic Publications- buffalo by two and three times, respectively in LAP Lambert Academic Publishing GmbH & Co. KG, Heinrich- Germany. FS-I (crop + livestock) while, in FS-III (crop + Raghav, S. and Srivastava, S.K. 2013. Economics of livestock + orchard) number of cow and buffalo major cereal and tuber crops grown by the tribal can be doubled. In FS-IV, number of cow and farmers in tarai region of Uttarakhand. Journal buffalo can be increased two times over of Hill Agriculture. 4 (2): 78-82. existing situation under optimum plan while Raghav, S. and Srivastava, S.K. 2014a. Constraints number of goat remains the same. Across the in adoption of farming systems on tribal farms farming systems, potential to increase return in Uttarakhand. Journal of Hill Agriculture. 5 over variable cost over existing plan was (2): 174-178. highest in FS-IV (Livestock) (93.97 per cent) Raghav, S. and Srivastava, S.K. 2014b. Socio- economic status of tribal farm households under followed by FS-I (Crop + livestock) (78.91 per different farming systems in the plain region of cent) and FS-II (6.11 per cent) while, through Uttarakhand, India. International Journal of

821 Basic and Applied Agricultural Research. 12 (2): Uttarakhand State Perspective Strategic Plan 2009- 160. 27. Watershed Management Directorate, Srivastava, S.K. 2011. Land use pattern in North- Dehradun. Retrieved on December 12, 2014 east and Eastern hill states of India-A from http://foodprocessingindia.co.in. comparative study. Journal of Hill Agriculture. 2 (2): 219-223. State Planning Commission, Government of Uttarakhand, 2011-12. Retrieved on January 5, Received: May 20, 2015 2015 from http://spc.uk.gov.in/ Accepted: August 30, 2015

822 Indian J Econ Dev DOI: 10.5958/2322-0430.2015.00090.6 Volume 11 No. 4 (2015): 823-832 Research Article

MARKETING OF POTATO IN JALANDHAR DISTRICT OF PUNJAB

Amritpal Kaur and M.S. Sidhu*

ABSTRACT

Potato is principal vegetable crop in Punjab. During 2012-13, about 70 percent of the area under vegetables was under potato crop in the state. The study has been undertaken to study the marketing system and price spread of potato in Jalandhar district of Punjab. The study revealed that per holding consumption of potato was about nine per cent of the production and the marketed surplus was about 91 percent. The sale pattern of potato brought out that its maximum quantity was sold in the wholesale market (Punjab). Apni Mandi was the most efficient one due to direct sale of the produce. The study suggested that modern market infrastructure may be built up with public-private partnership to bring efficiency in marketing of potato. Potato growers may prefer cooperative/group marketing for sale of their produce in the distant markets.

Keywords: Marketed surplus, marketing cost, sale pattern, supply chain JEL classification: E23, E31, M31, O13, P42, Q13, Q18

INTRODUCTION production of vegetables in 2011-12 stood at India with its wide variability of climate and over 37.34 lakh tonnes from an area of 114 soil, produces a large range of horticulture thousand hectares put to vegetable cultivation crops, such as fruits, vegetables, tropical tuber in Punjab. In spite of this seemingly high level crops, ornamental crops, medicinal and of production, the per capita consumption of aromatic plants, cocoa, etc. India has been vegetables in India is only about 140 grams, growing vegetables from several centuries and which is far below the minimum daily is the second largest producer of vegetables requirement of 300 gram per person as per the in the world (after China), accounting for nearly dietary guidelines by National Institute of 14 per cent of the vegetable production in the Nutrition, ICMR, Hyderabad (Anonymous, world. More than 70 kinds of vegetables 2009). Vegetables are an important part of diet belonging to different groups namely of vegetarian as well as non-vegetarian cucurbits, cole crops, solanaceous vegetables, population of our society. Thus, the role of root and leafy vegetables are grown in the vegetables in improving the dietary standards country (Salaria and Salaria, 2010). The of the people becomes all the more vital. Potato is staple food in Europe and North *Ph.D. Scholar and Former Professor (Agricultural America and a vegetable crop in the Economics) respectively, Department of Economics developing world including India. During the and Sociology, Punjab Agricultural University, 2012-13, the area under vegetables was about Ludhiana -141004 122 thousand hectares in Punjab. Out of this, Email: [email protected] 70 per cent area was under potato crop. The

823 production of potato was about 2132 thousand of potato was obtained from the selected metric tonnes in the state during the 2012-13. farmers. The primary data were collected from Punjab’s share in the India’s potato production the sample farmers related to the year 2011-12. was about five per cent in 2012-13. Kufri A random sample of 10 wholesalers and 15 Pukhraj variety of potato occupied first retailers were selected from Jalandhar market. position with regard to area and production For detail in this regard, refer to the Ph.D. thesis followed by Kufri Jyoti in Jalandhar district. of the first author (Kaur, 2015). The secondary Empirical studies have shown that a large data were obtained from the reputed published number of the intermediaries are involved in and unpublished sources. the movement of the horticulture produce from RESULTS AND DISCUSSION producer to consumer, who appropriate a large Profile of Potato Growers proportion of the price paid by consumer and The study brought out that about 51 per the share of producer becomes very low. In cent of the selected farmers were in the age the case of perishables, the storage of which group of 40 to 60 years. About 10 per cent of is very difficult, the producer’s share in the farmers were illiterate and 90 per cent were consumer’s price is in the range of 30 to 60 educated, a majority of them had education percent and the market efficiency is low from 9th to 10+2 level. The study revealed that (Government of India, 2001, Anantia, 2008, majority of the farmers (about 79 per cent) were Jairath, 2008, and Dastagiri et al., 2009). The having tractors as source of draft power. About marketing of vegetables is quite complex and 94 per cent of the respondents had exclusively risky due to the perishable nature of the tubewell irrigation, whereas about six per cent produce, seasonal production and its of them had both canal as well as tubewell bulkiness. Therefore, the present study has irrigation. The study revealed that average size been undertaken to study the marketing system of operational holding of the potato growers and price spread of potato in Jalandhar district was about 7.69 ha. The share of the area owned of Punjab. and leased in was about 63 and 40 percent DATA BASE respectively. In the first stage, Jalandhar district was Marketed Surplus of Potato selected randomly from the major potato The information regarding per holding growing districts in Punjab for the study. In production, consumption and marketed the second stage, three development blocks surplus of potato is given in Table 1. A perusal were selected randomly from Jalandhar district. of the Table 1 revealed that per holding In the third stage, three villages were again consumption of potato was about nine per cent randomly selected from each block. Twelve of the production and the marketed surplus farmers were selected randomly from each was high to the extent of about 91 per cent. selected village. To make the sample design The small category of the farmers had the self-weighting, the number of farmers selected maximum marketed surplus (94.04 percent) from each size category was in proportion to followed by the large farmers (91.24 percent) their number in that category. The farmers were and medium farmers (88.54 percent). There is categorized into small, medium and large no doubt that total consumption in absolute categories on the basis of area under potato terms was more in the case of large farmers by using cube root frequency method. The (261.44 q) as compared to 142.31 q and 30.11 q ultimate sample consisted of 63 small, 27 for medium and small farmers respectively. But medium and 18 large farmers (total sample of in percentage terms, total consumption of 108 farmers). The information related to potato was more in the case of medium farmers marketing system, price spread of potato, and (11.46 percent) followed by large farmers (8.76 problems related to production and marketing percent) and small farmers (5.96 percent).

824 Table 1: Per holding production and Table 2: Sale pattern of potato of the marketed surplus of potato with the selected respondents, 2011-12 selected farmers, 2011-12 (No.) (q) Particulars Farm category Particulars Small Medium Large Average Small Medium Large Overall Production (I) 505.55 1241.75 2985.91 1102.99 Sale at the 31 3 3 37 Family Consumption 1.22 1.55 1.72 1.39 village/farm (49.21) (11.11) (16.67) (34.26) (II) (0.24) (0.12) (0.06) (0.13) Sale in the 30 24 11 65 Quantity kept for 28.53 140.33 259.11 94.91 wholesale market (47.62) (88.89) (61.11) (60.19) Seed (III) (5.64) (11.30) (8.68) (8.60) (Punjab) Payment in kind to 0.36 0.43 0.61 0.42 Sale in Apni Mandi 5 1 - 6 labour (IV) (0.07) (0.03) (0.02) (0.04) (7.94) (3.70) (5.56) Total consumption 30.11 142.31 261.44 96.72 Sale in distant - 2 6 8 (II+III+IV) (5.96) (11.46) (8.76) (8.77) market (Delhi (7.41) (33.33) (7.41) Marketed surplus 475.44 1099.44 2724.47 1006.28 market) (I-V) (94.04) (88.54) (91.24) (91.23) Sale to private - 1 4 5 Figures in the parentheses indicate percentage to the total company (Contract (3.70) (22.22) (4.63) potato production. farming) Sale to organized - - 1 1 retailer (Punjab) (5.56) (0.93) The ultimate income of the farmer depends *Multiple responses. Figures in the parentheses indicate the percentage to the total upon the size of marketed surplus. Higher the number of respondents of each category. level of marketed surplus, the higher will be the level of income and vice-versa. High marketed surplus of potato clearly indicates followed by medium farmers (7.41 percent). that less quantity of this crop was either kept The study indicated that about 49.21, 16.67 for consumption or for seed purpose for the and 11.11 percent of the small, large and next year, etc. Almost the entire production medium farmers sold potato in the village or at was sold either through wholesalers or self- the farm itself. It showed that most of the small sale. farmers used to sell their produce in the village Sale Pattern of Potato or at the farm because of high transportation The farmers generally sell their produce in cost for the sale in the market. About six per those markets where they can get high price. cent of the selected farmers sold their produce The relevant information about the farmers in Apni Mandi. About five and one per cent of selling potato in the various markets is given the total respondents sold their produce to in Table 2. The maximum number of farmers the private companies and organized retailers (60.19 percent) in the sample sold their produce respectively. at the wholesale market (Punjab) followed by The information regarding the sale pattern sale in the village or farm itself (34.26 percent). of the selected potato growers is shown in The results also revealed that about seven Table 3. The highest quantity of potato (about per cent of the farmers sold their produce in 58 per cent) was sold by the growers in the Delhi market as Delhi is the biggest consuming wholesale market (Punjab) followed by sale at market in north India. It also operates as a the village or farm itself (about 19 per cent). distribution market for the entire northern The sale in the distant market was 15.49 areas of the country. More number of large percent. The sale in the Apni Mandi was about farmers sold their produce in the Delhi market three per cent. The reason behind low sale in as compared to the medium farmers. None of Apni Mandi was that the growers had to stay the selected small farmers sold his produce in for long hours for sale of the potato in Apni distant market (Delhi). About 33 percent large Mandi. There were other constraints too. farmers sold their produce in the Delhi market Therefore, majority of the farmers preferred to

825 Table 3: Sale pattern of potato of the caused by the seasonal nature of production selected respondents, 2011-12 and arrivals of potatoes. The other reasons (q) were high charges for cold stores and semi- Particulars Farm category Overall perishable nature of potatoes. Moreover, the Small Medium Large Sale at the 229.07 54.97 274.90 193.18 cold store owners prefer large farmers in their village/farm (48.18) (5.00) (10.09) (19.20) business dealings due to huge surpluses with Sale in the wholesale 214.71 952.89 1343.44 587.38 them (Sidhu and Singh, 2005). The small and market (Punjab) (45.16) (86.67) (49.31) (58.37) the marginal farmers are not on their priority Sale in Apni Mandi 31.66 42.33 - 29.05 due to low volume of produce and unsound (6.65) (3.85) (2.89) Sale in distant market - 37.82 875.37 155.35 financial position (ibid). (Delhi Market) (3.44) (32.13) (15.44) It is important to mention here that the Sale to private - 11.43 127.78 24.15 actual arrival of potato may be more in company (contract (1.04) (4.69) (2.40) Jalandhar district. This often happens due to farming) Sale to organized - - 102.98 17.16 unrecorded sale of the produce. The traders retailer (Punjab) (3.78) (1.70) are always interested to avoid the market fee, Total 475.44 1099.44 2724.47 1006.28 rural development fund, etc. An earlier study (100.00) (100.00) (100.00) (100.00) conducted at the Punjab Agricultural Figures in the parentheses indicate the percentage to the total quantity of potato sold University has also shown large scale evasion of market fee in the grape grown area of the sell potato in the wholesale market (Punjab) or state (Gill, 1990). The study further highlighted at the village/farm itself. Moreover, farmers sold that hardly one-fourth of the total arrival of about two per cent of their produce to private grapes were shown in the official records and companies and organized retailers in each the rest 75 per cent were sold without paying category. any market fee and rural development funds Market Arrival of Potato (ibid). Another press report had revealed a During the period of November to March, large scale evasion of market fee and rural the market arrivals were the highest creating development fund in the fruit and vegetable glut like situation in the market. The lean period market at Amritsar (Rambani, 2004). Within a existed from April to October in arrival. The week of shifting of that fruit and vegetable data regarding arrival of potato in Jalandhar market from one place to another, a six fold city market from 1993-94 to 2012-13 has been increase in market fee, rural development fund, given in Table 4. As Jalandhar is an assembling etc. has been registered (ibid). This showed market of potato, therefore, from this market, the massive tax evasion on the part of fruit the produce is sent to various consuming and vegetable traders. This phenomenon is markets within, as well as, outside the state by common all over Punjab and elsewhere also. the traders. The arrival of potato had varied Hence, we can say that actual market arrival of from 6.59 lakh q in 1993-94 to 2.88 lakh q in potato may be more than the one shown in the 2012-13 (Table 4). There have been inter-year official record of the concerned market fluctuations in the arrival of potato. committee. The arrival of potato was the highest in Prices of Potato the post-harvest period. The lowest market The information regarding average prices arrival was 0.06 lakh q in April in the year 2005- of potato in Jalandhar city market from 1993- 06 and the maximum arrival was 2.75 lakh q in 94 to 2012-13 is given in Table 5. The lowest December in the year 2001-02. The seasonal price of potato was `75 per q in December, indices of arrivals were found to be highest in 1999 whereas, the highest price was `1000 per the month of December (282) and the lowest in q in October, 2009. The prices started upward June (42). The seasonal changes were mainly movement in the month of May and reached

826 Table 4: Arrival of potato in Jalandhar city market, 1993-94 to 2012-13 (000 q) Year April May June July August Sept Oct Nov Dec Jan Feb March Total 1993-94 36.8 35.4 24.4 20.5 31.1 31.0 34.0 58.0 214.2 78.1 33.7 61.8 659.0 (5.58) (5.37) (3.70) (3.11) (4.72) (4.70) (5.16) (8.80) (32.50) (11.85) (5.11) (9.38) (100.00) 1994-95 36.0 55.9 28.3 25.4 23.9 24.7 29.0 76.6 164.0 52.7 43.2 104.2 663.9 (5.42) (8.42) (4.26) (3.83) (3.60) (3.72) (4.37) (11.54) (24.70) (7.94) (6.51) (15.70) (100.00) 1995-96 76.2 40.5 18.0 17.0 22.0 24.6 27.6 74.7 169.8 61.8 51.6 74.4 658.2 (11.58) (6.15) (2.73) (2.58) (3.34) (3.74) (4.19) (11.35) (25.80) (9.39) (7.84) (11.30) (100.00) 1996-97 39.6 44.9 18.2 16.0 24.8 25.0 23.4 109.0 97.0 33.2 27.4 51.3 509.8 (7.77) (8.80) (3.57) (3.14) (4.86) (4.90) (4.59) (21.38) (19.03) (6.51) (5.37) (10.06) (100.00) 1997-98 51.7 65.7 31.0 22.0 27.5 31.0 42.1 117.2 160.9 89.6 - 108.8 747.5 (6.92) (8.79) (4.15) (2.94) (3.68) (4.15) (5.63) (15.68) (21.53) (11.99) (14.56) (100.00) 1998-99 44.7 74.3 31.9 24.6 23.0 24.2 27.8 55.7 128.7 102.5 91.9 160.0 789.3 (5.66) (9.41) (4.04) (3.12) (2.91) (3.07) (3.52) (7.06) (16.31) (12.99) (11.64) (20.27) (100.00) 1999-00 46.9 84.6 26.0 34.3 43.1 25.1 26.1 76.6 267.2 132.3 110.9 180.6 1053.7 (4.45) (8.03) (2.47) (3.26) (4.09) (2.38) (2.48) (7.27) (25.36) (12.56) (10.52) (17.14) (100.00) 2000-01 52.8 98.2 28.2 44.7 45.5 37.6 45.9 87.1 267.2 103.0 91.2 144.6 1045.9 (5.05) (9.39) (2.70) (4.27) (4.35) (3.59) (4.39) (8.33) (25.55) (9.85) (8.72) (13.82) (100.00) 2001-02 53.7 98.4 31.9 44.6 45.5 37.6 39.3 104.4 275.3 101.8 93.6 123.2 1049.3 (5.12) (9.38) (3.04) (4.25) (4.34) (3.58) (3.75) (9.95) (26.24) (9.70) (8.92) (11.74) (100.00) 2002-03 58.0 81.0 36.0 28.2 27.7 18.2 28.2 95.4 177.0 84.0 63.1 147.4 844.2 (6.87) (9.59) (4.26) (3.34) (3.28) (2.16) (3.34) (11.30) (20.97) (9.95) (7.47) (17.46) (100.00) 2003-04 50.2 58.2 38.4 34.6 40.0 46.1 27.1 105.0 191.5 72.0 60.6 122.2 845.9 (5.93) (6.88) (4.54) (4.09) (4.73) (5.45) (3.20) (12.41) (22.64) (8.51) (7.16) (14.45) (100.00) 2004-05 48.0 41.2 15.9 18.3 22.5 19.4 19.9 90.1 191.5 111.4 51.2 69.3 698.7 (6.87) (5.90) (2.28) (2.62) (3.22) (2.78) (2.85) (12.90) (27.41) (15.94) (7.33) (9.92) (100.00) 2005-06 6.0 93.3 32.0 29.4 32.9 34.0 42.2 64.5 144.5 91.2 57.6 59.3 686.9 (0.87) (13.58) (4.66) (4.28) (4.79) (4.95) (6.14) (9.39) (21.04) (13.28) (8.39) (8.63) (100.00) 2006-07 39.1 34.1 25.4 24.2 34.1 20.7 26.9 79.3 66.7 41.7 31.4 39.5 463.1 (8.44) (7.36) (5.48) (5.23) (7.36) (4.47) (5.81) (17.12) (14.40) (9.00) (6.78) (8.53) (100.00) 2007-08 41.8 18.9 11.8 11.1 14.8 14.4 15.0 71.4 119.4 59.2 32.0 50.1 459.9 (9.09) (4.11) (2.57) (2.41) (3.22) (3.13) (3.26) (15.53) (25.96) (12.87) (6.96) (10.89) (100.00) 2008-09 35.3 35.3 21.4 28.8 15.8 22.4 24.8 47.7 132.4 21.3 29.9 48.8 463.9 (7.61) (7.61) (4.61) (6.21) (3.41) (4.83) (5.35) (10.28) (28.54) (4.59) (6.45) (10.52) (100.00) 2009-10 41.6 21.4 8.6 15.4 15.0 20.0 14.0 70.0 80.7 33.6 32.2 44.1 396.6 (10.49) (5.40) (2.17) (3.88) (3.78) (5.04) (3.53) (17.65) (20.35) (8.47) (8.12) (11.12) (100.00) 2010-11 22.9 22.7 11.0 13.7 16.7 11.4 13.9 28.8 61.4 41.9 22.2 40.8 307.4 (7.46) (7.39) (3.59) (4.45) (5.43) (3.72) (4.52) (9.36) (19.97) (13.64) (7.23) (13.26) (100.00) 2011-12 23.9 24.8 12.9 12.9 14.2 15.4 18.6 36.5 76.1 34.3 42.1 41.1 352.8 (6.78) (7.03) (3.66) (3.66) (4.03) (4.37) (5.28) (10.35) (21.55) (9.71) (11.94) (11.64) (100.00) 2012-13 15.1 13.1 9.7 9.9 12.0 12.5 14.3 41.2 71.6 41.6 20.7 26.1 287.8 (5.23) (4.55) (3.37) (3.46) (4.18) (4.33) (4.97) (14.30) (24.88) (14.44) (7.20) (9.08) (100.00) S.I. 75 93 42 44 49 46 50 139 282 130 93 156 1200 Source: Market Committee, Jalandhar city . Figures in the parentheses indicate the percentage to total S.I.: Seasonal indices the highest level during the months of August prices were the highest in the lean period when to October. the arrivals were low. The supply of the The seasonal indices of prices were the vegetables in the cities is uneven and uncertain, highest in the month of October (133) and the which results into fluctuations in their prices. lowest in the month of January and February It was also noticed that most of the farmers (68). There is inverse relationship between sold their produce in the post-harvest period arrivals and prices of potato which greatly and did not store it. Even if they stored potato, affected the income of the farmer. Thus, potato they stored very less quantity which resulted

827 Table 5: Average price of potato in Jalandhar city market, 1993-94 to 2012-13 (`q-1) Year April May June July August Sept Oct Nov Dec Jan Feb March 1993-94 130 215 230 215 235 263 280 250 155 110 150 150 1994-95 140 175 225 225 275 275 275 265 125 125 163 160 1995-96 175 275 350 285 325 350 475 250 125 190 215 250 1996-97 285 328 350 350 450 350 375 400 375 240 225 350 1997-98 160 105 115 200 275 150 150 125 90 175 NA 275 1998-99 325 450 450 550 700 700 800 600 225 150 100 100 1999-00 150 355 325 300 250 160 150 175 75 90 100 115 2000-01 100 100 150 150 125 125 100 115 90 105 125 275 2001-02 300 275 350 300 300 350 350 325 200 200 225 275 2002-03 200 300 300 350 400 225 200 200 125 150 175 200 2003-04 175 200 200 175 200 200 175 250 150 150 200 200 2004-05 225 300 275 300 400 500 450 210 150 140 225 400 2005-06 400 300 350 300 400 450 450 400 400 325 340 450 2006-07 425 650 450 525 550 650 725 375 275 250 340 365 2007-08 375 525 490 800 800 950 725 450 400 350 415 300 2008-09 290 250 340 270 250 400 400 150 145 170 175 350 2009-10 350 450 650 590 700 800 1000 800 550 650 300 175 2010-11 351 299 330 300 212 291 372 450 372 269 298 321 2011-12 299 336 409 446 314 369 347 290 212 274 244 278 2012-13 425 579 671 667 699 636 613 566 357 367 428 390 S.I. 84 104 112 116 127 129 133 105 71 68 68 84 Source: Market Committee, Jalandhar city . S.I.: Seasonal indices in creating a glut in the market in the peak throughout the year. season led to fall in prices. The farmers were Distribution Channels and Price Spread of forced to sell their produce in post-harvest Potato period due to various reasons. The traders The distribution channels are the routes exploited the farmers by purchasing potato through which the agricultural commodities from them at the lowest prices during the peak reach from producers to consumers. The price season. They used to store it and afterwards spread is the gap between the price paid by sold it during the lean period at higher prices the consumers and the price received by the to the consumers and to the producers for seed producer at a particular point of time (Acharya purposes. In this way, both consumer and and Agarwal, 2008). The price spread studies producer suffer; the former by paying higher can be helpful in studying the efficiency of prices and later by not receiving remunerative the marketing system. A reasonable price prices. To save them from this situation, the spread ensures better returns to the producers prices should be stabilized throughout the year and a regular supply to consumers at by providing more storage facilities to the reasonable prices. Consequently, production farmers at cheaper rates and at the same time is encouraged by increasing aggregate demand by increasing the retention power of the for a commodity. This is more for the foods, producers, so that they can store maximum of which are liked by every consumer if made their produce during the post-harvest period available at reasonable rates (Sidhu and Gill, and sell it during the lean period. In such a 1989). The price spread of potato has been way, the producers can get better prices for worked out for the month of February, 2012 their produce and also the consumers can get (peak season of potato marketing), when a the potato at almost constant prices major portion of produce was sold. The price

828 spread through three main supply chains of about `50 and `54 per q, respectively which potato has been worked out for the study. The was about seven per cent for each of the important supply chains studied were as wholesaler and retailer. The margin of the under: wholesaler (about `70 per q) was less on (i) ProducerD WholesalerD RetailerD account of high volume of business as Consumer compared to retailer (about `146 per q) who (ii) Producer DRetailer DConsumer handled low volume of the business. (iii)Producer D Consumer (Apni Mandi) The price spread of potato in Supply The Supply Chain-I (Producer Chain-II (Producer DRetailerDConsumer) has DWholesaler D Retailer D Consumer) has been presented in Table 7. The producer’s sale been discussed in Table 6. price of potato was `450 per q in Jalandhar A perusal of the Table 6 reveals that market which was 60 per cent of the producer’s sale price of potato was `430 per q consumer’s purchase price (`750 per q). The in Jalandhar market, which was about 57 per expenses borne by the producer were about cent of the consumer’s purchase price. The `73 per q which was about 10 per cent of the expenses borne by the producer were about consumer’s purchase price. `73 per q which was about 10 per cent of the The net price received by the producer was consumer’s purchase price. The net price about `377 per q which was about 50 per cent received by the producer was about `357 per of the consumer’s purchase price. The q which was about 48 per cent of the expenses borne by the retailer were about `101 consumer’s purchase price. The expenses per q which were about 14 per cent of the borne by the wholesaler and retailer were consumer’s purchase price. The retailer’s

Table 6: Price spread in Supply Chain-I *of potato in Jalandhar market, February 2012 Particulars Price (`q-1) Percent share in consumer's price Producer’s sale price/ wholesaler’s purchase price 430.00 57.33 Expenses borne by the producer 72.80 9.71 Grading, filling, stitching etc. 20.52 2.74 Cost of gunny bag 36.70 4.89 Loading and transportation cost 13.17 1.76 Unloading charges 2.41 0.32 Net price received by the farmer 357.20 47.63 Expenses borne by the wholesaler 50.20 6.69 Market fee @ 2% 8.60 1.15 Rural development fund @ 2% 8.60 1.15 Commission to the commission agent @ 5% 21.50 2.87 Grading expenses 4.00 0.53 Miscellaneous expenses 7.50 1.00 Margin of the wholesaler 69.80 9.31 Wholesaler’s sale price/ retailer’s purchase price 550.00 73.33 Expenses borne by the retailer 54.20 7.23 Transportation cost 12.25 1.63 Labour 3.70 0.49 Rent of the shop/rehri 2.75 0.37 Packing cost 14.00 1.87 Loss, wastage and spoilage @ 2.50% 13.75 1.83 Miscellaneous cost 7.75 1.03 Margin of the retailer 145.80 19.44 Retailer’s sale price/ consumer’s purchase price 750.00 100.00 *Supply Chain I: Produce D WholesalerDRetailer D Consumer

829 margin was about `199 per q which was 26.50 of the Financial Commissioner (Development), per cent of the consumer’s purchase price. The Punjab, in February, 1987 (Sidhu et al., 2012). margin of the retailer was high in Supply Chain- The major objective of this scheme was to II as compared to Supply Chain-I, because in increase producer’s share in the consumer’s Supply Chain-II, there was no role of the purchase price particularly for perishable wholesaler. Table 7: Price spread in Supply Chain-II* of potato in Jalandhar market, February 2012 Particulars Price (`q-1) Percent share in consumer's price Producer’s sale price/ retailer’s purchase price 450.00 60.00 Expenses borne by the producer 72.80 9.71 Grading, filling, stitching, etc. 20.52 2.74 Cost of gunny bag 36.70 4.89 Loading and transportation cost 13.17 1.76 Unloading charges 2.41 0.32 Net price received by the farmer 377.20 50.29 Expenses borne by the retailer 101.25 13.50 Market fee @ 2% 9.00 1.20 Rural development fund @ 2% 9.00 1.20 Commission to the commission agent @ 5% 22.50 3.00 Transportation cost 12.25 1.63 Labour 12.75 1.70 Rent of the shop/rehri 2.75 0.37 Packing cost 14.00 1.87 Loss, wastage and spoilage @ 2.5% 11.25 1.50 Miscellaneous cost 7.75 1.03 Margin of the retailer 198.75 26.50 Retailer’s sale price/ consumer’s purchase price 750.00 100.00 *Supply Chain-II: Producer D Retailer D Consumer)

The price spread of potato in Supply commodities like vegetables. Another aim was Chain-III (Apni Mandi ) market has been to supply fresh vegetable to the consumers at worked out in Table 8. It is important to mention low price in comparison to the prevailing here that there is no middleman involved in market price through traditional supply chains the sale of farm produce in Apni Mandi. There (ibid). is direct sale of the produce by the producer A perusal of Table 8 reveals that producer’s to consumer. This scheme was introduced in sale price/ consumer’s purchase price was `700 major cities/towns of Punjab at the initiative per q in Apni Mandi of the Jalandhar district. Table 8: Price spread in Supply Chain-III*of potato in Apni Mandi of Jalandhar market, February 2012 Particulars Price (`q-1) Percent share in consumer's price Producer’s sale price 700.00 100.00 Expenses borne by the producer 65.27 9.32 Grading, filling, stitching, etc. 20.52 2.93 Cost of packing 3.75 0.54 Transportation cost 15.25 2.18 Loading and wastage 6.00 0.86 Packing cost (carry bags) 14.00 2.00 Miscellaneous expenses 5.75 0.82 Net price received by the producer 634.73 90.68 Consumer’s purchase price 700.00 100.00 *Supply Chain III: ProducerDConsumer

830 The expenses borne by the producer were CONCLUSIONS about `65 per q which were about nine per Punjab’s share in the India’s potato cent of the consumer’s price. The net price production was about five per cent in 2012-13. received by the producer, (`634.73 per q) was Kufri Pukhraj variety of potato occupied first about 91 per cent of the consumer’s purchase position with regard to area and production price. As compared to Supply Chain-I and II, followed by Kufri Jyoti in Jalandhar district. the producer’s share in Supply Chain-III was The marketed surplus of potato was high due more on account of direct sale by the producer to its perishable nature. The maximum number to the consumer. But this is also a fact that of selected farmers sold their produce at the major share of the vegetables can’t be sold wholesale market (Punjab) followed by sale in through Apni Mandi, because the traditional the village or farm itself. Supply Chain-III (Apni wholesalers and retailers have their own role Mandi) was the most efficient market in in vegetable marketing. It is a part of the Indian Jalandhar district due to direct sale of produce culture that traditional vegetable hawkers sell from producer to consumer. There is inverse various vegetables at the doorsteps of relationship between arrivals and prices of consumers in various localities of cities and potato. Potato export may be encouraged from towns. the state to save the potato growers from price Marketing Efficiency of Potato uncertainties. In the mid-day meal scheme for The marketing efficiency of potato under the school going children all over the country, different supply chains has been worked out potato may also be included. The potato by using Acharya’s index of marketing varieties suitable for processing may be efficiency and it is shown in Table 9. The developed for the Punjab farmers. Contract profitability of the crop is the guiding force for farming will also reduce the price risk of the resource allocation decisions of the farmers, farmers. It is suggested that potato farmers which apart from production efficiency, may form informal cooperatives/ groups. As a depends upon the prices received by the large group, their per-unit transportation cost producers in terms of consumer’s rupee (Sidhu will definitely come down. This will encourage et al., 2011). potato cultivation in the state. To encourage The perusal of the Table 9 reveals that the small and medium farmers to take their Supply Chain-III was the most efficient in produce in distant markets outside the state, Jalandhar district because market efficiency cooperative/group marketing may be was 9.72 in this chain as compared to 1.01 in encouraged. This will increase income of the Supply Chain-II and 0.90 in Supply Chain-I. farmers. Modern market infrastructure may be More efficiency in case of Apni Mandi was built up with the public-private partnership to due to direct sale of produce from producer to bring efficiency in the marketing of potato as consumer. well as other vegetables. REFERENCES Table 9: Marketing efficiency of potato Acharya, S.S. and Aggarwal, N.L. 2008. Agricultural under different supply chains (`q-1) Marketing in India, Oxford & IBH Publishing Co. Pvt. Ltd., New Delhi. Particulars Supply Chain Anantia. 2008. What is India’s share in global I II III Producer's sale price 430.00 450.00 700.00 vegetable and fruit market? Culled from Consumer's purchase price 750.00 750.00 700.00 www.managementparadise.com Total marketing costs 177.20 174.05 65.27 Anonymous. 2009. Data on vegetable production Total margins of intermediaries 215.60 198.75 - in Punjab, Department of Horticulture, Punjab, Net price received by the 357.20 377.20 634.73 Chandigarh. farmer Dastagiri, M.B., Kumar, B.G., and Diana, S. 2009. Marketing efficiency 0.90 1.01 9.72 Innovative models in horticulture marketing in

831 India. Indian Journal of Agricultural Marketing. July 12: 4. 23: 83-94. Salaria, A.S. and Salaria, B.S. 2010. Horticulture at Gill, K.S. 1990. Marketing of grapes in Punjab. a glance. Jain Publishers, New Delhi, India. Research Report. Department of Economics and Sidhu, M.S. and Gill, K.S. 1989. Price spread of Sociology, Punjab Agricultural University, Blood-Red Malta in Punjab. Indian Horticulture Ludhiana. 36: 11-15. Government of India. 2001. Report of the working Sidhu, M.S. and Singh, S. 2005. Production and group on horticulture development for Tenth Five marketing of potato in Punjab. PSE Economic Year Plan (Main Report) Planning Commission, Analyst. 25: 103-118. New Delhi. Sidhu, R.S., Sidhu, M.S., and Singh, J.M. 2011. Jairath, M.S. 2008. Enhancing farmers’ linkage to Marketing efficiency of green peas under markets. Indian Journal of Agricultural different supply chains in Punjab. Agricultural Marketing. 22: 355-356. Economics Research Review. 24: 267-273. Kaur, A. 2015. An economic analysis of seed Sidhu R.S, Sidhu M.S., and Singh, J.M. 2012. management and marketing of potato in Punjab, Marketing efficiency of potato under different Ph.D. Thesis approved by Punjab Agricultural supply chains in Punjab. Indian Journal of University, Ludhiana: 1-126. Agricultural Marketing. 26: 14-24. Kaur, S. 2005. Diseases of potato: Potato cultivation in Punjab. Punjab Agricultural University, Ludhiana: 20-28. Rambani, V. 2004. Mandi shift unearths massive Received: August 31, 2015 tax evasion. Hindustan Times, Chandigarh: 80: Accepted: October 15, 2015

This paper is based on the Ph.D. (Agricultural Economics) Dissertation of the first author approved by Punjab Agricultural University, Ludhiana141004 (India) in 2015.

832 Indian J Econ Dev DOI: 10.5958/2322-0430.2015.00091.8 Volume 11 No.4 (2015): 833-842 Research Article

SUGARCANE PRODUCTION SCENARIO IN INDIA WITH PARTICULAR REFERENCE TO PUNJAB

A.K. Brar* and P. Kataria**

ABSTRACT

The present study, conducted to examine the over time changes in area, production and productivity of sugarcane in India with particular reference to Punjab, has been based on secondary data. The secondary data pertaining to the area, production and productivity of sugarcane for the study period 1980-81 to 2013-14 were culled from various published sources. The study highlighted that area as well as production of sugarcane in India nearly doubled during the study period. The production profile of sugarcane in major sugarcane producing states of India revealed that Uttar Pradesh, Maharashtra, Karnataka and Tamil Nadu are the major sugarcane producing states of India. In 2013-14, contribution of Punjab to sugarcane production of India has been reported as 1.8 per cent. The productivity of sugarcane in Punjab is less than that of India. The study further highlighted the declining compound annual growth rate for sugarcane area, production and productivity for India as well as Punjab during the later part (2000-01 to 2013-14) of the study period. Notwithstanding, the declining growth rates of area under sugarcane, the proportion of net sown area allocated to sugarcane cultivation has increased from what it was in the beginning of the study period, both in the case of India and Punjab. Decomposition analysis illustrated the role of increasing the sugarcane acreage in a bid to increase the sugarcane production in the country at large and Punjab in particular.

Keywords: Decomposition, India, Punjab, sugarcane. JEL Classification: C23, O47, Q10, Q18

INTRODUCTION contents. Production of sugar by boiling the Sugarcane has been one of the most cane juice was first discovered in India, most important crops cultivated widely in India likely during the first millennium BC (Sharpe, since times immemorial. Sugarcane was 1988). Sugarcane is the world’s largest crop originally grown for the sole purpose of by the quantity of production. As per FAO chewing in South-eastern Asia and the Pacific. estimates for 2012, it was cultivated on about The rind was removed and the internal tissues 26.0 million hectares, in more than 90 countries, sucked or chewed due to high sugar and juice with a worldwide harvest of 1.77 billion tons. Brazil is the largest producer of sugarcane in *Research Scholar and **Senior Economist (QM), the world accounting for 39.1 per cent of the Department of Economics and Sociology, Punjab global output. The next four major producers, Agricultural University, Ludhiana-141 004 in descending order of production were India Email: with 19.6 per cent share in global output

833 followed by China (6.73 per cent), Thailand semi-tolerant to salinity and can be (5.34 per cent) and Pakistan (3.17 per cent). successfully grown on all types of well drained Sugarcane is the main source of sugar, gur soils, it is being considered a viable and khandsari in India. As per the rough diversification option in Punjab agriculture, estimates, about two-thirds of the total which has been dominated by paddy-wheat sugarcane produced in India is consumed for crop rotation resulting in many ecological making gur and khandsari and only one third problems. The present study is a modest effort of it goes to sugar factories. It also provides to bring into lime light the characteristics raw material for manufacturing alcohol. features of sugarcane economy of India and Broadly, there are two distinct agro-climatic Punjab. regions of sugarcane cultivation in India, viz.; METHODOLOGY tropical and subtropical. Tropical region with In order to examine the overtime changes nearly 45 per cent of the sugarcane acreage in area, production and productivity of and average productivity of 77 tons per hectare sugarcane in Punjab vis-a-vis India, accounted for 55 per cent of the total secondary data has been taken resort to. The sugarcane production in the country. The sub- time frame for the study of changes in the tropical region, characterized by lower production profile of sugarcane in the country productivity of about 63 tons per hectare at large was selected as 1980-81 to 2013-14. accounted for only 45 per cent of total The information on pertinent variables has production of sugarcane on 55 per cent of been extracted from the various issues of country’s sugarcane acreage (Anonymous, Statistical Abstract of Punjab, Agricultural 2013). Statistics at a Glance, Economic Survey of The tropical sugarcane region includes the India. states of Maharashtra, Andhra Pradesh, Tamil The dispersion and growth analysis were Nadu, Karnataka, Gujarat, Madhya Pradesh, carried out by working out the coefficients of Goa, Pondicherry and Kerala. The area under variation and compound growth rates for sugarcane cultivation during 2000-01 has been sugarcane production parameters for Punjab recorded at 4.32 million hectares, which and India for three periods viz. increased to 5.02 million hectares in 2013-14. Period I: 1980-81 to 1990-91, During the same period, the sugarcane Period II: 1990-91 to 2000-01, and production also increased from 295.96 million Period III: 2000-01 to 2013-14. tons to 348.38 million tons, primarily due to The observed increase/ growth in marginal increase in sugarcane yield from production of sugarcane crop over the pre- 68.51to 69.40 tons per hectare during the same determined time periods has been decomposed period (Anonymous, 2014). Major states into area effect, yield effect and interaction producing sugarcane in India are Uttar effect. Pradesh, Maharashtra, Karnataka, Tamil Nadu, RESULTS AND DISCUSSIONS Andhra Pradesh, Gujarat and Punjab. Sugarcane is an important cash crop being In Punjab, sugarcane occupied an area of cultivated in India since times immemorial. 89 thousand hectares in 2013-14. The districts, India is the second largest producer of Gurdaspur and Hoshiarpur of Punjab are well sugarcane in the world, first being Brazil. Its ahead of all other sugarcane cultivating production profile has seen myriad changes districts of the state contributing significantly overtime. (47.2 per cent to area and 45.6 per cent to Trends in Area, Production and Productivity production) to the state’s sugarcane economy of Sugarcane in India (Anonymous, 2014). This section includes the trends in area, Since sugarcane is a relatively stable crop, production and productivity of sugarcane in

834 India as well as Punjab. The trends in area, revealed that it was highest for sugarcane area production and productivity of sugarcane in (10.25 per cent) and production (13.68 per cent) India, from 1980-81 to 2013-14 have been during 2000-01 to 2013-14. Taking into account presented in Table 1. It can be seen that in the entire period under consideration, i.e. 1980- 1980-81, area under sugarcane was 2.67 million 81 to 2013-14, the coefficients of variation for hectares, which increased to 3.69 million sugarcane area, production and productivity hectares in 1990-91 and further to 4.32 million were worked out to be 17.70, 23.25 and 7.42 hectares in 2000-01. The area under sugarcane percent, respectively. in India has been reported as 5.02 million The growth analysis has been carried out hectares in 2013-14, up from 2.67 million by working out the compound annual growth hectares at the start of the study period in 1980- rates for area, production and productivity of 81. sugarcane for different time periods and the Similarly, production of sugarcane has also results are presented in Figure 1. During the increased during the same period. It was 154.25 period 1980-81 to 1990-91; the area, production million tons in 1980-81, which increased to and productivity of sugarcane marked the 241.05 million tons in 1990-91 and further to highest growth rates as compared to other 348.38 million tons in 2013-14.The production periods under consideration. The compound of sugarcane more than doubled in the study annual growth rates of area, production and period (1980-81 to 2013-14), which visually productivity of sugarcane observed during seems to be attributed to increase in this period were 3.29, 4.53 and 1.24 percent productivity from 57.77 tons per hectare in respectively. The period 2000-01 to 2013-14 1980-81 to 69.40 tons per hectare in 2013-14. marked the lowest compound annual growth Among the five major producers of sugarcane rates with respect to all the three parameters in the world; namely, Brazil, India, China, i.e. 1.16 percent for area, 1.26 percent for Thailand and Pakistan, India ranks fourth in production and a meager 0.10 percent for sugarcane productivity. Sugarcane sugarcane productivity. Considering the entire productivity in India is also less than the global period of study, starting from 1980-81 and sugarcane productivity recorded at 71.10 tons ending by 2013-14, the compound annual per hectare (Anonymous, 2015).Coefficients growth rates of the three parameters viz. area, of variation were worked out and the results production and productivity have been

Table 1: Production profile of sugarcane in India, 1980-81 to 2013-14 Year Area Production Productivity (million ha) (million t) (tha-1) 1980-81 2.67 154.25 57.77 1985-86 2.85 170.65 59.88 1990-91 3.69 241.05 65.33 1995-96 4.15 281.10 67.73 2000-01 4.32 295.96 68.51 2005-06 4.20 281.17 66.95 2010-11 4.88 342.38 70.16 2011-12 5.04 361.04 71.63 2012-13 4.99 341.20 68.38 2013-14 5.02 348.38 69.40 Coefficient of variation (%) 1980/81-1990/91 9.06 13.27 5.34 1990/91-2000/01 7.27 9.60 3.88 2000/01-2013/14 10.40 13.76 4.80 1980/81-2013/14 17.70 23.25 7.42

835 5 4.53 4.5 1980/81 to 1990/91 4 1990/91 to 2000/01 2000/01 to 2013/14 3.5 3.29 1980/81 to 2013/14 3 2.5 2.5 2.07 2 1.93 1.59 1.5 1.26 1.16 1.24 1 0.48 0.57 0.5 0.1 0 Area Production Productivity Figure 1: Compound annual growth rates (%) of area, production & productivity of sugarcane in India

recorded at 1.93, 2.50 and 0.57 percent per productivity of the state being the lowest as annum, respectively. compared to the other major sugarcane The production profile of sugarcane in producing states. Tamil Nadu has the highest major sugarcane producing states of India for sugarcane productivity of 102.71 tons per the TE 2013-14 reveals that Uttar Pradesh, hectare among the major sugarcane producing Maharashtra, Karnataka and Tamil Nadu are states of the country. Further, Tamil Nadu, the major sugarcane producing states of India Maharashtra, Karnataka and Andhra Pradesh (Table 2). These four states, collectively have sugarcane productivity higher than that account for 78.3 percent of the country’s of the country as a whole. The sugarcane sugarcane acreage and contribute 80.2 per cent productivity in Uttar Pradesh, Punjab and to the total sugarcane production. Uttar Gujarat is conspicuously lower than that of Pradesh, the largest sugarcane producing state the country. Punjab has very meager of India, alone accounts for 43.8 percent of the contribution to the country’s sugarcane country’s sugarcane area and 37.7 percent of acreage and production. Punjab, with a the country’s sugarcane output, the sugarcane productivity level of 65.18 tons per hectare,

Table 2: Area, production and productivity of sugarcane in major sugarcane producing states of India, TE 2013-14 States Area Production Productivity million ha % of India million t % of India (tha-1) India 5.02 350.21 69.76 Uttar Pradesh 2.20 43.8 131.98 37.7 59.99 Maharashtra 0.96 19.2 77.25 22.1 80.47 Karnataka 0.43 8.5 36.82 10.5 85.63 Tamil Nadu 0.34 6.8 34.92 9.9 102.71 Andhra Pradesh 0.19 3.9 15.81 4.5 83.21 Gujarat 0.19 3.7 12.69 3.6 66.79 Punjab 0.08 1.7 5.48 1.6 65.18 Others 0.62 12.4 35.26 10.1 56.87

836 which is below the country’s average, in 2005-06 to 1.68 percent in 2010-11 and produces only 1.6 percent of country’s increased further to 1.94 percent in 2011-12. In sugarcane from 1.7 percent of country’s 2012-13, only 2.0 per cent of the NSA in Punjab sugarcane area. was allocated to sugarcane cultivation. The Importance of Sugarcane in the Cropping story is different if we talk about the importance Pattern of India and Punjab of sugarcane in the cropping pattern of the How strategic a particular crop to the country as a whole. In 1980-81, as little as 1.91 economy of a particular state/country or a per cent of the net sown area was under geographical location is, can be gauged by sugarcane cultivation in the country, which the proportion of cultivable area allocated to increased to 2.58 per cent in 1990-91 and further that crop. The importance of sugarcane in the to 3.06 percent in 2000-01. In 2011-12, the cropping pattern of Punjab as well as India proportion of NSA allocated to sugarcane over the years has been reflected in Table 3. cultivation was 3.58 percent in the country, Although, the area under sugarcane in Punjab the highest observed during the study period. has increased from 71 thousand hectares in Despite of the fact that the importance of 1980-81 to 101 thousand hectares in 1990-91 sugarcane crop is improving in the country, and further to 121 thousand hectares in 2000- the same does not quite hold good in the case 01, the proportion of net sown area allocated of Punjab. to sugarcane has not increased much during Trends in Area, Production and Productivity this period. It can be seen that in 1980-81, about of Sugarcane in Punjab 1.69 percent of the net sown area (NSA) in The trends in area, production and Punjab was under sugarcane cultivation, which productivity of sugarcane in Punjab from 1980- increased to 2.39 per cent in 1990-91 and further 81 to 2013-14 have been presented in Table 4. to 2.85 percent in 2000-01. The area under As reported earlier also, area under sugarcane sugarcane declined from 84 thousand hectares in Punjab was 71 thousand hectares in 1980- in 2005-06 to 70 thousand hectares in 2010-11. 81, which increased to 101 thousand hectares The sugarcane acreage in Punjab further in 1990-91 and further to 121thousand hectares increased to 83 thousand hectares in 2012-13, in 2000-01, but thereafter it started declining. but the proportion of NSA allocated to This can be attributed to delayed payment sugarcane cultivation has not shown any issues in case of marketing of sugarcane crop considerable increase. and increased importance of paddy-wheat The proportion of sugarcane acreage to rotation in Punjab. In 2010-11, area under the net sown area declined from 2.01 percent sugarcane declined to 70 thousand hectares

Table 3: Overtime changes in Net Sown Area (NSA) apportioned to sugarcane in Punjab and India, 1980-81 to 2012-13 Year Punjab India NSA Sugarcane area NSA Sugarcane area ('000 ha) '000 ha % of NSA ('000 ha) '000 ha % of NSA 1980-81 4191 71 1.69 140000 2670 1.91 1985-86 4197 78 1.86 140900 2850 2.02 1990-91 4218 101 2.39 143000 3690 2.58 1995-96 4158 150 3.61 142200 4150 2.92 2000-01 4250 121 2.85 141400 4320 3.06 2005-06 4170 84 2.01 141160 4200 2.98 2010-11 4158 70 1.68 141580 4880 3.45 2011-12 4134 80 1.94 140801 5040 3.58 2012-13 4150 83 2.00 140801 4990 3.54

837 Table 4: Trends in production profile of sugarcane in Punjab with respect to India, 1980-81 to 2013-14 Year Area Production Productivity (tha-1) '000 ha % of India '000 t % of India India Punjab 1980-81 71 2.7 3920 2.5 57.77 55.21 1985-86 78 2.7 5030 2.9 59.88 64.49 1990-91 101 2.7 6010 2.5 65.33 59.50 1995-96 150 3.6 9720 3.5 67.73 64.80 2000-01 121 2.8 7770 2.6 68.51 64.21 2005-06 84 2.0 4860 1.7 66.95 57.86 2010-11 70 1.4 4170 1.2 70.16 59.57 2011-12 80 1.6 5653 1.6 71.63 70.66 2012-13 83 1.7 5919 1.7 68.38 71.31 2013-14 89 1.8 5520 1.8 69.40 62.02 Coefficient of Variation (Percent) 1980/81-1990/91 13.56 13.41 5.91 1990/91-2000/01 24.45 24.59 4.01 2000/01-2013/14 27.79 27.59 7.79 1980/81-2013/14 25.26 25.20 6.25 and started increasing thereafter, primarily due and further to 97.2 lakh tonnes in 1995-96. The to governmental initiatives, to reach 80 sugarcane production exhibited a decline thousand hectares by 2011-12 and further to thereafter and was recorded at 41.7 lakh tonnes 89 thousand hectares recorded in 2013-14. in 2010-11.The sugarcane production picked Similarly, sugarcane production also up to reach 59.2 lakh tons in 2012-13. marked an increase from 1980-81 to 1995-96 Sugarcane productivity in Punjab has also and thereafter declined continuously from undergone marked fluctuations. During the 1995-96 to 2010-11. In 1980-81, the sugarcane entire study period, the productivity of production in the state was 39.2 lakh tonnes sugarcane hovered between 55.21 tonnes per which increased to 60.1 lakh tons in 1990-91 hectare (observed in 1980-81) to 71.3 tonnes

5 4.34 1980/81 to 1990/91 1990/91 to 2000/01 4 3.59 2000/01 to 2013/14 1980/81 to 2013/14

3 2.6

1.82 2 1.04 0.75 0.78 1 0.69 0.35 -2.34 -0.27 -2.6 0 Area Production Productivity -1

-2

-3 Figure 2:Compound annual growth rates (%) of area, production & productivity of sugarcane in Punjab

838 per hectare attained in 2012-13. The year 2012- in other words all the three parameters viz. area, 13 has been exceptionally good year in terms production and productivity have declined of sugarcane productivity (71.31 tha-1) of during 2000-01 to 2013-14, the rate of decline Punjab, which was even higher than the being the highest in the case of productivity. country’s average of 68.38 tons per hectare. Taking into account the entire period under In 1980-81, Punjab’s share in total area consideration, area under sugarcane has under sugarcane in India was 2.7 percent which increased at a growth rate of 0.69 percent, remained the same till 1990-91, increased to 2.8 production of sugarcane at 1.04 percent with percent in 2000-01 and again dwindled down productivity growth pegged at mere 0.35 to 1.4 percent in 2010-11. It has been reported percent per annum. that Punjab accounted for 1.8 percent of total The district wise area, production and sugarcane area of India in 2013-14. Similarly, productivity of sugarcane in Punjab is shown the percentage share of Punjab in the country’s in Table 5. It can be seen that Gurdaspur and sugarcane production ranged from 2.5 percent Hoshiarpur districts of Punjab have the highest in 1980-81 to 3.00 percent in 1990-91, which area (21 thousand ha in each district) under increased further to 3.5 percent in 1995-96. In sugarcane cultivation. Though productivity the late nineties, the proportion of Punjab’s (in terms of gur) in both the districts is less sugarcane production to that of India started (6143 kgha-1 and 5857 kgha1) than that recorded declining and in 2010-11 it was 1.2 percent. In for the state as a whole (6202 kgha-1). These 2013-14, contribution of Punjab to sugarcane two districts together contribute 45.65 percent production of India has been reported as 1.8 to the state’s sugarcane output. Other major percent. The productivity of sugarcane in sugarcane producing districts in Punjab are Punjab is less than that of India. In 1980-81, Jalandhar, S.B.S Nagar and Amritsar with 10, 6 sugarcane productivity was 55.21 tonnes per and 5 thousand hectares of area under hectare in Punjab, while it was 57.77 tonnes sugarcane cultivation. These three districts per hectare in India. In year 2013-14, the collectively contribute 23.37 percent to the sugarcane productivity has been reported as state’s sugarcane production. The 69.40 and 62.02 tonnes per hectare in India and productivity of sugarcane (in terms of gur) is Punjab respectively. The coefficient of the highest in Patiala district of Punjab (7667 variation calculated for area, production and kgha-1), closely followed by Sangrur and productivity of sugarcane in Punjab were 25.26, Ludhiana. Fazilka district accounts for 2.25 25.20 and 6.25 percent, respectively for the percent of the sugarcane acreage and 2.54 entire study period (highest being 27.79, 27.59 percent of the state’s sugarcane production and 7.79 percent during 2000-01 to 2013-14). with productivity of 7000 kg of gur per hectare. The findings of the growth analysis of Decomposition Analysis for Sugarcane w.r.t. sugarcane production in Punjab have been India and Punjab presented in Figure 2. Compound annual The decomposition analysis has been growth rates of area and production of carried out to decompose the change in sugarcane in Punjab were the highest for the sugarcane production into its constituents, to period 1980-81 to 1990-91, as compared to all account for area, yield and interaction effect. the other time periods, highlighting the The results of this analysis for India and increasing importance of sugarcane in the Punjab have been presented in Table 6. The cropping pattern of the state. The growth rate decomposition analysis for India reveals that was 3.59 percent for area and 4.34 percent per during the period 1980-81 to 1990-91, the annum for production of sugarcane. During increase in production of sugarcane to the tune the period 2000-01 to 2013-14, all the three of 86.8 million tons is largely due to the area parameters have marked a negative growth rate, effect (67.9 percent), while yield and the

839 Table 5: District wise area, production and productivity of sugarcane in Punjab, 2013-14 Districts Area Production Productivity* ('000 ha) ('000 t) (kgha-1) Gurdaspur 21 129 6143 (23.60) (23.37) Hoshiarpur 21 123 5857 (23.60) (22.28) Jalandhar 10 63 6300 (11.24) (11.41) S.B.S. Nagar 6 33 5500 (6.74) (5.98) Amritsar 5 33 6600 (5.62) (5.98) Pathankot 4 19 4750 (4.49) (3.44) Kapurthala 4 27 6750 (4.49) (4.89) Sangrur 3 22 7333 (3.37) (3.99) Patiala 3 23 7667 (3.37) (4.17) Fatehgarh sahib 3 20 6667 (3.37) (3.62) Fazilka 2 14 7000 (2.25) (2.54) Rupnagar 2 13 6500 (2.25) (2.36) Ludhiana 2 14 7000 (2.25) (2.54) Others 3 19 6333 (3.37) (3.44) Punjab 89 552 6202 (100.00) (100.00) Figures in parentheses are the percentage to the state *Production and productivity in terms of gur interaction of yield and area has contributed acreage, as enunciated by area effect of 69.9 23.2 and 8.9 percent, respectively. In the period percent. The yield and interaction effect for 1990-91 to 2000-01, the increase of 54.9 million this period have been recorded as 16.0 and tonnes in country’s sugarcane production can 14.1 percent, respectively, implying thereby be attributed primarily to area effect to the tune that during this period, sugarcane production of 74.9 percent, followed by yield (21.4 percent) has increased largely due to the increase in and interaction (3.78 percent) effect. Further, area under sugarcane cultivation. The increase in the period 2000-01 to 2013-14, the area effect in yield of sugarcane has also contributed to on sugarcane production was 163.7 percent, the increase in sugarcane production in India which is quite pronounced as compared to but not as much as area has. previous periods and high enough to offset In Punjab, during 1980-81to 1990-91, the negative yield and interaction effect. increase in sugarcane production (2.09 million Taking in to account the entire study period t) was primarily due to increase in area as i.e. from 1980-81 to 2013-14, it can be observed enunciated by area effect, which was as high that increase in the country’s sugarcane as 79.3 percent (this effect got diminished in production to the extent of 194.13 million tonnes the decade of 90’s), and yield and interaction has been propelled significantly by the effect being 14.6 and 6.2 percent, respectively.

840 Table 6: Decomposition of change in sugarcane production of India and Punjab into area, yield and interaction effect Period Change in production Effect, % (million t) Area Yield Interaction India 1980-81 to 1990-91 86.80 67.9 23.2 8.9 1990-91 to 2000-01 54.90 74.9 21.4 3.7 2000-01 to 2013-14 52.42 163.7 -61.9 -1.8 1980-81 to 2013-14 194.13 69.9 16.0 14.1 Punjab 1980-81 to 1990-91 2.09 79.3 14.6 6.2 1990-91 to 2000-01 1.76 67.6 27.0 5.4 2000-01 to 2013-14 -2.25 83.8 12.7 3.4 1980-81 to 2013-14 1.60 62.1 30.2 7.7

In the period 1990-91 to 2000-01, the area, yield of net sown area allocated to sugarcane and interaction effect, have been recorded as cultivation has increased from what it was in 67.6, 27.0 and 5.4 percent, respectively. The the beginning of the study period, both in the period 2000-01 to 2013-14 marked a decline in case of India and Punjab. Unlike many western the sugarcane production by 2.25 million or major sugarcane growing countries, tonnes, attributed mainly to area effect (83.8 sugarcane is the major source of sugar in our per cent) followed by yield effect to the tune country and therefore, any mismatch between of 12.7 percent and relatively smaller (3.4 demand and supply of sugar in the country percent) interaction effect. Considering the assumes significance at the national level and entire study period starting from 1980-81 to influences the economics of sugarcane 2013-14, sugarcane production in Punjab has cultivation to a great extent. Further, the increased by 1.60 million tonnes. The increase decomposition analysis illustrated the role of in area under sugarcane cultivation has increasing the sugarcane acreage in a bid to contributed much to the increase in sugarcane increase the sugarcane production in the production (area effect of 62.1 percent), while country at large and Punjab in particular. the contribution of increase in productivity has REFRENCES been recorded at 30.2 per cent. This analysis Anonymous. 2010. Statistical Abstract of Punjab, illustrates the role of increasing the sugarcane Economics and Statistical Organization, acreage in a bid to increase the sugarcane Government of Punjab, Chandigarh, India. Anonymous. 2011. Vision 2030, Indian Institute of production in the country at large and Punjab Sugarcane Research, Lucknow, U.P., India. in particular. Anonymous. 2012. Statistical Abstract of Punjab, CONCLUSION Economics and Statistical Organization, Sugarcane has been one of the most Government of Punjab, Chandigarh, India. important crops cultivated widely in India Anonymous. 2013. Status paper on sugarcane, since times immemorial. Although India Directorate of Sugarcane Development, produces one-fifth of global sugarcane, the Department of Agriculture and Cooperation, study highlighted the declining compound Ministry of Agriculture, Government of India, annual growth rate for sugarcane area, Lucknow, U.P., India. Anonymous. 2014a. Agricultural Statistics at a production and productivity for India as well Glance, Directorate of Economics and Statistics, as Punjab during the later part (2000-01 to Department of Agriculture and Cooperation, 2013-14) of the study period, 1980-81 to 2013- Ministry of Agriculture, Government of India, 14. Notwithstanding the declining growth New Delhi, India. rates of area under sugarcane, the proportion Anonymous. 2014b. Statistical Abstract of Punjab,

841 Economics and Statistical Organization, Sethi, A.S. and Kanwar, R.S. 1986. Diversification Government of Punjab, Chandigarh, India. of factors affecting sugarcane and sugar Anonymous. 2015. Report on Price policy for production in India. Agricultural Situation in sugarcane: 2015-16 Sugar Season, Commission India. 40: 1103-06. for Agricultural Costs and Prices, Department Sharpe, P. 1998. Sugarcane: Past and present. of Agriculture and Cooperation, Ministry of Southern Illinois University Carbondale / Agriculture, Government of India, New Delhi. Ethnobotanical Leaflets. Kumawat, L. and Prasad, K. 2012. Supply response of sugarcane in India: Results from all-India and state-level data. Indian Journal of Agricultural Econonmics. 67:585-99. Lal, J. 1985. Factors responsible for sugarcane area Received: August 05, 2015 and yield fluctuations. Indian Sugar. 35: 7-12. Accerpted: October 10, 2015

842 Indian J Econ Dev DOI: 10.5958/2322-0430.2015.00092.X Volume 11 No. 4 (2015): 843-850 Research Article

TREND ANALYSIS IN MARKET ARRIVALS AND PRICES OF MOTH BEAN IN RAJASTHAN

Subhita Kumawat* and I.P. Singh**

ABSTRACT

The seasonal indices analysis of arrivals and prices revealed that when major portion of the produce was received in the market, the prices were at the lowest. There was a significant negative correlation between prices and arrivals of Moth bean in Nagaur and Jodhpur. However, there was negative but non-significant correlation in Merta and Jaipur markets. However, in Nokha market, the correlation between arrivals and prices was positive but non-significant. Trend analysis of arrivals of moth bean in Nagaur, Merta, Jodhpur and Jaipur markets showed decreasing trend (non- significant) except Jodhpur market where it was significant. However, the trend values of arrivals in Nokha market showed positive but non-significant trend over the years. During the peak period, the prices of moth bean are depressed. Therefore, government should enhance efforts in procurement of moth bean at MSP in the peak period. Farmers should also avail of marketing loans to withhold their produce for some time so that they can get remunerative price during mid period and lean period. Credit facilities should also be given to farmers under Gramin Bhandaran Yojana to have scientific storage facilities at village level.

Keywords: Integration, seasonal variation, market, moth bean, trend JEL Classification: C32, C81, C87, Q13, Q18

INTRODUCTION in India has been pushed to marginal lands Moth bean was used for preparing soup and rainfed areas. The major area under pulses and several confectionary items like papad, lies in Madhya Pradesh (20 percent), Rajasthan bhujia, namkeen, wada, etc. which are used (17 percent), Maharashtra (14 percent), Uttar as daily snacks. Moth bean is source of food, Pradesh (10 percent), Karnataka (9 percent), feed, fodder and green manuring. The medical Andhra Pradesh (8 percent), Chhattisgarh (4 uses of moth bean, especially in reducing fever, percent), Bihar (3 percent) and Tamil Nadu (3 as well as the narcotic property of its roots are percent). India ranks first in the world in terms well known. India is the largest producer and of pulse production and produces 25 percent consumer of pulses in the world. Pulses are of total world’s production. These states grown on an average of about 23 million ha contribute 80 percent of total pulse production. area in India. Over the years, pulses cultivation Agriculture in Rajasthan state is primarily rainfed. The period of monsoon is short, *Ph.D. Scholar and Professor, Department of around three months. The cultivated area under Agricultural Economics, College of Agriculture, kharif season is about 61 percent of the total SKRAU, Bikaner-334006 cultivated area which, to a large extent, is E-mail: [email protected] dependent on rains which remain scanty and

843 irregular. The ground water table in the state is Hence, they have enormous human and rapidly going down. Nearly, 30 percent of political implications, especially in developing agricultural area is under irrigation. In countries. Prices of farm goods affect income Rajasthan, the pulses occupied 4197.72 and living standards of farmers, rural labourers thousand ha of area and production was and the non-farming population. They also 2471.10 thousand tonnes in 2013-14 affect the prices of non-farm goods and foreign (www.rajasthankrishi.gov.in). These are mainly trade. However, one way to throw some light cultivated in arid and semi-arid districts on this issue is to analyze the market including Nagaur, Jaipur, Jodhpur, Sikar, Pali, performance by studying market integration Jhunjhunu and Ajmer. Wide fluctuations have (Mukhtar and Javed, 2007). The degree to been observed in both area and production of which consumers and producers would benefit pulse crops in Rajasthan mainly due to change depends on how domestic markets are in weather and climatic conditions. It is integrated with world markets and how observed that during the years with favourable different regional markets are integrated with weather conditions, pulse production each other (Varela et al., 2012). increases to double of the average production. Although, several studies have been done Markets are glutted and prices of pulses slump empirically using co-integration techniques down. The increased production does not which concern the market integration of result in increased income to the producers. agricultural commodities in India (Aulukh, The situation becomes worse during bad 1983, Birukal , 2001, Acharya, 2003, Kumar, agricultural years. These conditions create 2003, Virender et al., 2004, Khunt et al., 2006, malpractices in pulse marketing to the Jaya, 2009, Kolur et al., 2012, Sekhar, 2012, disadvantage of producer sellers. Thombre et al.,2013, and Sharma et al., 2014) It is a well-known fact that Indian only a little work has been carried out on the agriculture is characterised by wide variations empirical evaluation of moth bean market in output of major crops which subsequently integration. The study was carried out with lead to wider fluctuations in market arrivals. objective, to study marketing pattern and price The extent of fluctuations in market arrivals behaviour of Moth bean in Nagaur district of largely contributed to the price instability of Rajasthan. major crops. In order to device the appropriate METHODOLOGY ways and means for not only reducing the For the present study, Nagaur, Merta city, degree of fluctuations in the prices of Nokha, Jodhpur and Jaipur markets of agricultural products but also increasing the Rajasthan were selected. Secondary data in quantity of market arrivals, there is need to respect of arrivals in different markets and have a perfect understanding about the wholesale prices of Moth bean prevailing in behaviour of prices of different agricultural these markets were obtained from Directorate products over a period of time. Marketing plays of Economics and Statistics, Government of an important role in the economic development Rajasthan, Jaipur. The relationship between as it stimulates production, avoids prices in different markets has been studied unnecessary fluctuation in output and prices. using simple correlation (Chahal et al., 2004 The past trends in market arrivals of and Andhalkar et al., 2011).To study the commodities are also useful in understanding relationship between market arrivals and the present and to forecast the future. Prices wholesale prices of moth bean in the selected play a vital role in predominantly agricultural markets, simple correlation coefficient was economies like India. Prices of farm products worked out. fluctuate more than those of industrial goods Market Integration due to heavy dependence on natural factors. Market integration was studied by zero

844 order correlation matrix approach using were higher in lean period and lower in mid correlations between prices of selected pulse and peak period (Table 1). crops in primary wholesale markets, secondary In Nokha market, the peak period arrivals wholesale markets, and terminal wholesale were in the range of 26.25 to 52.89 percent. In market in the state. the mid period, it ranged from 31.12 to 41.58 Trend Analysis percent and in the lean period, the arrivals were To work out seasonal effects in prices, in the range of 5.53 to 39.68 percent (Table 1). linear trend method was used. Linear trend The prices in Nokha market followed the same method is described as under; trend as in Nagaur and Merta market.In

Pt = a +b Tt +Ut Jodhpur market, (Table 1), the peak period where; arrivals were in the range of 31.37 to 50.78

Pt = Price during the year. percent. th Tt = Serial no. assigned to the t year. In the mid period, it ranged from 26.47 to

Ut = Random disturbance term. 35.75 percent and in the lean period, the arrivals Decomposition Analysis were in the range of 15.87 to 35.22 percent. Seasonal fluctuations were calculated by The prices in different periods followed same eliminating trend, cyclic and irregular trend as in Nagaur, Nokha and Merta market. fluctuations. The seasonal indices were In Jaipur market, the peak period arrivals calculated by the multiplicative model. were in the range of 68.88 to 88.99 percent RESULTS AND DISCUSSION (Table 1). In the mid period, it ranged from 6.83 With a view to examine the marketing to 22.96 percent and in the lean period, the pattern of moth bean, the crop year was split arrivals were in the range of 3.00 to 9.23 percent. up into three periods viz., (i) Peak marketing The prices, in general were higher in lean period period (September to December) when majority as compared to mid and peak periods. The of producers, especially the small farmers sell pattern of arrivals was almost the same in all their produce, (ii) Mid marketing period the markets. The maximum arrivals were in the (January to April) when producers of average peak period followed by mid period and lean financial position sell because they cannot period. In most of the years, the prices were withhold their produce any longer and (iii) Lean lower in the peak and mid periods than those marketing period (May to August) when the on lean period barring a few exceptions. producers of only sound financial position sell Seasonal Variations in Arrivals of Moth bean their produce. The perusal of Table 1 indicates in Selected Markets that in Nagaur market, the arrivals in the peak A number of studies have examined the period were the maximum during the last 10 seasonal variation in arrivals and prices of years (2004-05 to 2013-14). These were in the agricultural commodities in India (Agarwal and range of about 22.58 (2012-13) to 86.27 percent Sharma, 1990, Singh et al., 1995, Sushila (2006-07) of the total arrivals and the least in Srivastava and Brahm,1996, Jambhale et al., the lean period, ranging between 6.55 percent 2008, and Jalikatti et al., 2013). In order to (2009-10) to 27.01 percent (2012-13). ascertain the long run seasonal variation in In Merta city market, the arrivals of moth the arrivals of moth bean in the selected bean followed more or less the same pattern markets, seasonal indices for arrivals were as in Nagaur. The peak period arrivals ranged calculated. The seasonal indices of monthly between 22.27 to 93.11 percent. arrivals of moth bean in the selected markets In the mid period, it ranged from 5.56 to are presented in Table 2. The results clearly 52.84 percent and in the lean period, the arrivals indicate the existence of seasonality in arrivals were in the range of 0.99 to 24.89 percent. The of moth bean in all the markets. Highest arrivals similar trend was observed in prices, which of moth bean in Nagaur market were observed

845 Table 1: Per cent arrivals and prices of moth bean (`q-1) Year Market Arrivals Price Peak period Mid period Lean period Peak period Mid period Lean period Nagaur market 2004-05 53.29 20.94 25.77 362.65 378.68 459.27 2005-06 62.94 19.43 17.63 335.85 366.70 497.56 2006-07 86.27 6.81 6.92 339.02 421.66 439.28 2007-08 55.38 19.51 25.11 317.98 386.93 495.32 2008-09 62.32 19.94 17.74 299.40 386.81 513.57 2009-10 81.02 12.43 6.55 323.96 391.21 481.54 2010-11 54.47 20.27 25.26 315.16 410.05 474.96 2011-12 45.32 38.17 16.51 358.75 403.47 437.93 2012-13 22.58 50.41 27.01 356.43 382.57 460.99 2013-14 81.03 12.18 6.79 290.64 383.90 525.50 Merta city market 2004-05 77.08 17.30 5.62 354.72 383.76 461.60 2005-06 83.34 12.22 4.44 324.17 433.16 442.97 2006-07 93.11 5.56 1.33 384.52 387.96 427.80 2007-08 78.56 19.44 2.00 335.03 378.11 486.71 2008-09 67.43 25.49 7.08 368.92 409.68 421.23 2009-10 76.66 13.32 10.02 247.31 413.07 539.60 2010-11 46.49 52.52 0.99 242.52 342.19 615.15 2011-12 49.79 42.52 7.69 371.49 400.51 427.86 2012-13 22.27 52.84 24.89 288.98 424.23 486.63 2013-14 75.94 15.46 8.60 352.59 414.44 432.99 Nokha market 2004-05 41.28 31.12 27.60 384.00 396.40 419.60 2005-06 38.87 32.99 28.14 386.13 398.26 415.59 2006-07 52.89 41.58 5.53 366.35 399.25 434.39 2007-08 42.35 39.15 18.51 367.50 412.50 420.00 2008-09 42.47 31.59 25.94 372.72 409.09 418.18 2009-10 40.41 37.92 21.67 360.00 410.00 430.00 2010-11 41.37 31.15 27.48 343.75 417.14 439.29 2011-12 40.15 31.34 28.51 340.00 416.00 444.00 2012-13 26.25 34.07 39.68 367.50 407.75 424.75 2013-14 29.38 32.88 37.74 369.04 407.98 423.09 Jodhpur market 2004-05 40.63 28.07 31.30 365.21 411.30 423.91 2005-06 38.20 30.45 31.35 324.85 388.57 486.85 2006-07 46.09 30.97 22.94 374.76 396.17 429.06 2007-08 40.73 28.11 31.16 373.73 402.27 423.98 2008-09 39.19 33.79 27.02 370.60 405.27 424.12 2009-10 38.05 35.75 26.20 323.71 394.42 481.97 2010-11 39.76 26.47 33.77 290.15 435.08 474.77 2011-12 44.08 33.57 22.35 375.52 403.49 421.25 2012-13 31.37 33.41 35.22 352.77 414.14 433.22 2013-14 50.78 33.35 15.87 367.50 407.75 424.75 Jaipur market 2004-05 74.73 18.68 6.59 379.48 402.00 419.09 2005-06 71.37 19.40 9.23 325.02 386.44 488.99 2006-07 80.26 16.74 3.00 396.62 397.05 406.59 2007-08 76.20 15.13 8.67 391.35 395.18 414.07 2008-09 84.23 10.64 5.13 377.90 405.00 417.13 2009-10 68.88 22.96 8.16 340.47 417.85 441.74 2010-11 86.14 8.89 4.97 392.96 400.00 407.03 2011-12 86.89 8.57 4.54 382.75 405.65 411.61 2012-13 85.72 8.17 6.11 394.37 400.34 405.37 2013-14 88.99 6.83 4.18 392.81 400.61 406.77

846 Table 2: Seasonal indices of arrivals of analyse the long run seasonal variations in Moth bean in different markets the prices of moth bean in the selected markets, (Percent) season indices for prices were computed. The Month Markets seasonal indices of monthly prices of moth Nagaur Merta Nokha Jodhpur Jaipur city bean in the selected markets are presented in January 105.74 197.11 108.99 129.97 51.97 Table 3. The higher seasonal price indices February 84.08 99.75 92.62 86.99 34.56 observed in Nagaur market were in the months March 67.71 54.07 102.98 77.79 26.84 of May, June, July and August with values of April 62.73 73.05 97.10 95.37 19.64 115.63, 122.48, 119.09 and 119.22, respectively. May 44.71 22.09 94.96 94.66 13.12 June 49.29 21.55 127.43 78.81 9.35 July 65.04 39.26 99.22 90.90 28.34 Table 3: Seasonal indices of prices of Moth bean in different markets August 38.05 42.39 50.57 55.82 16.30 (Percent) September 48.33 70.69 75.00 74.51 110.28 Month Markets October 253.39 229.09 108.71 113.41 468.03 Nagaur Merta Nokha Jodhpur Jaipur November 207.39 198.47 118.99 137.91 281.05 city December 173.55 152.48 123.42 163.86 140.52 January 97.99 95.99 103.02 101.18 100.54 February 109.46 97.12 105.78 104.76 101.34 during the peak period. The higher market March 97.96 106.04 106.12 102.24 101.48 April 89.03 99.77 96.84 102.76 101.49 arrival indices (more than 100) in Nagaur market May 115.63 121.69 107.76 110.04 102.42 were observed during the months of October June 122.48 124.42 108.09 113.28 103.49 to January, the highest being observed in July 119.09 127.26 107.42 112.28 103.94 month of October (253.39). The range of arrival August 119.22 127.09 127.26 111.57 104.62 indices varied between 38.05 to 207.39. Arrival September 82.37 71.94 89.87 86.56 96.38 indices reached the peak during December October 77.23 74.54 88.21 82.83 94.63 November 82.77 70.69 88.37 80.57 95.05 (163.86) in Jodhpur market and decreased to December 86.77 83.43 89.59 91.95 94.59 55.82 in August. Highest arrivals of moth bean in Jaipur market were observed during the Lower seasonal price indices were month of October (468.03). The range of arrival observed during the months of October (77.23). indices was 9.35 to 281.05 during different Merta city market witnessed the highest months of the year. seasonal price index during July (127.26). Seasonal Variations in Prices of Moth bean Nokha market witnessed the highest seasonal in Selected Market price index in August (127.26). Jodhpur market The seasonal indices of moth bean crops witnessed the highest seasonal price index in in Rajasthan. The data on wholesale as well as June (113.28). The higher seasonal price indices farm harvest prices of all the pulse crops were in Jaipur market were in the months of May, collected during the period of 1972-1987. The June, July and August with values of 102.42, results indicated that price indices were the 103.49, 103.94 and 104.62, respectively. The lowest during peak arrival months (April-May lower seasonal price index was observed months for gram and October-November during the month of December (94.59). The months for moth, moong and urad pulse crops) variation in the price of moth bean in the peak and highest during sowing season months of season and lean season in the selected markets the crop (October-November for gram and does not appear to be significantly higher June-July for moth, moong and urad). Arhar because moth bean is grown under rainfed (long duration kharif pulse crop) depicted conditions. The farmers are not sure of next minimum prices during January-February harvest because of climatic reasons. Therefore, months and maximum in the month of October they retain moth bean till they get the next (Agarwal and Sharma, 1990). In order to harvest of the crop.

847 Relationship between Market Arrivals and Table 5: Trends in Moth bean arrivals and Prices prices, 2004-05 to 2013-14 The degree of relationship between market Markets Intercept b SE (b) arrivals and prices of moth bean was studied Arrivals NS by computing correlation coefficients. The Nagaur 106.50 -5.28 -1.62 Merta city 148.82 -7.23NS -5.09 results of correlation analysis, given in Table Nokha 54.20 8.95NS 2.54 4, reveal a negative correlation between prices Jodhpur 109.90 -7.15** -0.91 and arrivals in Nagaur, Jodhpur, Merta and Jaipur 830.50 -65.41NS -71.88 Jaipur markets which is generally a rule rather Prices than exception. Nagaur 105.80 22.16** 3.10 Merta city 115.43 26.21** 3.03 Nokha 109.86 21.69** 2.79 Jodhpur 109.10 23.55** 2.71 Table 4: Prices and arrivals correlation of ** moth bean in the selected markets Jaipur 90.71 25.27 2.98 **significant at 5 per cent level. Market Correlation NS: Non-significant Nagaur -0.847*** Merta city -0.298NS Nokha 0.040NS in Nokha market showed positive but non- Jodhpur -0.860*** significant trend. The reason for this is that NS Jaipur -0.329 Nokha is in Bikaner district which has large *** Significant at one percernt level. NS: Non-significant tracts of rain-fed areas, where there is no replacement of moth bean with any competing However, in Nokha market, the correlation crop. between arrivals and prices was positive but The price trend in Nagaur, Merta, Nokha, non-significant. This is mainly because Nokha Jodhpur and Jaipur markets, the trend in prices is in Bikaner district which is largely rain-fed was increasing and significant. This was and moth bean is a part of their staple diet. mainly due to declining trends in arrivals of Trends in Arrivals and Prices of Moth bean in moth bean in these markets. the Selected Market Market Integration Several studies have been done empirically Market integration implies the relationship using linear trend techniques which concern among the spatially separated markets. the market trend of agricultural commodities Markets differ in the extent of integration and, in India (Hosamani et al., 2000, Dayakar et al., therefore, there may be a variation in their 2003, Yogisha et al., 2007, and Shruthi et al., degree of efficiency. The extent by which 2013) only a little work has been carried out on prices of a commodity move together over a the empirical evaluation of moth bean market period of time in different markets located at trend. varied distances from each other is an indicator Trend analysis of arrivals of moth bean in of market integration for the commodity. In Nagaur, Merta city, Nokha, Jodhpur, and Jaipur integrated marketing system, price of a markets is presented in Table 5. The table commodity in one market is responsive to price reveals that trend values of arrivals in Nagaur, change in another market and as such, price Merta, Jodhpur, and Jaipur markets showed differences between the markets should not decreasing trend (non-significant) except exceed the transportation and handling costs. Jodhpur market where it was significant. The The analysis of movement in prices of a major reason for this is replacement of moth commodity in different markets helps in bean with other competing crops in the region ascertaining as to what extent the marketing due to installation of tube wells in these system is efficient in respect of that commodity. regions. However, the trend values of arrivals The market integration was studied by making

848 Table 6: Correlation between prices of Birukal, B.Y. 2001. Statistical analysis of price and moth bean in sample markets arrivals of cotton in selected regulated markets Market Nagaur Merta Nokha Jodhpur Jaipur of Northern Karnataka. M.Sc. Thesis submitted city to University of Agricultural Sciences, Dharwad. Nagaur 1.00 0.941*** 0.984*** 0.994*** 0.988*** Chahal, S.S., Singla R., and Kataria P. 2004. Merta city 1.00 0.901*** 0.966*** 0.929*** Marketing efficiency and price behaviour of Nokha 1.00 0.982*** 0.991*** green peas in Punjab. Indian Journal of Jodhpur 1.00 0.989*** Agricultural Marketing. 18 (1): 115-128. Jaipur 1.00 Directorate of Economics and Statistics. 2014. ***significant at one per cent level Government of Rajasthan. Jaipur. Hosamani, S.B., Gumagolmeth, K.C., and zero order correlation matrix. The correlation Savadathi, P.M. 2000. Trends in arrivals and price of groundnut and cotton in Dharwad between prices of moth bean in different market. International Convention of Agricultural markets was studied and is presented in Table Marketing Management-Challenges in the 6. The results reveal that prices of moth bean Millennium, August, MANAGE, Hyderabad. in Nagaur market were highly integrated with 21(2):315-317. Jodhpur, Jaipur, Nokha and Merta. Nokha Jalikatti, N.V., Patil, B.L., Basavaraj, H., Kunal, market was highly integrated with Jaipur and L.B., Yeledhalli, R.A. and Kataraki, P.A. 2013. Jodhpur. Overall, all markets which were Spatial and temporal variations in arrivals and spatially separated show higher degree of prices of onion in Northern Karnataka-An market integration. econometric analysis. Karnataka Journal of Agricultural Sciences. 26 (4): 565-566. CONCLUSIONS Jambhale, A.A., Talathi, J.M., and Patil, H.K. 2008. The study reveals that during peak period, Seasonality in arrivals and prices of selected farm the prices of moth bean are depressed. commodities. International Journal of Therefore, government should enhance efforts Agricultural Sciences. 4 (1): 207-210. in procurement of moth bean at MSP in the Khunt, K.A., Gajipara, H. M., and Vekariya, S. B. peak period. The farmers should also avail of 2006. Price behaviour of major vegetables in marketing loans enable them to withhold their Gujarat state. Indian Journal of Agricultural produce for some time so that they can get Marketing. 20 (1): 45-48. remunerative price during mid period and lean Kolur, A.B., Yeledalli, R.A., Gamanagatti, P.B., and Kolur, A.S. 2012. Market arrivals and price period. Credit facilities should also be given behaviour of wheat in Karnataka. International to farmers under Gramin Bhandaran Yojana to Journal of Commerce and Business have scientific storage facilities at village level. Management. 5 (1): 69-72. REFERENCES Kumar, S. 2003. An economic analysis of onion Acharya, S.S. 2003. Price integration of wholesale production in India. M.Sc. Thesis submitted to markets for food grains and oilseeds in India: CCS Haryana Agricultural University, Hisar. Indian Journal of Agricultural Marketing. 3 (1): Kumari, R.V. 2009. Economic analysis of maize 385-387. price behavior in Andhra Pradesh. Indian Journal Agarwal, N. and Sharma, K.C. 1990. Price behaviour of Agricultural Marketing. 2 (2): 137-146. of pulse crops in Rajasthan. Indian Journal of Mukhtar, T. and Javed, M.T. 2007. Market Agricultural Marketing, 4 (2):128-139. integration in wholesale maize markets in Andhalkar, G.K., Ulemale, D.H., Tayade, N.P., and Pakistan. Regional and Sectoral Economics Mokhale, S.U. 2011. Arrival and prices of major Studies. 8 (2): 85-98. pulses in selected A.P.M.C. of Amravati district. Rao, B.D., Kumar, K.A.B., and Mathew, B. 2003. International Research Journal of Agricultural Trends in production, prices and market arrivals Economics and Statistics. 2 (1):126-131. of sorghum versus completing crops-A critical Aulukh, H.S. 1983. Changing food chain market analysis. Indian Journal of Agricultural structure in India. BR Publishing Corporation, Marketing. 17 (1): 85-92. Delhi. Sekhar, C.S. 2012. Agricultural market integration

849 in India: An analysis of select commodities. Food (M.S.). Agriculture Update. 8 (1/2): 122-124. Policy. 37 (3): 309-322. Varela, G., Carroll, E.A., and Iacovone, L. 2012. Sharma, S. and Singh, I.P. 2014. Behaviour of market Determination of market integration and price arrivals and prices of pearl millet in Rajasthan. transmission in Indonesia. Policy Research Journal of Rural Development (Hyderabad). 33 Working Paper 6098. Poverty Reduction and (3): 351-358. Economic Management Unit, World Bank. Shruthi, M. and Krishnamurthy, K.N. 2013. Kumar, V., Sharma, H.R, and Singh, K. 2005. Statistical study of trends in arrivals and prices Behaviour of market arrivals and prices of of maize in selected markets of Karnataka. selected vegetable crop: A study of four Mysore Journal of Agricultural Sciences. 47 (4): metropolitan markets. Agricultural Economics 791-796. Research Review. 80 (6): 271-290. Singh, B.B., Singh, R.K.P., and Yadav, P.N. 1995. Yogisha, G.M., Karnool, N.N., Kumar, H.S.V., and Seasonal variation in arrivals and their effect on Basavaraja, H. 2007. Trends and seasonal price of wheat in Bihar. Indian Journal of variations in arrivals and prices of potato in Agricultural Marketing. 9 (1): 1-3. Kolar District. Indian Journal of Agricultural Srivastava, S. and Brahm, P. 1996. Analysis of Marketing 69 (4): 26-28. trends in market, arrivals and prices of pigeon pea in Uttar Pradesh. Indian Journal of Pulses Research. 9 (1): 104-106. Thombre, A.P. and More, S.S. 2013. Market arrivals Received: March 31, 2015 and prices of pigeon pea in Marathwada region Accepted: October 09, 2015

850 Indian J Econ Dev DOI: 10.5958/2322-0430.2015.00093.1 Volume 11 No. 4 (2015): 851-860 Research Article

SPATIAL PRICE TRANSMISSION IN GROUNDNUT MARKETS OF RAJASTHAN

Richard Kwasi Bannor and Madhu Sharma*

ABSTRACT

The study focused on assessing the spatial price transmission between groundnut markets pairs in Rajasthan state of India using monthly groundnut price series of 10 markets from 2005 to 2014. The secondary data used for this study was sourced from AGMARKNET database. The descriptive statistics, Johansen bivariate co-integration approach, error correction model and the unrestricted vector autoregressive model were used for the analysis. The coefficient of variance results indicate Sikar market has low volatility of 18.17 percent compared to 34.78 percent in Niwai market which is the highest. The co-integration tests results as shows Bikaner and Bhilwara, Jaipur and Bhilwara, Jodhpur and Laslot, Jodhpur and Niwai and Jodhpur and Sikar are not integrated in the long run. Results from the error correction model showed the lowest speed of adjustment towards long run equilibrium was from Jaipur to Sikar at rate of 8.2 percent. The highest speed of adjustment was 87 percent, running Fatehnagar to Bikaner market towards long run equilibrium, followed by a speed of adjustment of 50.3 percent running from Jodhpur to Sri Madhpour market towards along run equilibrium in a period of at most two months.

Keywords: Co-integration, groundnuts, price transmission JEL Classification: C21, C23, C32, F14, Q13

INTRODUCTION Burma, Argentina and Indonesia are the major Groundnut is grown on a large scale in producers of groundnut globally. India almost all the tropical and sub-tropical exported 5.35 lakh metric tonnes of groundnut countries of the world. Production of oilseeds worth `4,065.38 crores during the year 2012- has grown almost double in last 12 years from 13 with major export destinations such as 176 lakh metric tonnes in 2000-01 to 321 lakh Indonesia, Vietnam Social Republic, Malaysia, metric tonnes in 2012-13. The total world Philippines and Thailand (Anonymous, 2015). production of groundnut in 2012-13 is India is the second largest producer of approximately 37.19 million tonnes with India groundnuts in the world. Indian groundnuts contributing about 9,472,000 million metric are available in different varieties, Bold or tonnes (Anonymous, 2015 and FAOSTAT, Runner, Java or Spanish and Red Natal. The 2015). China, India, United States, Nigeria, main groundnut varieties produced in India are Kadiri-2, Kadiri-3, BG-1, BG-2, Kuber , *Ph.D. Scholar (Agribusiness) and Professor GAUG-1, GAUG-10, PG-1 , T-28, T-64, Chandra, Institute of Agribusiness Management, SK Chitra, Kaushal, Parkash, Amber, etc. (APEDA, Rajasthan Agricultural University, Bikaner-334006 2015). Email: [email protected] Groundnut is the major oil seed crop in

851 India and it plays a major role in bridging the patterns, given that the differences between vegetable oil deficit in the country. Groundnuts prices is explained by the transfer and in India are available throughout the year due transaction costs as groundnut flows between to a two-crop cycle harvested in March and the locations. The spatial market integration October. Groundnut production in kharif measures the extent to which markets at (2012-13) was 26.20 lakh tonnes and the same geographically distant locations (such as was 41.75 lakh tonnes in Rabi 2011-12 in India. between districts) share common long-run The major producers of groundnut are Gujarat price or trade information on a homogenous (26.34 percent), Andhra Pradesh (19.08 commodity. Such markets are connected by percent), Rajasthan (17.68 percent), Tamil arbitrage and this is reflected in the price Nadu (9.54 percent), Karnataka (7.63 percent), information of the respective state groundnut Madhya Pradesh (7.25 percent) and markets. Maharashtra (5.34 percent) (Anonymous, Thus, if different markets move in similar 2015). The major groundnut growing districts patterns then the markets have high potential in Rajasthan are Bikaner, Jaipur, Jodhpur and of integration, hence, efficiency. Efficiency of Sikar. markets and marketing channels are essential Groundnut production in the state occurs for realizing the impact of different agricultural mostly during the months of October and and economic policies such as macro November but demand for both processing economic, micro economic or trade policy. On and raw consumption exists throughout the the other hand, if the markets price series are whole year. The phenomenon of production found to have a negative co-movement in price, at one time and demand on regular basis causes a tentative no market integration is suggested variation in prices over the months or seasons and possibly market segmentation (Rashid, of the year (Agarwal and Satya, 1994). 2004). Markets that are segmented spatially The cumulative average growth rate of isolate economic agents and households groundnut prices during period 2005 to 2014) across space and limit the transmission of price ranges from 7.16 to 10.14 percent. Out of the incentives and the associated positive welfare selected markets for this research, Sikar and impact as a result of lower prices. In addition, Bikaner recorded the least cumulative growth non-integrated markets may send wrong price rate in price from 2005-2014 with 7.613 and 7.63 information signals to producers and other percent, respectively and Jaipur recorded a actors in the marketing chain which may result higher CAGR of 10.14 percent. in incorrect production and marketing The growth or otherwise in groundnut decisions. Segmentation of markets or lack of prices do affect the fortunes of farmers, groups market integration either in the long run or short and for that matter the whole economy. Thus, run can be attributed to prohibitive transaction prices have profound effect on growth, equity costs related to poor infrastructure in remote and stability in developing economies. Prices areas, damaged roads, damaged bridges, of agricultural commodities affect production unequal access to reliable information between potential of groundnut. Prices also serve as a and amongst producers, traders and means to aggregate output of varied nature consumers, inadequate storage facilities, high and help in monitoring the movements in volatility of prices, imperfect competition, and aggregate agricultural productions, farm incomplete or missing institutions for risk incomes and inflationary trends. Again, prices management like credit and insurance. usually give important indications on whether Having said that, the need for price analysis markets are integrated or not. The groundnut cannot be underemphasized as it affects markets are integrated or efficient, if prices agricultural production profoundly. Spatial among different spatial markets move in similar price analysis is beneficial in assessing the

852 effects of both national and state government variation, unit root test, co-integration policies on groundnut production, marketing technique (Johansen Co-integration Test), as well as on the farmers’ income. It is also of error correction, and vector autoregressive importance to potential investors in making models. The coefficient of variation was used decisions on when and where to sell or buy to determine volatility of prices in the various groundnut from in the state. markets, Augmented Dickey Fuller Tests (ADF) Notwithstanding, the importance spatial test, Philips-Perron (PP) test and DF-GLS test price integration of groundnut markets, little were used for the stationarity tests. Johansen to no research using cointegration, error Co-integration Test was used to test for long correction model and Grangar Causality run integration between variables that are Approach to the best knowledge of the authors stationary of the same order and Error has been done to understand whether the correction model, Vector Autoregressive model major groundnuts markets in Rajasthan are were used for short run causality analysis. integrated or not. Given that prices drive Test for order of econometric integration (unit resource allocation and output mix decisions root test) by economic actors, the main objective of the A stationary series is one with a mean value study is to analyze the dynamics of price which will not vary with the sampling period. transmission relationships between groundnut In contrast, a non-stationary series will exhibit markets in Rajasthan. a time varying mean (Juselius, 2006). Before METHODOLOGY examining integration relationships between Sources of Data or among variables, it is essential to test for The secondary data used for this study unit root and identify the order of stationarity, was sourced from AGMARKNET database. denoted as I(0) or I(1). This is necessary to This database is under the Directorate of avoid spurious and misleading regression Marketing and Inspection of the Ministry of estimates. The framework of ADF methods is Agriculture of Government of India. The data based on analysis of the following model: set of 10 markets namely Bhilwara, Bikaner, n Δp t  α  βp t1  γT   δk Δp tk  μ t ....(1) Jaipur, Fatehnagar, Jodhpur, Laslot, k1

Nimbahera,Niwai, Sri Madhpour, and Sikar of Here, pt is the groundnut price series being Rajasthan were sourced, covering monthly investigated for stationarity, D is first groundnut prices from January 2005 to difference operator, T is time trend variable, mt November 2014. In all they were cumulative represents zero- mean, serially uncorrelated, 119 observations. This period covers post random disturbances, k is the lag length; a, b,

WTO agreement era in India where a lot of g, and dk are the coefficient vectors. Unit root programs, policies, and strategies by various tests were conducted on the b parameters to governments have been adopted to revamp determine whether or not each of the series is groundnut production and marketing both in more closely identified as being I(1) or I(0) at the state and national level. process. Test statistics is the t-statistics for b. Estimation of the relationship between The test of the null hypothesis of Equation (1) groundnut markets in Rajasthan shows the existence of a unit root when b=1 Johansen co-integration test procedure against alternative hypothesis of no unit root The approach adopted by the researcher when b  1. The null hypothesis of non- was shaped by the approach adopted by stationarity is rejected when the absolute value Mafimisebi et al. 2014 and Kwasi and Kobina, of the test statistics is greater than the critical

2014. The data analytical techniques that were value. When pt is non-stationary, it is then used in this study comprised of descriptive examined whether or not the first difference of statistics such as means and coefficient of pt is stationary (i.e. to test Dpt-Dpt -1 J (1) by

853 repeating the above procedure until the data vector. Throughout, p is restricted to be (at were transformed to induce stationarity. most) integrated of order one, denoted I(1), DF-GLS test for a unit root in a time series where I(j) variable requires jth differencing to was used in addition to Augmented Dickey make it stationary. Equation (2) tests the co- Fuller(ADF) and Philips-Perron (PP) Test. It integrating relationship between stationary performs the modified Dickey-Fuller t-test series. Johansen and Juselius (1990) and (known as the DF-GLS test) proposed by Juselius (2007) derived two maximum likelihood Elliott, Rothenberg, and Stock (1996). statistics for testing the rank of  , and for Essentially, the test is an Augmented Dickey- identifying possible co-integration as the Fuller test, except that the time series is following equations show: transformed via a generalized least squares m (GLS) regression before performing the test. λ trace τ T  In(1  λi )...... (4) Elliott, Rothenberg, and Stock (1996) and later ir1 λ τ, τ 1 Tln(1 - λ )...... (5) studies have shown that this test has max r1 significantly greater power than the previous Where  is the co-integration number of versions of the augmented Dickey-Fuller test. pair-wise vector, t is ith eigen value of matrix

The Philips-Perron (PP) test is similar to . T is the number of observations. The ltrace the ADF test. PP test was conducted because is not a dependent test, but a series of tests the ADF test loses its power for sufficiently corresponding to different -value. The lmax large values of k, the number of lags. It includes tests each eigen value separately. The null an automatic correction to the Dickey-Fuller hypothesis of the two statistical tests is that process for auto-correlated residuals. The there is existence of co-integration relations regression is as follows: while the alternative hypothesis is that there Δy  b b y  u ...... (2) is existence of more than  co-integration t 0 1 t1 t relations. This model was used to test for, (1) Where y is the groundnut price series t integration between various groundnut market being investigated for stationarity, b and b 0 1 price series in Rajasthan. are the coefficient vectors and u is serially t Test for Granger-causality: After undertaking correlated. Testing for Johansen co-integration co-integration analysis of the long run linkages (trace and eigenvalue tests): If two series are of the various variables, and having identified individually stationary at same order, the they are linked, an analysis of statistical Johansen co-integration model can be used to causation was conducted. The causality test estimate the long run co-integrating vector uses an error correction model (ECM) of the using a Vector Auto regression (VAR) model following form: of the form: m k1 i i j i p j  β0  β1p (t 1)  β2p (t 1)  δkΔp (t  k) ΔP t ΓiΔpt1  Πpt1 μt ...... (3) k1 i1 n j Where pt is a n  1 vector containing the  ΔσΔhhp (t  h)  μt series of interest (the three variable series) at h1 Where m and n are number of lags time (t), D is the first difference operator Gi and P are nxn matrix of parameters on the ith and kth determined by Akaike Information Criterion lag of : (AIC).If the null hypothesis that say Bikaner groundnut market prices in Rajasthan j do not  k   k  Granger cause Jaipur groundnut market prices pt , i   Ai .- Ig and    Ai - Ig .....(4) i1  i1  in Rajasthan i is rejected (by a suitable F-test)

Ig is the identity matrix of dimension g, a is that h = 0 for h = 1, 2….n and =0, this indicates Bikaner groundnut market price j Granger-cause constant term, ut is n  1 white noise error

854 Jaipur groundnut market prices in Rajasthan i fluctuations over the space and are called as (Mafimisebi et al., 2014) spatial variation. These two kinds of price RESULTS AND DISCUSSION variations play a significant role in cropping The results presented in Table 1, show that, pattern of the farmers as well as in the stability the mean price of groundnut in Indian Rupees of income in the agriculture sector. Large (`) per quintal from the period of 2005 to 2014 fluctuations in the prices of a commodity may for the ten markets across Rajasthan was result in switching over of the farmers to some lowest at `2558.52 in Bhilwara market. The other remunerative crops. On the other hand, highest average was recorded at price of stable price level of the commodity provides `3132.017 in Jodhpur market. The minimum incentives to the producers to increase the price was recorded in Sri Mahdhpour market production of that commodity. However, In at price of `1189.91 with highest price recorded general the volatility is low to medium in Jodhpur market at price of `5108.05. indicating normal volatility in prices of Coefficient of variation indicates Sikar market agricultural commodities. This results agree has low volatility of 18.17 percent compared with studies by Nayyar and Sen (1994) and to 34.78 percent in Niwai market, which has Chand (1999 and 2001), who argued that, there been the highest. Volatility in Bikaner, Bhilwara is evidence of a much lower degree of and Fatehnagar and Nimbahera market prices agricultural price variability at the national level can make the investment in recommended in in Indian markets, as compared to the world groundnut production risky for farmers. markets. Volatility in these markets are caused by The study first examined each variable time predominantly non-availability of groundnut series for evidence of non-stationarity in order pods during most part of the year and to proceed with co-integration approach. At fluctuating groundnut production hence level 0, all the major groundnut market price affecting the capacity utilization of groundnut series in Rajashtan were stationary but Niwai processing units. This can lead to market. However, when the constant term was fragmentation of capacities, unfavorable scale suppressed, all the market prices were not of economies and large idle capacity which is stationary. DF-GLS test, Augmented Dickey not good for the growing groundnut industry. Fuller (ADF) and Philips-Perron (PP) showed The variation in agricultural arrivals is also a similar results (Table 2). contributing factor. The price variation can be The study went further to test the unit root related to the trend of arrival levels which in Niwai market price series at first difference. shows fluctuations over time and are called as DF-GLS test, Augmented Dickey Fuller (ADF) temporal variation, and the other comprises of and Philips-Perron (PP) showed similar results

Table 1: Descriptive statistics of groundnut price series (`q-1) Variable Mean Standard deviation Minimum Maximun Coefficient of variatiom (%) Bhilwara 2558.52 782.26 1290.18 4775.35 30.57 Bikaner 2731.19 838.15 1500.56 4882.52 30.69 Jaipur 2942.42 752.96 1450.00 5000.00 25.59 Fatehnagar 2794.28 930.79 1558.37 5108.05 33.31 Jodhpur 3132.02 781.86 1425.00 5284.33 24.96 Laslot 2631.03 626.45 1339.04 4667.61 23.81 Nimbahera 2616.84 854.28 1466.44 4939.18 32.65 Niwai 2654.70 923.35 1386.54 4736.10 34.78 Sri Madhpour 2829.49 857.94 1189.91 5480.00 30.32 Sikar 2637.67 479.38 1287.00 4750.14 18.17

855 Table 2: Unit Root Testing equilibrium is about two months at most (Test statistics) The co-integration tests results as Market Price level 1(0) intercept with trend presented in the Table 4 showed that Bikaner prices ADF statistics PP statistics DF-GLS and Bhilwara, Jaipur and Bhilwara, Jodhpur and CV=-3.448 CV=-3.448 CV=-3.011 Bhilwara -3.493 -3.493 -3.480 Laslot, Jodhpur and Niwai and Jodhpur and Bikaner -3.717 -3.493 -3.480 Sikar were not integrated in the long run that Jaipur -7.076 -7.076 -6.808 was, there is no long run relationship between Fatehnagar -9.612 -9.636 -9.612 these two markets. Jodhpur -6.178 -6.178 -5.573 However, for the rest of the market pairs, Laslot -5.987 -5.987 -5.673 the results show otherwise (cointegration Nimbahera -5.652 -5.652 -1.444 Niwai -2.816 -2.816 -2.848 between market pairs). This means that, most Sri Madhpour -5.771 -5.771 -5.770 of the groundnut market prices in Rajasthan Sikar -6.242 -6.242 -5.274 move closely together in the long run although

Ho: Variables are not stationary or has unit root

H1: Variables are stationary or does not have unit root NB: If the absolute value of ADF, PP, DF-GLS Test Statistics is less than their 5% critical value we accept null hypothesis. It is also when Table 4: Co-integration results for market the MacKinnon approximate p-value for Z(t) is insignificant. pairs Variables Trace 5% Rank CI/NCI as indicated in Table 3. statistics CV BikanerBhilwara 12.66 15.41 0 NCI Before the cointegration analysis, suitable BikanerJaipur 2.04* 3.76 1 CI number of lags should be selected. The number BikanerFatehnagar 2.48* 3.76 1 CI of lags were selected by applying five different BikanerLaslot 2.96* 3.76 1 CI multivariate lag selection criteria: the Akaike BikanerNimbahera 1.56* 3.76 1 CI BikanerNiwai 1.72* 3.76 1 CI information criterion (AIC), the Hannan-Quin BikanerSri Madhpour 2.25* 3.76 1 CI information criterion (HQIC), and the Schwarz’s BikanerSikar 2.33* 3.76 1 CI Bayesian information criterion (SBIC), FPE and BikanerJodhpur 2.28* 3.76 1 CI LR. A vector autoregression (VAR) on the JaipurFatehnagar 1.61* 3.76 1 CI JaipurBhilwara 11.89* 15.41 0 NCI differenced series was conducted and lag JaipurJodhpur 2.47* 3.76 1 CI length of the model with the least AIC, HQIC, JaipurNimbahera 3.18* 3.76 1 CI LR and FPE values chosen as the appropriate JaipurLaslot 3.41* 3.76 1 CI lag length to be included in the co-integration JaipurNiwai 1.64* 3.76 1 CI JaipurSri Madhpour 1.72* 3.76 1 CI test. The test indicated the right maximum lag JaipurSikar 2.59* 3.76 1 CI length for the analysis was 2 lags. This JodhpurBhilwara 3.40* 3.76 1 CI indicates the maximum time for price to be JodhpurFatehnagar 2.91* 3.76 1 CI transmitted from one groundnut market to the JodhpurLaslot 13.68 15.41 0 NCI * other in the long run or to move into long run JodhpurNimbahera 3.14 3.76 1 CI JodhpurNiwai 13.97 15.41 0 NCI JodhpurSri Madhpour 2.65* 3.76 1 CI Table 3: Unit Root Testing at first JodhpurSikar 13.642 15.41 0 NCI Source: Author's computation difference At rank 0: (Test statistics) Ho: There is no co-integration between the variables

Market First difference 1(1) intercept with trend H1: There is co-integration between the variables price ADF PP statistics DF-GLS test NB: We accept null hypothesis when trace statistics or max statistics CV=-3.448 statistics statistics is less than the 5% Critical value at rank 0. At rank 1: CV=-3.448 CV=-3.012 H : There is (1) co-integration of the variables at rank 1 Niwai -7.226 -10.734 -7.281 o H1: There is no 1 co-integration of the variables at rank 1. Source: Author's computation from time series data analysis NB: We accept null hypothesis when trace statistics or max Ho: variables are not stationary or has unit root statistics is less than the 5% Critical value at rank 1. H1: Variables are stationary or does not have unit root NB: If the absolute value of ADF, PP, DF-GLS Test Statistics is less CI: Co-integration than their 5% critical value we accept null hypothesis. It is also when NCI: No co-integration the MacKinnon approximate p-value for Z(t) is insignificant. CV: Critical Value

856 in the short run they may drift apart, indicating 2008) high efficiency between the market pairs in the The results however, disagree with Wilson state at long run. (2001) as quoted by Acharya and Agarwal Commenting on the increase of marketing (2014) that reveal that groundnut markets efficiencies in India, Bethla (2008) argued that, integration continues to be low in India. there is evidence in favour of an improvement The error correction model was used to in the short-run and long-run relationships analyse the variables that were co-integrated between the wholesale market prices of wheat, in the long run. The results from the error rice, sugar and groundnut in the post-reform correction model showed that, the lowest period with a larger set of states in India to be speed of adjustment towards long run integrated with each other. equilibrium was from Jaipur to Sikar at rate of Again, Bathla (2006), argued that the 8.2 percent. The highest speed of adjustment Government of India recognizing the growing was 87 percent, running from Fatehnagar to inefficiencies in the marketing system, which Bikaner market towards long run equilibrium. may dissuade gains from trade liberalization This is followed by a speed of adjustment of that is after WTO, initiated a number of reforms 50.3 percent running from Jodhpur to Sri in agriculture markets. To start with, it reduced Madhpour market towards along run tariffs below the required level for a good equilibrium in a period of at most two months number of commodities and removed all (Table 5). The high speed of adjustment quantitative barriers to agricultural imports by towards long run equilibrium within a period 2001, which led to a greater openness of of two months can be attributed to MSP and markets during the nineties and early 2000 procurement policy of the government, which compared to the eighties, and increased the helps absorb price shocks and bring stability, external trade. Contributing to the high market particularly in the states where procurement integration between market pairs in Rajasthan operations are effectively undertaken (Jha et can also be contributed to expanded India al., 1997 and Chand and Jha, 2001). national policy on handling and storage of Agricultural futures exchanges and food grains and oil seeds, which involves the commodity exchange have can also be private sector in building storage capacities attributed to the increase of the efficiency of for holding stock and preventing farmers from markets with an added advantage of advance distress sales during bumper harvest. In price discovery and effective forward linkages addition, government initiative of ensuring the like warehousing. National Commodity and entry of private sectors in the storage of Derivatives Exchange Limited, National Multi agricultural commodities such groundnut and Commodity Exchange of India Limited just to also relaxation of restrictions on stock limits mention few are all involved in groundnut and inter-state movement of groundnut and exchange contributing to increase in other essential commodities has contributed groundnut market integration. to the high spatial transmission between Increased in competition in the groundnut markets markets in Rajasthan is also a determining Contract farming, establishment of direct factor in the efficiency of groundnut markets. sale or purchase centres has also contributed Rajkot (Gujarat), Ahmedabad (Gujarat), Gondal significantly in increasing the spatial price (Gujarat), Junagarh (Gujarat), and Delhi which transmission between groundnuts markets in are the major trading centers of groundnut and the state. Summing it up, market integration derivatives in India are in a state which are for farm commodities across the Indian states neighbours to Rajasthan state. These markets has improved in the post-reform period players interaction with the Rajasthan market compared to the pre-reform period (Bethla, has a major influence on the high efficiency of

857 Table 5: Vector Error Correction (VECM) Model results for the co integrated variables Market Pairs p-value Error Correction Short run model/Causality Causality Remarks Term Prob>Chi Direction BikanerJaipur 0.019 -0.183 0.000 Unidirectional Short run BikanerFatehnagar 0.000 -0.868 0.000 Unidirectional Short run BikanerLaslot 0.000 -0.399 0.045 Bidirectional Short run BikanerNimbahera 0.000 -0.462 0.000 Bidirectional Short run BikanerNiwai 0.010 -0.216 0.001 Bidirectional Short run BikanerSri Madhpour 0.940Ns -0.006 0.0028 Bidirectional Short run BikanerSikar 0.535Ns -0.163 0.0391 Bidirectional Short run BikanerJodhpur 0.837Ns -0.012 0.0040 Bidirectional Short run JaipurFatehnagar 0.001 -0.353 0.000 Bidirectional Short run JaipurJodhpur 0.038 -0.168 0.000 Bidirectional Short run JaipurNimbahera 0.006 -0.273 0.000 Bidirectional Short run JaipurLaslot 0.000 -0.349 0.000 Bidirectional Short run JaipurNiwai 0.008 -0.278 0.000 Unidirectional Short run JaipurSri Madhpour 0.002 -0.318 0.000 Bidirectional Short run JaipurSikar 0.053 -0.082 0.000 Bidirectional Short run JodhpurBhilwara 0.000 -0.493 0.000 Bidirectional Short run JodhpurFatehnagar 0.001 -0.287 0.000 Bidirectional Short run JodhpurNimbahera 0.002 -0.118 0.000 Bidirectional Short run JodhpurSri Madhpour 0.001 -0.503 0.000 Bidirectional Short run Source: Author's computation,AB=Bidirectional, AB=A causes B, AB=B causes A Ho: No short run causality running from variable A to B H1: Short run causality running from A to B or variable A causes changes in variable B in the short run NB: Reject null hypothesis when the Prob> chi value is > 5% groundnut markets in the state. Also, the in Rajasthan. They predominantly use the increasing number of small to large processing channel of Producer D Wholesaler D units especially in areas of Bikaner, Nokha, and Processors whereas in Jharkhand, it was Niwai in Tonk district NABARD (2011) has FarmerD Commission agentD DealerD influence on the increase efficiency of the WholesalerDConsumer (NABARD, 2011). groundnut markets. The gains in efficiency in both production, The analysis for co-integrated markets processing and marketing of oilseeds will shows, there is short run causality between ultimately reduce domestic prices of edible oils the markets used in the study with most for consumers (Brennan and Bantilan, 2003) having bidirectional causality except Jaipur to and also increase competitiveness and reduce Bikaner, Fatehnagar to Bikaner and Jaipur to surge in large scale import of edible oils, which Niwai which had unidirectional causality (Table will justify non-market distorted seed subsidy 6). The short run causality means that a change (controversial case between India and WTO in one of the prices of the market pairs, results, threatening the Bali Agreement) to seed in an instantaneous less than two months production and distribution companies and reflection in the other market pair resulting in capital subsidy to processing units, which will high efficiencies of the groundnut markets. It go long run to reduce India’s huge dependence is therefore not surprising that NABARD on oil import. (2011) argued that, the net income per acre The unrestricted vector autoregressive earned by the sample farmers in their research (VAR) model was run for the non-co-integrated was highest in the State of Rajasthan (`10,442) market pairs. The results of the Wald tests followed by Chattisgarh(`9,925), Gujarat show that groundnut price series between (`7,963), and Jharkhand (`5,663). This is Bikaner and Bhilwara causes each other in the because of the type of channel majority of the short run whereas Jodhpur causes Laslot and sample farmers use in selling their produce is Jodhpur also causes Niwai in less than two

858 Table 6: Vector autoregressive (VAR) to Sikar at rate of 8.2 percent. The highest model for the non co-integrated variable speed of adjustment was 87 percent, running Granger Causality Wald Tests Fatehnagar to Bikaner market towards long run Markets pairs Prob>F Direction Short run equilibrium. This is followed by a speed of causality adjustment of 50.3 percent running from BikanerBhilwara 0.004 Bidirectional Short run JaipurBhilwara 0.092NS - No short run Jodhpur to Sri Madhpour market towards along JodhpurLaslot 0.009 Unidirectional Short run run equilibrium in a period of at most 2months. Jodhpur Niwai 0.016 Unidirectional Short run However, most of the non co-integrated JodhpurSikar 0.073NS - No short run markets are also not integrated in the short Source: Author's computation, AB=Bidirectional, AB=A causes run depicting inefficiencies between those B, AB=B causes A Ho: No short run causality running from variable A to B H1: Short market pairs in terms of price transmission. run causality running from A to B or variable A causes changes in variable B in the short run In the context of policy implications, NB: Reject null hypothesis when the p-value is > 5% Government should continue to invest in domestic groundnut production, warehousing, months though in the long run they drift apart. processing factories and other infrastructure However most of the non co-integrated to be able to maintain and sustain the efficiency markets are also not integrated in the short of the groundnut markets in Rajasthan and its run depicting inefficiencies between those marketing channels. The Government of India market pairs in terms of price transmission. cross cutting programmes and policies on CONCLUSIONS oilseeds especially for groundnuts marketing The study has shown, the mean price of is paying of positively, which should be groundnut in Indian Rupees (`) per quintal acknowledged and encouraged to make India from the period of 2005 to 2014 for the ten more competitive not only in Rajasthan but markets across Rajasthan was lowest at across the states and in the world. However, `2558.52 in Bhilwara market. The highest the ongoing government programs including average was recorded in at price of `3132.017 ISOPOM largely concentrated on oilseed in Jodhpur market. The minimum price was production with little emphasis on the recorded in Sri Mahdhpour market at price of processing sector needs to be corrected; as `1189.91 with maximum price recorded in the sustenance of efficiency of groundnut Jodhpur market at price of `5108.05. The processing units is crucial in sustaining coefficient of variation indicates Sikar market groundnut market efficiency. has low volatility (18.17 percent) compared to The Government should also in addition Niwai market (34.78 percent) which is the to policies undertaken, create the development highest. This study also explored long run and of oilseed clusters with best transport and short run causality of groundnut market in infrastructure facilities (which will encourage Rajasthan state of India for the time period groundnut along with other oilseed crops like using the Johansen Bivariate Co-integration sunflower/mustard) to reduce transaction Approach, Error Correction Model and the costs for both farmers and processors. Lastly, Unrestricted Vector Auto-regressive Model. research should be conducted to determine The co-integration tests results as showed that the efficiency of Gujarat and Rajasthan markets Bikaner and Bhilwara, Jaipur and Bhilwara, as it has significant influence on groundnuts Jodhpur and Laslot, and Jodhpur and Sikar marketing in Rajasthan. are not integrated in the long run. However REFERENCES for the rest of the market pairs, the results show Acharya, S.S. and Agarwal, N.L. 2014. Agricultural otherwise. The results from the error correction marketing in India (5th edition). Oxford and IBH model showed the lowest speed of adjustment Publishing Corporation Private Limted, New towards long run equilibrium was from Jaipur Delhi.

859 Agarwal, N.L. and Satya, P.K. 1994. Price behaviour Jha, R., Bhanumurthy, K.V., Nagarajan, H.K., and of groundnut in markets of Rajasthan. Bihar Seth, A.K. 1997. Market interventions in Indian Journal of Agricultural Marketing. 2 (4): 309- agriculture. Indira Gandhi Institute of 318. Development Research, Mumbai: RP-97-269. Anonymous. 2015. Retrieved from Johansen, S. and Juselius, K. 1990. Maximum www.agropedia.iitk.ac.in on 17/01/2015 likelihood estimation and inference on co- APEDA. 2015. Retrieved from www.apeda.gov.in integration with application to the demand for on 21/01/2015. money. Oxford Bulletin of Economic Statistics. Bathla, S. 2008. Agriculture reforms and market 52: 231-254. integration: A spatial analysis of food and non- Juselius, K. 2006. The co-integrated VAR model: food commodities. Journal of Social Economic Methodology and applications. Oxford Development. 10 (2):197-221. University Press, London. Bathla, S. 2006. Trade policy reforms and openness Kwasi, B.R. and Kobina, B.J. 2014. Cassava of Indian agriculture: Analysis at the commodity markets integration analysis in the Central level. South Asia Economic Journal. 7 (1):19- Region of Ghana. Indian Journal of Economics 53. and Development. 10 (4): 319-329. Brennan, J.P. and Bantilan M. 2003. Price and yield Mafimisebi, T.E., Agunbiade, B.O., and Mafimisebi effects of spill-overs in international agricultural O.E. 2014. Price variability, co-integration and research: Evidence from ICRISAT and Australia. exogeniety in the market for locally produced Agricultural Economics. 28 (203): 87-97. rice: A sase study of Southwest Zone of Nigeria. Burman, R.R., Dubey, S.K., Jha, G.K., Singh, G., Retrieved from www.dx.doi.org. and Sharma, M.K. 2012. Analysis of production NABARD. 2011. Commodity specific study- gap, marketing and processing status and groudnut. Department of Economics an analysis associated constraints for major oilseeds in the research. Karnatak Orion Press fort, Mumbia, states of Rajasthan and Gujarat. Journal of India. Community Mobilization and Sustainable Nayyar, D. and Sen, A. 1994. International trade Development. 7 (2):198-209. and agricultural sector in India. In: Bhalla, G.S. Chand, R. 1999. Effects of trade liberalization on (Ed.) Economic Liberalization and Indian agriculture in India, commodity aspects. Working Agriculture. New Delhi. Paper No 45. The CGPRT Centre Working Rashid, S. 2004. Spatial integration of maize markets Paper Series, September 1999. Retrieved from in post-liberalized Uganda. Discussion Paper www.ageconsearch.umn.edu. No.71 (May), IFPRI, Market Trade and Chand, R. 2001. Trade liberalization, WTO and Institution Division, Washington. Indian agriculture. Mittal Publishers, New Reddy, A.A. and Bantilan, M. 2011. Delhi. Competitiveness and efficiency in groundnut Oil Chand, R., and Jha, D. 2001. Trade liberalisation, sector of India. ICRISAT Policy Brief No. 14. agricultural prices and net social welfare. In: Reddy, A.A. and Bantilan M.C.S. 2012. Acharya, S.S. and Chaudhari, D.P. (eds), Indian Competitiveness and technical efficiency: Agricultural Policy at the Crossroads. Rawat Determinants in the groundnut oil sector of India. Publications, Jaipur. Food Policy. 37:255-263. Elliot, G., Rothenberg, T.J., and Stock, J.H. 1996. Efficient tests for an autoregressive unit root. Econometrica. 64: 813-836. FAOSTAT. 2015. Retrieved from Received: January 30, 2015 www.faostat3.fao.org on 21/07/2015 Accepted: September 21, 2015

860 Indian J Econ Dev DOI: 10.5958/2322-0430.2015.00094.3 Volume 11 No.4 (2015): 861-868 Research Article

RESEARCHING THE RELATIONSHIP BETWEEN FINANCIAL AND REAL SECTORS IN INDIA

Bhanu Pratap Singh* and Alok Kumar Mishra**

ABSTRACT

The aim of the study is to empirically examine Schumpeter’s view on finance and growth nexus in Indian setting. The study utilizes quarterly time series data for the period spanning from 1993:Q3 to 2013:Q2. After studying the construction of Indian financial system, both banking sector and stock market growth indicators are considered as the placeholder for financial sector growth. Nevertheless, real GDP growth is considered to represent the economic growth. Along with financial sector development and economic growth indicators, some control variables, namely; total government final consumption expenditure as a percentage of GDP, total trade as a percentage of GDP and inflation is applied as a control variable. Johansen maximum likelihood procedure of cointegration is employed to ensure the long run dynamics among the set of considered variables. Toda and Yamamoto (1995) Granger’s Causality tests is applied to look into short-run causal relationship.The study concludes, Schumpeter’s view on finance and growth nexus does not hold well in the context of Indian economy.

Keywords: Cointegration, economic growth, financial and real sectors. JEL Classification: C58, D53, E44, O57

INTRODUCTION and comprehensive reforms were introduced In the summer of 1991, Indian economy was in 1992-93. Past four decades was the regime suffering from adverse fiscal and external of credit control. In order to extend the banking imbalances which in turn ended with balance facilities in rural areas the Imperial Bank of India of payments (BOP) crisis. New Economic was partially nationalised on July 1, 1955 and Policy (NEP) was announced on July 24, 1991, named the State Bank of India along with other under the conditional credit provided by 8 banks. In line with providing financial International Monetary Fund (IMF) to inclusion and have more control over the overcome the crisis. Financial sector reforms banks 14 commercial banks which had their were integral part of the NEP which was reserve more than rupees 50 crores were initiated to check rigidities and weakness in nationalised on 19th July, 1969. After two the financial system. After 20 days, on August decades of financial reconstruction, Indian 14, 1991 a high-level Committee under the economy has become more open and has Chairmanship of M. Narasimhan was set up witnessed substantial increase in the size of financial markets. Now the question arises, *Doctoral Fellow and **Assistant Professor, School what effect do above changes in the financial of Economics, University of Hyderabad, sector have on economic growth? The study Hyderabad,Telangana-560046 (India) empirically investigate the nexus of financial Email: [email protected] development and economic growth in India

861 using quarterly time series data for the period Schumpeter’s theory of economic 1996:Q3 to 2013:Q3. development deals with three things, namely; The survey is organized as, Section 2 innovations give rise to wave like movements, discusses the theoretical underpinnings pioneering entrepreneurs are the agents of behind the finance-growth nexus. Empirical creative destruction and bank credit is the pre- literature is provided in section 3. The requisite for foundation of new enterprise and description on the data and methodology of financing innovative investment. In his system the study are discussed in section 4. Empirical dynamics are strongly related to the findings are reported in section 5 and the study phenomena of economic growth and concludes with section 6. pioneering entrepreneurs are the main carrier Theoretical Underpinnings of economic development which acts as an The debate on the contribution of finance endogenous force in the economy. to economic growth in the economics literature The Figure 1 is a diagrammatic can be traced back to second decade of the representation of Schumpeter’s model of 20th century. Schumpeter (1911) was perhaps economic growth. In the model he assumes a first to examine how well developed banking perfectly competitive equilibrium in a system spurs economic growth. Goldsmith stationary state. In such an economy, there is (1969) with his financial interrelation ratios did no existence of economic profits, savings, first notable empirical study to examine the investment, interest rates and involuntary nexus of financial development and economic unemployment. The equilibrium is growth. McKinnon (1973) and Shaw (1973) characterized by the term circular flow in with their seminal work propounded the idea which same goods are produced in an of financial repression. According to them it economy in a same manner. is not the cost of capital (interest rate) but distortions (credit control) in the domestic capital market which is responsible for the low growth in less developed countries (LDCs). The classical views of Schumpeter (1911), Goldsmith (1969), McKinnon (1973) and Shaw (1973) have given vital importance to banking sector development in the process of economic growth. There were extremely divergent views by the economists who believed it’s not worth discussing finance (Lucas, 1988) and where enterprise leads finance follows (Robinson, 1952). Despite of multiple perspectives on this nexus, different school of economists Figure 1: Schumpeter’s model of economic acknowledged the role of finance as a facilitator development. in the economic system. It helps to minimize Source: www.slideshare.com transaction and information cost in the Empirical Literature economy. The literature on finance growth nexus can The analysis of theoretical issues related be divided into three categories namely, cross- to the economic crisis in 1905 motivated country and panel studies, microeconomic Schumpeter to write The theory of economic studies and country case studies. development (Schumpeter 1911). In his To start with cross-country studies masterpiece he initiated the debate with the Goldsmith (1969) was the first economist to distinction between statics and dynamics. analyse the nexus using annual time series data

862 for 35 countries for the period 1863 to 1963. were constrained from investing in profitable Financial interrelation ratio was used as a growth opportunities. The main focus was to proxy for financial development which was study long-term debt and external equity in found to be positively associated with funding firms growth. Both banking sector economic growth. The major pitfall of the study growth and stock market liquidity indicators was that it failed to ascertain the direction of were found positively associated with the causality. King and Levine (1993 a, b, c) using excess growth of firms. annual time series data for 76 countries for the Jayaratne and Strahan (1996) and Haber period 1960 to 1989 examined the relationship (1991, 1997) did country case study for US, with better proxies for financial intermediation Italy, Brazil, and Mexico, respectively. Acharya in a multivariate regression framework. The et al. (2009), Chakraborty (2010) and Singh and major finding of the study suggested a strong Mishra (2014) did country case study in Indian positive relationship between financial depth context. Singh and Mishra (2014) used both indicators and different real sector indicators. banking and stock market development The work was further extended by Atje and indicator as a proxy for financial development Jovanovic (1993) and Levine and Zervos in a multivariate framework for the period (1998) by adding several measures of stock spanning from 1988 to 2011. The major finding market and banking development indicators. of the study suggests financial development Levine and Zervos (1998) used several stock neither in short-run nor in the log-run promotes market growth indicators to analyse finance- growth in the Indian context. growth nexus. After looking into literature review large Microeconomics studies can be further number of studies either cross-country or segregated into firm-level and industry-level country case study is done using annual time surveys. Rajan and Zingales (1998) attempted series data. The current study is an attempt to industry-level study in which they found the empirically examine the Schumpeterian notion industry which were more dependent on of financial development and economic growth external finance they were more benefited from using quarterly data in the Indian context. financial exploitation. Study analysed 36 Description of Variables and Methodology of industries in 42 countries for the period 1980 the Study to 1990. Empirical findings of the study With a view to examine relationship suggests financial development dis- between financial development and economic proportionately boosts the growth of growth, real gross domestic product (GDP) industries that were naturally heavy users of growth rate is considered to represent external finance. Wurglur (2000) used industry- economic growth. The considered variable is level data for 65 countries from 1963 to 1995 also used in previous empirical studies and computed an investment elasticity that conducted by Odhiambo (2010) and Adusei gauges the extent to which a country increases (2012). The financial development indicator is investment in developing industries, decreases divided into two parts, namely banking sector investment in declining industries. The study and stock market development indicators. The concludes, the country with increasing proportion of domestic credit provided by the financial development has more investment in banking sector as a percentage of GDP is taken a growing industry and the country with lower to represent the banking sector liquidity levels of financial development had decreased indicator. King and Levine (1993a), Levine and investment in declining industries. Zervos (1996), Beck et al. (2000), and Levine Demirguc-Kunt and Maksimovic (1998) et al. (2000) has also used this indicator in used firm level data for 26 countries from 1980 their empirical studies. Broad money as a to 1991 and analysed degree to which firms percentage of GDP is considered to represent

863 the financial depth indicator. Most of the dynamics, after which an Error Correction previous surveys, namely, Goldsmith (1969), Model (ECM) is developed to check the long- King and Levine (1993a), Rousseau and run equilibrium among the set of financial Wachtel (2000), Rioja and Valev (2004), and development, economic growth and other Levine et al. (2000) also considered broad control variables. The Wald and Toda and money as a share of GDP as a proxy for size of Yammoto Granger’s causality test in the VAR the banking sector. Market capitalization as a block exogeneity form is used as the percentage of GDP is considered to represent diagnostic test for the long run equilibrium the stock market liquidity indicator. The relationship. turnover ratio is taken an additional stock RESULTS AND DISCUSSION market development indicator to represent the As a prerequisite for time series analysis, size of capital market. Besides, trade openness, the unit root properties of all the concerned general government final consumption variables is examined using the Augmented expenditure as a percentage of GDP, Wholesale Dickey Fuller test at their respective trend and Price Index (WPI) are used as control variables intercept. The result are described in the Table in the model. The detailed description of these 2. The results shows all variables are non- variables is given in the Table 1. stationary at level. This provides the credence The following model is employed to to use Johansen Maximum Likelihood examine the nexus of finance and growth in procedure to look into long run equilibrium the Indian context. WPI is taken as an average relationship among the variables. on weeks with base year 2004-05 for all The l trace and l max test is conducted to commodities. The data on other variables is find maximum number of co-integrating vectors sourced from RBI, the World Bank and EPW Research Foundation. The real GDP growth Table 2: Unit Root Estimation at level rate is found negative in some quarters. Hence, Variable ADC log-linear model is not employed. Trend & Intercept P-value Lag-length GRGDP -2.29 0.43 5 GRGDPt= a + b1RDCt + b2RBMt + b3RMARKt RDC -2.05 0.56 6 +b 4TURNRATIOt + b 5RTOt + RBM -0.73 0.97 6 b6LRGFCEt + b7WPIt + Ut...... (i) RMARK -1.9 0.64 2 At inception, before performing long-run TURNRATIO -0.87 0.95 2 and short-run tests, the Augmented Dickey RGFCE -1.12 0.92 9 Fuller unit root test was conducted to look RTO 0.13 0.96 6 into stationary properties of the variables. The WPI 0.39 0.99 3 Note: Mackinnon critical values for ADF at both 1% and 5% seasonality issue is very prominent in quarterly level of significance are -4.15 and -3.51 respectively. series which is taken care by Census 12 method. Johansen’s Maximum Likelihood and presented in Table 3. To start with l trace procedure is applied to examine long-run test, the trace value at null hypothesis r = 0 is

Table 1: Description of variables Variable Description GRGDP Growth rate of real GDP RDC Domestic credit by banking sector (Public + Private) as a share of GDP RBM Broad money as a share of GDP RMARK Market capitalization as a share of GDP TURNRATIO Turnover ratio (Turnover /Market Capitalization) RGFCE Government final consumption expenditure RTO Trade openness WPI Whole sale price index

864 Table 3 : Johansen Cointegration Test l Trace Critical Val l Max Critical Val * * H0 Test H1 Test Value 5% P-Value H0 Test H1 Test Value 5% P-Value r=0 r>0 419.29 159.53 0.00 r=0 r=1 141.04 52.36 0.00 r71 r>1 278.23 125.61 0.00 r=1 r=2 99.45 46.23 0.00 r72 r>2 178.78 95.75 0.00 r=2 r=3 72.75 40.08 0.00 r73 r>3 106.03 69.82 0.00 r=3 r=4 40.86 33.88 0.01 r74 r>4 65.16 47.85 0.00 r=4 r=5 29.73 27.58 0.03 r75 r>5 35.44 29.79 0.01 r=5 r=6 21.05 21.13 0.05 r76 r>6 14.39 15.49 0.07 r=6 r=7 14.38 14.26 0.05 Note: * Implies McKinnon-Haug-Michelis (1999) p-values

419.29 which is higher than the critical value at Table 4: Error Correction Model 5 percent level of statistical significance. Variables Coefficients p-value Therefore, it rejects the null hypothesis and Constant 0.002286 0.50 ecm1 -0.005082 0.77 accepts alternative hypothesis r>0. Similarly t-1 null hypothesis r>6, the trace value 14.39 is D(GRGDP)-1 -0.624223 0.00 D(GRGDP)-2 -0.434937 0.00 lower at 7 percent level of significance. D(RDC)-1 -0.000565 0.37 Therefore, null hypothesis was accepted D(RDC)-2 5.49E-05 0.92 implying r = 6. Now moving to l max test, the l D(RBM)-1 0.000599 0.31 max value at null hypothesis r = 0 is 141.04 was D(RBM)-2 -0.000189 0.74 higher than the critical values at the 5 percent D(RMARK)-1 4.95E-05 0.31 level of significance. In the similar fashion, the D(RMARK)-2 -4.57E-05 0.38 D(TURNRATIO)-1 0.00023 0.24 null hypothesis r = 6, the l max value 14.38 D(TURNRATIO)-2 0.000323 0.09 was slightly higher than the critical value at 5 D(RGFCE)-1 -0.001014 0.51 percent level of significance. Therefore, it can D(RGFCE)-2 -0.001196 0.15 be concluded that there are six co-integrating D(RTO)-1 -0.00423 0.08 vectors existing among the set of considered D(RTO)-2 -0.000472 0.84 variables. D(WPI)-1 0.000695 0.75 D(WPI)-2 -0.002361 0.31 The above result does not give information Diagnostic Tests about the direction of causality. The Adjusted R-squared 0.30 p-value relationship can be either way, where finance F-statistic 2.66 0.004 leads enterprise follows (Robinson, 1952). In DW 2.19 order to check direction of causality in the long- JB Test 2.18 0.34 run framework the Error Correction Model Obs R Square LM Test 3.36 0.19 (ECM) is developed. The following long-run BPG Test 22.17 0.57 equation is developed to study ECM by which innovations were obtained.

D(GRGDP) = b 0+ b 1D(LDC) + b 2D(LBM) + there is no long-run equilibrium. Apart from

b3D(LMARK) + b4D(TURNRATIO) + GRGDP, TURNRATIO and RTO lagged co- b D(RGFCE) + b D(RTO) + b D(WPI) efficient all other short-run co-efficient are 5 6  7

+ b8D(GRGDP- GRGDP )-1 + et .....(ii) insignificant. The different diagnostic test is The result of ECM is reported in the Table applied to check the model. The adjusted R2 4. The result shows the speed of adjustment for the model is 0.30. The LM and DW test coefficient (ecm1t-1), which is speed of performed were used to check autocorrelation adjustment towards the long-run equilibrium in the model. The DW value is 2019, which state is -0.005082 (negative), which is desirable shows that the autocorrelation is not serious but insignificant. Hence, the model shows that in the model. BPG test is applied to check the

865 heteroskedasticity, the results shows no promote growth in the Indian economy. spilling heteroscahdasticty. At the end JB test The Toda Yammoto Granger’s causality is applied to check the normality and result test in the VAR block exogeneity form is applied shows the model is normally distributed. to look into short-run dynamics among the After ECM, Wald test was applied to check considered financial development and joint influence of financial variable on economic growth indicators. This test is used economic growth. The results are reported in as a diagnostic test for the long-run equilibrium Table 5. The joint influence of single financial test because as per the necessary precondition variable with lagged terms and joint financial for the co-integration test, if there exists a long- variables with lagged terms is checked. In the run equilibrium relationship between the set next step financial variables are divided into of variables, then there should be a two groups, namely, banking sector unidirectional causality among the variables, development indicators (RDC and RBM) and if the theory holds good. capital market development indicator The Wald Test statistics of the causality (RMARK and TURNRATIO). test are reported in the Table 6. The results For both categories of indicators their revealed that no causality is existing among respective joint influence were found to be financial development and economic growth insignificant. At last the joint influence of all indicators except GRGDP to TURNRATIO. The the financial development indicators were tried unidirectional causality running from economic to check on GRGDP and the results show the growth (GRGDP) to stock market development joint influence of all combined financial variable (TURNRATIO) shows Robinson view holds is insignificant. The result shows neither good, where enterprise leads, finance follows. banking sector nor capital market was able to It is also because there is huge capital inflows in Indian capital market in the form of foreign Table 5: Wald Test institutional investment (FIIs). The returns in Dependent variable D (GRGDP) Indian capital market is higher than developed Independent variables Chi-square p-value D(RDC)-1 0.79 0.67 D(RDC)-2 Table 6: VAR Granger Causality/Block D(RBM)-1 1.08 0.582 Exogeneity D(RBM)-2 Dependent variable Excluded Chi-square p-value D(RMARK)-1 1.44 0.49 GRGDP RDC 10.56 0.39 D(RMARK)-2 RBM 12.75 0.24 D(TURNRATIO)-1 3.14 0.21 RMARK 17.58 0.06 D(TURNRATIO)-2 TURNRATIO 11.16 0.35 D(RDC)-1 2.03 0.73 RDC GRGDP 5.52 0.85 D(RDC)-2 RBM 9.01 0.53 D(RBM)-1 RMARK 13.61 0.19 D(RBM)-2 TURNRATIO 4.01 0.95 D(RMARK)-1 4.93 0.29 RBM GRGDP 10.38 0.41 D(RMARK)-2 RDC 9.71 0.47 D(TURNRATIO)-1 RMARK 18.17 0.05 D(TURNRATIO)-2 TURNRATIO 5.93 0.82 D(RDC)-1 5.94 0.65 RMARK GRGDP 10.32 0.41 D(RDC)-2 RDC 6.28 0.79 D(RBM)-1 RBM 5.83 0.83 D(RBM)-2 TURNRATIO 1.88 1.00 D(RMARK)-1 TURNRATIO GRGDP 25.60 0.00 D(RMARK)-2 RDC 50.29 0.00 D(TURNRATIO)-1 RBM 50.12 0.00 D(TURNRATIO)-2 RMARK 34.15 0.00

866 economies. Adusei, M. 2012. Financial development and CONCLUSIONS economic growth in India: Is Schumpeter right. The long-run and short-run diagnostics British Journal of Economics, Management and result shows, financial development neither in Trade. 2 (3): 265-278. Atje, R. and Jovanovic, B.1993. Stock markets long-run nor in the short-run promotes growth and development. European Economic Review. in the Indian context. The findings are also in 37 (2-3): 632-40. line with, Singh and Mishra (2014) who studied Beck, T., Demirguc-Kunt, A., and Levine, R. 2000. the same relationship using annual data in the A new database on financial development and Indian context. The unidirectional causality structure. World Bank Economic Review. 4: 597– from economic growth to stock market 605. development supports Robinson view. Hence, Chakraborty, I. 2010. Financial development and Schumpeter view on finance growth nexus economic growth in India: An analysis of the does not holds well in the context of Indian post reform period. South Asia Economic Journal. 11 (2): 287-308. economy. Demirgüç-Kunt, A. and Maksimovic, V. 1998. Law, The financial friction in the form of finance, and firm growth. Journal of Finance. institutional factors are the major reason 53 (6): 2107-2137. behind the inability of finance to promote Goldsmith, R.W. 1969. Financial structure and economic growth in the Indian market. There development. Yale University Press, New is huge indication of agency element in priority Haven and London. sector lending-in particular agricultural sector Haber, S.H. 1991. Industrial concentration and the lending. The NPA of banks engaged in priority capital markets: A comparative study of Brazil, sector lending (Agriculture) increased Mexico, and the United States, 1830–1930. The Journal of Economic History. 51 (03): 59-580. substantially after reforms (1991). The Indian Haber, S.H. 1997. Financial markets and industrial banks engaged in agricultural lending takes development: A comparative study of huge risk because of managerial mischief governmental regulation, financial innovation (Singh and Mishra (2014)). On the other hand and industrial structure in Brazil and Mexico, there is miscalculation of risk (credit, market 1840-1940. In: Haber, S.H. (ed.) How Latin and operational) on corporate loans because America fell behind? Stanford University Press, of traditional way of risk assessment (expert Stanford, CA. system analysis). Jayaratne, J. and Strahan, P.E. 1996. The finance- There is unidirectional causality from the growth nexus: Evidence from bank branch deregulation. Quarterly Journal of Economics. real sector to the financial sector in the short- 111 (3): 639-70. run, which shows finance responds to King, R. and Levine, R. 1993a. Financial changing demands of the real sector. If intermediation and economic development, in government have to promote finance-led capital markets and financial intermediation. economic growth it have to build a resilient Centre for Economic Policy Research, Colin financial system by promoting financial Mayer and Xavier Vives London. inclusion, checking financial frictions and King, R. and Levine, R. 1993b. Finance and growth: come with a better risk management framework Schumpeter might be right. Quarterly Journal which should be in line with global regulatory of Economics. 108: 717-738. King, R. and Levine, R. 1993c. Finance, framework (Basel III). entrepreneurship and growth: Theory and REFERENCES evidence. Journal of Monetary Economics. 32: Acharya, D., Amanullah, S., and Joy, S. 2009. 513-542. Financial development and economic growth Levine, R., and Zervos, S. 1996. Stock markets, in Indian states: An examination. International banks, and economic growth. American Research Journal of Finance and Economics: Economic Review. 88: 537–58. 24.

867 Levine, R. 1997. Financial development and Robinson, J. 1952. The rate of interest and other economic growth: Views and agenda. Journal essays. MacMillan, London. of Economic Literature. 35: 688-726. Rousseau, P.L. and Wachtel, P. 2000. Equity Levine, R., Loayza, N., and Beck. T. 2000. markets and growth: Cross-country evidence Financial intermediation and growth: Causality on timing and outcomes, 1980-1995. Journal and causes. Journal of Monetary Economics. of Banking and Finance. 24 (12): 1933-1957. 46 (1): 31-77. Schumpeter, J.A. 1911. The theory of economic Lucas, R.E. 1988. On the mechanics of economic development. Harvard University Press, development. Journal of Monetary Economics. Cambridge. 22 (1): 3-42. Shaw, E.S. 1973. Financial development and McKinnon, R.I. 1973. Money and capital in economic deepening. Oxford University Press, economic development. Brookings Institution, New York. Washington DC, USA. Singh, B.P. and Mishra, A.K. 2014. Nexus of Odhiambo, N.M. 2010. Finance-investment- financial development and economic growth in growth nexus in South Africa: an ARDL-bounds India: Revisiting Schumpeter. Indian Journal testing procedure. Economic Change and of Economics and Development. 10 (3): 246- Restructuring. 43: 205-219. 255. Rajan, R.G. and Zingales, L. 1998. Financial Wurgler, J. 2000. Financial markets and the dependence and growth. American Economic allocation of capital. Journal of Financial Review. 88 (3): 559-586. Economics. 58: 187-214. Rioja, F. and Valev, N. 2004. Finance and the sources of growth at various stages of economic development, economic inquiry. Western Economic Association International. 42 (1): Received: June 16, 2015 127-140. Accerpted: October 08, 2015

868 Indian J Econ Dev DOI: 10.5958/2322-0430.2015.00095.5 Volume 11 No. 4 (2015): 869-876 Research Article

AN ECONOMIC ANALYSIS OF SOYBEAN CULTIVATION IN HOSHANGABAD DISTRICT OF MADHYA PRADESH

Punit Kumar Agarwal and O.P. Singh*

ABSTRACT

Among the different soybean growing states Madhya Pradesh is forerunner in the case of soybean production in India. Out of total national area and production of soybean, contribution of Madhya Pradesh was 56.03 and 51.43 percent in 2011-12. The area of soybean increased tremendously in M.P. along with increased productivity but at present, its productivity has reached a plateau because of low adoption of recommended technology and due to several other constraints. The objective of present study was to estimate the cost and return structure and resource use efficiency of soybean cultivation in Madhya Pradesh. The average cost of cultivation was observed `25454.66 on overall farms and it was highest on small farm followed by medium and large farms, respectively. An average per hectare gross return from soybean was observed highest on large farm compared to small and medium farms. The cost of production of soybean was 1397.28 per quintal at overall level. The benefit-cost ratio was lowest in the case of small and highest in the case of large farmers.

Keywords: Cost of cultivation and production, B-C ratio, resource use efficiency. JEL Classification: A11, D24, M11, Q01, Q12, Q18

INTRODUCTION India and it was increased to 10.10 million Soybean is one of the important oilseed hectares and 12.21 million tonnes respectively crops grown in different parts of India during by the year 2011-12. In 1970-71, per hectare the kharif season. The soybean crop was yield of soybean was 426 kg and it was introduced in India during 1970-71. Soybean increased to the level of 1208.91 kg during production is mainly confined to Madhya 2011-12 (Anonymous, 2013). Pradesh (also known as soybean bowl of Soybean is a richest source of protein with India), Maharashtra, Rajasthan, Andhra 40 percent protein and 20 percent oil has now Pradesh, Karnataka, Uttar Pradesh, and been recognized all over the world as a potential Chhattisgarh. In 1970-71, total area under supplementary source of edible oil, it also soybean cultivation was 0.03 million hectares contains a large amount of lecithin and a fair and production was 0.01 million tonnes in amount of fat-soluble vitamins. The five major soybean producing countries in the world are *Research Scholar and Assistant Professor, USA, Brazil, Argentina, China and India. In Department of Agricultural Economics, BHU 2011, world’s soybean production was 251.5 Varanasi, 221005 million metric tonnes. Out of world’s total Email: [email protected] soybean production, about 33 percent

869 soybean came from USA. The contribution of MATERIALS AND METHODS Brazil, Argentina, China and India was 72, 48, Sampling Procedure 13.5 and 11 million metric tonnes, respectively Madhya Pradesh was selected purposively in 2011 (Agarwal and Singh, 2014). Madhya because more than 50 percent of the total area Pradesh is one of the major soybean producing and production of soybean in India comes from Indian states. The soybean crop was Madhya Pradesh alone. There are 38 major introduced in Madhya Pradesh in 1975-76. The soybean producing districts in Madhya crop is mainly grown during the kharif season Pradesh. For the selection of district, under rain-fed condition. The inter-annual compound growth trend of soybean area was variation in area and production of soybean calculated for each district. Based on the crop is mainly depends on the rainfall pattern. growth trend all the districts were classified During the good monsoon year farmers are into three groups namely, (1) relatively high allocating more area under the crop, whereas performing districts (growth rate more than 10 it was reduced during the poor monsoon year percent); (2) relatively average performing and this affects overall production of soybean. districts (growth rate five to ten percent); and In India, area and production under soybean (3) relatively low performing districts (growth crop was 10.10 million hectares and 12.21 rate up to five percent). The relatively average million tonnes, respectively in 2012. Out of total performing districts were Tikamgadh, Mandla, area and production in India, share of Madhya Shadol, Ujjain Hoshangabad and Bhopal. From Pradesh was 56.03 and 51.43 percent, the relatively average performing districts, respectively (GOI, 2013). Soyabean research Hoshangabad district was selected randomly. has played an important role in the last three Two blocks were selected and criteria for decades in augmenting its area and production. selection of black were based on area under The area of soyabean increased tremedously soybean cultivation in the block. Out of two in the M.P. alongwith increased productivity blocks, one block was selected based on but now the productivity has reached plateau highest area under soybean cultivation and due to low adoption of recommended package second block was selected based on lowest of practices which in turm become impediment area under soybean cultivation. The names of in its cultivation. Due to its nutritive value and selected blocks were Bankhedi and Seoni favorable agro-climatic condition there is Malwa. On the basis of area allocation under ample scope to increase the production of soybean cultivation one village from each soybean in the state. In this regard, there is selected block was selected for study. From need to understand the profitability from each selected village, 20 farmers were selected soybean cultivation. The overall objective of randomly. While selecting farmers, precaution the present study was to find out cost and was taken that selected farmers are growing return structure and resource use efficiency soybean crop. The data were collected through of soybean cultivation in Hoshangabad district personal interview method with the help of a of Madhya Pradesh. The specific objectives pre-tested comprehensive schedule for of present study were: soybean crop from sample farmers. The (i) to identify the socio-economic characters reference year of the study was 2011-12. of soybean growers in Hoshangabad Analytical Procedure district of MP, To identify the socio-economic characters (ii) to work out the cost and return structure of soybean growers in Hoshangabad district of soybean in Hoshangabad, and of Madhya Pradesh, descriptive statistics such (iii) to determine the input-output relationship as percentage, mean, minimum, maximum and of soybean in Hoshangabad district of average were used to analyze the socio- Madhya Pradesh. economic characteristics of the respondents

870 To estimate the cost of cultivation, those farmers having up to two hectares of following methodology was used: land holding were classified as small farmers.

Cost C1: It includes cash and kind expenses The land holding size was considered for actually incurred by cultivators which are as medium category farmers was 2.0 to 4.0 follows: hectares, whereas in the case of large farmers, 1. Value of hired human labour the land holding size was more than 4.0 2. Value of owned/hired machine labour hectares. The total sample size of soybean 3. Value of owned/purchased manures growers was 40. Out of this, numbers of 4. Value of fertilizers medium farmers were highest and it contributed 5. Value of owned/purchased seed about 47.50 percent to total sample size. The 6. Value of plant protection chemicals number of small and large farmers was 22.50 7. Land revenue and 30.00 percent, respectively (Table 1). 8. Interest on working capital Table 1: Sample size of respondents in Cost C2: Cost C1+ Imputed value of family human labour + rent paid for leased in land + Hoshangabad interest on value of owned capital assets Type of farmers Number Percent Small (up to 2 ha) 9 22.50 (excluding land) + rental value of own land Medium (2-4 ha) 19 47.50 Cost C3: Cost C2 + 10 percent of Cost C2 to Large (>4 ha) 12 30.00 account for managerial remuneration to the Total 40 100.00 farmer. To determine the resource use efficiency Family Size To study the input-output relationship in The average family size on overall sampled soybean crop, modified Cobb-Douglas farm families was worked out to be 7.39. The production function was fitted. The Cobb- average number of family members was largest Douglas production function was converted in large farms (9) followed by medium (8) and into log-linear form and the coefficients were lowest on small farms thai is, 5.99 (Table 2). estimated by using ordinary least squares The contribution of old age group (>50 years) (OLS) method. In logarithmic form, it assumed which is an indicator of farming experience, a log-linear equation as under: was highest in the case of medium farmers

Log Y = log a+ b1log X1 + b2log X2 + b3log X3 (20.95 percent) followed by large farmers (19.76

+ b4 log X4 + b5 log X5 + b log e percent) and lowest for small farmers (16.69 where, percent). The middle age group (18-50 years) Y = Yield (qha-1) was considered as main work force on the farm. -1 X1 = Human labour (man daysha ) Out of total family members, the contribution -1 X2 = Machinery labour (`ha ) of this group was 40.73, 41.90 and 40.60 percent

X3 = Seeds (kg) for small, medium and large farmers,

X4 = Chemicals fertilizers (kg) respectively. The rest of the family members

X5 = Plant protection chemicals (kg) were children (Table 2). a = Constant term µ Table 2: Age wise distribution of family e = Error/disturbance term members b1 to b5 = Elasticity coefficients of respective Farm category Age group Total inputs or regression coefficients of Old Middle Child

factor input. Small Farmers (n1=6) 16.67 33.33 50.00 100.00 Medium Farmers (n =8) 25.00 37.5 37.5 100.00 RESULTS AND DISCUSSION 2 Large Farmers (n =8) 25.00 37.5 37.5 100.00 Classification of sample farmers according 3 Large Farmers (n =8) 25.00 37.5 37.5 100.00 to land holding size 4 Overall Farmers (N=7) 14.28 42.86 42.86 100.00 For the classification of sample farmers, Old: > 50 year, Middle age: 15-80 year and , 18 Childern

871 Education level of sample farmers Table 4: Average size of land holding and Education is one of most important factors cropping intensity of soybean growers in achieving rapid socio-economic Particulars Farm category Overall development and increasing social order Small Medium Large founded on the value of freedom, social justice No. of farmers 9.00 19.00 12.00 40.00 Total cultivated 11.90 58.30 92.00 162.20 and equal opportunity. Education parameter area (ha) plays a significant role in farming practices Percent 7.33 35.94 56.72 100.00 which shows the awareness level of farmers Average 1.32 3.06 7.66 4.01 about the adoption of improved practices in Cropping 287.88 277.12 248.04 259.85 production of crops. It was observed from the Intensity (%) Table 3 that out of total sample farmers (40), only 2.50 percent farmer was illiterate and rest economically well-off are in a position to adopt of the farmers was literate (97.50 percent ). Out improved farm practices using mechanical of the total farmers only five per cent who were power. On the other hand, small, and medium above the graduation level. The percentage of farmers even they desired to use mechanical the farmers who did intermediate was highest power, but they cannot use it due to small size in overall farmers. of land holding and economically poor. Average size of land holding was showed Table 3: Education level of sample farmers positive relationship with increasing size of in the study area (Percent) farms. It was 1.32, 3.06, and 7.66 hectares in Particulars Farm category Overall the case of small, medium, and large farmers Small Medium Large (N=40) respectively. The cropping intensity was (n =9) (n =19) (n =12) 1 2 3 highest on small farms (287.88 percent) Illiterate - 5.26 - 2.50 Primary 22.22 5.26 - 7.50 followed by medium farms (277.12 percent) and Secondary 33.33 15.78 8.33 17.50 lowest on large farms (248.04 percent). On High School 22.22 10.52 41.66 22.50 overall average, cropping intensity was 259.85 Intermediate 11.11 26.31 33.33 25.00 percent (Table 4). UG 11.11 31.57 8.33 20.00 PG - 5.26 8.33 5.00 Input use pattern for soybean cultivation Total 100.00 100.00 100.00 100.00 The cost of cultivation and cost of production of any crop are the most important In the case of small farmers highest aspect of the farm economy both at micro and percentage was observed in secondary macro level, it provides guideline to the education group (33.33 percent) while in government in promulgating the price policy medium category the percentage of both for factors of production and the produce. respondents who did U.G. was highest (31.57 The cost was worked out in two broad heads percent) Number of respondents who did high namely variable cost and fixed cost. The school was highest in the case of large farmers variable cost included cost of human labour that is, 41.66 percent. (Table 3) (family and hired), machinery labour, seeds, Size of land holding manures, fertilizers, pesticides, herbicides, and The distribution of land holding according interest on working capital. On the other hand, to various categories of sample farmers has fixed cost involved land revenues, rental value greater importance in the study. It helps in of owned land (Vishwakarma, 2003). The mechanization of the agricultural farm and present study shows average cost of adoption of improved agronomic practices. cultivation per ha for soybean in was `25885.85 This leads to save time and labour resulting followed by medium farmers (`25731.08) and augmentation of socio-economic condition of large farmers (`24693.60) in Hoshangabad the farmers. The large farmers generally district. The similar results were reported by

872 Verma, (2008) for rapeseed/ mustard production medium farmers (22.82 percent) and lowest in in Dholpur district Rajasthan. the case of large farmers (22.65 percent). In the Out of total cost of cultivation incurred by case of overall farm the contribution of variable the large farmers, largest share was spending and fixed cost was 67.18 and 23.73 percent, on variable cost (68.26 percent) followed by respectively (Table 5). medium farmers (68.09 percent) and small Among the different inputs of soybean farmers (63.89 percent), while in the case of production in Hoshangabad district, the share fixed cost, small farmers has the highest of hired human labour to total cost of expenditure (27.02 percent) followed by cultivation was higher in the case of medium

Table 5: Input use pattern of soybean growers in Hoshangabad district of MP (ha-1) Particulars Small Medium Large Overall Quantity Value (`) Quantity Value (`) Quanity Value (`) Quantity Value (`) Human Labour (Man days) Hired 10.625 1842.08 18.14 3895.85 13.22 2288.48 16.38 2951.54 (7.12) (15.14) (9.27) (11.60) Family 4.3 814.21 1.34 291.84 0.22 44.41 1.65 335.15 (3.15) (1.13) (0.18) (1.32) Machinery 3791.67 2848.68 4135.42 3446.88 Labour (14.65) (11.07) (16.75) (13.54) Seeds (kg) 109.72 4494.44 117.76 4764.47 117.19 4556.25 111.41 4641.25 17.36 (18.52) (18.45) (18.23) Manures (q) 1.66 150 2.5 45 (0.61) (0.18) Urea 80.56 652.78 95.11 824.25 44.79 403.13 75.18 659.33 (2.52) (3.20) (1.63) (2.59) DAP 55.56 1270.83 38.53 842.11 18.75 420.83 44.55 812.19 (4.91) (3.27) (1.70) (3.19) SSP 144.68 796.3 202.63 1117.76 315.1 1708.33 222.66 1222.6 (3.08) (4.34) (6.92) (4.80) Total fertilizers 2719.91 2784.12 2532.29 2694.12 (10.51) (10.82) (10.25) (10.58) Plant 1.25 562.5 1.26 585.71 1.31 776.83 1.25 637.83 protection (l) (2.17) (2.28) (3.15) (2.51) 1.22 1830.79 1.18 1839.67 1.23 1880.2 1.26 1849.83 Herbicide (l) (7.07) (7.15) (7.61) (7.27) Total working 16055.6 17010.35 16363.88 16601.59 capital (62.02) (66.11) (66.27) (65.22) Interest on 481.67 510.31 490.92 498.05 working capital (1.86) (1.98) (1.99) (1.96) 16537.27 17520.66 16854.8 17099.64 Sub total (63.89) (68.09) (68.26) (67.18) Rental value 6990.74 5868.42 5590.28 6037.5 of land (27.01) (22.81) (22.64) (23.72) Land revenue 4.58 2.82 3.65 3.46 (0.02) (0.01) (0.01) (0.01) Sub total 6995.32 5871.24 5593.93 6040.96 (27.02) (22.82) (22.65) (23.73) Managerial 2353.26 2339.19 2244.87 2314.06 cost (9.09) (9.09) (9.09) (9.09) Total cost 25885.85 25731.08 24693.6 25454.66 (100.00) (100.00) (100.00) (100.00) Figures in parentheses show the percentage of the total cost

873 farmers (15.14 percent) followed by large rental value of own land for soybean crop. The farmers 9.27 percent and in the case of small average rental value of own land was worked farmers it was 7.12 percent, while in the case of out to be `6037.50 for overall farms and it was family labour, small farmer were spending 3.15 varied with the size of farms. In the case of percent of total cost followed by medium small farmers, rental value of own land was farmers (1.13 percent) and large farmers (0.18 highest (`6690.74) followed by medium farmers percent). Out of total cost of cultivation, the (`5868.42) and lowest for large farmers with share of machinery labour was highest for large `5590.28. The land revenue paid by small farmer farmers were 16.75 percent followed by small (4.58) was highest followed by large farmers farmers (14.65 percent) and lowest for medium (3.65) and medium farmers (2.82). It was farmers (11.07 percent). There was marginal observed that, medium farmers was spending difference between the seed quantity used by highest amount on human labour, seeds and large farmers (117.76 kg per ha) and medium fertilizers, while large farmers was spending farmers (117.19 kg per ha). The small farmer larger amount on machinery labour and plant used lowest quantity of seeds (109.72 kg per protection chemicals (Table 5). ha) as compared to medium and large farmers. Return from soybean cultivation Average expenditure incurred on seed was The yield of main product was highest on estimated to be highest in the case of medium large farms 19.27 quintal per hectare, whereas farmers (`4764.47) followed by large farmers it was 18.05 quintal for medium farmers and (`4556.25) and lowest in the case of small lowest for small farms with 17.18 quintal per ha farmers (`4494.44). (Table 6). In the case of by-product yield, it The farmers in study area were using urea was increasing with increasing size of farms. as a nitrogenous fertilizer, DAP, and SSP were An average yield from main product on overall used as phosphatic fertilizer and none of the farms was 18.22 quintal per ha. The price of farmers were used potash fertilizer. The average the main product was observed almost similar expenditure incurred on fertilizers was highest on all categories of farms. It was `2260 per in case of medium farmers (`2784.12) followed quintal on overall farms. The per quintal price by small farmers (`2719.91) and lowest by large of by-product was observed `30.56, `30.53 and farmers (`2532.29). `33.92 for small, medium and large category of The plant protection chemical includes farmers respectively. The gross income insecticides and herbicides. The average showed increasing trend with increasing size expenditure on plant protection chemicals was of farms and it was ranging between `40078.77 registering positive and direct correlation with (small farms) to `45520.77 (large farms). size of farm. Percentage share of expenditure The net income over Cost C1 was observed incurred on plant protection chemicals to total `24355.71, `24822.88 and `28710.38 per ha cost of cultivation was highest in case of large for small, medium and large farmers, farmers (3.15 percent) followed by medium respectively. Similarly, net income per hectare farmers (2.28 percent) and small farmers (2.17 over Cost C2 also showed the similar patterns. percent). The large farmers were spending Net returns per hectare over Cost C3 was highest on herbicide (`1880.20) among `17184.44 on overall farms. Net income per different groups of farmers followed by hectare was highest on large farms ( `20827.17) medium farmers (`1839.67) and small farmers followed by medium farms (`16320.62) and (`1830.79). lowest in the case of small farms (`14192.91).

The rental value of own land was estimated The cost of production (Cost C3) showed on the basis of price prevailing in the village decreasing trend with increasing size of farms. for leased in land. Again it was divided by the It was relatively higher in the case of small number of crops taken during the year to get farmers (`1507.10 per quintal) followed by

874 Table 6: Cost and return structure of Table 7: Regression coefficient of soybean growers in Hoshangabad production function in soybean Particulars Farm category Overall Particulars Regression Standard Small Medium Large coefficient error Cost (` ha-1) Intercept (a) 0.257 0.330 Human labour, x (Man days) 0.025NS 0.059 C1 15723.06 17228.82 16810.38 16764.49 1 Machinery labour, x (`) 0.096* 0.068 C2 23532.59 23391.89 22448.72 23140.6 2 Seed, x ( ) 0.378*** 0.082 C3 25885.85 25731.08 24693.6 25454.66 3 ` *** Yield (q) Fertilizer, x4 (`) 0.153 0.028 NS Main product 17.18 18.05 19.27 18.22 Plant protection chemical x5 (`) 0.066 0.076 By qroduct 43.77 45.95 49.52 46.53 Sum of elasticites 0.718 - Price (`q-1) Coefficient of Multiple 0.658 Main Product 2255.56 2252.63 2275 2260 Determination By product 30.56 30.53 33.92 31.55 *** and * significant at 1 and 20 per cent level Gross income (` ha-1) NS: Non-significant Main product 38741.26 40648.99 43841.15 41171.01 By product 1337.513 1402.709 1679.621 1468.091 (regression coefficient) attached to the variable Total 40078.77 42051.7 45520.77 42639.1 human labour (0.025), plant protection Net income over (` ha-1) Cost C 24355.71 24822.88 28710.38 25874.61 chemicals (0.066) application turned out to be 1 positive in soybean production. However, as Cost C2 16546.17 18659.81 23072.04 19498.5

Cost C3 14192.91 16320.62 20827.17 17184.44 the coefficient of these resources was Cost of production (` q-1) statistically non-significant means these Cost C 915.41 954.76 872.32 920.25 1 resources had no effect on the yield level of Cost C 1370.09 1296.3 1164.91 1270.26 2 soybean. In the study area uses of seed (kg) has medium farms (`1425.93 per quintal) and large significantly influenced the production of farms (`1281.40 per quintal). The benefit-cost soybean indicated by their significant ratio shows the income receives against per regression coefficient. The regression rupees investment. The benefit-cost ratio for coefficient of seeds found to be significant at soybean cultivation was found to be 1.68 for one per cent probability level which indicates overall category of farmers. The benefit-cost that, one per cent increase in the seeds would ratio also exhibiting increasing trend with bring about 0.378 percent increase in the yield increasing size of farms and it was 1.55, 1.63 (Table 7) and 1.84 for small, medium and large farmers The elasticity coefficient of fertilizers used respectively. The similar results were reported for soybean production was also turned out by the Verma, (2008) in his study on economics to be positive and shown significant effect on of production and marketing of rapeseed/ yield. The regression coefficient of fertilizers mustard in Dholpur Rajasthan. was found to be significant at one per cent Resource Use Efficiency probability level which indicated that one per The value of Coefficient of Multiple cent increase in fertilizers (quantity) would Determination (R2) of soybean crop was 0.658 bring about an increase of 0.153 percent in the in Hoshangabad. It indicates that, 65.8 percent yield, keeping the other variable resources variation in logarithmic value of output per constant in the equation at their geometric hectare was explained by the independent mean levels. variables included in the equation. While rest The coefficient of elasticity of production of the variation in the output were explained (regression coefficient) attached to the variable by those factors which had not been taken in machinery labour (0.096), application turned to considerations (Table 7). out to be positive in soybean production. The coefficient of elasticity of production However, the regression coefficient of this

875 resource was statistically significant at twenty role in soybean production with high yield. per cent which means that one per cent Therefore, it is required that farmers should increase machinery labour would bring about apply micronutrient fertilizer to the soybean an increase in the yield by 0.096 percent, crop. keeping the other variable resources constant REFERENCES considered in the equation at their geometric Anonymous. 2013. Agricultural statistics at a mean levels (Table 7). glance. Department of Agriculture and The return to scale (sum of the production Cooperation, Ministry of Agriculture, elasticity) was found less than one (0.718), Government of India, 2013 available at showed that, the production process is of www.dacnet.nic.in Agrawal, P.K. and Singh, O.P. 2014. An economic decreasing returns to scale. It indicates that analysis of soybean cultivation in Narsinghpur simultaneous increase of one per cent in factors district of Madhya Pradesh, India. Indian of production yields less than one per cent Journal of Agriculture Research. 48 (3): 185- increase in the output. 191. CONCLUSIONS Sharma, H.O., Soni, S.N., and Khare, P. 2006. It is concluded that the soybean Determinants of adoption of soybean production production which play a vital role in total technology by the cultivatiors at different regions oilseed production for food and nutritional of India. Agricultural Situation in India. 62 (10): security of the district. The average cost of 671-675. Verma, D.K. 2008. Economics of production and cultivation was observed to be highest on small marketing of rapeseed-mustard in Dholpur farm followed by medium farm and lowest on district, Rajasthan. Ph.D. Thesis. Department large size farm due to resources constraints. It of Agricultural Economics, Institute of has been observed at the time of survey that, Agricultural Sciences, Banaras Hindu University, farmers are growing soybean under rain-fed Varanasi. condition and none of the farmers were Vishwakarma, R.K. 2003. Production and marketing applying irrigation water to soybean crop. Due of cauliflower in Varanasi district, UP. M.Sc to uneven distribution of rainfall, soybean Thesis (Agricultural Economics), Department crop faced water stress during the critical of Agricultural Economics, Institute of Agricultural Sciences, Banaras Hindu University, stages of crop production cycle, resulting low Varanasi. yield. To sustain growth in soybean production, soybean growers should provide some irrigation when required. None of the soybean growers were applying micronutrient Received: March 12, 2015 (S, Zn) to soybean crops, but it plays important Accepted: October 07, 2015

876 Indian J Econ Dev DOI: 10.5958/2322-0430.2015.00096.7 Volume 11 No. 4 (2015): 877-886 Research Article

SUSTAINABLE DEVELOPMENT THROUGH FARMING SYSTEM APPROACH: A STUDY OF TRIBAL REGION IN CENTRAL GUJARAT

Mahammadhusen K.* , S. Jadav** and V.B. Darji**

ABSTRACT

In this study cost and returns, income and employment generation under different farming system has been analyzed. Major farming systems identified in the study area was FS-I (crop enterprises), FS-II (crop enterprises and animal husbandry), FS-III (crop enterprises and poultry) and FS-IV (crop enterprises, animal husbandry and poultry). The per farm results of the study indicated that the gross returns and net returns was found highest in FS-IV while least in FS-I. In case of employment generation, FS-IV generated higher employment (448.53 man days and 21.36 bullock pair days) followed by FS-II, FS-III and FS-I.

Keywords: Crops, employment generation, farming system, returns JEL Classification: O13, Q10, Q15, Q56

INTRODUCTION marginal farmers. The approach aims at A farming system is the result of complex increasing income and employment from small- interactions among a number of inter- holdings by integrating various farm dependent components, where an individual enterprises and recycling crop residues and farmer allocates certain quantities and qualities by-products within the farm itself (Singh et of four factors of production, namely land, al., 2006). Integrated farming systems (IFS) are labour, capital and management to which he often less risky, if managed efficiently, they has access. Farming systems research is benefit from synergisms among enterprises, considered a powerful tool for natural and diversity in produce, and environmental human resource management in developing soundness (Lightfoot, 1990). Poor technology countries such as India. This is a adoption, paucity of resources, low income and multidisciplinary whole-farm approach and very depletion of soil fertility are major obstacles effective in solving the problems of small and affecting the progress and productivity in overall development of the tribal area. Raising agricultural crops and collection of forest *Ph.D. Research Scholar, Department of produce are the means of livelihood of tribal. Agricultural Economics, Junagadh Agricultural The basic thought behind the selection of this University, Junagadh-362001( Gujarat) and topic was using integrated farming system **Associate Professors, Department of Agricultural Economics and Department of Agricultural approach to investigate enterprise Statistics, Anand Agricultural University, Anand- combination among the crops, animal 388 110. husbandry, poultry, etc. in the study area. Email: [email protected] Keeping this problem of tribal area, the specific

877 objectives were to study socio economics produced FYM was valued at market prices. characteristics, identify the major farming While cost incurred in the purchase of systems, work out the economics of the major fertilizers, actual prices plus transport and other farming systems and income and employment incidental charges were taken. generation under different farming systems Plant protection chemicals: The actual were analysed. purchase price of plant protection chemicals, MATERIAL AND METHODS purchased by the respondents. Data and Sampling Feeds and fodder: Accounted at purchase A multi-stage sampling technique was prices plus transport costs and self-produced adopted. In the first stage Panchmahal and feeds were evaluated at actual costs. Dahod districts were chosen purposively on Labour: Hired labour was accounted for at the the basis of highest maize area and at the actual wages paid by the farmers. Family human subsequent stages, two talukas having highest labour was imputed at the prevailing wage area under maize from each selected district. rates. Labour in all enterprises was converted Thus, total four talukas were selected. At the into man days by multiplying female and child next stage three villages were selected labour by 0.70 and 0.50, respectively. Bullock randomly from the each selected talukas. Thus, labour, both owned and hired, was accounted Total twelve villages were selected for the at the prevailing hire rates. study. Finally, from each selected village, 10 Marketing costs: These were the costs maize growers were selected at random. As incurred by the farmers in cleaning, grading, such sample consisted of total 120 packing, transporting and selling their respondents. The primary data for the study products. were collected through personal interview Miscellaneous costs: These were the other method with help of pre-tested comprehensive incidental costs incurred in the operation of interview schedule for the year 2013-14. Total enterprises. These included cost incurred on fixed cost and total variable cost are used for purchase of ropes, baskets, repairs and determining the cost and return under different maintenance of implements used, etc. farming systems. Interest on working capital: This was Establishment Cost calculated on the entire working cost of the It included cost on building, machinery enterprise at the prevailing bank rate of interest and equipments. The actual cost of animals 12 per cent per annum and was computed for purchased and the imputed value at prevailing half of the cropping period. market rates for animals born on the farm were Fixed Cost considered as establishment costs. It remained fixed irrespective with the level Variable Costs of output. It include: These were the costs incurred by the Land revenue: Where field crop enterprises farmers for the enterprise, which were were involved, the land revenue was productive. Broadly, these were the actual accounted at the rates fixed by the costs along with incidental charges incurred government. towards labour and material costs. These Land rent: The prevailing land rents for include: agricultural enterprises were imputed for the Seeds: The actual purchase price plus sample, since all land holdings were observed transportation costs, incurred if any, and farm to be owner operated. produced seeds were imputed at prevailing Depreciation: Depreciation on buildings used market rates. for the storage purpose was calculated at the Farmyard manure and fertilizer: FYM was rate of 5 per cent on kachcha and 2 per cent on valued at the actual purchase price and self- pakka buildings considering economic

878 productive period of crop. Life span and junk RESULTS AND DISCUSSION value for various agricultural implements and Based on the farming activities taken up machinery were decided in consultation with by the sample respondents there were four the respondents. Consequently, the farming system identified in Dahod and depreciation was calculated using the straight Panchmahal districts. Preliminary visits to the line method as shown below: district indicated that along with maize, other Purchasevalue  Junk value agricultural enterprises such as cultivation of Depreciation  Lifespan other field crops, like pigeon pea, wheat etc., Interest on fixed capital: This was calculated livestock enterprises and poultry, are also at the rate of 10 per cent per annum on the taken up. Several visits to the study area were book value of the asset/livestock, as the case made prior to taking up detailed primary data may be, for the study year. collection. With the given total number of Electricity costs: These were levied on respondents (120) basically maize growers, 24 horsepower basis and were included under had the crop enterprises that is, maize, pigeon fixed costs. pea, wheat, etc. which considerd as Farming Returns System (FS)-I, 36 respondents had combined The returns from crop, animal husbandry the crops with animal enterprises that is FS-II, and poultry, were evaluated at the actual price 32 respondents had poultry with crops, named obtained for them. The same method was also as FS-III and remaining 28 respondents had followed for the evaluation of by-products of adopting all above three enterprises as various enterprises. combined for maximization of resources

Table 1: Socio economic characteristics of sample farms (Percent) Particular Farming Systems FS-I FS-II FS-III FS-IV

(n1=24) (n2=24) (n3=24) (n4=24) Average age (years) 44.54 41.8 43.66 44.36 Family size Male 45.30 46.25 43.01 42.00 Female 39.10 36.55 3.77 37.50 Children 15.60 17.20 23.22 20.50 Total 100.00 100.00 100.00 100.00 Average size of the family (No.) 4.04 3.72 3.79 4.00 Education level Illiterate 16.67 13.89 18.75 10.72 Primary 41.67 38.89 40.63 35.71 Secondary 29.16 30.55 25.00 25.00 College 12.50 16.67 15.62 28.57 Total 100.00 100.00 100.00 100.00 Occupational pattern Agriculture 62.50 - - - Agriculture and other activities 37.50 - - - Agriculture and allied activities - 83.33 81.25 71.43 Agriculture and allied activities +other activities - 16.67 18.75 28.50 Total 100.00 100.00 100.00 100.00 Fully Irrigated 52.21 55.10 61.65 49.26 Semi irrigated 43.78 41.32 37.59 45.81 Rain fed 4.01 3.58 0.76 4.93 Average size of land holding (ha) 2.49 3.63 2.65 4.06

879 efficiency termed as FS-IV. in all the systems, constituting 45.30, 46.25, Socio Economic Characteristics of Sample 43.01 and 42.00 percent, respectively in FS-I, Farms II, III, and IV. The farm structure viz., size of family, With pertaining to education level, the educational status of head of the family, proportion of illiterates was found to be organizational participation, irrigation facility highest in FS-III (18.75 percent) compared to etc., affect the economy of the different farming FS-I (16.67 percent), FS-II (13.89 percent) and systems and also the adoption of improved FS-IV (10.72 percent). Literate respondents technology to a considerable extent. The size possessed education level ranging from of family has a bearing on the supply of labour primary to college level. In all the four farming force on the farm as well as the family systems majority of farmers had higher share consumption needs which is prime factor in in primary education level. As a pattern of land adaptation of different systems of farming. holding per farm was concerned, about 52.21, Educational status affects the cultivator’s 55.10, 61.65 and 49.26 percent of cultivable land response to change of cropping patterns and was under fully irrigated condition and their improved technology with the proportion of semi irrigated land was 43.78, combination of probable sources of income. 41.32, 37.59 and 45.81 percent while, proportion These aspects of socio economic of rain-fed land was 4.01, 3.58, 0.76 and 4.93 characteristics of sample farm for sample percent in FS-I, II, III, and IV, respectively. cultivators have, therefore, been analyzed and Average size of land holding was observed presented in Table 1. It can be seen from the fully irrigated in all the systems. Table 1 that, majority of the farmers belonged Cropping Pattern to middle age group in all the selected major The results of major crops grown in kharif maize based farming systems. The average age season include maize and pigeon pea among of the sample respondents was 44.54, 41.80, all the farming systems are presented in Table 43.66, and 44.36 years in FS- I, FS-II, FS-III and 2. The percentage area under maize and pigeon FS-IV, respectively. The family composition of pea in FS-I, which contributed to 43.74 and sample farmers in the study area revealed that 19.94 per cent of total cropped area, where as the proportion of adult male per family was in FS-II, maize and pigeon pea contributed to higher compared to adult female and children 44.38 and 19.30 per cent, in FS-III, maize and

Table 2: Major farming systems followed by the respondent in study area (ha) Particulars FS-I FS-II FS-III FS-IV Area (%) Area (%) Area (%) Area (%) Kharif season Maize 1.71 43.74 2.53 44.38 1.74 44.61 2.69 43.6 Pigeon pea 0.78 19.94 1.1 19.3 0.92 23.59 1.37 22.2 Sub total (a) 2.49 63.68 3.63 63.68 2.66 68.2 4.06 65.8 Rabi season Wheat 1.42 36.32 2.07 36.32 1.24 31.8 2.11 34.2 Sub total (b) 1.42 36.32 2.07 36.32 1.24 31.8 2.11 34.2 Gross cropped 3.91 100 5.7 100 3.9 100 6.17 100 area/farm (a+b) Net cropped area/farm 2.49 3.63 2.66 4.06 Cropping intensity (%) 157.023 157.02 147.00 151.97 Allied enterprises (No. per farm) Poultry birds - - 19.37 20.75 Animals - 6.06 - 6.57

880 pigeon pea contributed 44.61 and 23.59 per cent through the total cost (TC), gross returns, net of the total cropped area, respectively. In FS- returns and input-output ratio. Moreover, total IV, maize and pigeon pea contributed 43.60 and cost was composed of total variable cost (TVC) 22.20 per cent in the total cropped area, and total fixed cost (TFC). respectively. During rabi season, wheat was Costs and returns structure of different the major crop in all the four farming systems enterprises under FS-I with a share of 36.32, 36.32, 31.80 and 34.20 per The costs incurred and returns realized cent respectively, with FS-I, II, III, and IV. from different crop enterprises and their shares Cropping intensity was found to be 157.03, in total cost and returns were calculated and 157.02, 147 and 151.97 per cent in FS-I, II, III, presented in Table 3. The results presence in and IV, respectively. Table 3 perceived that among crop enterprises Non-crop enterprises in Different Farming mainly three major crops viz. maize and pigeon Systems pea in kharif and wheat in rabi were observed. The perusal of Table 2 shows that, poultry The result showed that among the three major and animal husbandry are the major non crop crops, expenditure made as variable cost, fixed enterprises adopted by respondents in study cost and total cost found highest in rabi wheat area. The average number of poultry birds per and it was 42.87 per cent, followed by kharif farm was 19.37 in FS-III and 20.75 in FS-IV, while maize 39.55 per cent and kharif pigeon pea 17.58 the average number of animals per farm was per cent accordingly to the total variable cost. 6.06 and 6.57 in FS-II and FS-IV, respectively. Moreover, total fixed cost in wheat also found Evaluation of Different Maize based Farming highest (46.28 percent), followed by kharif Systems maize (40.88 percent) and kharif pigeon pea In most of the farming systems, net area (12.84 percent). Among the three crop was found different, therefore at the end, the enterprises, highest share in total cost was in evaluation was carried out on one rupee basis. rabi wheat (43.96 percent) followed by kharif Here considering criteria, costs and returns maize (39.97 percent) and comparatively less structures, income and employment generation contribution in kharif pigeon pea (16.07 and diversification indexes of different maize percent). The total cost of the farming system related farming systems were used for as a whole was `50775.99 and the gross return evaluation of different farming systems. was `105654.33. The share of different crop Economics of farming systems were analyzed enterprises to the net returns was highest in

Table 3: Costs and returns structure of different enterprises under FS-I (`farm-1) Particulars Kharif Rabi Farming system as a Maize Pigeon pea wheat whole Total variable cost 13683.9 6080.4 14833.18 34597.47 (39.55) (17.58) (42.87) (100.00) Total fixed cost 6613.08 2077.89 7487.55 16178.52 (40.88) (12.84) (46.28) (100.00) Total cost 20296.98 8158.29 22320.72 50775.99 (39.97) (16.07) (43.96) (100.00) Gross returns 44342.61 16235.86 45075.86 105654.33 (41.97) (15.37) (42.66) (100.00) Net returns 24045.63 8077.58 22755.14 54878.34 (43.82) (14.72) (41.46) (100.00) Input-output ratio 2.18 1.99 2.02 2.08

881 kharif maize (43.82 percent) followed by rabi (21.78 percent), kharif maize (18.18 percent) wheat (41.46 percent) and kharif pigeon pea and pigeon pea (8.60 percent). While as per (14.72 percent). Net returns generated from FS- total fixed cost, kharif maize had found highest I, were lower than any other combinations of cost (32.19 percent), followed by rabi maize farm enterprises. The probable reason might (31.94 percent), animal husbandry (26.51 be that FS-1 had crop enterprises alone. Non- percent) and kharif pigeon pea (9.36 percent). crop enterprises like animal husbandry and Among the combinations of four enterprises poultry found absent. The results were more in FS-II, the share in total cost ranged between accorded to the resume of work done by Trivedi 8.78 per cent in kharif pigeon pea to 45.37 et al. (2008) and Hugar and Palled (2008). The percent in animal husbandry. As far as net net returns obtained from the farming system returns contribution was concerned, it was as a whole was `54878.34. maximum in animal husbandry (59.19 percent), The return per rupee of expenditure was followed by rabi wheat (18.01 percent), kharif observed highest in kharif maize (2.18), maize (16.47 percent) and kharif pigeon pea followed by rabi wheat (2.02) and in kharif (6.33 percent). pigeon pea (1.99). For the farming system as a The returns per rupee of expenditure were whole return per rupee of expenditure was observed to be higher in animal husbandry found 2.08. (2.75), followed by kharif maize (2.02), rabi Costs and returns structure of different wheat (1.99) and kharif pigeon pea (1.97). enterprises under FS-II Return per rupee for farming system as a whole The per farm cost and returns of these was observed 2.33. The by-products of crop enterprises were calculated and depicted in the enterprises were used as input in animal Table 4. It can be seen from the table that husbandry enterprise and farm manure respondents under FS-II were grown crop produced from animal husbandry used in crop enterprises such as maize and pigeon pea in enterprises. Thus, two enterprises function as kharif and wheat in rabi along with animal complimentary enterprises. This farming husbandry. The total cost of the farming system showed highest net returns when system was `142891.05 with `334279.35 as compared with FS-I and FS-III. The attributed gross income and `191388.30 received as net reason was the lion’s share of animal husbandry return. The major contribution in total variable enterprise in total net returns under this system, cost was incurred in animal husbandry which was not included in FS-I and FS-III. enterprise. Similar results were found by Singh et al. (2007) It was 51.44 percent, followed by rabi wheat and Jojo et al. (2013).

Table 4: Costs and returns structure of different enterprises under FS-II (`Farm-1) Particulars Kharif maize Kharif pigeon pea Rabi wheat Animal husbandry Farming system as a whole Total variable cost 19653.65 9291.22 23550.74 55616.2 108111.81 (18.18) (8.60) (21.78) (51.44) (100.00) Total fixed cost 11196.44 3251.03 11110.15 9221.62 34779.24 (32.19) (9.36) (31.94) (26.51) (100.00) Total cost 30850.09 12542.25 34660.89 64837.82 142891.05 (21.59) (8.78) (24.26) (45.37) (100.00) Gross returns 62365.11 24663.01 69135.83 178115.4 334279.35 (18.66) (7.38) (20.68) (53.28) (100.00) Net returns 31515.02 12120.77 34474.94 113277.58 191388.3 (16.47) (6.33) (18.01) (59.19) (100.00) Input-output ratio 2.02 1.97 1.99 2.75 2.33 Figures in parentheses indicate percentage to respective total

882 Costs and returns structure of different combination, it is difficult to generate larger enterprises under FS-III return over day and year, but it is possible to There were mainly three crop enterprises save the rupee, needed for purchasing of with poultry combined under this farming vegetables on day basis throughout the year system. Costs and returns for enterprises were which do not possible by crop enterprises only calculated and presented in Table 5. The total in FS-I. cost of cultivation for farming system as a The return per rupee of expenditure was whole was `58446.70 which comprises found in descending order from poultry (2.34), `41132.66 and `17314.03 as variable and fixed kharif maize (2.23), kharif pigeon pea (2.10), and cost respectively. Whereas `124858.88 rabi wheat (2.02). On overall basis the input – received as gross returns and `66412.19 as net output ratio was 2.14, which was comparatively returns. The farming system as a whole higher than FS-I but lower than FS-II and FS- indicated that the share of the rabi wheat cost IV. This is in conformity with the results found highest (37.66 percent) and lowest in obtained by Channabasavanna et al. (2009). poultry (8.21 percent) in total cost. Further, Costs and returns structure of different with the existing combination of enterprises in enterprises under FS-IV FS-III, the contribution toward net returns was In FS-IV, five enterprises such as three crop maximum in kharif maize (38.67 percent) enterprises along with poultry and animal followed by rabi wheat (33.90 percent), kharif husbandry were identified. Costs and returns pigeon pea (17.76 percent) and poultry (9.67 were calculated and depicted in Table 6. Results percent). revealed that total gross return and net return The lower share of poultry in variable, fixed received from farming system as a whole was and total cost may be due to traditional `384883.96 and `220797.00, respectively. adaptation of this enterprise. The attributed Whereas, respective amount of total variable, reason for lowest contribution of poultry in fixed and total cost was `119388.77, `44698.19, return was that majority of the respondents and `164086.97 in farming system as a whole. allocate resources towards poultry as backyard Among the five enterprises, contribution in poultry. They keep poultry birds to fulfil as an total cost was observed highest in animal additional need of the family in their daily diet. husbandry (46.78 percent), followed by rabi Although total eggs and meat productions per wheat (22.06 percent), kharif maize (19.27 year consider as output and income was percent), kharif pigeon pea (8.83 percent) while calculated on the basis of market price. Farming it was lowest in poultry enterprises (3.06 system with such type of enterprise percent). In this farming system farmers

Table 5: Costs and returns structure of different enterprises under FS-III (`Farm-1) Particulars Kharif maize Kharif pigeon pea Rabi wheat Poultry Farming system as a whole Total variable cost 13368.19 8072.92 14913.98 4777.57 41132.66 (32.50) (19.63) (36.26) (11.61) (100.00) Total fixed cost 7506.67 2686.95 7099.26 21.15 17314.03 (43.36) (15.52) (41.00) (0.12) (100.00) Total cost 20874.87 10759.87 22013.24 4798.72 58446.7 (35.72) (18.41) (37.66) (8.21) (100.00) Gross returns 46553.8 22556.91 44526.39 11221.78 124858.88 (37.29) (18.06) (35.66) (8.99) (100.00) Net returns 25678.94 11797.04 22513.15 6423.06 66412.19 (38.67) (17.76) (33.90) (9.67) (100.00) Input-output ratio 2.23 2.1 2.02 2.34 2.14 Figures in parentheses indicate percentage to respective total

883 maintained the dairy enterprises and poultry systems (2.35) which is higher than FS-I, II, along with crop enterprises as the subsidiary III, and IV. The probable reason might be the one, using the unused resources on the same share of animal husbandry in total cost and area of fixed resources. It provides products total returns under this system. Poultry like milk, dung, eggs and meat as a regular and enterprises also generate the income which continuous output over day to day which used was lower compared to other enterprises but as consumption as well as source of income farmer received the regular income. Thus, there throughout the year. However, the share in is need to suitably modify the development gross return, net return and input-output ratio approach and to consider improving whole was found highest in animal husbandry (50.80, farm production with livestock, poultry, and 53.79, and 2.55, respectively). It was mainly mixed crops. The results of the study are in due to continuous flow of income generated corroboration with earlier findings of using the by-product of the crop enterprises Kandasamy (1998), Ramrao et al. (2006) and and operated by underutilized family man Trivedi et al. (2008). power. Income and Employment Generation The returns per rupee of expenditure were Net farm income observed to be highest in animal husbandry The net farm income and employment (2.55). The ratio for kharif pigeon pea and derived from identified farming systems are poultry was found similar (2.23) but in poultry furnished in the Table 7. The result indicated it was realized using 3.06 percent while in kharif that the per hectare net income vary from pigeon pea it was 8.83 percent in total cost in `22039.49 in FS-I to `54383.49 in FS-IV and it farming system as whole (`164086.97). It may was `52724.05 in FS-II and `25061.20 in FS- be due to lower variable and fixed cost III. The ratio of gross income and total cost compared to all other enterprises in this per hectare found increased as the number of system. Looking to the evaluated results enterprises increased such as the ratio of FS-I, related to input-output ratio in this farming II, III, and IV, were estimated to be 2.08, 2.34, system was found higher in both animal 2.14, and 2.35, respectively. Income earned husbandry and poultry enterprises indicating from FS-I showed lower net income may be the influence of such enterprises in this due to crop enterprises alone a source of combination. income. The probable reasons for higher Moreover, the ratio as a whole also income earned from FS-IV, was due to inclusion observed to be higher as compared to other of more enterprises, and income earned from

Table 6: Costs and returns structure of different enterprises under FS-IV (`Farm-1) Particulars Kharif Kharif pigeon Rabi Animal Poultry Farming system as maize pea wheat husbandry a whole Total variable cost 19945.92 10637.82 24867.08 58937.83 5000.12 119388.77 (16.71) (8.91) (20.83) (49.37) (4.18) (100.00) Total fixed cost 11678.33 3845.17 11321.83 17827.04 25.82 44698.19 (26.13) (8.60) (25.33) (39.88) (0.06) (100.00) Total cost 31624.25 14482.99 36188.92 76764.87 5025.94 164086.97 (19.27) (8.83) (22.06) (46.78) (3.06) (100.00) Gross returns 69559.66 32334.03 76262.95 195523.2 11204.12 384883.96 (18.07) (8.40) (19.81) (50.80) (2.92) (100.00) Net returns 37935.41 17851.03 40074.02 118758.33 6178.19 220797 (17.18) (8.08) (18.15) (53.79) (2.80) (100.00) Input-output ratio 2.2 2.23 2.1 2.55 2.23 2.35 Figures in parentheses indicate percentage to respective total

884 allied activities such as animal husbandry and The attributed reason may be due to crop poultry make it more profitable system along enterprises furnished employment during crop with highest per rupee expenditure. season only. While in the case of FS-IV, total Both animal husbandry and poultry found employment was found higher and probable subsidiary source of income for farmer reason for highest employment may be due to throughout the year. This system integrate more number of labour utilized in animal crop enterprises along with non-crop husbandry. The pattern of labour utilization in enterprises and used crop enterprises by- animal husbandry and poultry is continuous product such as fodder for animal husbandry, which provide the higher employment farm manure taken from animal husbandry as compared to FS-1. source of organic manure for land and utilized Bullock labour employment in crop production as complimentary activities. It could be seen from the Table 7 that under Such crop production provides straws to the four identified farming systems, FS-IV animal husbandry and poultry as feed. This required highest bullock labour (21.36 pair complimentary use of farm resources among days) followed by FS-II (21.14 pair days), FS- different farm enterprises make it best possible III (19.31 pair days) and FS-I (17.67 pair days). use of resources by increases the overall farm CONCLUSIONS productivity. Mainly four farming systems were Human labour employment observed in the study area. The proportion of To increase employment generation at the male was higher as compared to female, primary farm level which ensures the better livelihood education was found more in all the systems and earning capacity which plays an important whereas college education was observed role in the realization of any farm family goals higher in those farm which adopted crop + AH in farming system. The quantum of employment + poultry. The gross income, net income and generated under various farming systems by return per rupee was found higher in FS-IV, the farmers in the study area was worked out followed by FS-II, III, and I means combination for human labour and the details are given in of allied activities with crop enterprises will Table 7. It could be seen from Table 7 that under increase the profitability of tribal farmers the identified farming systems, FS-IV required because animal husbandry and poultry highest number of human labour (448.53 md generated regular income. Moreover, FS-IV per farm), followed by FS-II (362.87 md per system had provided higher both human and farm), FS-III (196.07 md per farm) and FS-I bullock employment. It can be concluded that (158.75 md per farm). tribal farmer should adopt poultry as well as It clearly revealed from the table that animal husbandry which not only make them employment generation under FS-1 which had supplementary sources of income but provide crop enterprises only found lowest compared higher man days for employment and utilize to any other enterprises found in study area. the farm resources in complementary manner.

Table 7: Income and employment generation in different farming systems (`Per farm) Particulars FS-I FS-II FS-III FS-IV Total cost 50776 142891 58447 164087 Gross returns 105654 334279 124859 384884 Net income 54878 191388 66412 220797 Input output ratio 2.08 2.33 2.14 2.35 Employment human labour (Mandays) 158.75 362.87 196.07 448.53 Bullock labour (Pair days) 17.67 21.14 19.31 21.36

885 REFERENCES Sabita Jojo, N., Singh, B.K., and Prakash, J. 2013. Channabasavanna, A.S., Biradar, D.P., Prabhudev, Economic analysis of different mixed production K.N., and Hedge, M. 2009. Development of farming systems in Simdega district of profitable integrating farming system model for Jharkhand. Journal of Economic and Social small and medium farmers of Tungabhadra Development. 9 (1): 57-59. project area of Karnataka. Karnataka Journal Singh, K., Bohra, J.S., Singh, T.K., Singh, J.P., Singh, of Agriculture Science. 22(1): 25-27. Y., and Singh, C.S. 2007. Productivity and Hugar, H.Y., and Palled, Y.B. 2008. Studies on economics of integrated farming system in maize-vegetable intercropping systems. irrigated agro ecosystem of Eastern Uttar Karnataka Journal of Agriculture Science. 21 Pradesh. Indian Journal of Agronomy. 52 (1): (2): 162-164. 11-15. Kandasamy, O.S. 1998. An economic analysis of Singh, K., Bohra, J.S., Singh, Y., and Singh, J.P. integrated farming system in Dharmapuri district 2006. Development of farming system models of Tamil Nadu. Farming System. 14 (1&2): 29- for the North-eastern Plain Zone of Uttar 33. Pradesh. Indian Farming. 56 (2): 5-11. Lightfoot, C. 1990. Integration of aquaculture and Trivedi, M.M., Mishra, R.K., Patel, M.V., and Patel, agriculture: A route to sustainable farming A.M. 2011. Integrated livestock farming systems, Naga. The ICLARM Quarterly. 13 (1): systems. In: Seminar of agribusiness potential 9-12. of Gujarat. Anand agricultural University, Ramarao, W.Y., Tiwari, S.P., and Singh, P. 2006. Anand, March 17-18. Crop-livestock integrated farming system for the marginal farmers in rainfed region of Chhattisgarh in Central India. Livestock Received: March 08, 2015 Research for Rural Development. 18 (7): 1-4. Accepted: September 15, 2015

886 Indian J Econ Dev DOI: 10.5958/2322-0430.2015.00097.9 Volume 11 No. 4 (2015): 887-894 Research Article

ECONOMIC ANALYSIS OF MILK PRODUCTION IN PARBHANI DISTRICT OF MARATHWADA REGION IN MAHARASHTRA-A STUDY OF SMALL SCALE FARMS

Ravi Shrey* and S.H. Kamble**

ABSTRACT

The present paper attempts to examine the economics of milk production on small farms in Parbhani district of Marathwada region of Maharashtra state. A sample of 60 farmers was selected through multistage sampling technique. The major finding of economic analysis revealed that the composition of milch animals on selected farm was 180, which includes 23 local cows, 60 crossbreed cows and 97 buffaloes. At overall level total fixed investment on milch animals was `117541.19 of which share of crossbreed cow was highest (46.48 percent) followed buffalo (34.86 percent) and local cow (18.66 percent). Whereas, working capital investment per animal per annum was highest in the case of crossbred cow (`30833.13) followed by buffalo (`29815.42) and found lowest for local cow (`16987.29). The analysis also revealed that per liter cost of milk production was highest in case of local buffalo (`26.05) followed by local cow (`24.14) and lowest in case of crossbred cow (`14.52) with input- output ratio of 1.42 for crossbred cow followed by local buffalo (1.18) and then local cow (0.86).

Keywords: Buffalo, cost, crossbred cow, local cow, return, small farms JEL Classification: M11, O13, O33, Q12, Q16

INTRODUCTION said to be most complete food item because of Milk is considered as wholesome food. its great biological importance as it contains a Milk products have a significant place in the variety of nutrients and these nutrients in milk people’s diet in general and sick people in help make it nature’s most nearly perfect food. particular. The minerals in milk are essential It improves human nutrition and plays an for body building and its maintenance. Milk is important role to achieve food security. India has two percent of the geographical area of the world and supports about 18 percent of * Ph.D Scholar, Department of Agricultural the world cattle population, largest cattle Economics, College of Agriculture, Indira Gandhi population than any other country in the world. Krishi Vishwavidyalaya, Raipur-492012 (Chhattisgarh) and **Associate professor, India is world’s largest milk producer (132.4 Department of Agricultural Economics, College of mt), but accounting for only 16 percent of Agriculture, Vasantrao Naik Marathwada Krishi world’s total milk production and was the Vidyapeeth, Parbhani-431402 (Maharashtra) world’s largest consumer of dairy product. The Email ID: [email protected] milk production in the country was 17.0 million

887 tonnes during 1950-51. A number of initiatives et al., 2002 and Shergill, 2006) reported that under taken by the government helped feed cost was the major cost component in the improving the productivity of milk over the total expenses, and there were a large inter farm- period. At the beginning of 12th Five Year Plan group variations regarding cost of milk (2012-13), the total milk production was 132.4 production and earnings. Beside, this the study mt. The milk production increased from 127.9 on maintenance cost per milk animal (Autkar mt in 2011-12 to 132.4 mt in 2012-13 registering et al., 1995) showed that the major items of a growth of 3.5 percent. The per capita maintenance cost were feed, human labour, and availability of milk was at 130 gm per day in interest on working fixed capital. 1950-51. Which were increased from 225 gm The study of economics of milk production per day in 2003-04 to 299 gm per day in 2012-13 has assumed a great importance as seeing with an average annual growth rate of 2.7 increasing urban population, leading to percent (BAHS, 2014). maladjustment between demand and supply Dairy farming provides an opportunity of and consequently a sharp rise in milk prices. generating employment and income to peoples Therefore, the study of economics of milk mainly rural one. It is an important source of production is of practical interest to milk subsidiary income to small and marginal producers in pointing out the directions to farmers, landless labours, rural housewives bring down the cost of production of milk, thus and rural unemployed youth. Misra and Pal ensuring good margin of returns to producer (2003) reported that 25 percent of the total and fair price to the consumer, indirectly earnings of villagers come from dairying as a governing the supply and demand position of subsidiary activity of crop livestock integration milk. There are number of research studies in rural area. The cost and return structure of conducted to assess the economics of milk dairying is an important aspect for producers production using various economic or and consumers to get better remunerative econometric techniques (Chandra and prices for milk sale and purchasing price by Agarwal, 2000, Kumar and Rai, 2008, Kulkarni consumers at reasonable and cheaper rates. and Hembade, 2010, Venkatesh and Sangeetha, Dairying in India has traditionally been a small 2011, and Ghule et al., 2012). Hence, keeping holders’ enterprise. As the demand for milk and in view the importance of dairying the present milk product is increasing rapidly, many study was undertaken to estimate cost of milk commercial dairy farms have come up in the production of small scale farm. country. The pattern of investment on a dairy DATA AND METHODOLOGY farm largely depends on the returns obtained Sampling Technique and Data Description from them. Past studies on economics of milk The data was collected for the research production (Chand et al., 2002 and Kumar, during November-December 2012 with multi- 2003) showed that the share of investment on stage sampling technique. Parbhani district animal remains the highest followed by was selected purposively as study area in the buildings and equipment. In dairying the profit first stage, in the second stage Parbhani tehsil of the firm can be maximized either through was selected on the basis of highest milk maximization of returns or minimization of cost. production. In third stage four villages were Individual producer have very little control selected on the basis of highest milk production over returns and being largely dependent on in the tehsil. And in fourth stage small farmers external environment of the firm. Hence cost including marginal farmers were selected minimization is an important tool in the hands randomly from each village. Thus, 60 farmers of producer through which profit could be were selected for the study. A well-structured maximized. The studies on the cost of schedule was used for personal interview from production of commercial dairy farms (Chand sample farmers.

888 Functional Analysis investment of respective enterprise. Cost concepts used in dairy enterprises Estimation of returns The cost of milk production on the farm Dairy enterprise was grouped in two categories viz. fixed cost Value of milk production and value of dung and variable costs. were considered as returns in dairy enterprise. Fixed costs The values were estimated by taking selling It includes costs of all fixed assets utilized price of milk and market price of FYM (dung). in milk production such as cattle shed, Milk Estimation of per farm expenditure and income cane, other utensils, etc. Per farm expenditure and income was Variable costs estimated by using following formulae: Costs of feed, fodder, labour charge, a) Per farm expenditure on dairy enterprise: veterinary aids, etc. C  c x Estimation of Costs and their Valuation i i where, Labour C = Expenditure on dairy enterprise (per The hired human labour as well as bullock farm) labour was evaluated on the basis of actual c = Total cost of maintenance of ith milch wages paid to labour. Whereas, the valuation i animal of family human labour and bullock labour was x = Number of milch animals of ith category done by considering the prevailing wages rates i b) Per farm income from dairy enterprise: (`100 man per day) in the study area. Purchased inputs Y  cixi The valuation of purchased inputs like where, feeds and fodder was done on the basis of Y = Income from dairy enterprise (per farm) th actual payment. yi = Total income per animals of i category th Farm produced inputs xi = Number of milch animals of i category The farm produced input like manures; RESULTS AND DISCUSSION feed, fodder, etc. were evaluated on the basis The present paper attempts to find out the of market price prevailing in the study area. economics of milk production of the different Depreciation on capital assets milch animal categories viz. local cows, The depreciation on farm building, crossbred cows and buffaloes, and the detail implements and machinery, hand tools etc. was results of the study were presented under the worked out by adopting straight line method following under headed. of depreciation. While calculating the Distribution of Milch Animals depreciation following formula was used. The information regarding distribution of Purchaseprice  Junk value total milch animals on selected farms is Annualdepreciation  presented in Table 1. The results showed that Expectedlife there were a total 180 milch animals with all the While, depreciation on dairy enterprises selected farmer among which 12.78 percent (23 was calculated @ 20 per cent per year for animal, @ 20 per cent on cattle shed per year Table 1: Distribution of milch animals on and @ 10 per cent per year on dairy equipment. selected farms Whereas expected life of dairy animals, cattle (n=60) shed and dairy equipment were taken as 5, 6 Types of milch animal Numbers Percentage and 3 years, respectively. Local cow (0.38) 23 12.78 Interest Crossbred cow (1.0) 60 33.34 Local buffalo (1.62) 97 53.89 The interest on fixed capital was worked Total milch animals (3) 180 100.00 out @ 10 per cent per annum on the fixed Figures in parantheses are average number animals

889 animals) were local cows, 33.34 percent (60 Working capital investment in dairy animals) were crossbred cows and 53.89 The working capital investment includes percent (97 animals) were local buffaloes. The the value of feed and fodder, family labour, average numbers of milch animals per farm hired human labour and miscellaneous items. came out to be three. The details are presented in Table 3. The results Maintenance Cost of Different Categories of revealed that the working capital per animal Milch Animal per annum was highest in the case of Fixed capital investment in dairy crossbred cow (`30833.73), out of which 19.46 Fixed capital investment on dairy consists percent invested on family labour, 16.98 of value of animals, value of building, percent on green fodder, 23.23 percent on dry equipments and other tools used in dairy and fodder, 27.97 percent on concentrates, 11.51 presented in Table 2. The perusal of Table 2 per cent was interest on working capital @ 13 revealed that in the case of investment on per percent rate and 0.84 percent on medicine and milch animal, total investment was `97455.62 miscellaneous items. of which highest investment was on crossbred In the case of local buffalo total working cow (41.56 percent), followed by buffalo (38.19 capital was `29815.42. Out of which 24.99 percent) and local cow (20.25 percent). percent invested on family labour, 14.72 Whereas, in the case of investment on cattle percent on green fodder, 27.29 percent on dry shed per milch animal, total investment was fodder, 20.78 percent on concentrates, 11.51 `18873.34 of which maximum investment was percent was interest on working capital @ 13 on crossbred cow shed (71.97 percent), percent rate and 0.74 percent on medicine and followed by buffalo (17.82 percent) and local miscellaneous items. The lowest working cow (10.20 percent). capital per animal per annum was observed for local cow. It was `16987.29, out of which 17.69 Table 2: Fixed capital investment per animal percent invested on family labour, 26.51 with respect to different categories percent on green fodder, 30.56 percent on dry (`animal-1) Types of Animal Cattle Dairy Total fodder, 12.98 percent on concentrates, 11.51 Animals value shed equipment percent was interest on working capital @ 13 Local cow 19739.13 1926.08 263.48 21928.69 percent rate and 0.75 percent on medicine and (20.25) (10.20) (22.23) (18.66) miscellaneous items. Whereas, at overall level Crossbred 40500 13583.34 541.34 54624.68 total working capital investment was cow (41.56) (71.97) (45.67) (46.48) Buffalo 37216.49 3363.92 380.41 40960.82 `77639.44, out of which highest capital (38.19) (17.82) (32.09) (34.86) invested on dry fodder was 26.40 percent, Total 97455.62 18873.34 1185.23 117514.19 followed by concentrate 21.93 percent, family (100.00) (100.00) (100.00) (100.00) labour 21.19 percent, green fodder 18.19 Figures in the parentheses are percentages to total fixed percent, 11.51 percent was interest on working investment capital @ 13 percent rate and 0.80 percent on Similarly, in the case of dairy equipment medicine and miscellaneous items. Hence, it investment per milch animal, total investment was evident from Table 3 that farmers were not was `1185.2 of which maximum investment aware of balance feeding practice and not was on crossbred cow (45.67 percent), properly use green fodder, dry fodder and followed by buffalo (32.09 percent) and local concentrates. They usually use lower cow (22.23 percent). Whereas, at overall level quantities of all feeds and fodder, which results total investment was `117541.19 of which the lower yield. share of crossbred cow was (46.48 percent), Similar findings were also reported by followed by buffalo (34.86 percent) than local Badal and Dhaka (1998) which estimated only cow (18.66 percent). 45.91 percent of total cost on feeding of buffalo

890 Table 3: Working capital investment per animal with respect to different categories (`animal-1) Particulars Units Local cow Crossbred cow Buffalo Overall total Quantity Value Quantity Value Quantity Value Family labour Manday-1 30.06 3006 60.1 6001 74.5 7450 16476 (17.69) (19.46) (24.99) (21.19) Green fodder Quintals 22.51 4502 26.18 5236 21.94 4388 14126 (26.51) (16.98) (14.72) (18.19) Dry fodder Quintals 12.98 5192 17.92 7164 20.34 8136 20492 (30.56) (23.23) (27.29) (26.40) Concentrates Quintals 1.47 2205 5.75 8625 4.13 6195 17025 (12.98) (27.97) (20.78) (21.93) Medicine and ` - 128 - 257.84 - 219.25 605.09 Miscellaneous (0.75) (0.84) (0.74) (0.80) Interest on working ` - 1954.29 - 3546.89 - 3429.17 8930.35 capital @13 percent (11.51) (11.51) (11.51) (11.51) Total working ` - 16987.29 - 30833.73 - 29815.42 77635.44 capital investment (100.00) (100.00) (100.00) (100.00) Figures in the parentheses are percentages to total working investment in Gopalganj district of Bihar. Shiyani and worked up average milk yield of 2.50 litres for Pandya (1999) reported that more than 70 indigenous cow and 3.50 litres for buffalo. percent of total cost was shared by feed and Cost and return in milk production (Per fodder cost in Gujarat and Kulkarni and animal per annum) Hembade (2010) reported that on feeds and The cost and return was worked out by fodder more than 70 percent expenditure of considering variable costs and fixed costs total cost was occurred as farmers followed together per animal per annum. While, for their own feeding practices. estimating per animal returns, value of milk, Production traits of different categories of value of dung/manure and value of calf upto animal one year was considered and presented in The perusal of Table 4 shows the average Table 5. milk yield per day in litres, lactation period, Per animal per annum cost and returns Dry period and inter calving period in days. structure in local cow milk production While working up average milk yield in study It is observed from Table 5 that, the total area it was worked out to be 4.61 litres for cost of milk production of local cow was indigenous cow, 10.89 litres for crossbred cow worked out to `23539.58 of which 72.16 per and 5.88 litres for that of buffalo. cent was variable cost and 27.84 per cent was Similar findings were also reported by Raj fixed cost, while gross returns worked out to and Gupta (1994) while studying economics `20375.4 of which `19502.4, `765, and `108 of milk production in Churu district of were from milk, dung/manure and calf up to Rajasthan. Kulkarni and Hembade (2010) also one year, respectively. It was revealed from Table 5 that in the case of local cow total cost Table 4: Production traits of different and gross return were lowest against categories of animal crossbred cow and buffalo milk production. (lday-1) Per animal per annum cost and returns Animal category Milk yield Local cow 4.61 structure in crossbred cow milk production Crossbred cow 10.89 The perusal of Table 5 revealed that, the Local buffalo 5.88 total cost of crossbred cow milk production

891 Table 5: Annual cost and return in milk production of different categories (`animal-1) Animal category Cost Amount Returns fromn Amount Local cow a) Variable cost 16987.29 a) Milk 19502.40 (72.16) b) Dung/manure 765.00 b) Fixed cost 6552.29 c) One year calf 108.00 (27.84) d) Gross return 20375.40 c) Total cost 23539.58 e) Net profit - (100.00) Crossbred cow a) Variable cost 30833.13 a) Milk 69956.67 (65.37) b) Dung/manure 1277.00 b) Fixed cost 16333.28 c) One year calf 720.00 (34.63) d) Gross return 66953.67 c) Total cost 47166.41 e) Net profit 19786.66 (100.00) Buffalo a) Variable cost 29815.42 a) Milk 48451.80 (70.88) b) Dung/manure 1047.00 b) Fixed cost 12250.13 c) One year calf 375.00 (29.12) d) Gross return 49873.8 c) Total cost 42065.55 e) Net profit 7808.25 (100.00) Figures in the parentheses are percentages to total cost was worked out to be `47166.41 of which 65.37 animals Table 6 revealed that the milk percent was variable cost and 34.63 percent production from a single crossbreed cow was was fixed cost. While, gross return worked out 3247.83 liters with total cost of `47166.41 and to `66953.67 of which `74371.45, `1277 and gross returns of `66953.67 including value of `720 were from milk, dung/manure and calf dung and calf, followed by milk production, upto one year, respectively, whereas, the net which was 1615.06 liters with total cost of profit obtained was `19786.66. It was clearly `42065.55 and gross returns of `49873.80. observed from Table 5 that in the case of Whereas, in the case of local cow milk crossbred cow total cost and gross return were production from a single cow was 975.12 liters higher against local cow and buffalo milk with total cost of `23539.58 and gross returns production. of `20375.40. Per animal per annum cost and returns The per liter cost of milk production of structure in buffalo milk production different milch animals was also shown in It was also observed from Table 5 that, the Table 6 and revealed that, per liter cost of milk total cost of local buffalo milk production was production was highest in the case of local `42065.55 of which 70.88 percent was variable buffalo (`26.05) followed by local cow (`24.14) cost and 29.12 percent was fixed cost with a and lowest in the case of crossbred cow gross return of `49873.8 of which `48451.8, (`14.52). The analysis of per liter cost of milk `1047 and `375 were received from milk, dung, production of different milch animals indicated manure and sale of calf respectively. The net that, for producing a liter of milk, minimum cost profit worked out to the farm was `7808.25. Cost and return in milk production (Per liter) Table 6: Annual cost of milk production per The per liter cost of milk production would litre for different milch animals Animal category Total cost Milk yield Cost give the real picture of dairy enterprise. The (`) (Litre) (`l-1) per liter cost of milk production was calculated Local cow 23539.58 975.12 24.14 by dividing net maintenance cost by quantity Crossbred cow 47166.41 3247.83 14.52 of milk produced. Regarding returns from milch Buffalo 42065.55 1615.06 26.05

892 was incurred on crossbred cow and maximum level, total fixed investment on milch animals cost was incurred on local buffalo. This picture was `117541.19 of which share of crossbreed was reached because of low milk yield than cow was 46.48 percent followed by buffalo crossbred cow. These results are in close 34.86 percent and local cow 18.66 percent. conformity with results of Menbhekar et al. Whereas at overall level total working capital (1995), Gauraha (1995), Nandlal and Rana investment was `77635.44, out of which (1995), Kulkarni and Hembade (2010), and Kaur highest capital expended on dry fodder was and Kaur (2013) as they reported that cost of 26.40 percent, followed by concentrates 21.93 milk production per litre was higher in the case percent, family labour 21.19 percent, green of buffaloes as compared to indigenous cows. fodder 18.19 percent, interest on working Profitability of different categories in milk capital @ 13 percent rate (11.51 percent) and production medicine and miscellaneous items 0.80 percent. The output-input relationship (B:C ratio) The analysis showed that at overall level total for different category of milch animals was cost of milk production per animal per annum studied and results are presented in Table 7. was higher in the case of crossbred cow The results showed that, output-input ratio (`47166.41) followed by buffalo (`42065.55) was highest (1.42) in the case of crossbred and local cow (`23539.58). The result also cow followed by local buffalo (1.18) and lowest revealed that per liter cost of milk production in the case of local cow (0.86). was higher in the case of local buffalo (`26.05) followed by local cow (`24.14) and lowest in Table 7: Output-input ratio for different the case of crossbred cow (`14.52). While milch animals Type of milch animal Output-input ratio considering the fact about profitability of milch Local cow 0.86 animal’s results revealed that, crossbreed cow Crossbred cow 1.42 was most profitable as its input-output ratio Local buffalo 1.18 was more 1.42 followed by local buffalo (1.18). Whereas, the B: C ratio of local cow was 0.86, This output-input relationship indicated which indicates that the local cow milk that maintaining crossbred cow for dairy production was not profitable. enterprise was most profitable in study area REFERENCES followed by local buffalo. Whereas, Autkar, V.N., Kumar, K.R., and Thokal, M.R. 1995. maintaining local cow was not remunerative in Towards livestock economy in Vidharbha Region study area. These results are more similar with of Maharashtra. Indian Journal of Agricultural results of Kumar and Rai (2008) and Kaur and Economics. 50 (3): 325-326 Badal, P.S. and Dhaka, J.P. 1998. An analysis of Kaur (2013) who were reported that the net feeding pattern on cost of milk production in profit in the case of buffaloes was higher as Gopalgang district of Bihar. Indian Journal of compared to indigenous cows. The farmers in Dairy Science. 51 (2): 121-126. study area were rearing non profitable local Basic Animal Husbandry Statistics. 2014. cows and local buffaloes on their farm, because Department of Animal Husbandry, Dairying and farmers require draft power for different farm Fisheries, Ministry of Agriculture, Government operations and transport. Another reason of India, New Delhi. might be preference of milk of their local breeds Chand, K., Singh K., and Singh R.V. 2002. Economic for home consumption. analysis of commercial dairy herds in Arid region of Rajasthan. Indian Journal of Agricultural CONCLUSIONS Economics. 57 (2): 224-233. The results revealed that the composition Chandra, A. and Agarwal, S.B. 2000. Cost and of milch animals on selected farm was 180 of returns from milk production in Farrukhabad which 23 were local cows, 60 were crossbreed district of Uttar Pradesh. Indian Journal of Dairy cows and 97 were local buffaloes. At overall Science. 53 (4): 310-316.

893 Gauraha, A.K. 1995. Comparative economics of Journal of Agricultural Economics. 50 (3): 364. milk production in urban and rural area of Misra, R.K. and Pal, P.K. 2003. Prospects and Madhya Pradesh. Indian Journal of Agricultural constraints of dairying in rural Bengal: A case Economics. 50 (3): 365-366. study. Indian Dairyman. 55 (12): 55-59. Ghule, A.K., Verma, N.K., Cahuhan, A.K., and Nandlal, R.S. and Rana, A.S. 1995. Production and Sawale, P. 2012. An economic analysis of disposal of buffalo milk in rural areas of district investment pattern, cost of milk production Rohtak of Haryana: An economic analysis. profitability commercial farms in Maharashtra. Indian Journal of Agricultural Economics. 50 Indian Journal of Dairy Science. 65 (4): 329- (3): 367-368. 336. Raj, D. and Gupta, J.N. 1994. An economic analysis Kaur, S. and Kaur, P. 2013. Comparative economics of milk production in Churu district of of milk production among different breeds of Rajasthan. Indian Journal of Dairy Science. 47 milch animals in Punjab. Indian Journal of (4): 294-301. Economics and Development. 9 (4): 312-317. Shergill, H.S. 2006. Commercial dairy farming in Kulkarni, R.V. and Hembade, A.S. 2010. Cost and Punjab: Problems and strategy for further net returns from milk production in Beed district development. Institute for Development and of Maharashtra state. Indian Journal of Animal Communication, Chandigarh: 23-29. Nutrition. 27 (4): 396-400. Shiyani, R.L. and Pandya, H.R. 1999. Relative share Kumar, A. and Rai, D.C. 2008. Cost and returns of different factors in buffalo production in from milk production in Faizabad district of coastal areas of Porbander. Indian Journal of Uttar Pradesh. Indian Journal of Animal Dairy Science. 52 (1): 46-50. Nutrition. 25 (4): 369-372. Venkatesh, P. and Sangeetha, V. 2011. Milk Kumar, A.P. 2003. Economics of milk production production and resource use efficiency of dairy and marketed surplus of milk in Vellore district farms at the Madurai district of Tamil Nadu. of Tamil Nadu. M.Sc. Thesis. NDRI (Deemed Journal of Community Mobilization and University), Karnal, Haryana. Sustainable Development. 6 (1): 25-30. Menbhekar, M.V., Alshi, M.R., and Joshi, C.K. 1995. Relative economics of milk production from local vis-a-vis cross bred cow: A study in Received:March 28, 2015 the vicinity of Akola city, Maharashtra. Indian Accepted: July 21, 2015

894 Indian J Econ Dev DOI: 10.5958/2322-0430.2015.00098.0 Volume 11 No. 4 (2015): 895-900 Research Article

INCOME, SAVING, INVESTMENT, AND CONSUMPTION PATTERN OF FARM HOUSEHOLDS IN KARNAL DISTRICT OF HARYANA

Jitender K. Bhatia, Dalip Bishnoi, R.K. Khatkar, J.C. Karwasra and V.K. Singh*

ABSTRACT

The findings of the study revealed that on an average, the operational holding was 3.34 ha which varies between 1.40 ha to 7.24 ha on small and large category of farms and land rent was found to be `75916 per hectare. The leased in land was higher on large farms (1.23 ha), followed by medium (0.37 ha) and small category of farms (0.06 ha). More than seventy per cent farmers highlighted that lower income and savings, lower access to institutional credit and inadequate loan, higher cost of new machinery, lower output prices of agricultural crops, higher prices of inputs, higher rate of interest, higher family consumption expenditures, higher cost of education and small size of holding hindered the capital formation on farms. Hence, there is a need to encourage the investment on farms through liberal financing at lower rate of interest and making higher public investment on agricultural infrastructure development. The investment on productive farm assets was having positively significant relationship with income earnings and size of operational holding, while, non-significant with literacy and age of the respondent.

Keywords: Borrowed fund, capital formation, income, investment, saving JEL Classification: C81, Q10, Q12, Q18

INTRODUCTION industries. So far the overall development of In spite of all the planned development of the economy and stable social order is last five decades, agriculture still supports the concern, a high rate of capital formation in majority population. Therefore, development agriculture is a prerequisite factor. In the of agriculture is a key to national economic changed globalization scenario it is a must to development. An economic development attain a high rate of capital formation to brings not only significant changes in socio- improve competitive strength of the Indian economic and cultural life but also in the level agriculture. of living in long run (Thakur and Singh, 2006). The prosperity of people in India is very Moreover, it provides demand to the much interlinked with progress in agriculture manufacturing sector and meets the demand in view of the predominantly agrarian character of raw material of a large number of agro-based of the economy. The modernization of agriculture and its improvement with the help of technical advances necessitates a *Department of Agricultiral Economics, CCS Haryana Agricultural University, Hisar-125004 consistent and significant growth in the capital Email: [email protected] investment. The country is making an all-out

895 effort to increase agricultural productivity in Kurali, and Rambha during the year 2010-11. order to complete globally. Then, 30 farmers each were selected randomly As such Haryana has made tremendous from the sample villages and categorized into progress in agriculture sector as it has turned small, medium and large categories by using from food deficit to surplus and has become cuberoot cummulative frequency method. In one of the major contributors to Central Pool. all, 53 farmers of small holding (average size of But new farm technology may be scale neutral, operational holding 1.40 ha), 40 of medium size it is also capital intensive. The differential (average size of operational holding 3.27 ha) adoption rate of new technology exhibits and 27 large category (average size of perceptible changes in the pattern of income operational holding 7.24 ha) formed. The distribution, standard of living, saving, primary data pertaining to assets, income dimension and investment decisions among earning, consumption expenditure, investment different categories of farming community. In and saving pattern of selected households order to sustain the rate of growth of were collected for the year 2010-11.Tabular agriculture, bringing desirable socio-economic analysis was used to analyze the data. Besides, changes, it is important to examine the Chi-square test was applied to examine the changing pattern of expenditure and relationship between farm investment with investment of increased income generated in income of farm family household, size of the new economic era. The need for studies operational holding, literacy level and age. on income, saving, investment, and RESULTS AND DISCUSSION consumption pattern in developing country Investment Pattern like us is felt especially because development The perusal of Table 1 revealed that farm brings about significant changes in size and investment as borrowed fund was found higher structure of population, urbanization, attitudes on large farms (`144381) followed by medium of various social classes and in the pattern of farms (`50698) and small farms (`29334). The consumption (Vatta and Sidhu, 2007). higher borrowed funds on large farms may be Thus, the present study was undertaken attributed to their better repaying capacity. On with the aim to examine the income, saving, an average the borrowed fund constituted investment and expenditure pattern on farm major portion of investment on crop enterprise households in Haryana in the changed (50.10 percent), combined harvester and laser scenario required to take into account for future land leveler (17.18 per cent), livestock (14.22 planning. The specific objectives of the study percent), tractor (10.97 percent), underground were: water conveyance pipes (5.38 percent) and i. to examine the present status of income, tube well (2.15 percent). Similar, trends were saving, investment, and consumption observed on different category of farms. On expenditure on farm households, an average, the investment on farm productive ii. to estimate the level of income and its assets such as tractor, irrigation structure, distribution pattern, and combine harvester/laser land leveler, farm iii. to identify the socio-economic constraints machinery-implements, etc. was estimated to faced in farm investment. be `25125, `16938, `16667, and `6867 of the METHODOLOGY total productive investment during 2010-11, In order to achieve the stipulated respectively. There is positive relationship objectives of the study a multistage random between investment on productive farm assets sampling techniques was adopted. The study and size of operational holding (Table 1). was conducted in Karnal district Northern Income Pattern zone of Haryana. The names of four villages On an average `614172 were earned by the selected randomly were Taraori, Mohdinpur, sampled farm-family (Table 2). The overall

896 Table 1: Farm investment pattern on different categories of farms in Karnal district of Haryana (`farm-1) Particulars Small farm Medium farm Large farm Overall Owned Borrowed Owned Borrowed Owned Borrowed Owned Borrowed fund fund fund fund fund fund fund fund Tractor 14222 4740 19980 7020 23766 10678 18289 6836 (16.50) (16.16) (13.57) (13.85) (8.86) (7.40) (12.40) (10.97) Farm machinery/ 4849 - 7975 - 9185 - 6867 - implements (5.63) (5.41) (3.42) (4.65) Combined/Laser - - 3000 4500 22037 40925 5958 10708 land leveler (2.04) (8.88) (8.21) (28.35) (4.04) (17.18) Tubewell 4214 1163 7405 1345 7394 1680 5993 1340 (4.89) (3.96) (5.03) (2.65) (2.76) (1.16) (4.06) (2.15) Underground pipe 1793 1793 4009 4009 5463 5463 3357 3357 line for water (2.08) (6.11) (2.72) (7.91) (2.04) (3.78) (2.28) (5.38) applications* Live stock 23785 11715 29984 7496 44546 5300 30523 8865 (27.59) (39.94) (20.36) (14.79) (16.60) (3.67) (20.69) (14.22) Crops 37332 9924 74935 26328 155945 80335 76554 31235 (43.31) (33.83) (50.88) (51.93) (58.12) (53.64) (51.89) (50.10) Figures in parentheses denote per cent to the grand total *50 per cent subsidy on underground pipe line for water application family income earning pattern shows that on from crop enterprises was 74.07, 67.18 and 45.83 an average 64.91 percent was contributed by percent, 12.05, 17.49 and 29.28 percent from crop enterprise followed by livestock (18.10 livestock rearing, 6.94, 9.95 and 15.43 percent percent), services (10.05 percent), hiring out through services followed by hiring out machinery (4.11 percent) and non-farm machinery income and non-farm activities activities (2.84 percent). The average income which came out to be 3.98, 3.08, 5.97 and 2.96, earned was `1069939, `691624, and `323534 2.30 and 3.50 percent, respectively (Table 2). by large, medium and small farm-family Consumption Pattern categories, respectively. The income earned It was noticed that overall the consumer goods constituted the major share `109549 Table 2: Farms family income of different (61.90 percent) of expenditure incurred per categories of farms in Karnal district of Haryana house hold per annum followed by education (` per household/annum) of the children `29275 (16.54 percent), Income Farm category consumer durables `21901 (12.38 percent), source Small Medium Large Overall social ceremonies `5907 (3.34 percent), others Crop 148285 464641 792507 398687 `5654 (3.19 percent), health care `2370 (1.34 Enterprise (45.83) (67.18) (74.07) (64.91) percent) and house repair/construction `2319 Hiring out 19304 21325 42593 25218 machinery (5.97) (3.08) (3.98) (4.11) (1.31 percent). The results revealed that there Livestock 94718 120933 128914 111150 was a positive relationship between (29.28) (17.49) (12.05) (18.10) expenditure incurred and size of holding. The Services 49906 68850 74259 61700 average expenditure was `238166, `182832, (15.43) (9.95) (6.94) (10.05) and `141381 by large, medium and small farm- Non-Farm 11321 15875 31667 17417 family categories respectively. The expenditure activities (3.50) (2.30) (2.96) (2.84) Total 323534 691624 1069939 614172 on consumer goods was (59.81, 59.86 and 65.68 (100.00) (100.00) (100.00) (100.00) percent) while 16.06, 16.26 and 17.23 percent Figures in parentheses denote per cent to the total was expended on education, 13.41, 14.19 and

897 Table 3: Expenditure incurred on various heads in Karnal district of Haryana (` per household/annum) Farm category Income source Small Medium Large Overall 92865 109445 142452 109549 Consumer Goods (65.68) (59.86) (59.81) (61.90) 13750 25936 31927 21901 Consumer Durables (9.73) (14.19) (13.41) (12.38) Health care 2055 2472 2836 2370 (1.45) (1.35) (1.19) (1.34) Education 24362 29730 38246 29275 (17.23) (16.26) (16.06) (16.54) 4254 6573 8164 5907 Social Ceremonies (3.01) (3.60) (3.43) (3.34) 1628 2822 2929 2319 House repair/construction (1.15) (3.60) (1.23) (1.31) 2467 5854 11612 5654 Other items (1.74) (3.20) (4.88) (3.19) Total 141381 182832 238166 176974 (100.00) (100.00) (100.00) (100.00) Figures in parentheses denote per cent to the total

Table 4: Income utilization pattern of sample farms in Karnal district of Haryana (`farm-1) Sr. No. Income source Farm category Small Medium Large Overall 1. Fixed investment on farm 39960 73268 147589 75280 (12.35) (10.59) (13.79) (12.26) 2. Crop enterprise 47256 101263 236280 107789 (14.61) (14.64) (22.08) (17.55) 3. Live Stock enterprise 35500 37480 49846 39388 (10.97) (5.42) (4.66) (6.41) 4. Household assets 19145 26242 34866 25048 (5.92) (3.79) (3.26) (4.08) 5. Family expenditure 92865 109445 142452 109549 (28.70) (15.82) (13.31) (17.84) 6. Social ceremonies 4254 6573 8164 5907 (1.31) (0.95) (0.76) (0.96) 7. Education 24362 29730 38246 29275 (7.53) (4.30) (3.57) (4.77) 8. Health care 2055 2472 2836 2370 (0.64) (0.36) (0.27) (0.39) 9. House repair/ 1628 2822 2929 2319 construction (0.50) (0.41) (0.27) (0.38) 10. Other items 2467 5854 11612 5654 (0.76) (0.85) (1.09) (0.92) 11. Loan Repayment 29334 50698 144381 62341 (9.07) (7.33) (13.49) (10.15) 12. Total (Sum of 1 to 11) 298826 445847 819201 464920 (92.36) (64.46) (76.57) (75.70) 13. Income 323534 691624 1069939 614172 (100.00) (100.00) (100.00) (100.00) 14. Saving (13 minus 12) 24708 245777 250738 149252 (7.64) (35.54) (23.43) (24.30) Figures in parentheses denote per cent to the total

898 9.73 percent on consumer durables followed SUMMARY AND CONCLUSIONS by social ceremony (3.43, 3.60 and 3.01), other The findings of the study revealed that the items 4.88, 3.20 and 1.74 percent, health care farm investment as borrowed fund was found 1.19, 1.35 and 1.45 and house repair/ to be higher on large farms (`144381) followed construction 1.23, 3.60 and 1.15 percent, by medium farms (`50698) and small farms respectively (Table 3). (`29334) due to the better repaying capacity Utilization Pattern on the large size category farms. On an average, The overall income utilization pattern of the investment on farm productive assets such income earned per farm during 2010-11 is as on tractor, irrigation structure, combined presented in Table 4. The results indicated that harvester/laser land leveler and farm machinery highest share was cornered by savings (24.3 and implements were `25125 (30.44 percent), percent), followed by family expenditure (17.84 `16938 (20.52 percent), `16667 (20.19 percent), percent), crop enterprise (17.55 percent), fixed and `6867 (8.32 percent) of the total productive investment on farm assets (12.26 percent), loan investment, respectively. There is positive repayment (10.15 percent), livestock enterprise relationship between investment on (6.41 percent), education (4.77 percent), productive farm assets and size of operational household assets (4.08 percent), social holding. On an average, `614172 were earned ceremonies (0.96 percent), other items (0.92 by the sampled farm-family. The overall family percent), health care (0.39 percent) and house income earning pattern shows that on an repair/construction (0.38 percent). average 64.91 percent was contributed by crop Saving Pattern enterprise followed by livestock (18.10 The saving constituted `149252 (24.30 per percent), services (10.05 percent), hiring out cent) of total earnings while total expenditure machinery (4.11 percent) and non-farm was `464920 (75.70 per cent) of total farm activities (2.84 percent). It was noticed that household earnings. The saving on small farms overall the consumer goods constituted the was found to be `24708 (7.64 per cent) of total major share `109549 (61.90 percent) of family earnings, on medium `245777 (35.54 per expenditure incurred per house hold per annum cent) and on large category farm-family it was followed by education of the children `29275 `250738 (23.43 per cent) which indicated that (16.54 percent), consumer durables `21901 income earned on small farms was just to meet (12.38 percent), social ceremonies `5907 (3.34 out the various obligations of the family and percent), others `5654 (3.19 percent), health having very less to save (Table 4). care `2370 (1.34 percent) and house repair/ Relationship between Investment and Income construction `2319 (1.31 percent). The results The Chi-square test indicated that revealed that there was a positive relationship investment on productive farm assets was between expenditure incurred and size of having positively significant relationship with holding. The saving constituted `149252 (24.3 income earnings and size of operational percent) of total earnings while total holding, while non-significant with literacy and expenditure was `464920 (75.70 percent) of age of the respondent (Table 5). total farm household earnings. The saving on small farms was found `24708 (7.64 percent) Table 5: Relationship between investment of total family earnings, on medium `245777 and income, size of holding, literacy and age (35.54 percent) and on large category farm- Investment /Variables Chi-square value family it was `250738 (23.43 percent) which Income 10.32** indicated that income earned on small farms Size of land holding 21.96** was just to meet out the various obligations of literacy 1.65 the family and having very less to save. The Age 2.11 **Significant at 5 percent evel Chi-square test indicated that investment on

899 productive farm assets was having positively Schultz, T.P. 1999. Women’s role in the agricultural significant relationship with income earnings household: Bargaining and human capital. and size of operational holding, while non- Center Discussion PAPER No. 803, Economic significant with literacy and age of the Growth Center, Yale University Singh, S. 2012. Rural development and socio- respondent. economic status of rural household in Haryana. REFERENCES Asian Journal of Research in Social Science and Rao, A.V. and Bhanu, B.S. 2012. Consumption, Humanities. 2 (3): 88-106 expenditure pattern of rural households-A case Vatta, K. and Sidhu, R.S. 2007. Income study of Guntur district in Andhra Pradesh. diversification among rural household in Punjab: International Journal of Multidisciplinary Dynamics, impacts and policy implications. The Educational Research. 1 (1): 237-245. Indian Journal of Labour Economics. 50 (4): Thakur, D.S. and Singh, S. 2006. Extent of absolute 723-736 poverty among the different socio-economic groups in the rural areas of Himachal Pardesh: A nutrition and nutrition plus approach. The Indian Journal of Economics. 87 (345): 317- Received: February 16, 2015 338. Accepted: July 07, 2015

900 Indian J Econ Dev DOI: 10.5958/2322-0430.2015.00099.2 Volume 11 No. 4 (2015): 901-906 Research Article

IMPACT ANALYSIS OF JOINT FOREST MANAGEMENT PROGRAMME ON RURAL HOUSEHOLD INCOME IN UTTARAKHAND

Bishwa Bhaskar Choudhary and S.K. Srivastava*

ABSTRACT

Joint Forest Management (JFM) is operating with main objective of forest management and empowerment of local livelihoods. The present study was carried out in Nainital district of Uttarakhand. An investigation run for two years and beneficiaries were categorized into two income groups (low and high). The annual family income was estimated for both the periods. The share of each source of income in annual family income was computed and compared. The JFM comprised second largest source of income with 25.48 percent share in annual family income after implementation of JFM programme and hence, it needs to be strengthened.

Keywords: Income groups, joint forest management, Uttarkhand JEL Classification: D31, D63, E24, Q10, Q23

INTRODUCTION household income is derived from these Forests cover 31 per cent of total land area forests (Bhattacharya et al., 2010). worldwide, which is an area just over 4 billion It was not possible for the Forest hectares (FAO, 2011). Forests support almost Department, even armed with strict forest 90 per cent of the world’s terrestrial biodiversity protection laws, to safeguard a large (Parker et al., 2008). Forest and tree cover in component of forests from the large number India is 78.29 m ha, which is 23.81 percent of of local users, given the small number of the geographical area of the country. India has forestry personnel throughout the country. only 1.8 percent of the global forest area but After that the Government realised that it can has to support 16 percent of the world’s human effectively protect forests only by soliciting population (CIA World Fact Book, 2011). people’s participation in forest management. The importance of forest management is The Forest Policy, formulated in 1988 based obvious considering that 40 percent of India’s on these experiences, gave priority to the forests provide a home to around half of India’s needs of the forest dependent communities tribal population (indigenous people in India) and subsequently on June 1, 1990, the and between 20 to 50 percent of their Government of India issued the guidelines for implementation of Joint Forest Management * Ph.D. Scholar, Department of Economics, Statistics (JFM) programme as policy decision. The JFM and Management, NDRI, Karnal-132001 and program is described as A forest management Associate Professor, Department of Agricultural Economics, GBPUAT, Pantnagar, U.S. Nagar- strategy under which the government 263145(Uttarakhand) represented by the Forest Department and the Email : [email protected] village community enter into an agreement

901 to jointly protect and manage forestlands income) based on their annual family income adjoining villages and to share within a year (2011) by dividing the difference responsibilities and benefits (Government of of two extreme incomes (highest annual family India, 2002). income and lowest annual family income) in In Uttarakhand, JFM started in 1992 when two equal parts. The low income group it was a part of UP. There are 10,107 JFM beneficiaries were with annual family income committees managing about 0.86 m ha of forest, less than `74802.50 and high income group which is about 27 percent of the forest area of with more than `74802.50. The investigation the Uttarakhand. Around 0.6 million families on different aspects of selected sample was of Uttarakhand are involved in the JFM conducted for two separate years, one just programme, of which around 15,000 families before establishment of JFM programme (2001) belongs to scheduled tribes (Ojha et al., 2009). in the villages and another, latest year after Nainital, where present study is conducted as JFM programme (2011). the first district in implementation of JFM The primary data were collected from the programme in Uttarakhand (Sarin et al., 2008). sample beneficiaries on pre-structured But no study has been conducted regarding schedule through the personal contact for two impact of JFM on household income in the separate years, one just before implementation district. About nine percent of total forest area of JFM and another, latest year after of the district comes under the programme implementation of JFM programme (2011). The (Ojha, 2009). The previous studies from data were collected related to family size, land different parts of the country show that the holding, livestock holding, sources and programme is focussed more on forest amount of family income, expenditure pattern, management rather than another important employment activities of family members, aspect that is livelihood of forest dependent number and type of livestock reared, amount people. Hence, the present study aims to of feed, fodder and medical expenses of estimate the share of income derived from JFM livestock, cropped area, cropping pattern, programme among different income groups of manure and fertilizer, seeds, etc. for both the beneficiaries. periods that is before and after implementation METHODOLOGY of JFM programme. Sampling Framework Analytical Framework The study area was confined to Nainital The annual family income of beneficiaries district of Uttarakhand. The JFM programme included net income from crop enterprises, is going on in all the eight blocks of Nainital livestock enterprises, JFM Programme and district. Out of the eight blocks, one block non-farm sources in the study area. To namely Bhimtal was selected randomly for the eliminate the inflationary effect the income was study. From the selected block, list of villages estimated at constant prices at 2010-11 price having JFM programme was obtained from equivalents after adjusting the values. The Block Forest Office and out of them two annual family income was estimated for both villages, Bhaktura and Junestate were selected the periods that is, before and after randomly. From each selected village, list of participation of beneficiaries in JFM beneficiaries through JFM programme was programme as follows. obtained from the Sarpanch of the respective village. From the each village, 30 beneficiaries 4 were selected randomly. So, the total sample AFI = I  j size comprised of 60 beneficiaries. j1 The sample beneficiaries were categorized where, into two income groups (low income and high AFI = Annual family income

902 th Ij = Net annual income from j source in income from livestock is due to increase in j = Sources of income (crop enterprises, number of livestock and shift in feeding livestock enterprises, income from JFM pattern from stall feeding to grazing after Programme and non-farm income implementation of JFM programme. The sources) income from non-farm source increased from The share of each source of income in `20800 before JFM to `24420 after annual family income for different income implementation of JFM programme (17.40 groups of the beneficiaries were computed and percent) increase in income from non-farm has compared across income groups using been noticed. This shift in income from non- averages and percentages. The paired sample farm sources was mainly due to expansion of t-test was applied to see the impact of JFM on business by the beneficiaries after annual family income and income from different implementation of JFM programme. sources. This exercise was done for whole There is a noticeable change in the sample population as well as individual income proportion of income from different sources in groups of sampled population. annual family income. Non-farm income RESULTS AND DISCUSSION appeared the main source of family income The family income after implementation of comprising 78.08 percent before and 58.34 JFM programme includes income from crop percent share after JFM programme. The JFM enterprise, livestock enterprise, income from comprised second largest source of income JFM programme and non-farm sources of with 25.48 percent share in annual family income income in the study area. The contribution of after implementation of JFM programme. different sources of income in annual family The average income from crop enterprise income of the sample population is shown in across income groups is presented in Table 2. Table 1. The results show that the annual family The results indicate that the average income income increased from `26636 before JFM from crop enterprise for overall beneficiaries programme to `41868.57 after implementation increased significantly by 38.68 percent from of the JFM programme that is, annual family `405 before JFM to `561.66 after income increased by 57.18 percent. The implementation of JFM programme. The highest increase in income is noticed in the average income from crop enterprise for low case of income from crop enterprise (38.68 and high income groups also increased percent). significantly from `320.97 and `1329.33 before The income from livestock enterprise has JFM programme to `406.21 and `2271.61 after also increased by 14.44 percent after implementation of JFM programme, implementation of the programme. The increase respectively. This is due to the adoption of

Table 1: Annual family income according to source of income (` household-1) Source of income Before JFM After JFM Change over before JFM period Crop enterprises 405 561.66 156.66 (1.50) (1.30) [38.6] Livestock enterprises 5431.36 6216 784.64 (20.30) (14.80) [14.4] JFM - 10670.91 - - (25.40) - Non-farm sources 20800 24420 3620 (78.08) (58.30) [17.4] Overall (Annual family income) 26636.36 41868.57 15232.21 (100.00) (100.00) [57.1] Figures in parentheses ( ) show percentage to total and [ ] percentage change over before JFM period

903 Table 2: Average income from crop enterprise across income groups before and after JFM Programme (` household -1) Income group of Income before JFM Income after JFM Percentage change over Mean beneficiaries programme programme before JFM period difference Min. Max. Mean Min. Max. Mean Low income group 309.9 900.4 320.9 390.1 1390.1 406.2 26.55 85.24** High income group 1045.4 1800.5 1329.3 1650.5 2578.9 2271.6 70.88 942.28* Overall 309.9 1800.5 405 390.1 2578.9 561.6 38.68 156.66* * and ** significant at 1 and 5 percent level better cultural practices by both income groups This is due to rearing of more number of (high income group and low income group) of livestock by high income group beneficiaries beneficiaries after implementation of JFM after implementation of JFM Programme. There programme. is no significant increase in average income An average livestock income across from livestock and the average income has been income groups before and after JFM found to increase only by 6.98 per cent in the programme is presented in Table 3. The case of low income group (`4698.12 before JFM average income from livestock sector Programme to `5021.9 after JFM Programme). increased significantly from `5431.66 before The net non-farm income according to JFM Programme to `6216 after JFM Programme income groups of beneficiaries is presented in for overall beneficiaries registered an increase Table 4. An average non-farm income increased of 14.44 percent in net livestock income is significantly by 17.40 percent as such it noticed for overall beneficiaries after increased from `20800 before JFM Programme implementation of JFM Programme. An average to `24420 after implementation of JFM income from livestock also increased Programme for overall beneficiaries. An significantly by 43.33 percent in the case of average income from non- farm sources also high income group (`13500.6 before JFM to increased significantly across the income `19351.1 after JFM). groups after implementation of JFM

Table 3: Average livestock income across income groups before and after JFM Programme (` household -1) Income group of Income before JFM Income after JFM Percentage change Mean beneficiaries programme programme over before JFM difference Min. Max. Mean Min. Max. Mean period Low 3520.7 9420.2 4698.12 7580.4 12390 5021.9 6.98 323.78 High 8600.8 15520.34 13500.6 14100.2 21400.6 19351.1 43.33 5850.5* Overall 3520.7 15520.34 5431.66 7580.4 21400.6 6216 14.44 784.34** * and ** significant at 1 and 5 percent level

Table 4: Average non-farm income across income groups before and after JFM Programme (`household-1) Income group of Income before JFM Income after JFM Percentage change Mean beneficiaries programme programme over before JFM difference Min. Max. Mean Min. Max. Mean period Low income group 12490.8 39000.6 18918.1 13080 48960.0 21852.0 15.50 2933.82** High income group 32000.4 47070.7 41500.02 37760 58705.5 52668.0 26.91 11167.98* Overall 12490.8 47070.7 20800.00 13080 58705.5 24420.0 17.40 3620.00** * and ** significant at 1 and 5 percent level

904 Table 5: Average annual family income across income groups (`household-1) Income group of Income before JFM programme Income after JFM programme Percentage Mean beneficiaries change over difference before JFM Min. Max. Mean Min. Max. Mean period Low income group 15040.36 62789.54 21982.5 19810.8 67890.3 37252.18 69.46 15269.68** High income group 74905.23 95220.38 77831.7 79670.12 119232.75 92648.86 19.03 148166*** Overall 15040.36 95220.38 26636.6 19810.8 119232.75 41868.57 57.18 15231.9* * , ** and *** significant at 1 , 5 and 10 percent level

Programme. This significant increase in income business by the beneficiaries. However, the from non-farm sources across income groups per cent increase in income from different was due to expansion of business by the sources of income was less in the case of low beneficiaries over time. income groups as compare to high income After analyzing the change in income from groups of beneficiaries. different sources across income groups, an Based on the findings of the study the attempt has been made to see the change in following policy implications have emerged: annual family income across different income 1. Joint Forest Management (JFM) groups. The average annual family income of programme is a potent source which can the different income groups is presented in generate an additional income for local Table 5. The results revealed that the average people hence it needs to be strengthened. annual family income increased significantly 2. The main beneficiaries of JFM comprise by 57.18 percent at the overall level. Annual disadvantaged and low income group of family income in the case of high income group the society. It is found that the high of beneficiaries increased only by 19.03 income groups are gaining more benefit percent after the JFM Programme and also from the programme. Hence there is need found to be significant statistically. to focus more on low income group people This is due to the small number of for overall development of the society. beneficiaries in high income group category. REFERENCES Annual family income of low income group of Bhattacharya, P. and Pradhan, L. 2010. Joint forest beneficiaries significantly increased by 69.46 management in India: Experiences of two percent after implementation of the JFM decades. Resources, Conservation and Programme. This is due to increase in income Recycling. 54 (4): 469-480. of low income group of beneficiaries from the CIA World Factbook. 2011. Available at JFM Programme. www.cia.gov. accessed on November 25, 2011. CONCLUSIONS AND POLICY FAO. 2011. Global forest resource assessment- main report and key findings, available at www.fao.org. IMPLICATIONS accessed on March 8, 2012. The results of the study show that the Government of India. 2002. Joint Forest annual family income increased by 57.18 Management: A decade of partnership, percent after implementation of JFM RUPFOR, Ministry of Environment and Programme. The JFM comprised second Forests, New Delhi. largest source of income with 25.48 percent Ojha, C.S. and Mukherji, S.D. 2009. Old roots share in annual family income after new shoots: A study of joint forest management implementation of JFM Programme. The in Uttarakhand. Indian Forester. 127 (7): 737- income from JFM Programme resulted in 742. Ojha, H. 2009. Village voices, forest choices: Joint adoption of better cultural practises, rearing forest management. Journal of Forest and more number of livestock and expansion of Livelihood. 8 (1): 155-169.

905 Parker, C., Mitchell, A., Trivedi, M., and Mardas, joint management in Uttarakhand. Indian N. 2008. Institutions, forest management, and Journal of forestry. 14 (6): 34-41. sustainable human development-Experiences from India. Environment, Development and Sustainability. 5 (7): 353-367. Sarin, M. and Bisht, R. 2008. Disempowerment in Received: February 12, 2015 the name of participatory forestry-Village forests Accepted: August 10, 2015

906 Indian J Econ Dev DOI: 10.5958/2322-0430.2015.00100.6 Volume 11 No. 4 (2015): 907-913 Research Article

IMPACT OF MNREGA ON HOUSEHOLD INCOME EMPLOYMENT GENERATION, LABOUR SCARCITY AND MIGRATION: A STUDY IN DAHOD DISTRICT OF GUJARAT

Macwan J.D. and Zala Y.C.*

ABSTRACT

The present study conducted in Dahod district of Gujarat by using multistage sampling techniques during 2012-13. The results revealed that the MNREGA programme had significantly positive impact on the income of the MNREGA beneficiaries as compared to non-beneficiaries and the income of the MNREGA beneficiaries was significantly influenced by the number of migrants in the households, number of livestock units owned, number of person days employed in agriculture, non-agriculture and MNREGA. The additional income (`5559) earned from MNREGA works was not much more but it strengthened the food and nutritional security of the vulnerable section of the society. The MNREGA programme generated employment on an average 37.28 person days per job demanding household from that the share of Schedule Tribe households and women was 61.14 and 46.46 percent, respectively. After implementation of MNREGA programme, the labour scarcity (35.10 percent) was created in farming and subsequently wage rate of agriculture operations was also increased. The programme reduced the migration up to some extent and shortened the length of out-migration period.

Keywords: MNREGA, income, employment, labour scarcity, migration. JEL Classification: J21, J22, J61, R23

INTRODUCTION problems in rural India. Therefore, the India is a country of villages and about 50 Government of India enacted several per cent of the villages have very poor employment schemes to eradicate the chronic economic condition (Bordoloi, 2011). poverty and to provide the employment to the Indebtedness, unemployment, starvation, rural poor people in India. The Mahatma Gandhi migration, farmer’s suicide and low National Rural Employment Guarantee Act productivity in agriculture are the main (MNREGA) is one of them employment scheme (Mohanty, 2012). It is the world’s biggest employment *Senior Research Fellow, Department of Agricultural guarantee programme and aims at enhancing Economics, B.A. College of Agriculture and Principal and Dean, International Agri-Business livelihood security of households in rural areas Management Institute, Anand Agricultural of the country by providing 100 days of University, Anand-388 110 (Gujarat) guaranteed wage employment in a financial Email: [email protected] year to every household whose adult members

907 volunteer to do unskilled manual work (Vanitha Looking to the relative share of each taluka to and Murthy, 2011). Another aim of this act is the total registered workers in MNREGA, two to create assets in rural areas like road talukas namely, Jhalod and Limkheda were connectivity, water conservation, land selected as they constitute higher number of development, irrigation facilities, etc. The MNREGA workers. choice of works suggested in this Act address Both primary and secondary data sources causes of chronic poverty like drought, were used for the study purpose. The primary deforestation, soil erosion so that the process data regarding income, labour scarcity and of employment generation becomes on a migration were collected from the selected sustainable basis. respondents with help of pre-tested interview The Act came into force on 2nd February schedules. The secondary data on employment 2006 in 200 backward districts of the country. days were collected from MNREGA web site From 1st April 2007, it was extended to 130 more www.nrega.nic.in for the year 2012-13. A total districts. The Act has been extended to all the 150 respondents (60 MNREGA beneficiaries remaining 266 districts (barring urban districts) and 60 MNREGA non-beneficiaries and 30 with effect from 1st April 2008 (Shah and farmers who hire the agriculture labour) spread Makwana, 2011). It is the biggest poverty over six villages, covering two talukas of alleviation programme in the world which is Dahod district were selected. Using started with an initial outlay of `11,300 crores beneficiaries and non-beneficiaries approach, in year 2006-07 and now it is `33,000 crores in tabular analysis was applied with t-test. 2012-13 (www.nrega.nic.in). The following linear multiple regression The MNREGA provides guarantee of 100 model (Income function) was used to analyze days of wage employment in a year to every the factors influencing to the household rural household who is willing to do unskilled income of the MNREGA beneficiaries manual work. This unique feature of the scheme (Sivashakti et al., 2011). has absorbed not only the labour having no Y=b0+b1x1+b2x2+b3x3+b4x4+b5x5+b6x6+b7x7+ui employment but also the labour working earlier where, in the agricultural fields, making it difficult for Y = Total income of the households (`) the farmers to carry out agricultural operations x1 = Educational status of household head (Harish et al., 2011). (Number of schooling years)

MNREGA programme also arrest out x2 = Number of migrants in the family seasonal/distress migration which has been a (persons/household) significant source of employment for rural x3 = farm size (ha) population (Mohanty, 2012). The right from x4 = Livestock units owned (No.) the beginning, this programme was started in x5 = Employment in agriculture (person- Dahod district which is one of the tribal days/household/year) districts of Gujarat. Considering importance of x6 = Employment in non-agriculture the programme particularly in tribal area, (person-days/household/year) impact of MNREGA on household income, x7 = Number of man-days employed in employment generation, labour supply in MNREGA (person-days/household/ agriculture and on migration were chosen as year) major objectives. b0, b1… b7= Regression coefficients and

METHODOLOGY ui = Error term with usual polpulation. The present study was undertaken in the RESULTS AND DISCUSSION Dahod district of Gujarat during the year 2012- Impact of MNREGA on the Household’s 13. For selection of respondents a multi-stage Income sampling design was adopted for the study. Household income determines the

908 standard of living and ensure the growth of contributed 8.05 percent to the total assets for sustain the realized prosperity in household’s income in Karnataka. the long run. As income increases, household The non-agriculture labour income was welfare improves through increase in significantly lower (`4734.16) for MNREGA consumption expenditure which leads to the beneficiaries as compared to MNREGA non- food and nutritional security of the people. beneficiaries. The income from livestock was Therefore, it is required to examine the found significantly higher (`10226.03) for the impact of MNREGA on the household’s MNREGA beneficiaries as compared to income. In this context, source-wise income of MNREGA non-beneficiaries. This might be due MNREGA beneficiaries and non-beneficiaries, to the decreasing migration in MNREGA the factors influencing household income and beneficiaries and consequently MNREGA expenditure pattern of the MNREGA beneficiaries were able to keep more number beneficiaries are discussed in the following of livestock units. sub-sections: It can be concluded that because of the Sources of household’s income MNREGA programme the beneficiaries of The results given in Table 1 reveal that the MNREGA earned significantly more income MNREGA beneficiaries had highest income (`7845.86) than MNREGA non-beneficiaries. from agriculture (30.24 percent), followed by Finally, the results revealed that the MNREGA non-agricultural labour (30.23 percent), programme had significantly positive impact livestock (27.68 percent), MNREGA wages on the income of the MNREGA beneficiaries (7.43 percent) and agriculture labour (4.41 as compared to non-beneficiaries. percent). The MNREGA non-beneficiaries had Determinants of household’s income highest income from non-agriculture labour To find out the factors influencing to the (40.84 percent), followed by agriculture (37.94 MNREGA beneficiaries household’s income, percent), livestock (15.64 percent) and an income function was fitted and the results agriculture labour (5.55 percent). MNREGA obtained are presented in Table 2. The results beneficiaries earned additional income revealed that 63 percent of the variation in total `5558.78 (7.43 percent of total income) by income of the household was explained by working in MNREGA programme. Similarly, fitted model using the independent variables. Harish et al. (2011) found that MNREGA The independent variables had the expected

Table 1: Source-wise income of the sample households (`year-1) Sources of income MNREGA beneficiaries MNREGA non-beneficiaries Difference t-value

(n1=60) (n2=60) 22612.16 25399.58 -2787.5 1.02 NS Agriculture (30.24) (37.94) 3302.71 3720.00 -417.28 0.25NS Agriculture labour (4.41) (5.55) 22607.5 27341.66 -4734.16 3.66** Non-agriculture labour (30.23) (40.84) 20699.53 10473.50 10226.03 3.56** Livestock (27.68) (15.64) 5558.78 - - - MNREGA wages (7.43) 74780.61 66934.75 7845.86 2.12** Total (100.00) (100.00) Figures in parentheses are percentages to the total ** Significant at 5 percent level of significance NS: Non-significant

909 Table 2: Determinants of household income of MNREGA beneficiaries Variables Coefficients t-values Intercept 2005.74 0.209 NS Educational status of head (x1) 2377.94 1.303 ** Number of migrants (x2) 4142.37 2.011 NS Farm size (x3) 9658.76 0.950 ** Number of livestock owned (x4) 1950.61 2.393 ** Employment in agriculture (x5) 54.32 2.239 ** Employment in non-agriculture (x6) 120.27 2.264 ** Employment in MNREGA (x7) 211.14 2.068 R2 0.63 ** Significant at 5 percent level. NS: Non-significant relationship with the total income of the enhance the livelihood security of the rural household. households by guaranteeing 100 days of wage The variables such as number of migrants employment in a year. One of the judging in the households, number of livestock units criteria for the success of MNREGA is number owned, number of man-days employed in of person days of employment generated per agriculture, number of man-days employed in job demanding household (Shah and non-agriculture, and number of man-days Makwana, 2011). employed in MNREGA turned out to be It is seen from the Table 3 that total 65665 significant statistically. However, the households demanded employment under educational status of household head and farm MNREGA programme and MNREGA created size did not influence the total income of the employment of 24,48,618 person days in the MNREGA beneficiaries’ household. Similar year 2012-13. It means that against the results were also reported by Sivashakti et al. guarantee of 100 person days, MNREGA (2011) in Tamil Nadu. programme generated on an average 37.28 Employment generated person days per job demanding household The major objective of the MNREGA is to (Table 3).

Table 3: Households issued job-cards under MNREGA in Dahod district, 2012-13 Taluka Cumulative No. of HHs issued job-cards Cumulative No. of HH employment SC ST Others Total Demanded Provided Dahod 405 32651 7273 40329 9333 8507 (15.15) Devgadh Bariya 231 2152 24522 26905 9216 8086 (10.11) Dhanpur 386 14850 13297 28533 7508 6967 (10.72) Fatepur 336 33687 9091 43114 9471 7374 (16.20) Garbada 248 28459 5211 33918 9889 8886 (12.74) Jhalod 323 46965 2310 49598 9010 6524 (18.94) LImkheda 995 17331 25342 43668 11238 10189 (16.41) Total 2924 176095 87046 266065 65665 56533 (1.10) (66.18) (32.72) (100.00) (24.68) (86.09) Source: www.nrega.nic.in Figures within the parentheses indicate percentage to total

910 Table 4: Employment generated under MNREGA in Dahod district, 2012-13 Taluka Cumulative person days generated No. of HH completed 100 days SC ST Others Total Women (till the reporting month) Dahod 2076 271404 67831 341311 164589 636 Devgadh Bariya 1200 42846 372122 416168 198393 1668 Dhanpur 4788 180868 167048 352704 165153 963 Fatepur 1337 287196 37375 325908 138537 720 Garbada 2584 344751 28665 376000 176667 813 Jhalod 646 205795 10279 216720 95178 356 LImkheda 7147 164178 248482 419807 199206 879 Total 19778 1497038 931802 2448618 1137723 6035 (0.81) (61.14) (38.05) (100.00) (46.64) (10.68) Source: www.nrega.nic.in Figures within the parentheses indicate percentage to total)

The low level of generation of employment empowered the women in some extent. days per job demanding households reflects Impact of MNREGA on labour supply in low level of performance of MNREGA. It might agriculture be due to low level of operation of the Major crops grown in the study area were programme and frequent suspension/ maize and pigeon pea in kharif; wheat and gram stoppages of works under MNREGA. It was in rabi. The information regarding crop-wise also found that in Dahod district, total person labour scarcity after implementation of days of employment generated through MNREGA is given in Table 5. Schedule Tribes households had very high The results revealed that for the maize crop contribution (61.14 percent). About 38 per cent 24.07 labour days required per acre, while of total person days of employment were labour availability after MNREGA generated by others (Table 4). implementation was 15.70 labour days, with a Thus, it can be concluded that the labour scarcity of 34.77 percent. In the case of MNREGA succeeded in providing enhanced pigeon pea, labour scarcity was 35.79 percent. employment and livelihood security to under For the rabi crops that is wheat and gram, the privileged households. For empowering the labour scarcity was 37.54 and 31.83 percent, women, there is a provision in the scheme to respectively. The average labour scarcity was provide at least one third employment to about 35.10 percent after implementation of women. MNREGA programme. It is seen that in Dahod district, the During the survey, it was also observed employment share of women was higher (46.46 that because of scarcity in agriculture labour, percent) than provision made in guidelines. the wage rates of agriculture operations were The good level of women participation in increased from `60-80 to `100-150. Thus, it can MNREGA works is definitely a sign of positive be concluded that after implementation of development for women community and it MNREGA programme the labour scarcity was

Table 5: Crop-wise labour scarcity after implementation of MNREGA in Dahod district (labour-days acre-1 year-1) Crops Kharif crops Rabi crops Maize Pigeon pea Gram Wheat Labour required 24.07 20.84 20.73 19.50 Labour availability after MNREGA 15.70 13.38 14.13 12.17 Labour scarcity after MNREGA implementation 8.37 7.46 6.60 7.32 Labour scarcity (%) 34.77 35.79 31.83 37.54 Average labour scarcity 35.10

911 created in farming and subsequently wage rate observed in 76.66 per cent households while it of agriculture operations was also increased. was 86.70 percent in Non-MNREGA Similarly, Vanitha et al. (2011) found that the households. The number of members migrated overall labour scarcity for agriculture work was from beneficiaries group was also lower (98 33.11 per cent per annum after the members) as compared to MNREGA non- implementation of MNREGA in Karnataka. beneficiaries (104 members). From the migrated Impact of MNREGA on migration MNREGA beneficiaries, 82.60 percent By securing livelihood, MNREGA also households were migrated up to 4 to 6 months. mitigates seasonal/distress migration which While in case of MNREGA non-beneficiaries, has been a significant source of employment 73.08 percent households were migrated up to and income for a large proportion of rural 7 to 9 months. population. In the study area, the regular Thus, it was found that MNREGA employment opportunities also motivate many shortened the length of out-migration period of them to migrate to other districts like Baroda, in the study area. Overall, the finding reveals Anand, Ahmedabad, Surat, Rajkot, Saurashtra. that MNREGA was succeeded up to some The perusal of Table 6 shows that in the extent to reduce the migration. Similar findings case of MNREGA beneficiaries, migration was were reported by Shah and Makwana (2011)

Table 6: Impact of MNREGA on migration in sample district Particulars MNREGA beneficiaries MNREGA non-beneficiaries

(n1=60) (n2=60) Migration in total number of households 46 52 (76.66) (86.70) Total members of households migrated Male members 55 62 (56.12) (59.61) Female members 43 42 (43.88) (40.39) Total members 98 104 (100.00) (100.00) Duration of migration 1 to 3 months 4 - (8.70) 4 to 6 months 38 6 (82.60) (11.54) 7 to 9 months 4 38 (8.70) (73.08) 10 to 12 months - 8 (15.38) Total 46 52 (100.00) (100.00) Reasons for migration Building construction 18 13 (39.13) (25.00) Road construction 14 18 (30.43) (34.62) Agriculture work 4 6 (8.70) (11.54) Industrial work 10 15 (21.73) (28.85) Total 46 52 (100.00) (100.00) Figures in the parentheses are percentages to the total

912 that MNREGA helpful in shortening the length in Assam. Study No. 138. Agro-economic of out-migration period in Gujarat. Research Centre for North East India, AAU, CONCLUSIONS Jorhat, Assam. The study has revealed that MNREGA Devi, T.S., Balasubramanian, R., and Kumar, B.G. 2011. Employment, income and labour supply programme had significantly positive impact decision of rural households: An economic on the income of the MNREGA beneficiaries analysis of MGNREGS in Tamil Nadu. as compared to non-beneficiaries. MNREGA Agricultural Economics Research Review. 24: programme had generated on an average 37.28 473-484. person days per job demanding household in Harish, B.G., Nagaraj, J., Chandrakanth, M.G., the year 2012-13. The total person days of Murthy, P.S.S., Chengappa, P.G., and employment generated through ST Basavaraj, G. 2011. Impact and implication of households had very high contribution (61.14 MNAREGA on labour supply and income per cent) and the employment share of women generation for agriculture in central dry zone of Karnataka. Agricultural Economics Research was higher (46.46 per cent) than provision made Review. 24: 485-496. in guidelines. The average labour scarcity was Mohanty, S. 2012. Mahatma Gandhi National Rural about 35.10 per cent after implementation of Employment Guarantee Act (MGNREGA) and MNREGA programme subsequently the wage tribal livelihoods: A case study in Sundargarh rate of agricultural operations was increased district of Odisha. M.Sc. Thesis (unpublished). from `60-80 to `100-150. MNREGA was Department of Humanities and Social Science. succeeded up to some extent to reduce the National Institute of Technology. Rourkela. migration. For improving the performance of Odisha. the programme and achieving 100 person days Shah, V.D. and Makwana, M. 2011. Impact of NREGA on wage rates, food security and urban employment guarantee target, it was suggested migration in Gujarat. Study No. 141. Agro- to stop the frequent suspension/stoppages of economic Research Centre. Anand. works and to create sound permanent quality Vanitha, S.M. and Murthy, P.S. 2009. Economic work under MNREGA. For solving the problem analysis of MGNREGA Programme in Mysore of labour scarcity in agriculture after district of Karnataka. Department of Agricultural implementation of MNREGA programme, Economics. University of Agricultural Sciences. measures were suggested like implementing GKVK. Bengaluru (Karnataka). proper work calendar by arranging MNREGA www.nrega.nic.in work during lean season of agriculture operations. REFERENCES Bordoloi, J. 2011. Impact of NREGA on wage rates, Received: July 13, 2015 food security and rural urban migration- A study Accepted: October 10, 2015

This paper is part of the thesis submitted to the Anand Agricultural University, Anand- 388110 for the award M.Sc. (Agriculture) Degree, 2014.

913 914 Indian J Econ Dev DOI: 10.5958/2322-0430.2015.00101.8 Volume 11 No. 4 (2015): 915-921 Research Article

PAIRS TRADING IN FINANCIAL STOCK FUTURES: AN EMPIRICAL INVESTIGATION IN INDIAN STOCK MARKET

Navdeep Aggarwal and Mohit Gupta*

ABSTRACT

Pairs trading is a strategy that relies on the existence of strong arbitrage. For application of this strategy, India offers a unique opportunity as on one side it suffers from weak efficiency and on the other side it became the largest market in terms of equity volume trading. In the absence of any concrete findings in this regard, pairs trading was carried out using futures contracts available on financial stock futures including banks. Using Gatev’s (2006) methodology and holding periods of maximum two weeks, average positive excess returns of 3.71 percent were produced by the pairs trading portfolio. While systematic market risk, size risk or the value risk could not explain these returns, it may still be premature to attribute these returns only to mean reversion, as it is possible that pairs trading profits may be related to patterns in returns that are known to earn significant profits.

Keywords: Fama-French (1993) model, pairs trading, stock futures JEL Code: D53, E44, G11

INTRODUCTION on short term price reversal and involves Statistical arbitrage is a trading strategy finding two stocks that have a long term that employs time series methods to identify relationship and tend to follow a similar trading relative mispricing between securities. This pattern. When the stock prices diverge from strategy has been in use since the mid-1980’s that specified trading pattern, one stock will and has been one of the major tools employed become overvalued in relation to the other by both hedge funds and investment banks. stock (Gatev et al., 2006). Many bank proprietary operations now focus Long position is then taken in the lower to varying degrees on statistical arbitrage priced stock and short position in the higher trading. One technique under the umbrella of priced stock. The trade is completed or closed statistical arbitrage is pairs trading. by taking an exit in each of the positions when Pairs trading has been one of the most the two assets have returned to their original popular quantitative arbitrage strategies or long run equilibrium path-therefore this employed by hedge fund managers. It is based strategy utilizes the concept of mean reversion as stated by Hillebrand (2003). Profit is *Assistant Professors, School of Business Studies, captured from the short-term or temporary Punjab Agricultural University, Ludhiana-141004 anomaly that arose in pair of stock prices. (Punjab) Vidyamurthy (2004) emphasized that this Email: [email protected] to and away movement from the long-run

915 equilibrium relationship between a pair of available for stocks. It thus, blends the financial assets does not depend on the characteristics of both developed and movement of the overall market, therefore, emerging economies and should be an pairs trading strategy is a market-neutral interesting case to test the performance of investment strategy. pairs trading, especially in the absence of any Pairs trading is a strategy that relies on the concrete evidence. This paper therefore, aims existence of strong arbitrage forces so that the to assess the performance of pairs trading mispricing of the stocks in a pair are eliminated strategy in Indian stock market. (that is, their prices converge) after the position REVIEW OF LITERATURE is opened. It is an established fact that market The concept of pairs trading was first characteristics like short sales constraints, implemented by Nunzio Tartaglia’s quant options trading and the size of transaction group at Morgan Stanley in the mid-1980s costs impact the strength of arbitrage forces (Vidyamurthy, 2004). in a market (Yuksel et al., 2010). Also, prior After a very positive start, the strategy research indicates that there is a considerable however, began to show contradictory results difference, in general, between emerging and and the team was dismantled. But pairs trading developed markets regarding these continued to fascinate academics and characteristics. practitioners equally. A large and still growing Charoenrook and Daouk (2005) reported body of research has focused on the that 95 percent of developed countries allowed performance of pairs trading in different short sales, compared to 31 percent of emerging markets (Nath, 2003, Hong and Susmel, 2004, economies. Moreover, options trading was Andrade et al., 2005, Gatev et al., 2006, Perlin, available in 91 percent of developed countries 2009, and Do and Faff, 2010). but only in 19 percent of emerging countries. In their pioneering paper, Gatev et al. (1999) Compared to developed markets, emerging reported statistically significant profits from a markets had higher transaction costs by margin simple pairs trading strategy implemented in of 1.0 to 1.5 percent (Domowitz et al., 2001). U.S. equity market during 1962-1997. They This line of reasoning implies that both confirmed robustness of their results with arbitrage forces and the returns from pairs conservative estimates of transaction costs trading strategy should be weaker in emerging and concluded that pairs payoffs were not markets (Yuksel et al., 2010). exactly linked to a classical mean reversion India, a strong emerging market, offers a effect. Gatev et al. (2006) then extended their unique case. This is so because on one side it analysis up to the year 2002 and recorded suffers from weak efficiency (Gupta and Basu, average annualized excess returns of up to 11 2007) and has suffered from major scams percent. They opined that such returns from including Harsha Mehta scam, Ketan Parekh the pairs strategies were basically a scam, and the latest involving National Spot compensation to arbitrageurs for enforcing the Exchange Limited. On the other side, the law of one price. country is a constituent of the BRICS nations In Brazilian financial market, Perlin (2009) and has attracted huge investments and concluded that pairs trading performed well trading activity from all over the world to the with an emphasis that the positive excess extent that National Stock Exchange (NSE) returns were not the result of chance. Do and gained the top spot in world bourses in terms Faff (2010) replicated Gatev’s et al. (1999) of volume of equity trade (World Federation methodology during the period of 2000-2009 of Exchanges). and reported that the strategy was still While there are short sales constraints, profitable but with a declining trend. They both options and futures contracts are attributed this trend to a worsening of arbitrage

916 risks and increasing market efficiency. After cointegration for pairs selection. Papadakis and incorporating the impact of trading costs they Wysocki (2007) widened the scope of concluded that after 2002 pairs trading was methodology of Gatev et al. (2006) by largely a loss making proposition. Using high examining the impact of accounting frequency pairs trading, Bowen et al. (2010) information events (that is earnings concluded that returns from pairs trading were announcements and analyst forecasts) on the highly sensitive to transaction costs and speed level of returns of the pairs trading strategy. of execution. They also showed that the most Several academic studies proposed of returns occur in the first and last hour of frameworks to implement pairs trading rather trading. than provide empirical evidence of the In order to expand the scope of pairs effectiveness of pairs trading. Vidyamurthy trading, Mori and Ziobrowski (2011) compared (2004) for example, explained the link between the performance of pairs trading in the US REIT pricing theory and pairs trading providing an (real estate investment trust) market and the implementation strategy based on equity market during 1987-2008. They found cointegration. that the REIT market provided superior profits Elliott et al. (2005) proposed an analytical between 1993 and 2000, which disappeared framework for pairs trading applying a mean- later on. Alsayed and McGroarty (2012) found reverting Gaussian Markov chain model. Huck that pairs trading was an important price (2010) applied multi-criteria decision correcting mechanism in American depository techniques for the selection of pairs for pairs receipt (ADR) market. Using pairs of UK stocks trading. and ADRs, they found that pairs trading DATA AND METHODOLOGY accounted for a 1.45 percent return in excess Pairs trading involves indentifying two of the risk free rate. In Finnish stock market, assets that enjoy a long term relationship and Broussard and Vaihekoski (2012) tested the taking long-short positions in case of profitability of pairs trading under different divergence from the relationship. In order to weighting structures and trade initiation ensure that our results are directly comparable conditions. They found that the returns from to developed markets, and to avoid any data- a pairs strategy were not related to market risk mining bias, our methodology closely followed and that lowering the threshold for opening a the work of Gatev et al. (2006) where in pairs pair increased the returns. trading was carried out over two periods-a A recent stream of literature on pairs pairs portfolio formation period, immediately trading focused on the optimization of the followed by trading period. different phases of the strategy and on the As the stock exchanges in Indian stock control of the variables that impinge upon its market do not allow short sales in stocks, we performance. Interesting models were implemented the strategy on the stock futures proposed by Huck (2010), Xie and Wu (2013), contracts available on National Stock and Goncu and Akyildirim (2015) in this Exchange of India (NSE) with a specific focus direction. Several studies made changes to the on financial stocks including banks. As of pairs trading methodology used by Gatev et date, these contracts are available on 28 such al. (2006). For example, Elliott et al. (2005) used stocks and an individual/institution can go a Gaussian Markov chain model for capturing long or short as desired. NSE offers stock the spread while Do et al. (2006) measured the futures contracts with maturities of one, two, spread using theoretical asset pricing methods and three months (for greater details, please and mean reversion. refer to www.nseindia.com). However, only one While Vidyamurthy (2004), Burgess (2005) month contracts were utilised as volumes tend and Haque and Haque (2014) applied to be low in longer maturities.

917 For selection of stock futures contracts one standard deviation and were then into sample, no restrictions such as industry, squared-off. Initial margin was assumed to be size, age, etc. were imposed; but no contract 20 percent in all cases and all open positions should have delisted during the study period. were marked-to-market on the basis of daily Using daily closing prices of these one closing prices; financing costs however, have month financial stock futures contracts been ignored. No positions were maintained between January 1, 2011 and December 31, for more than two weeks and if an open 2013, the price series of each stock futures position could not be squared-off before, it contract was first standardized by taking was squared off at the closing price of the last deviations from the mean and then dividing day of second week. by the standard deviation: The returns from the trades were calculated as follows: P  P P  i m P  P  P  P  TC s σ R  li lj sj si p k IM - MM where, P = Standardised price where, s th Rk = Returns from k pair Pm = Mean closing price over the pairs formation period Pli = Purchase price of stock futures th contract with long position Pi = Closing price for i day Pljb = Selling price of stock futures sp = Standard deviation of closing price over pairs formation period contract with long position Then, all possible pairs from these selected Psi = Purchase price of stock futures contracts were constructed. For each pair, the contract with short position sum of the squared deviations between their Psj = Selling price of stock futures standardized prices were calculated. Finally, contract with short position all pairs were sorted in ascending order of the MM = Marked-to-market margin payments sum of the squared deviations and the top 20 TC = Trading costs pairs were retained for trading. IM = Initial margin The trading period immediately followed To examine the risk of pairs trading and the the pairs formation period and continued till drivers of returns, the portfolio returns were December, 2014. For each of the selected pairs, risk adjusted using Fama and French (1993) trade was initiated when the difference three factor asset pricing model. The model is between the standardized prices of stock detailed below: futures contract forming a pair diverged by Re  a  b(Rm  Rf )  S(SMB) h(HML) e more than two historical standard deviations where, calculated using the prices during pairs Re = Excess return on pairs portfolio formation period. Rm = Returns from market portfolio The short position was created in the (S&PCNXNifty)] higher priced stock futures contract and long Rf = Risk free rate of return position was taken in the stock futures contract SMB = Size risk with lower price. As futures contracts are HM = Value risk available in predefined lot sizes, the number of a = Intercept showing value added by lots gone long/short was such that the the pairs strategy monetary value of the long and short position b = Measure of exposure of pairs was as close as possible. portfolio to systematic market risk Positions were maintained till the difference S = Measure of exposure to size risk of the standardized price series came down to (SMB)

918 h = Measure of exposure to value risk the same, standard deviation is an adequate (HML) measure of risk. While, the standard methodology was The maximum value of 7.14 percent and employed for calculation of different risk factors minimum value of -0.21 percent and the (Aggarwal and Gupta, 2007), one month consequent range of 7.35 percent shows that MIBOR rates were used as a proxy for risk free the risk was minimum in the case of pairs rate of interest. portfolio strategy and the maximum in the case RESULTS AND DISCUSSION of short only portfolio at 8.40 percent. The last In the following text we provide summary descriptive focused was the percentage of statistics for the pairs trading strategy. Along trades with negative returns. This was found with that, statistics have also been presented to be lowest in pairs portfolio at 24 percent for long only portfolio and short only portfolio. and the highest at 33 percent in the case of We present both average returns and standard short only portfolio strategy. deviation of returns for each strategy To consider alternative explanations for implementation. As the literature reports non- these short-term pairs trading profits net of normality in the returns from such strategies transaction costs, risk adjusted returns of the (Yuksel et al., 2010), higher moments of the strategy were estimated by employing the returns distribution including kurtosis and the Fama and French (1993) three-factor asset- skewness are also reported. In the light of the pricing model and the results of the same have same, volatility (as measured by standard been shown in the lower half of Table 1. deviation) may not be an effective measure of The foremost component is the intercept risk, alternative measures such as the maximum value a which signifies the value added by the and minimum of the returns are also presented. strategy over and the above the compensation The statistics returns from long only and for the risk factors considered in the model. short only portfolio strategies have also been presented. In order to explore the contribution Table 1: Descriptive statistics for returns of different risk factors towards the returns from pairs, long only and short only portfolio from each strategy implementation, results strategy from application of Fama-French (1993) asset (Percent) pricing model have also been shown. Table 1 Statistics Portfolio strategy Pairs Long Short presents the descriptive statistics for the only only monthly excess returns from the pairs portfolio Mean 3.71 3.64 1.21 strategy, long only portfolio strategy and short Standard deviation 7.62 12.01 9.24 only portfolio strategy. Skewness 1.47 1.10 -0.41 As seen from the Table 1, the pairs portfolio Kurtosis 6.54 4.84 3.42 strategy produced the highest mean returns Minimum -0.21 -0.35 -0.28 Maximum 7.14 5.23 8.12 of 3.71 percent while long only portfolio offered Range 7.35 5.58 8.40 3.64 percent followed by short only portfolio Trades with negative returns 24 29 33 at 1.21 percent. The standard deviation of Fama-French (1993) Model returns, which represents the risk of the Intercept (a) 2.90** -0.05NS 0.02NS ** ** ** strategy, was however, the lowest in the case Measure of exposure of pairs 0.07 0.61 -0.53 portfolio to systematic market of pairs portfolio at 7.62 percent. It was the risk (b) highest for the long only portfolio at 12.01 Measure of exposure to size 0.09NS 0.07NS 0.06NS percent. The values for the higher degree risk, SMB (S) moments, that is skewness and kurtosis clearly Measure of exposure to value -0.35NS 0.04NS 0.02NS show that returns distribution was not normal risk, HML (h) ** Significant at 5 percent level. in any of the three strategies. In the light of NS: Non-significant

919 As shown, the pairs portfolio strategy added further. a positive and significant value of 2.90 percent CONCLUSIONS over and above the market risk, size risk, and Pairs trading, one of the most popular value risk factors considered in the model. quantitative arbitrage strategies, is based on Long only portfolio and short only portfolio short term price reversal and involves finding strategies did not add any significant value, two stocks that have a long term relationship suggesting that the returns generated were and tend to follow a similar trading pattern. On mere compensation for the risks taken. This divergence from specified trading pattern, one also leads to the conclusion that the return stock becomes overvalued in relation to the generated by the pairs portfolio is not due to other. Long position is then taken in the lower long only or short only portfolios but due to priced stock and short position in the higher the strategy itself. priced stock. The trade is completed by taking The most important risk that any trader or an exit in each of the positions when the two investor assumes is the systematic market risk, assets have returned to long run equilibrium compensation for which is captured through path-therefore, this strategy utilizes the coefficient b. Larger is the value of b, larger is concept of mean reversion. As Indian capital the contribution of market movements towards market did not allow short sales of stocks, pairs the returns generated by a trading strategy. trading was implemented using one month The pairs portfolio strategy produced a small stock futures contracts available on financial but statistically significant value of 0.07 for b. stocks including banks. This suggests that market movements did not Using Gatev’s (2006) methodology, large have much impact on the returns generated positive returns ranging from -0.21 to 7.14 by the pairs portfolio strategy. This is in line percent per month, were produced by pairs with the pairs philosophy which suggests that portfolio for maximum holding periods of two it is a market neutral strategy. Large values of weeks. These were superior to returns provided coefficient b for long only portfolio and short by short only or long only portfolios. Further, only portfolio clearly show that these analyses demonstrated that systematic market strategies earned their returns mainly because risk, size risk or the value risk were not helpful of market movements. However, size risk and in explaining these returns. However, it may value risk did not have any significant still be premature to attribute these returns only contribution towards generated by any of the to mean reversion, as it is possible that pairs three strategies. trading profits may be related to patterns in Evidently, pairs portfolio strategy is able returns that are known to earn significant to provide positive excess returns net of profits. transaction costs. These returns are not due REFERENCES to the presence of a long only portfolio or short Aggarwal, N. and Gupta, M. 2007. Do mutual only portfolio, or due to market portfolio, but funds perform: An empirical investigation into due to the strategy itself, which has its roots Indian mutual funds. ICFAI Journal of Applied in mean reversion strategy. However, it may Finance. 13: 5-16. Alsayed, H. and McGroarty, F. 2012. Arbitrage still be premature to attribute these returns only and the law of one price in the market for to mean reversion, as it is possible that pairs American depository receipts. Journal of trading profits may be related to patterns in International Financial Markets, Institutions and returns that are known to earn significant Money. 22:1258-1276. profits. For example, two such patterns are the Andrade, S.C. and Pietro, V.D., and Seasholes, M negative first-order serial correlation and S. 2005. Understanding the profitability of pairs momentum in stock returns. Impact of these trading. Retrieved from www.ssrn.com. factors on returns needs to be investigated Bowen, D., Hutchinson, M.C., and O’Sullivan, N.

920 2010. High-frequency equity pairs trading: Goncu, A. and Akyildirim, E. 2015. Statistical Transaction costs, speed of execution, and arbitrage with pairs trading. Retrieved from patterns in returns. Journal of Trading. 5: 31- www.papers.ssrn.com. 38. Gupta, R. and Basu, P.K. 2007. Weak form Broussard, J.P. and Vaihekoski, M. 2012. efficiency in Indian stock markets. International Profitability of pairs trading strategy in an Business and Economic Research Journal. 6: illiquid market with multiple share classes. 57-63. Journal of International Financial Markets, Haque, S.M. and Haque, A.K.E. 2014. Pairs trading Institutions and Money. 22: 1188-1201. strategy in Dhaka Stock Exchange: Burgess, A. N. 2003. Using cointegration to hedge Implementation and profitability analysis. Asian and trade international equities. In: Dunis, C., Economic and Financial Review. 4: 1091-1105. Laws, J. and Naïm, P. (eds.) Applied Quantitative Hillebrand, E. 2003. A mean-reversion theory of Methods for Trading and Investment. John stock-market crashes. Working Paper. Center Wiley & Sons, Chichester: 41-69. of Finance and Risk Management, Gutenberg Charoenrook, A. and Daouk, H. 2005. A study of University, Mainz, Germany. market-wide short-selling restrictions. Retrieved Hong, G. and Susmel, R. 2003. Pairs-trading in the from www.ssrn.com. Asian ADR market. Retrieved from Do, B. and Faff, R. 2010. Does simple pairs trading www.ssrn.com. still work? Financial Analysts Journal. 66: 83- Huck, N. 2010. Pairs trading and outranking: The 89. multi-step-ahead forecasting case. European Do, B., Faff, R., and Hamza, K. 2006. A new Journal of Operational Research. 20: 1702-1716. approach to modeling and estimation for pairs Mori, M.and Ziobrowski, A.J. 2011. Performance trading. In: Proceedings of 2006 Financial of pairs trading strategy in the U.S. REIT market. Management Association European Conference: Real Estate Economics. 39: 409-428. 87-99. Nath, P. 2003. High frequency pairs trading with Domowitz, I., Glen, J., and Madhavan, A. 2001. U.S. treasury securities: Risks and rewards for Liquidity, volatility and equity trading costs hedge funds. Working Paper. London Business across countries and over time. International School, London (UK). Finance. 4: 221-255. Papadakis, G. and P. Wysocki. 2007. Pairs trading Elliott, J.V. 2005. Pairs trading. Quantitative and accounting information. Retrieved from Finance. 5: 271-276. www.ssrn.com. Elliott, R.J., Hoek, J.V.D., and Malcolm, W.P. 2005. Perlin, M.S. 2009. Evaluation of pairs-trading Pairs trading. Quantitative Finance. 5: 271-276. strategy at the Brazilian financial market. Fama, E.F. and French, K.R. 1993. Common risk Journal of Derivatives and Hedge Funds. 15: factors in the returns on stocks and bonds. 122-136. Journal of Financial Economics. 33: 3-56. Vidyamurthy, G. 2004. Pairs Trading, Quantitative Fama, E.F. and French, K.R. 1993. Common risk Methods and Analysis. John Wiley and Sons, factors in the returns on stocks and bonds. Canada. Journal of Financial Economics. 33: 3-56. Xie, W. and Wu, Y. 2013. Copula-based pairs trading Gatev, E.G., Goetzmann, W., and Rouwenhorst, strategy. Retrieved from www.ssrn.com. K.G. 2006. Pairs trading: Performance of a Yuksel, A., Yuksel, A. and Muslumov, A. 2010. relative value arbitrage rule. Review of Financial Pairs trading with Turkish stocks. Middle Studies. 19: 797-827. Eastern Finance and Economics. 7: 38-54. Gatev, E.G., Goetzmann, W.N., and Rouwenhorst, K.G. 1999. Pairs trading: Performance of a relative value arbitrage rule. NBER Working Paper No. 7032. 1050 Massachusetts Avenue, Received: April 07, 2015 Cambridge, Massachusetts, USA. Accepted: September 25, 2015

921 922 Indian J Econ Dev DOI: 10.5958/2322-0430.2015.00102.X Volume 11 No.4 (2015): 923-932 Research Article

PROSPECTS OF AGRITOURISM IN BIKANER DISTRICT OF RAJASTHAN

Aditi Mathur, Surjeet Singh Dhaka and Urmila*

ABSTRACT

The present study was carried out to examine the potential of agritourism in Bikaner district of Rajasthan. Tourism experts and agriprenuers were selected on the basis of judgemental sampling while tourists were conveniently selected. Agritourism potential of Bikaner has scored 5.7 out of 10 and property/farm/site had 4.78 out of 10 that indicating huge scope and potential for agritourism in study area but efforts are needed towards site, infrastructure, marketing, innovative ways of displaying agritourism products and services. Increasing number of foreign and domestic tourist in Bikaner create an opportunity for this new farm diversification or additional source of income for farmers and other participants. Criteria those are considered during location identification- near to water source, away from polluted environment, better connectivity, natural site, historical importance, agricultural prosperity, etc. Lack of credit facilities, lack of water resources, lack of specific policy and no clear policy about tax exemption are most important problems in establishment and operating agritourism centers.

Keywords: Agri-tourism,diversification, entrepreneurship, factor analysis JEL Classification: M13, M31, O13, Q22

INTRODUCTION Present Status in India Agritourism or agrotourism involves Agritourism is considered as the fastest any agriculturally based operation or activity growing sector in the tourism industry. The that brings visitors to a farm or ranch. concept has been successfully implemented Agritourism includes a wide variety of in states like Maharashtra, Kerala, Rajasthan, activities, including buying produce direct Jharkhand, Gujarat, and Himachal Pradesh. from a farm stand, picking fruit, feeding animals, Importance of Study or staying at a farm. Agritourism or agricultural Agriculture is a most important occupation tourism, is one alternative for improving the in the India. But, today it has become incomes and potential economic viability of unprofitable in some parts of the country due small farms and rural communities. the irregular monsoon, price fluctuations of agro-products, small farm size and some internal weakness of the agriculture sector. *Assistant Professor, Teaching Associate, and MBA Student (Agri Business Management), Institute of Hence, there is need to do some innovative Agri Business Management, S K Rajasthan activities in the agriculture, which will help Agricultural University, Bikaner, Rajasthan farmers and rural people. Agritourism is Email: [email protected] emerging as an important instrument for

923 sustainable human development including 2012, while Jaipur, Udaipur, Jaisalmer, poverty alleviation, employment generation, Ranakpur, and Jodhpur occupied first, second, environmental regeneration and development third, fourth and fifth postion respectively. of remote areas and advancement of woman Bikaner is at 10th position with 1.14 percent and other disadvantaged groups in the share in arrival of domestic tourist in Rajasthan country apart from promoting social during 2012 and top three destinations were integration and international understanding. shared by Ajmer, Mount Abu, and Pushkar, Agritourism doesn’t require any huge changes respectively. In the backdrop of these facts in the agricultural landscape; it is easily the present study was carried with the developed with the help of existing resources following specific objectives: on the farm. Bikaner district is recognized as a i. the need and scope of agritourism in desert region in India with rich history of Bikaner, culture and warm welcome to outsiders- from ii. to analyse the motivational factors for farm Atithee Devo Bhav to Padharo Mahare Desh. diversification in Bikaner, Bikaner district is well known for guar seed iii. to know the tourists’ expectations production and other crops like gram, regarding agritourism in Bikaner, and groundnut, moth (raw material for famous iv. the challenges for the agritourism bikaneri bhujia), moong, wheat and mustard. development in Bikaner. The details about Agro climatic features in METHODOLOGY Bikaner region is presented in Table 1. Area of Study Bikaner was purposively selected as an Table 1: Agro-climatic features of Bikaner area of study due to two major reasons. Firstly District according to Ministry of Tourism, Art and Particulars Agro-climatic features Culture-Government of India, Department of Zone IC-Hyper Arid Partial Irrigated Zone Tourism, Final Report on 20 years perspective Rainfall 100-350 mm plan for sustainable tourism in Rajasthan, Major crops Mostly rainfed crops like bajra,kharif pulses, guar etc. are grown during the kharif Bikaner has a huge potential for tourism sector season. Rabi crops like wheat, rape-seed and during the last two years a growth is and mustard. observed in arrival of foreign and domestic Types of soil Desert Soils and sand Dunes soil loamy tourist. Secondly, Bikaner also has a renowned coarse in texture & calcareous Districts Bikaner, Jaisalmer, Churu state agricultural university (SK Rajasthan Agricultural University) to facilitate the development of scientific agricultural practices The perusal of Table 2 revealed that in the region along with the increased growth Bikaner is at 6th position with 5.27 percent share of agriculture graduates to bring about growth in arrival of foreign tourist in Rajasthan during in the agripreneurial activity in the area.

Table 2: Top destinations for arrival of tourists in Rajasthan and Bikaner in 2012 Name of destination Foreign tourist arrival Position Domestic tourist arrival Position Jaipur 1st Ajmer 1st Udaipur 2nd Mount Abu 2nd Jaisalmer 3rd Pushkar 3rd Ranakpur 4th Jaipur 4th Jodhpur 5th Nathdwara 5th Bikaner Bikaner 6th 9th (5.27 % share) (1.14 % share) Source:http://www.rajasthantourism.gov.in

924 Research Approach Table 4: Data reliability statistics The research was exploratory in nature and Particulars Cronbach's Number of as an attempt was made for the first time to alpha items explore the possibility of such an agri business Foreign tourist expectations 0.782 28 in the region. Domestic tourist expectations 0.787 28 Motivation behind farm 0.700 19 Sampling procedure diversification Judgmental sampling was used for the Agritourism potential factors 0.735 40 selection of various tourism experts in the Source: Researcher's computation through SPSS 20 study area and suggestion was made by the Rajasthan Tourism Development Corporation, minimum standard of 0.70 (Nunnally and Bikaner. Only eight tourism experts were found Bernstein, 1994) specifies that with the in the study area. After that convenience acceptable reliability, the scale can be used for sampling was used for selection of foreign and the analysis. Based on these appraisals, domestic tourist (only those have awareness measures used in this study were within the about agritourism). Only twenty each tourist acceptable levels supporting the reliability (foreign and domestic) was selected, due to given in Table 4. less arrival of tourist during data collection. RESULTS AND DISCUSSION Selection of agripreneur was based on Pattern of Agritourism in Bikaner judgmental sampling, suggested by experts. An assessment of agritourism potential of The detail of which is provided in Table 3. Bikaner region, motivational factors behind farm enterprise diversification (agritourism) in Table 3: Detail of sampling procedure Bikaner, and tourist’s expectations regarding Sample unit and size Sampling techniques agritourism in Bikaner was carried out. The Foreign Tourists (20) Convenience Sampling perusal of Table 5 shows the rate of growth of Domestic Tourists (20) Convenience Sampling domestic tourism and foreign tourist as per Experts (8) Judgment Sampling the studies conducted by TCS Projections up Agripreneur (12) Judgment Sampling to year 2020.

Nature and Sources of Data Collection Table 5: Projected tourist growth in both primary and decondary data were Rajasthan collected to accomplish the objectives of the Year Domestic tourist Foreign tourist Total study. 2000 6.67 0.56 7.23 2005 10.18 0.75 10.93 Secondary Data 2010 14.05 0.96 15.01 Data have been furnished from related 2020 27.64 1.57 29.21 articles, research papers, reports of the Source: Tourism Annual Report 2012-2013, Department of Government of India and Rajasthan, as well as Tourism, Rajasthan Ministry of Agriculture and Ministry of Tourism. The results presented in Table 6 exhibited Primary Data the year by year arrival of foreign and domestic The primary data were collected from tourists in Bikaner is increasing, which is foreign and domestic tourists, various tourism directly related to the potential of agritourism experts and agripreneur in the Bikaner district in Bikaner. Because tourists are basic thruogh person interview method on well requirement for agritourism, their growth and stuctured schedule. increasing number in Bikaner creating an The Cronbach’s coefficient alpha was used opportunity for this new farm diversification to ensure reliability of the measures. The or additional source of income for farmers and Cronbach coefficient alpha value exceeding other participants.

925 Table 6: Arrival of tourists in Bikaner from Table 8: Agritourism decision matrix for 2001 to 2012 evaluating potential in Bikaner Year Domestic tourist Foreign tourist Part A: Regional characteristics Score 2001 185645 31441 1. Natural beauty 15.5 2002 165407 17060 2. Cultural and social characteristics 18.875 2003 181654 28081 3. Recreational offers 9.375 2004 177898 48712 4. Shopping 15.25 2005 197275 61123 5. Public infrastructure to support agritourism 15.25 2006 214716 65347 6. Attitude towards tourists 15.25 2007 224089 74961 7. Accessibility 14.125 2008 235206 77068 8. Existing tourism activity 10.375 2009 244547 59857 Gross total 114 2010 275191 74508 Divide gross total by standardizing factor 20 2011 277546 74820 Net regional characteristics score 5.7 2012 324988 76497 Part B: Property/Site characteristics Source: Tourism Annual Report 2012-2013, Department of 9. Farm features 14.25 Tourism, Rajasthan 10. Natural built features 15.5 11. Site infrastructure 9.75 Agritourism Potential of Bikaner Region 12. Business potential and Human resource 8.25 An assessment tool is adapted from the features Farm and Country Tourism on your Property: Gross total 47.8 Assessment Tool of the Sustainable Tourism Divide gross total by standardizing factor 10 Development, Queensland, Australia. Net property/site characteristics score 4.78 Source: Researcher's computation from field data This assessment tool offers a way to make a preliminary assessment about the ‘magnetism’ of region and property – the extent On the basis of results it was found that to which they can attract tourists as a result of potential of agritourism in Bikaner comes their characteristics. It is based on the premise under the site development with better that the region and property (infrastructure, marketing and infrastructure development facilities and farm stays) are the ‘foundation (Table 7). The potential of Bikaner has score stones’ for agritourism activities, the more 5.7 out of 10 and property/farm/site has 4.78 magnetic the region and property are, the more out of 10 that indicates huge potential for likely you are to develop a successful agritourism in study area has very good but agritourism business. we need efforts towards site, infrastructure, This tool consists of 60 statements that marketing, innovative ways of displaying are expressed in very positive terms and agritourism products and services. represent ideal situations. Part A offers a way Each square of the Agritourism Potential to examine the agritourism potential of the Grid can be interpreted according to the region. Part B offers a way to examine the following: agritourism potential of property or site. High Tourism Potential Agritourism potential grid helps to evaluate Balanced tourism structure between the potential of both the region and property attraction of the property and the region in and offers some interpretation of findings. which it is located. Results of the total scores are explained in the Site Development Potential Table 7 and 8. Bikaner region is very attractive to tourist,

Table 7: Agritourism potential grid score of Bikaner Characteristics Score Remark Regional 5.70 Site, infrastructure development and marketing innovations are required. Property/Site 4.78 - Source: Researcher's computation from field data

926 but our property must be further developed to of Table 9 shows that mean scores were found capitalize on the regional development. to be statistically significant . Market Development Potential: Our property Tourists’ expectations regarding agritourism has many suitable features for agritourism, in Bikaner region needs to be developed. The perusal of Table 10 exhibited the Low Tourism Potential: It appears that neither expectations of foreign tourists, which our property nor the region has sufficient included typically rural food, chance to be attractiveness for agritourism activities. involved in the farm, entertainment value, Improving the viability of our concept will be quality water, peace and quiet, on-site a huge undertaking. If we are committed to our restrooms, clean and green environment, project, proceed to the business planning for countryside accommodations, educational a more detailed analysis of what we need to value, attractive location and interaction with do. service providers have scored between 4.00 Motivational factors for farm diversification to 4.55, indication of extra importance of these in Bikaner factors, thereby requiring urgent intention of The results revealed a broad range of the agritourism is to be promoted. The perusal economic, intrinsic, and market-based goals of Table 10 shows that mean differences were related to respondents’ decisions to diversify significant statistically. their farms (Table 9). The goals with the The results presented in Table 11 revealed importance ratings were economic and intrinsic the expectations of domestic tourists which in nature specifically were: generate included among other peaceful and quiet, additional income (mean=3.92), continue attractive location, on-site restrooms, farming (mean=3.67), enhance personal/ entertainment value, quality food, typically family quality of life (mean=3.50), and increase/ rural food, security and trust, countryside diversify the market (mean=3.08). The perusal accommodations, participation in local

Table 9: Motivational factors behind farm enterprise diversification (agritourism) in Bikaner (n=12) Motivation behind farm enterprise diversification Mean score t-value Generate additional revenues 3.92 17.11*** Continue farming 3.67 16.32*** Enhance personal/family quality of life 3.5 17.98*** Increase/diversify the market 3.08 15.98*** Educate customers 2.92 15.11*** Respond to a market need/opportunity 2.83 13.68*** Keep the farm in the family 2.83 13.68*** Generate revenues from existing resources 2.75 15.33*** Generate revenues during off-season 2.67 18.76*** Interact with customers 2.5 9.57*** Make farm less dependent on outside factors 2.33 10.38*** Offset fluctuations in farm revenues 2.25 10.34*** Capitalize on an interest or hobby 2.25 8.07*** Reduce impacts of catastrophic events 2.08 9.10*** Provide employment for family members 2.08 7.24*** Provide a new challenge 2 7.27*** Enhance ability to meet financial obligations 1.92 6.67*** Reduce overall farm debt 1.83 7.61*** Provide current customers with new products 1.75 6.28*** *** Significant at one percent level

927 Table 10: Expectations of foreign tourists' for agritourism (n=20) Expectations Mean score t-value Typically Rural Food 4.55 39.87*** Chance to be involved in the farm 4.50 39.23*** Entertainment Value 4.50 39.23*** Quality water 4.30 29.27*** Peace and Quiet 4.25 26.53*** On-site restrooms 4.20 30.51*** Clean and Green Environment 4.15 27.67*** Countryside Accommodations 4.10 28.62*** Educational Value 4.05 26.39*** Attractive Location 4.00 22.51*** Interaction with Service Providers 4.00 19.49*** Quality Food 3.90 22.13*** Purchasing opportunities 3.90 19.13*** Security & Trust 3.80 16.91*** Interpersonal Congruency 3.70 17.92*** Participate in Local festivals 3.65 17.49*** Interact with rural people 3.55 16.81*** Continue of relationship with farmer 3.50 15.65*** Primary health care facilities 3.40 14.53*** Convenient Location 3.30 13.65*** Adequate parking 3.05 12.41*** Source: Researcher's computation from field data & SPSS 20 *** Significant at one percent level Based on a 5-point Likert scale where 1 = "extremely not important" and 5 = "extremely important"

Table 11: Domestic tourists' expectations (n=20) Expectations Mean score t-value Peace and quiet 4.65 42.50*** Attractive location 4.50 33.16*** On-site restrooms 4.35 29.00*** Entertainment value 4.20 26.99*** Quality food 4.15 24.91*** Typically rural food 4.10 23.27*** Security and trust 3.95 21.40*** Countryside accommodations 3.80 20.39*** Participate in local festivals 3.75 19.71*** Primary health care facilities 3.70 17.92*** Chance to be involved in the farm 3.60 17.12*** Clean and green environment 3.50 16.55*** Luxurious accommodations 3.40 15.29*** Convenient location 3.40 13.88*** Quality water 3.30 13.65*** Educational Value 3.25 12.03*** Interaction with service providers 3.15 11.49*** Continue of relationship with farmer 3.15 11.11*** Interpersonal congruency 3.10 10.39*** Interact with rural people 3.00 10.03*** Purchasing opportunities 3.00 9.75*** *** Significant at one percent level

928 festivals, primary health care facilities, and Table 12: Problems in establishment and chance to be involved in the farm have scored operating agritourism centers 4.65 to 3.60 therefore these are most important (n=20) and rank at the topin the factors which should Problems Mean t-value Lack of effective communication 2.90*** 18.20 be strongly considered for agritourism. The Poor supply of electricity 2.76*** 17.61 perusal of Table 11 shows that mean Lack of marketers 2.62*** 17.11 differences were significant statistically. Lack of managerial expertise 2.57*** 15.89 Challenges for the agritourism development Lack of rules and regulations 2.35*** 15.00 in Bikaner Small size of land holding 2.30*** 14.67 *** The results presented in Table 12 clearly Lack of subsidies and grants 2.28 14.67 Problem regarding guidance 2.27*** 14.26 indicated that, lack of effective Problem of residence 2.26*** 13.63 communication, poor supply of electricity, lack Lack of trained manpower 2.24*** 9.77 of marketing expertise and lack of managerial Lack of proper transport 2.08*** 10.21 expertise are least important problems (mean Lack of credit facilities 1.94*** 9.43 score ranging from 2.57 to 2.90), lack of rules Lack of water resources 1.79*** 8.45 Lack of Agritourism policies 1.68*** 8.11 and regulations, small size of land holding, lack No Tax exemption 1.49*** 6.26 of subsidies and grants, problems of expertise Source: Researcher's computation from field data & SPSS 20 *** Significant at one percent level guidance, problem of residential facilities, lack Based on a 5-point Likert scale where 1 = "extremely not important" of trained manpower and lack proper transport and 5 = "extremely important" facilities are important problems up to some extent (which mean score ranging from 2.08 to measure was 0.565. The Bartlett’s Sphericity 2.35) and lack of credit facilities, lack of water test also found highly significant. It provides resources, lack of specific policy and no clear support for validity of the factor analysis of policy about tax exemption are most important the data set and indicates that, factor analysis problems in establishment and operating of is appropriate. The perusal of Table 13 reveals agritourism centers. The perusal of Table 12 that, the Eigen Values associated with each shows that mean differences were significant linear component before extraction and after statistically. extraction. The rotation has the effect of In the present factor analysis test the KMO optimizing the factor structure and one

Table 13: Initial Eigen Values Component Initial Eigen Values Extraction sums of squared loadings Total % of variance Cumulative % Total % of variance Cumulative % 1 2.263 15.089 15.089 2.263 15.089 15.089 2 1.556 10.373 25.462 1.556 10.373 25.462 3 1.394 9.296 34.758 1.394 9.296 34.758 4 1.265 8.435 43.193 1.265 8.435 43.193 5 1.252 8.344 51.537 1.252 8.344 51.537 6 1.139 7.590 59.127 1.139 7.590 59.127 7 0.953 6.355 65.482 8 0.928 6.184 71.666 9 0.751 5.008 76.674 10 0.718 4.787 81.461 11 0.686 4.575 86.036 12 0.627 4.181 90.217 13 0.545 3.631 93.848 14 0.537 3.578 97.426 15 0.386 2.574 100.000 Source: Researcher's computation from SPSS 20

929 Table 14: Component matrix Factors Components 1 2 3 4 5 6 Problem regarding guidance .684 Lack of subsidies and grants .622 Lack of trained manpower .600 Lack of communication skills .525 Lack of marketers .506 Problem of residence .459 Lack of water resources .677 Lack of proper transport .641 Small size of land holding .680 Lack of rules and regulations -.434 Lack of Agritourism policies -.564 Tax exemption .531 Poor supply of electricity -.662 Lack of managerial expertise .519 Lack of credit facilities .632 % of Variance 15.08 10.37 9.29 8.43 8.34 7.59 % of Cumulative variance 15.08 25.46 34.75 43.19 51.53 59.12 Source: Researcher's computation from SPSS 20 consequence for these data are that the relative was used. As per this method, respondents importance of the three factors was equalized. have been asked to assign the rank for all the The perusal of Table 14 indicates that, lack factors and outcome of such ranking have been of expert guidance, lack of subsidies and converted into score value with the help of grants, lack of trained manpower, problem of the following formula: communication, lack of marketing expertise and 100(R  0.5) lack of residential facilities are the major Percentposition  ij problems for agritourism development in N j Bikaner. Where, Experts’ Rating of Strategic Measures to th th Rij = Rank given for the i factor by the j Develop Agritourism in Bikaner respondents To find out the most significant factors th Nj = Number of factors ranked by the j which influence the respondents while respondents. arranging tour, the Garrett’s ranking technique It can be observed from Table 15 that

Table 15: Experts' rating of strategic measures to develop agritourism in Bikaner according to Henry Garret table Strategic measures TGS AGS Rank Wild publicity of tourism centres 625 78.125 1 Take customer feedback 572 71.5 2 Trained staff for hospitality 441 55.125 3 Develop good relationship 428 53.5 4 Develop agri tourism centres on the basis of co-operative society 420 52.5 5 Preserve address book 340 42.5 6 Optimum charges for facilities 325 40.625 7 Develop different agro packages 294 36.75 8 Understand customer expectations 271 33.875 9 Develop innovative website 252 31.5 10 Source: Researcher's computation from Garret ranking TGS: Total Garret Score, AGT: Average Garret Score

930 experts rank wide publicity of tourism centres accommodations, participate in local festivals, as most significant strategic measure to primary health care facilities, and chance to be promote agritourism in Bikaner with 78.125 involved in the farm. Criteria those are average Garret score, coverage by newspapers, considered during location identification- near television internet and all other possible to water source, away from polluted advertisement means can be used. environment, better connectivity, natural site, The customers’ feedback has been historical importance, agricultural prosperity, considered as second important step to be etc. Lack of credit facilities, lack of water taken with average Garret score 71.50 in resources, lack of specific policy and no clear promotion of agritourism in Bikaner. Proper policy about tax exemption are most important training of staff for hospitality has been given problems in establishment and operating third rank with 55.125 average Garret Score. agritourism centers Family members can be trained for reception For the success in the agritourism sector of tourists. Development of good relationship following things are recommended: ranked as forth (average Garret Score 53.50). * Give a wide publicity of your tourism Fifth prominent strategy has been considered centre by newspapers, television, etc. Use as Agro-tourism centres should be developed all possible advertisement means. on the basis of cooperative society with 52.50 * Develop contacts with the schools, average Garret Score (Table 15). colleges, NGOs, clubs, unions, CONCLUSIONS organisations, etc. Agritourism potential of Bikaner has * Train your staff or family members for scored 5.7 out of 10 and property/farm/site had reception and hospitality. 4.78 out of 10 that indicates huge potential for * Understand the customers’ wants and agritourism in study area but efforts are needed their expectations and serve. towards site, infrastructure, marketing, * Charge optimum rent and charges for the innovative ways of displaying agritourism facilities on the commercial base. products and services. The goal with the * Use artificial local resources to entertain highest average importance ratings was and serve the tourist. economic and intrinsic in nature: generate * Develop your website and update time to additional income (mean=3.92), continue time for attract foreign tourists. farming (mean=3.67), enhance personal/ * Take their feedback and comments about family quality of life (mean=3.50), and increase/ the service and suggestions for more diversify the market (mean=3.08). These are development and modification. motivating factors for agripreneur towards farm REFERENCES diversification (agritourism). Foreign tourist’s Annual Report. 2012-13. of Department of Tourism expectations as typically rural food, chance to Rajasthan. Retrieved from www.prj.co.in on 13/ be involved in the farm, entertainment value, 04/2014 quality water, peace, on-site restrooms, clean Bahl, S. 2012. Strategic implications in agro-tourism with special reference to Punjab, International and green environment, countryside Journal of Research in Commerce and accommodations, educational value, attractive Management. 3 (12): 81-84. location and interaction with service providers Bouèková, B. 2008. Definition of agritourism. are scored 4.55 to 4.00 therefore these are most AgroTourNet ‘S Hertogen Bosch Available from: important, which should highly consider in the www.agrotournet.tringos.eu. agritourism.Domestic tourist expect peace, Butler, R.W., Hall, C.M., and Jenkins, J. 1998. attractive location, on-site restrooms, Tourism and recreation in rural areas. John entertainment value, quality food, typically Wiley and Sons Inc, Toronto: 5-12. . rural food, security and trust, countryside Che, D., Veeck, A., and Veeck, G. 2005. Sustaining

931 production and strngthening the agritourism Economic Research. 3 (1): 79-86. product: linkages among Michigan agritourism PlaceFirst. 2011. Agri-tourism in Southern Scotland. destinations. Agriculture and Human Values. 22 Scottish Natural Heritage Commissioned Report (3): 225-234. No. 463. Gopal, R., Varma, S., and Gopinathan, R. 2008. Talekar, P.R. 2012. Potential for development of Rural tourism development: Constraints and agro-tourism in Kolhapur district of possibilities with a special reference to Maharashtra. International Journal of Young agritourism. Paper presented in Conference on Researcher. 1 (1): 23-30. Tourism in India-Challenges Ahead, 15-17 May, Taware, P. 2009. Agri-tourism: Innovative 2008, IIMK. supplementary income generating activity for Hamilpurkar, S. 2012. Agri tourism in Karnataka- enterprising farmers. Retrieved from Issues constraints and possibilities. International www.scribd.com. Journal of Research in Commerce and Ubale, N.B. and Borate, H.V. 2012. Agri tourism: Management. 2 (7): 106-111. An innovative income generation avenue, Kumbhar, M.V. 2009. Agro-tourism: A cash crop Kurukshetra: India’s Journal on Rural for farmers in Maharashtra (India). MPRA Development. 60 (52): 21-25. Paper No. 25187. Retrieved from Veeck, G., Che, D., and Veeck, A. 2006. America’s www.mpra.ub.unimuenchen.de. changing farmscape: A study of agricultural Kumbhar, M.V. 2012. Tourists expectations tourism in Michigan. Professional Geographer. regarding agritourism: Empirical evidences from 58 (3): 235-248. Ratnagiri and Sindhudurg district of Konkan Zdenek, H., Václav, L., and Petr, B. 2009. ICT and (Maharashtra). Online International Inter- agritourism in Czech Republic. Conference disciplinary Research Journal. 2 (3): 82-91. Papers, Applied Studies in Agribusiness and Maetzold, J. 2002. Nature-based tourism and Commerce, Agroinform Publishing House, agritourism trends: Unlimited opportunities. Budapest. 45-48. www.kerrcenter.com. Retrieved on 10/02/14. Mohapatra, T. 2013. Agri-tourism: An innovative supplementary income-generating activity in Received: January 14, 2015 rural India. International Journal of Social and Accepted: September 24, 2015

932 Indian J Econ Dev DOI: 10.5958/2322-0430.2015.00103.1 Volume 11 No. 4 (2015): 933-938 Research Note

ECONOMICS OF VEGETABLE PRODUCTION IN MANIPUR

L. Priscilla* and S.P. Singh**

ABSTRACT

The results revealed that both the cost of cultivation and cost of production was found to be highest in the case of peas followed by cauliflower and cabbage. The cost incurred on human labour was found to be the major cost component in the cultivation of all the three vegetables suggesting that vegetables are labour-intensive crops. While the highest gross returns was reported in pea cultivation followed by cauliflower and cabbage cultivation, the net returns was found to be highest in case of cauliflower cultivation followed by pea and cabbage cultivation. High cost of seeds and unavailability of good quality seeds were cited as the major constraints faced by the vegetable growers. The study revealed that vegetable production was a remunerative enterprise, but it could be made more profitable if farmers are made aware of new and improved technologies for crop management. Also, human labour cost can be reduced by use of efficient tools and equipments which will lead to overall decrease in cost incurred in vegetable cultivation. To mitigate the production constraints, research and extension facilities in the state should be strengthened and efforts for timely supply of crucial inputs at reasonable price and in adequate quantity to the farmers should be undertaken.

Keywords: Constraints, cost, Manipur, returns, vegetables JEL Classification: C81, D22, D24, Q12

INTRODUCTION agriculture during 2012-13 (Economic Survey, India has a comparative advantage in 2013-14). The total production of horticulture terms of labour costs and diverse agro-climatic crops in India during 2012-13 was 268.85 mt conditions which provides ample opportunity from an area of 23.69 Mha. Out of this, the to grow a variety of horticultural crops (Kumar production of vegetables was 162.19 mt from et al., 2004). In the past one decade, the an area of 9.21 Mha (Indian Horticulture changes in cropping pattern are more towards Database, 2013) making India the second the horticulture sector (Mittal, 2007). This largest producer of vegetables after China. sector contributed 30.4 percent to GDP of Percentage share of vegetables production in the total horticulture production is higher (60.3 per cent during 2012-13) as compared to other * Ph.D. Scholar, Dairy Economics, Statistics and horticulture crops. As compared to the rest of Management Division, National Dairy Research Institute, Karnal and ** Professor, Department of the country, the record of crop diversification Agricultural Economics, G.B. Pant University of is more favorable in the north-east region of Agriculture & Technology, Pantnagar-263145 the country (Barah, 2007) which comprises of Email: [email protected] the states of Assam, Arunachal Pradesh,

933 Manipur, Meghalaya, Mizoram, Nagaland, of Horticulture and Soil Conservation, Manipur Sikkim and Tripura. have identified 2,77,064 ha as potential area Vegetables are an important component in for growing different horticulture crops like the food basket of the people of Manipur. The fruit, vegetable, spices, root and tuber crops, diverse agro-climatic conditions of the state aromatic, and medicinal plants, etc. Out of the ranging from the temperate to tropical, fertile nine districts of Manipur, Thoubal district was soils and abundance of rainfall offer an purposively selected as it had the highest environment conducive for the production of acreage (3723 ha) under vegetable cultivation, vegetables like cabbage, cauliflower, peas, which is 18.35 per cent of the total area under lady’s finger, brinjal, etc. Large scale cultivation vegetables in the state (Economic Survey of vegetables is done in the valley region of Manipur, 2010). Three vegetables, viz., the state after the harvest of paddy, which is cabbage, cauliflower, and peas were selected the staple food in the state. The total on the basis of area under their cultivation in production of horticulture crops in Manipur the district. during 2012-13 was 684.6 thousand t from an From Thoubal district, Thoubal block was area of 84.1 thousand ha, out of which the randomly selected. Three villages production of vegetables was 219.8 thousand viz.,Wangjing, Lamding and Wangbal were t from an area of 21.7 thousand ha (Handbook then selected randomly by simple random of Horticulture Statistics, 2014). The sampling. A list of farmers growing cabbage, productivity of vegetables has increased from cauliflower, and pea in each village was 6.2 t per ha in 2001-02 to 10.1 t per ha in 2012-13 prepared from which farmers were randomly (Indian Horticulture Database, 2014). Vegetable selected. A sample of 60 respondent farmers production is labour intensive yet more was drawn with the condition that the sample profitable which fits well in the small farm consisted of at least 15 farmers growing each production systems (Joshi et al., 2006). It is of the three vegetables. The cost of cultivation expected that vegetable production would of the selected vegetables was calculated as augment income and employment per the definition given by Commission on opportunities for the vegetable farmers of Agricultural Costs and Prices (CACP). The Manipur, majority of which falls under small gross returns and net returns were also and marginal category. Further, most of the computed. To analyze the constraints faced vegetables have a short crop-cycle and by the vegetable growers in the production of therefore provide returns round the year. To vegetables, multiple responses of producers be a remunerative enterprise, farmer should be were taken into consideration. The intensity able to get a considerable net profit over all of the constraints was found out by asking costs and ensure a satisfactory margin the respondents to give scores in terms of between the cost of various inputs and selling percentage (0-100 percent) to each constraint. price of the product. It thus, becomes The average of scores given by individual absolutely essential for the farmer to have respondent to each constraint was found out. knowledge about their production cost. For Then, the constraints were ranked according this, various inputs used in the production to the average percentage received. process needs to be analyzed so that low cost RESULTS AND DISCUSSION alternatives can be suggested which will Socio-economic Analysis enable the farmers to lower their production The socio-economic characteristics of the cost and have higher returns. farmers effect the organization and METHODOLOGY management of the farm as well as the The study was conducted in Manipur state production and disposal of the produce. An ans is based on primary data. The Department analysis of the socio-economic characteristics

934 of any region furnishes a base for further 50 years) constituting 36.67 per cent of the planning and development of agriculture. total sample. In respect of educational status, Under socio- economic aspects, classification majority of the sample belonged to middle based on gender, educational profile, family group (35 percent) followed by high school size, area under vegetables, operational size (26.67 percent), secondary education (18.33 of holdings, sources of irrigation, occupation, percent) and graduate and above (8.33 and income of the sample vegetable growers percent). Also, most of the sample were assessed and have been presented in respondents (95 percent) were found Table 1. The results reveal that a significant belonging to marginal and small category with proportion of the vegetable growers (45 only five percent of them having a land holding percent) belonged to the middle age group of of more than one hectare. The average size of 36-50 years followed by old age group (above operational holding for the respondents was found to be only 0.45 hectare. Hence, for the present study the respondent farmers were not Table 1: Socio-economic characteristics of grouped into categories. The results also show the sample vegetable growers (N=60) the distribution of area under vegetables in Variables Frequency Percentage the sample vegetable farms. It shows that the Age (Years) average area under each of the three Young (Upto 35) 11 18.33 vegetables is almost the same. The table also Middle (36 to 50) 27 45.00 revealed that majority of the respondents Old (Above 50) 22 36.67 (51.67 percent) had an annual income between Level of education Illiterate 7 11.67 `0.5 to `1.0 lakh. While, 38.33 per cent had an Middle 21 35.00 annual income of more than `1 lakh annually, High school 16 26.67 10 per cent had an annual income of less than Secondary 11 18.33 `0.5 lakh. Graduate & above 5 8.33 Cost and Returns Analysis Operational land holding (ha) The various cost incurred in the 0 – 0.25 18 30.00 0.25 – 0.5 27 45.00 production of the three vegetables is given in 0.5 – 1.0 12 20.00 Table 2. The costs is given under three main > 1 ha 3 5.00 components, viz., operational cost, material Area under vegetables (ha) cost and fixed cost. The operational costs Cabbage include costs incurred on human labour 0-0.25 35 85.37 0.26-0.50 6 14..63 (hired+owned) and machine (hired+owned). 0.51-1.0 - Material costs include cost incurred on seeds, Average area 0.296 fertilizers and manures, irrigation charges and Cauliflower plant protection chemicals. While for fixed 0-0.25 28 80.00 costs interest on working capital, rental value 0.26-0.50 6 17.14 of leased in land, rental value of owned land, 0.51-1.0 1 2.86 Average area 0.287 land revenue, depreciation, and interest on the Pea value of fixed capital assets were considered. 0-0.25 21 75.00 The cost incurred on human labour was 0.26-0.50 6 21.43 found to be the major cost component in the 0.51-1.0 1 3.57 cultivation of all sample vegetables accounting Average area 0.219 for nearly 28 percent of the total cost of Annual income (`) Less than 50000 6 10.00 cultivation for cabbage and cauliflower 50000- 100000 31 51.67 cultivation and about 25 percent for pea. Cost

More than 100000 23 38.33 A1 and A2 were found to be different in all the

935 Table 2: Comparison of the cost of cultivation of cabbage, cauliflower and peas (`thousand ha-1) Particulars Cabbage Cauliflower Peas A. Operational cost 1. Human labour (hired + owned) 23.67 24.17 22.56 (27.96) (28.36) (25.95) 2. Machine (hired + owned) 1.90 3.39 2.56 (2.24) (3.97) (2.94) Sub-total (2+3) 25.57 27.56 25.11 (30.20) (32.34) (28.89) B. Material cost 12.09 14.73 12.15 (14.29) (17.29) (13.98) C. Fixed costs 39.29 35.18 41.77 (46.42) (41.28) (48.05)

Cost A1 31.23 34.04 32.67

Cost A2 47.27 49.56 49.45

Cost B1 32.65 35.01 34.41

Cost B2 64.85 65.59 68.12

Cost C1 44.75 46.89 45.33

Cost C2 76.95 77.47 79.04

Cost of cultivation (Cost C3 ) 84.65 85.22 86.94 (100.00) (100.00) (100.00)

Figures in parentheses indicate percentages of total cost of cultivation, Cost C3 three cases as farmers were found to lease in cauliflower (`85218.14 per ha) and cabbage land for the cultivation of the crops. There was (`84647.99 per ha). Nandeshwar et al. (2013) considerable difference observed in between also reported that cost of cultivation of

Cost B2 and Cost C1 which indicated that cauliflower was higher than that of cabbage in vegetable production was a labour intensive a study conducted in Uttar Pradesh. activity. The total material cost was found to A summary of the returns from the be highest in the case of cauliflower (`14733.79 production of the three vegetables is given in per ha) followed by peas (`12151.09 per ha) Table 3. The highest gross returns was in pea and cabbage (`12096.29 per ha). Amongst the cultivation (`100873.70 per ha) followed by three vegetables, the highest fixed cost was cauliflower (`100490.50 per ha) and cabbage incurred for pea cultivation (`41771.49 per ha) cultivation (`96235.75 per ha). But the net followed by cabbage (`39289.96 per ha) and returns was found to be highest in the case of then cauliflower (`35179.95 per ha). Pea cauliflower cultivation (`15272.35 per ha) cultivation was found to have the highest cost followed by pea (`13933.44 per ha) and of cultivation (`86940.29 per ha) followed by cabbage (`11565.76 per ha). Cauliflower gave

Table 3: Returns over various costs for cabbage, cauliflower and peas (`q-1) Particulars Cabbage Cauliflower Peas Yield of product (qha-1) 119.99 105.78 103.85 Average price of product 802 950 971 Gross Returns (`000ha-1) 96.24 100.49 100.87 Net Returns (`000ha-1) 11.57 15.27 13.93

Cost of production at cost C3 705.61 805.62 837.17

Gross Return/Cost C3 1.14 1.18 1.16 Figures in parentheses indicate percentages of total cost of cultivation, Cost C3

936 the highest per rupee investment (1.18) losses due to non-availability of quality seeds, amongst the three vegetables. Also, the results inadequate irrigation facilities, insect pest reveal that the cost of production was highest incidence, etc. for peas (` 837.17 per q) followed by CONCLUSIONS AND IMPLICATIONS cauliflower (`805.62 per q) and cabbage The study concluded that vegetable (`705.61 per q). Similar result was reported by cultivation was a remunerative enterprise in Bala et al. (2011) in a study conducted in the study area as all the three vegetables were Himachal Pradesh (Table 3). found to give positive net returns to vegetable Constraints in Vegetable Production growers. The cost of cultivation per hectare The constraints faced by the vegetable was observed to be highest for pea followed growers along with their percentage intensities by cauliflower and cabbage. Amongst the as perceived by them are given in Table 4. High various cost components, the cost incurred cost of seeds and inadequate availability of on human labour was found to be the highest quality seeds were the major constraint faced indicating that vegetable cultivation is a labour by the vegetable growers with intensities of intensive activity. Vegetable production could 78.83 and 72.91 percent, respectively. The be made more beneficial if farmers are made farmers had to incur high cost in purchasing aware of new and improved technologies for quality seeds. crop management. To bring down the cost incurred on human labour, mechanization should be taken up with the support of the Table 4: Constraints in vegetable production and intensity government. The labour cost can be reduced Constraints Percent Rank and the enterprise can become more High cost of seeds 78.83 I remunerative if handy and efficient tools are Inadequate availability of quality seeds 72.91 II made available to the farmers for performing Inadequate availability of labour 63.83 III various intercultural operations like hoeing, High incidence of pest and diseases 62.17 IV weeding, etc. The production constraints are Inadequate irrigation facilities 59.83 V Bandhs and blockades 43.33 VI discouraging the vegetable producers to boost their production. There is a need to strengthen the research and extension facilities Some of the farmers were even found to in the state. Efforts for timely supply of crucial order the seeds from other state incurring still inputs at reasonable price and in adequate higher cost. The presence of defected seeds quantity to the farmers should be undertaken. in the seed packets bought was also common. If these issues are seriously considered than The vegetable production being a labour vegetable production will definitely receive a intensive enterprise, the unavailability of boost benefitting the growers and the state adequate labour especially during the can even increase its revenue through harvesting season was the third major vegetable export to other states. constraint. High incidence of pest and REFERENCES diseases, inadequate irrigation facilities, Anonymous. 2010. Economic Survey Manipur, Bandhs and blockades were the other Directorate of Economics and Statistics, constraints. Frequent bandhs and blockades Government of Manipur, Imphal. due to the unstable law and order condition of Anonymous. 2013. Agricultural Statistics at a Glance, Ministry of Agriculture, Department the state was a problem hindering timely of Agriculture and Cooperation, Directorate of purchase of inputs and also the marketing of Economics and Statistics, Government of India, harvested produce. Kumar et al. (2014) also New Delhi. reported that the most important problems in Anonymous. 2013a. Indian Horticulture Database, the production of vegetables in India were National Horticulture Board, Department of

937 Agriculture and Cooperation, Government of Evidence from a study on vegetable production. India, New Delhi. Agricultural Economics Research Review. 19 (2): Anonymous. 2013-14. Economic Survey, Ministry 219-222 Of Finance, Economic Division, Department of Kumar, S., Pal, S., and Joshi, P.K. 2004. Vegetable Economic Affairs, Government of India, New sector in India: An overview. In 13th Proc. Impact Delhi. of vegetable research in India, National Centre Bala, B., Sharma, N., and Sharma, R.K. 2011. Cost for Agriculture Economics and Policy Research, and return structure for the promising enterprise New Delhi: 3-9 of off-season vegetables in Himachal Pradesh. Mittal, S. 2007. Can horticulture be a success story Agricultural Economics Research Review. 24: for India? Working Paper No. 197. Indian 141-148 Council for Research on International Economic Barah, B.C. 2007. Strategies for agricultural Relations, New Delhi. development in the north-east India: Challenges and emerging opportunities. Indian Journal of Agricultural Economics. 62 (1):13-31 Joshi, P.K., Joshi, L., and Pratap, S.B. 2006. Received: April 09, 2015 Diversification and its impact on smallholders: Accepted: August 25, 2015

938 Indian J Econ Dev DOI: 10.5958/2322-0430.2015.00104.3 Volume 11 No. 4 (2015): 939-944 Research Note

CONTRACT FARMING-AN EFFICIENT MARKETING METHOD OF Ailanthus excelsa

A.Rohini*, S. Selvanayaki* and M.Padma Selvi**

ABSTRACT

Farmers in Tamil Nadu have resorted to cultivation of short rotation trees in their farm lands in place of or in addition to the agricultural crops as supplement source of income. This study was taken up to assess the marketing efficiency of Ailanthus excelsa tree. Vellore, Tuticorin and Coimbatore districts were purposively selected for the study and information were collected from 30 farmers in these districts. Rank based quotient, marketing efficiency analysis and Garrett ranking techniques were the tools used to obtain the results in light of the objectives set forth. Assured market and good price scored high as reasons for preference of tree cultivation. Concurrent margin method was employed to estimate marketing efficiency and it was found high in contract farming tie up with the match industries compared to the sales through traders. Farmers expected government support in the form of subsidy, improved contract farming arrangements with industries, insurance, loan facilities and high yielding short duration clones suited to their regions. Policy decisions favouring tree farming will increase the green cover in farm lands.

Key words: Agroforestry, contract farming, intermediaries, marketing efficiency JEL Classification: Q12, Q13, Q20, Q23

INTRODUCTION resources, it becomes necessary to explore the Agriculture besides farming comprises of possibilities of using alternate plant resources. forestry, floriculture, olericulture, fruit The producers tend to maximize the farm cultivation, dairy, poultry, mushroom, bee income and minimise the income variability. keeping, sericulture, fishery, etc. Today, This has resulted in growing number of farmers marketing, processing and distribution of to cultivate trees in their lands. Tree crops agricultural products had been accepted as a specifically grown in marginal farm lands have part of modern agriculture. With increasing not been tapped to their fullest potential. Major population and fast depletion of natural tree crops contribute substantially to the economy of many developing countries. Most commonly cultivated short rotation trees are * Assistant Professors, Department of Agricultural Eucalyptus grandis, Eucalyptus tereticornis, and Rural Management, Centre for Agricultural and Eucalyptus camaldulensis and Casuarina Rural Development Studies, Tamil Nadu Agricultural University, Coimbatore - 641 003 and equisetifolia and fast growing soft woods **Administrative Officer, Cheran Arts College, such as Cieba pentandra and Ailanthus Thelungupalayam, Coimbatore excelsa. Email: [email protected] Industrial demand for the short rotation tree

939 crops in Tamil Nadu is rising mainly for the three districts was enquired for the study. The pulp, pole, plywood and matchwood information from the sample respondents was industries. These trees are often promoted by gathered by personal interview method during the industries and/or driven by the market to the year 2013-14. Awareness on various demands. However, the market or industry marketing aspects of Ailanthus excelsa among driven demands had changed the cultivation the sample farmers was studied using rank pattern in the recent years. Ailanthus excelsa based quotient method (David, 2001). is a fast growing and an excellent safety F (n 1 i) matches industry species in India. The genus RBQ  i 100 N Ailanthus excelsa belongs to the family n Simarubaceae which consists of about 6-10 Where i 1 to n species. Ailanthus excelsa grows well in semi- Where, Fi is the frequency of sample arid and semi-moist regions and has been farmers for the ith rank of preference; N and n found suitable for planting in dry areas with represent the total number of sample farmers annual rainfall of about 400 mm. It is capable cultivating Ailanthus excelsa and total number of growing in poor soils under relatively low of preference identified, correspondingly. The rainfall, down to about 800 mm and susceptible factor with the higher RBQ score was to frost. In India, it is used for afforestation of considered the most preferred factor for poor sites, though on shallow soils its growth creating awareness among the farmers. is poor. It does not grow well on clay or Price spread analysis involved waterlogged areas. It is a strong light- computation of marketing cost and profit demander. It coppices well and produces root margin and their expression as a percentage to suckers. The wood is soft and light, weighing the consumers’ rupee (Goerge, 1972). 340-450 kg m-3. It could be used for packing Concurrent margin method was employed to cases and makes good match splints. The trees analyze the price spread in various marketing could be harvested from the fifth year of channels and information on prices prevailed planting for match industry. There is no study and the cost involved in marketing of Ailanthus on the present trend in the tree cultivation by excelsa at different stages of all identified farmers in Tamil Nadu and its impact on the marketing channels were collected from the potential of wood based industries. The farmers and other market intermediaries. The objectives of the study were to assess the cost of marketing included cost spent on marketing efficiency of Ailanthus excelsa in transport, loading and unloading, storage and Tamil Nadu and to study the constraints other incidental expenses incurred for encountered by farmers in cultivation and marketing the wood. Moreover, farmer’s share marketing of Ailanthus excelsa. in consumer’s rupee was also worked out in MATERIALS AND METHODS the estimation of price spread. Ailanthus excelsa was generally grown all Marketing Efficiency (Acharya and Agarwal, over the State in the recent years and 1994) specifically promoted in the districts of Vellore, FP MME Tuticorin and Coimbatore by the National (MC MM) Agricultural Innovative Project (NAIP) where operated at the Forest College and Research 1. MME is the modified measure of marketing Institute, Mettupalayam. Therefore these three efficiency suggested by Acharya districts were selected purposively and ten 2. FP is the price received by the farmer, sample farmers in each district were selected 3. MC and MM are marketing costs and employing snowball sampling method. Thus marketing margins incurred for Ailanthus a total sample of 30 farmers spread over the excelsa.

940 Garrett’s ranking (Sundar and Kombai Ailanthus excelsa tree based on the Raju, 2005) was adopted to analyze the information given by Forest College and constraints faced by Ailanthus excelsa Research Institute, Mettupalayam. This was growing farmers in terms of cultivation and followed by relatives, neighbours and forest marketing. The respondents were asked to rank department official recommendation. Hence, the given factors. The orders of merit given by farmers were guided by Forest College and the respondents were converted into ranks by Research Institute, Mettupalayam to practice using the following formula. tree cultivation for improving their farm income by networking with industries. 100R  0.5 Percent position  ij To identify the reasons for cultivating this N j particular tree was important and therefore an where understanding of the awareness on various th th Rij = Rank given for i attribute by j production and marketing aspects of Ailanthus individual excelsa among the sample farmers would help th N j = Number of attributes ranked by j to identify their scope of expansion in future individual in various areas (Table 2). The percent position of each rank obtained was converted into scores by referring the Table 2: Details of awareness on marketing Garrett table. Mean score was estimated for aspects of Ailanthus excelsa each of the factors. These mean scores for all (n=30) the factors were arranged in descending order Particulars Respondents (%) Aware Unaware and factor with the highest mean score wereas Price of Ailanthus excelsa 100.00 0 given first rank. The most important factors Marketing Practices 100.00 0 were thus identified. Method of cultivation 100.00 0 RESULTS AND DISCUSSION Contract farming or tie up with 75.00 25.00 Source of Information about Ailanthus industries excelsa Source of information would have Some of the factors like price, method of significant influence on the awareness and cultivation, information on contract farming cultivation of Ailanthus excelsa. From the and marketing were taken into account to industry point of view, source of information identify the awareness about Ailanthus to cultivate particular tree crop is essential for excelsa among the sample farmers. contract farming. It facilitates to get the All the farmers had knowledge about the required raw material in a consistent manner. price, markets, method of cultivation and only Hence, the details of the same were gathered, 25 per cent of them were unaware about analysed and presented in Table 1. contract farming or tie up with match It could be observed that majority of the industries. sample farmers (80 percent) cultivated Preference for Cultivating Ailanthus excelsa Table 1: Source of Information about The preference for cultivating Ailanthus Ailanthus excelsa excelsa by the sample farmers was analysed Source of information Respondents Percent using Rank Based Quotient (RBQ) method and (No.) discussed in this section. This would facilitate Forest college and research 24 80.00 in formulating strategies for effective institute Relatives 4.00 13.34 agroforestry model and for increasing tree Neighbour 1 3.33 crops in private lands. It could be observed Forest department 1 3.33 that assured market for the produce obtained Total 30 100.00 the highest score and was ranked as first major

941 Table 3: Preference for cultivating Ailanthus excelsa Reasons RBQ (%) Rank Assured Market for Ailanthus excelsa through contract farming 28.05 I Good price for Ailanthus excels 21.33 II Water scarcity 14.64 III Labour scarcity 11.45 IV Recommended by neighbour /extension worker /FC & RI /forest department 7.99 V Distress sale is avoided in trees 4.23 VI reason for cultivation of Ailanthus excelsa by would take up loading, unloading and sample farmers and it was followed by good transport, and finally the farmers would be paid price as second major reason (Table 3). This with the pre-determined or contract rate per was followed by water scarcity and labour tonne (Singh, 2000). scarcity as third and fourth based on the Marketing Efficiency scores. Recommendation by extension An understanding of prevailing marketing functionaries and avoidance of distress sale channels and its efficiency will give the were the other reasons for Ailanthus excelsa awareness about profitable marketing channel cultivation. In sum, sample farmers engaged for Ailanthus excelsa. Acharya’s Efficiency in Ailanthus excelsa cultivation as a means of Index Method was employed to find out the getting assured market and price. efficient marketing channel and presented in Prevailing Marketing Practices of the Sample Table 5. Farmers Table 5: Measurement of marketing The sample farmers had awareness on efficiency marketing of the produce after five years and (`t-1) the information collected on this aspect are Particulars Channel-I Channel-II given in Table 4. Consumer's purchase price 7000 7000 Total marketing Cost 3000 1500 Table 4: Marketing practices of sample Total net margins of intermediaries 1000 - farmers of Ailanthus excelsa Net price received by farmers 3000 5500 (n=30) Marketing efficiency (Ratio) 0.75 3.66 Marketing Practices Adopted Percentage Contract farming with match industries 76.66 Selling to local traders 23.34 Marketing Channel of Ailanthus excelsa for Total 100.00 Non-Contract Farmers Channel-I: ProducerDTraderDSplint and About three fourth of the farmers entered match industries into contract farming with industries and Marketing Channel of Ailanthus excelsa for remaining farmers were depending on local Contract Farmers traders for marketing of Ailanthus excelsa. The Channel-II: Producer DSplint and match contract tie up in marketing of trees to match industries wood industry was practised only during the Marketing efficiency is high in Channel-II recent years. compared to the Channel-I (Table 5).This was Contract Farming or Tie up with Industry mainly due to the presence of market The sample farmers (76 per cent) had intermediaries and their higher margin. entered into contract farming with a match Therefore, Channel B was profitable marketing industry (M/s. Vasan Match Works) in the channel for Ailanthus excelsa. Hence, contract study area. The contract between farmers and farming or tie up with match industries would industry was only for selling of trees after five benefit the farmers rather than marketing years, the industry would fell the trees, they through intermediaries.

942 Table 6: Constraints faced by sample farmers in marketing of Ailanthus excelsa Particulars Score Rank Absence of current price information 71.45 I Pest attack in Ailanthus excelsa affecting its yield and spreading to other field crops 65.57 II Complex procedures in obtaining loan from banks for tree crops 49.43 III No income for the initial period of five years 35.21 IV Contract with industries ends with lower price than the market price 22.03 V

Constraints faced by Sample Farmers avoiding complex procedures and Details of the constraints faced by sample documentation (60 percent), price support for farmers in Ailanthus excelsa cultivation and trees (57 percent) and high yielding short marketing were analysed using Garrett ranking rotation tees suitable to the regions made and presented in Table 6. available to them (50 percent). If these factors The major constraint faced by the sample are considered while framing strategies to farmers was absence of current price promote agroforestry in the nation it would be information and was ranked first. This was beneficial for the farmers and ultimately followed by pest attack in Ailanthus excelsa improve the green cover of lands. and it’s spread to other crops, difficulty in CONCLUSIONS getting loan from banks, no income for during The present contract farming arrangement the initial period and contract with industries is in a nascent stage and should be evolved resulting in lower price than the market price benefiting both the farmers and industries. The were the other constraints faced by the sample technical assistance including the supply of farmers. Thus, price formed the major factor planting material and other essential inputs that influenced the cultivation of tree crops. should find a place in the contract agreements. As there was no income generation during the Regarding the price determination, the establishing years, the farmers found it difficult industries should consider the rate feasible to to rely on tree cultivation. the farmers considering the cost of production. Suggestions Since, the farmers incur cost and do not get Farmers had certain expectations or any income in the initial years of cultivation, opportunities for encouraging tree cultivation Government support in the form of subsidy in farm lands for increasing their net income or and input supply with loan facilities would profitability is depicted in Table 7. encourage farmers to take up Ailanthus Major expectation of farmers was excelsa farming in greater extent. The market availability of subsidy from government (83 intelligence including price information of percent) followed by beneficial contract with Ailanthus excelsa should be made available industries mainly in price fixation and providing to farmers at their door steps and this will all necessary inputs (67 percent), loan and encourage them to venture into tree cultivation. insurance facilities to be made easily available Forest research institutes and forest

Table 7: Farmers' expectations in tree cultivation (n=30) Farmers' expectations Percentage Subsidy for initial establishment and maintenance of trees (subsidy for drip irrigation) 83 Beneficial contract tie-up with industries 67 Insurance and loan facilities for trees 60 Price support for trees 57 Availability of high yielding short rotation hybrid tree clones suitable to their area 50

943 department should encourage cultivation of determining agricultural price policy. Agricultural Ailanthus excelsa among the farmers as there Situation in India. 27 (9): 617-619. is an obvious gap between actual demand and Singh, S. 2000. Theory and practice of contract supply position. farming: A review. Indian Journal of Social and Economics. 2 (1): 228-246. REFERENCES Sundar, A. and Raju, S.K. 2005. Production and Acharya, S.S. and Agarwal, V.L.1994. Agricultural marketing constraints in Gloriosa superba in price analysis and policy. IBH Co. Private Tamil Nadu. Agricultural Marketing. 25 (2): 12- Limited, New Delhi. 14. David, D.A., Ravichandran, V., and Netaji, S.R. 2001. Knowledge and adoption of critical technologies among paddy growers. Journal of Extension Education. 11 (4): 2975-2977. Received: August 18, 2015 George, P.S. 1972. Role of price spreads in Accepted: October 20, 2015

944 Indian J Econ Dev DOI: 10.5958/2322-0430.2015.00105.5 Volume 11 No. 4 (2015): 945-950 Research Note

GROWTH AND INSTABILITY OF WHEAT PRODUCTION IN RAJASTHAN

Meera and Hemant Sharma*

ABSTRACT

An attempt has made to compare the growth and instability of wheat crop pre and post Green Revolution in Rajasthan. It was found that growth of wheat area and production was significant statistical during 1963-72, 1973- 82 and 2003-12. The highest growth of wheat production and productivity was 9.97 and 5.73 percent per annum during 1963-72 due to introduction of high yielding variety programme. Instability analysis indicated that wheat area was more stable than production and productivity. Instability of wheat production was highest during 1963-72 and lowest during 2003-12. The growth in area, production and yield during Green Revolution were 1.55, 4.23, and 2.63 percent, respectively, as against the pre-Green Revolution of -1.37, -2.09, and -0.72 percent. The coefficient of variation which is the indicator of instability, for area production and yield of wheat was higher in post-Green Revolution than pre-Green Revolution. Thus, policies should be made to reduce the risk in wheat production and more efforts are needed to ensure food and nutritional security put forth by the burgeoning population.

Keywords: Growth, instability, production, Rajasthan, wheat JEL Classification: O13, O43, O47

INTRODUCTION Bengal, Uttarakhand, Himachal Pradesh and Wheat is a major food staple in India, and Jammu & Kashmir. These States contribute is crucial to India’s food economy and security. about 99.5 percent of total Wheat production With wheat production of 85 to 90 million tons in the country. The remaining states, namely, annually and a large demand, India’s wheat Jharkhand, Assam, Chhattisgarh, Delhi and economy is now the second largest in the other North Eastern States contribute only world. Wheat is grown in India in an area of about 0.5 percent of the total Wheat production about 30 Million ha with a production of 93 in the country. In Indian agriculture, year-to- Million tonnes. The normal National year fluctuations in output and variations in productivity is about 2980 kg per ha. The major productivity across space have remained Wheat producing States are Uttar Pradesh, issues of significant concern for researchers Punjab, Haryana, Madhya Pradesh, Rajasthan, as well as policy makers. The adoption of green Bihar, Maharashtra, Gujarat, Karnataka, West revolution technologies not only led India towards attainment of self-sufficiency in *M.Sc. Scholar, College of Agriculture, SKRAU, foodgrains production but also invoked a large Bikaner-334006 and Assistant Professor, Agro- number of researchers to see its effect on Economic Research Centre, Anand-388 120 agricultural instability and regional variations Email: [email protected] therein. In past literature on Indian agriculture

945 on Indian Agriculture performance there were augment growth in the rural economy and concerns in the early 1980s about a possible associated secondary activities like food deceleration in foodgrains production (Alagh processing and retail trading. However, and Sharma1980 and Desai and Namboodiri agriculture-led rural industrialisation has not 1983). Mehra (1981) examined the question of received due attention from policy makers in instability in foodgrain production and found the country notwithstanding the fact that that instability had increased after the green maintaining the growth of agriculture as such revolution. Bhalla and Alagh (1978) studied was lost sight during 1990s (Sen, 1992, Bhalla the growth rates at the district level between and Singh, 2001, Rao, 2003, and Bhalla and 1962-65 and 1970-73, their main finding was Singh, 2009). In this context, Chand et al., 2008, that the yield effect was the major component has reported about the growth and instability of growth in most of 48 high growth districts. measurement on Indian Agriculture scenario Dev (1985) extended this analysis to 1978-78. and also has referred several economists Bhalla and Tyagi (1989) have examined growth (Mehra 1981, Hazell 1982, Dev 1987, and Rao in agriculture at the district and state levels et al., 1988), studied during 1980’s have came using averages of 1962-63 to 1964-65, 1970-71 into conclusion that agriculture production to 1972-73, and 1981-82 to 1983-84. They had become more unstable after the examined spatial aspects of association introduction of new agricultural technology. between growth and modern input use, and These dynamic changes underline the changes in regional concentration. Narain importance of studying the growth (1977) studied the growth rate of productivity performance and instability of wheat before by decomposing it and segregating the effects and after Green Revolution. of changes in cropping pattern and the spatial METHODOLOGY shifts of crops. The present study is based on the Although, the contribution of new secondary data area, production and yield of technology to the acceleration of wheat wheat in Rajasthan sourced from Directorate production is well recognized, there has been of Economics and Statistics, Rajasthan and some controversy over its contribution to Directorate of Economics and Statistics, overall food grain and agricultural production Department of Agriculture and cooperation, (Mitra, 1968, Minhas and Srinivasan, 1968, and Ministry of Agriculture, and Government of Dantawala,1978). Dantwala found that the India. The period of analysis is 1963-64 to 2012- HYV technology brought about significant 13. The entire period was sub-divided into five improvement in the productivity of cereal sub periods of ten years each. Further to study crops, but its overall effect on foodgrain the growth before and after Green revolution production, especially when evaluated in per introduction the period from 1956-57 to 1965- capita terms, is not significant. Various studies 66 was taken as pre-green revolution period have examined India’s food grain and wheat and from 1966-67 to 2012-13 as post- green economy such as Hazell (1982 and 1989), regulation period. Two different analyses have observed that production variability in world been carried out in the present study viz. (a) cereal and Indian foodgrains production Compound growth rates are calculated period- increased due to the adoption of modern wise to study the growth in area, production technology (Balakrishnan, 2000, Bhalla and and yield of wheat and (b) Variability in area, Singh, 2009, Reddy and Mishra, 2009, and production and yield of wheat is measured Vaidyanathan, 2010). through instability index. The sustained agricultural growth, which Compound growth rates (CGR) of area, is facilitated through constant policy and production and productivity of wheat were institutional support has the potential to worked for different periods as well as for entire

946 period of analysis by fitting exponential genotypes through well-established function. coordinated research system coupled with RESULTS AND DISCUSSION increase in area under irrigation and favourable Wheat Scenario in Rajashthan weather factors during the crop season. Till 1965s, country used to import massive Whereas, rising support price over years led quantities of wheat in the range of 8 to 9 mt. to the increase in wheat area. However, after the Government launched Growth of Area, Production and Productivity special schemes like high yielding variety Rajasthan, located at the western border (HYV) programmes through successive five- of the country, is the largest state in terms of year plans, that wheat production received the area (342.24 thousand km2) but ranks only necessary impetus. As a result of initiation of eighth in terms of population. This is primarily green revolution for wheat improvement because of the desert (and near-desert involving genetic, production and protection conditions) in six westernmost districts of the technologies has resulted in increasing state that cover more than half of the area of country’s productivity by 4 folds from 873 kg the state. All the others have either severely per ha to 3028 kg per ha. The realized wheat low water resources or nearly so. This poses a production of nation during 2012-13 was 9275.5 major threat to the people of the state because mt. It is expected that this trend is going to more than 60 per cent of irrigation is dependent remain more or less the same. on ground water; the low water reserves Over the years, state has achieved threaten agricultural production and income, significant quantitative increase in wheat livelihood of a vast majority of people and food production (Table 1). If we examine wheat area security. The seriousness of this issue has over the years it is clear that area increase is been further underlined by repeated visitation less than that of production and productivity. of drought conditions in large areas of the state. Wheat area increased from 1247 thousand ha Decadal growth rates of wheat area, in 1960-63 to 3063 thousand ha in 2012-13. But production and productivity were worked out wheat production increased nearly 9 folds and are presented in Table 2. The growth rate during the period of study. Wheat productivity of wheat area was negative during one decade increased to nearly 4 folds during the same only. But t-values indicated that this negative period. Wheat productivity was only 873.30 growth was statistically non-significant. kg per ha in the year 1962-63 and increased to Growth rate was positive in other periods as 3028 kg per ha in the year 2012-13. The increase well as overall period. Wheat area increased in productivity and production was more significantly during 1963-72, 1973-82 and 2003- prominent after 2002-03. An increased yield is 12, the growth rates were 4.01, 2.19 and 4.00 due to the factors like adoption of high yielding percent respectively. During other periods, growth rate of wheat area was non-significant. Growth rate of wheat area was positive and Table 1: Area, production and yield of significant statistically during overall period. wheat in Rajasthan Wheat production during 1963-2013 Year Area Production Yield witnessed significant positive growth rate ('000 ha) ('000 t) (kgha-1) 1962-63 1247.00 1089.00 873.30 except in period 1993-2002 wheat production 1972-73 1399.40 1753.50 1253.04 has increased but the growth was non- 1982-83 2069.80 3787.20 1829.74 significant statistically. Growth of wheat 1992-93 2250.80 5147.80 2287.10 production was highest during 1996-1972. 2002-03 1800.70 4878.00 2708.95 During overall period of analysis also wheat 2012-13 3063.00 9275.50 3028.00 Source: Directorate of Economics and Statistics, Ministry of production increased significantly at an annual Agriculture, Government of India, New Delhi. rate of 4.49 percent per annum. Wheat

947 Table 2: Trend in growth rate of area, and productivity and is given in Table 3. It is production and yield of wheat in Rajasthan clear from the analysis that instability in wheat (Percent) area was lower when compared to production Period Area Production Yield and productivity. The coefficient of variation 1963-64 to 1972-73 4.01** 9.97*** 5.73*** (3.45) (4.96) (6.31) for area was only 26.43 per cent whereas it was 1973-74 to 1982-83 2.19* 6.81*** 4.52*** 56.39 and 37.21 per cent in the case of (1.96) (5.02) (5.48) production and productivity, respectively. 1983-84 to 1992-93 0.50NS 4.94** 4.42** Instability in wheat area was the least during (0.37) (3.54) (4.18) 1973-82 followed by 1983-92. It was highest NS NS ** 1993-94 to 2002-03 -0.37 2.48 2.87 during 1963-72 followed by 2003-12. Instability (-0.16) (0.92) (2.34) 2003-04 to 2012-13 4.00*** 5.46*** 1.40** of wheat production was highest during 1963- (4.51) (6.69) (2.70) 72 and lowest during 2003-12. Similarly, 1963-64 to 2012-13 1.68*** 4.49*** 2.77*** coefficient of variation of yield was also highest (11.91) (23.05) (29.66) during 1963-72 followed by 1973-82 due to new ***, ** aand * Significant at 1, 5, and 10 percent level Figures in the parentheses indicates t-value technology and HYV programme initiated this NS: Non-significant period. PRE AND POST-GREEN REVOLUTION productivity also recorded positive growth Growth and Instability during all the periods. The growth in Green Revolution in India was introduced productivity was highest during the decade in the year 1966-67. Since then the area, of 1963-73. During this period wheat production and yield of wheat in India has productivity increased at a rate of 5.73 per cent undergone dynamic changes. A comparative per annum, this may be due to introduction of analysis of growth before and after the Green high yielding variety programme. The growth Revolution in Rajasthan suggests significant rate for overall period was also positive and increase in the growth in area, production and significant. The productivity increased at a rate yield during the post-green revolution than of 2.77 per cent per annum during the overall the pre-Green Revolution (Table 4). period. Similar results were obtained by Rao (1975), Narain (1976), Mehra (1981), Hazell Table 4: Growth rate of area, production (1982), and Rao et al. (1988) have pointed out and yield of wheat in Rajasthan-pre and post Green Revolution that the new strategy of agri-cultural Period Area Production Yield production based on high-yield varieties (HYV) 1956-57 to 1965-66 -1.37* -2.09* -0.72NS seed-fertiliser technology has contributed to (-1.26) (-1.15) (-0.72) the growth in production and productivity. 1966-67 to 2012-13 1.55*** 4.23*** 2.63*** The coefficient of variation for different (10.29) (21.12) (26.38) periods was worked out for area, production Overall 1.64*** 4.36*** 2.68*** (14.33) (23.18) (30.39) ***, ** and * Significant at 1, 5, and 10 percent level Figures in the parentheses indicates t-value Table 3: Decade-wise instability of area, NS: Non-significant production and productivity of wheat in Rajasthan The growth in area, production and yield (Percentage) during Green Revolution were 1.55,4.23, and Period Area Production Yield 2.63 per cent respectively, as against the pre- 1963-64 to 1972-73 15.57 33.13 18.37 Green Revolution of -1.37, -2.09, and -0.72 1973-74 to 1982-83 11.30 22.91 15.28 percent. 1983-84 to 1992-93 12.24 19.45 15.26 1993-94 to 2002-03 12.94 18.60 12.11 Instability in area, production and yield of 2003-04 to 2012-13 14.18 17.68 6.13 wheat pre and post green revolution in Overall 26.43 56.39 37.21 Rajasthan is given in Table 5. The coefficient

948 Table 5: Instability in area, production and reforms: Growth and welfare. Economic and productivity of wheat in Rajasthan-Pre and Political Weekly. 35 (12): 999-1004 Post Green Revelation Bhalla, G.S. and Tyagi, D.S. 1989. Patterns of Indian (Percentage) agricultural development- A district level study. Period Area Production Yield Institute for Studies in Industrial Development, 1956-57 to 1965-66 11.24 17.12 8.61 New Delhi. 1966-67 to 2012-13 22.80 50.20 32.92 Bhalla, G. S. and Singh, G. 2001. Indian agriculture: Over all 29.11 63.90 42.01 four decades of development. Sage Publications, New Delhi. of variation which is the indicator of instability, Bhalla, G.S. and Singh, G. 2009. Economic liberalisation and Indian agriculture: A statewise for area production and yield of wheat was analysis. Economic and Political Weekly. 44 (52): higher in post-Green Revolution than pre- 34-44. Green Revolution. Bhalla, G.S. and Alagh, Y.K. 1978. Performance of During post-Green Revolution period, the Indian agriculture: A district-wise study. New instability in the production of was highest Delhi. (50.20 percent) followed by yield (32.92 Chand, R. and Raju, S.S. 2008. Instability in Indian percent) and area (22.80 percent). Similar results agriculture during different phases of technology were obtained by Hazell (1982) examined the and policy. Discussion Paper in National question of instability in foodgrain production Professor Project, National Centre for Agricultural Economics and Policy Research, and found that instability had increased after ICAR, New Delhi. the green revolution. Dantwala, M.L. 1978. Future of institutional reform CONCLUSIONS and technical change in Indian agricultural The productivity increased at a rate of 2.77 development. Economic and Political Weekly per cent per annum during the overall period. special number (August):1299-1306. It is also worth pointing out that the instability Desai, G.M. and Namboodiri, N.V. 1983. The in area and yield of almost all decades moved deceleration hypothesis and yield-increasing in the same direction and their increasing trend inputs in Indian agriculture. Indian Journal of resulted in increase in instability. During post- Agricultural Economics. 38(4): 497-508. Dev, M.S. 1985. Direction of change in performance Green Revolution period, the instability in the of all crops in Indian agriculture in late 1970s-A production of was highest (50.20 percent) look at the level of district and agro-climatic followed by yield (32.92 percent) and area (22.80 regions. Economic and Political Weekly . 20 (51- percent).Hence, it may be said that the increase 52): A130-A136. in production of a particular crop due to a Dev, M.S. 1987. Growth and instability in spectacular increase in area and productivity foodgrains production: An interstate analysis. would accompany by the increase in Economic and Political Weekly. 22 (39) : A82- instability. Although, the wheat production A92. and productivity exhibited an increasing trend, Hazell, P.B.R. 1982. Instability in Indian foodgrain production. Research Report No. 30. it is associated with many problems like lack International Food Policy Research Institute, of irrigation facility and high input price. Washington, DC, U.S.A. Considering these facts policies should be Mehra, S. 1981. Instability in Indian agriculture in made to reduce the risk in wheat production the context of the new technology. Research and to make it profitable. Report 25. International Food Policy Resaerch REFERENCES Institute, Washington, D.C. Alagh, Y.K., and Sharma, P.S. 1980. Growth of crop Minhas, B.S. and Srinivasan, T.N. 1968. Food production: 1960-61 to 1978-79- Is it production trends and buffer stock policy. The decelerating? Indian Journal of Agricultural Statesman. November 14 and 15. Economics. 35 (2):104-118. Mitra, A. 1968. Bumper harvest has created Balakrishnan, P. 2000. Agriculture and economic dangerous illusions. The Statesman. October 14

949 and 15. the reforms regime. Agrarian crisis in India. Narain, D. 1977. Growth of productivity in Indian Oxford University Press, New Delhi. agriculture. Indian Journal of Agricultural Sen, A. 1992. Economic liberalisation and agriculture Economics. 32 (1): 1-44. in India. Social Scientist. 20 (11): 4-19. Rao, C.H.H. 1975. Techtiological change and Vaidyanathan, A. 2010. Agricultural growth in distribution of gains in Indian agriculture, India, role of technology, incentives, and MacMillan, Delhi. institutions. Oxford University Press, New Rao, C.H.H. 2003. Reform agenda for agriculture. Delhi. Economic and Political Weekly. 33 (29): 615-20. Rao, C.H., Ray, S.K., and Subbarao, K. 1988. Unstable agriculture and droughts- Implications for policy. Vikas Publishing House Priavte Limited, New Delhi. Received: June 06, 2015 Reddy, D.N. and Mishra, S. 2009. Agriculture in Accepted: October 05, 2015

950 Indian J Econ Dev DOI: 10.5958/2322-0430.2015.00106.7 Volume 11 No. 4 (2015): 951-960 Research Note

USE OF E-HEALTH INFORMATION: A CASE STUDY

Dhiraj Kumar and Sonia Bansal*

ABSTRACT

The Internet has emerged as the leading source of information in all walks of life including health care. The present study was conducted with the objective to ascertain utilization of the Internet for seeking health related information with a special emphasis on the change experienced in health behavior with its use. Questionnaires were randomly distributed to 130 postgraduate students/researchers. In total 102 questionnaires were received back and 100 of them were found relevant for analysis. Calculations were made manually. Almost all the students used the Internet to get health information. Diet and fitness were identified as the top most searched health topics by the students. More than 90 percent of the respondents found the retrieved information to be reliable. The study concluded that more than 80 percent respondents felt an improvement in understanding of symptoms, treatment of disease, health care needs, eating and exercising habits with the use of the Internet.

Keywords: e-health, health information, Internet, JEL Classification: I12, I15

INTRODUCTION The availability of vast amount of The Internet has become our everyday information on the Internet has given an companion because we consult the Internet impetus to its use in every sphere of life making to get all kinds of information without any it an indispensable part of our life. The Internet hesitation. The availability of the Internet via has transformed the ways of communication, wireless devices like mobiles, tablets, and research, education, business, finance, laptops has revolutionized its use and has entertainment, etc. Following similar tracks, made it accessible to a large number of people. transformation in health services, and According to Internet World Stats (2015) 42.3 information has also been noticed and health percent of world’s total population uses the has gone online, giving origin to e-health. Internet. This shows how deep it has According to Eysenbach (2001) e-health refers penetrated into our life. According to Oxford to the health services and information dictionaries (2013) the Internet is A global delivered or enhanced through the Internet computer network providing a variety of and related technologies. Now-a-days people information and communication facilities, have become health conscious and give special consisting of interconnected networks using attention to diet, fitness, hygiene, preventive standardized communication protocols. measures, cause, symptoms, and treatment of diseases. Easy availability of e-health *Junior Library Assistant and Assistant Librarian, information on all these topics has led to University Library, Guru Angad Dev Veterinary and increased use of the Internet for health Animal Sciences University, Ludhiana-141004 purposes by people worldwide. The people [email protected] surfing the Internet for health information can

951 be termed as online health seekers. Oxford approach to maintaining their health or the Dictionaries (2014) has designated these health of someone for whom they provide care. people as Cyberchondriac which means a Akerkar et al. (2005) conducted a study of person who compulsively searches the the subjects attending the outpatient clinic of Internet for information about particular real an urban private tertiary care hospital. Out of or imagined symptoms of illness. a total of 281 respondents surfing the Internet Various studies have been conducted nearly 75 percent acknowledged the use of the worldwide to determine the use of the Internet Internet for medical information. This study for seeking health information. Some of the revealed that most of the users (70 percent) studies found relevant to the present study looked up the Internet at their own, while 9 are discussed below: percent were advised by the physicians to use Pletneva et al. (2011) conducted an online it for medical information. survey in 2011. About 385 persons from 42 A survey of 120 students of Monash countries across the world participated in the University at the Clayton Campus, Australia survey. It was found that about 24 percent of was conducted by Kam et al. (2010) to assess the respondents tracked the Internet daily their behavior and attitudes when accessing upto six times to get e-health information and online health information. The study revealed 25 percent users doing the same a few times a that a significant number of university students week. About 70 percent of the participants (60.80 percent) had used the Internet for searched the Internet with a focus to get seeking health information, with varying general information about health issues, search techniques and frequencies of usage around 60 percent for long term chronic but General Practitioner was found to be the diseases and nearly half of the respondents most commonly used health information source for healthy lifestyle and nutrition. (73.00 percent). Similarly, 77 percent of adult Hanauer et al. (2003) in a study of 125 French-speaking population in the Paris students of urban community college in the metropolitan area was found to have consulted Boston area found that more than 43 percent a physician quite often or in most cases when of respondents searched the Internet for they had a health question by Renahy et al. health information. It was noticed that amongst (2008) and nearly half of the Internet users had those seeking health care information from the searched for e-health information during the Internet, 51.9 percent respondents searched previous three years. About 70.9 percent for diet/nutrition followed by 42.6 percent Lithuanian citizens considered health respondents for fitness/exercise. Similarly, Pew professionals as the most trusted source of Research Center (2014) in a survey of US adults health information in a study of 3000 ascertained that 72 percent of internet users inhabitants conducted by Maraziene et al. looked for online health information within the (2012), while 16.6 percent respondents past year. Specific diseases or conditions, considered the Internet as the second most treatments or procedures and doctors or other trusted source of information. Lithuanian health professionals were among the most people with higher education had a tendency commonly researched topics. About 60 percent to trust books, courses and lectures and the of U.S. adults tracked their weight, diet, or Internet more than their worse educated exercise routine on the Internet and 33 percent counterparts. adults tracked health indicators or symptoms Jimenez-Pernett et al. (2010) explored the like blood pressure, blood sugar, headaches use of the Internet by Spanish adolescents to or sleep patterns. About 46 percent of the seek healthcare information. Findings revealed respondents opined that tracking the Internet that nearly half the sample group (55.7 percent) for health information changed their overall was using the Internet to search for health

952 related information. The main reasons for information they found on the Internet. searching health information on the Internet About eighty percent of American internet were ease of use (36.9 percent), lots of users, or some 113 million adults, were found information (21.7 percent) and speed (14.7 to have searched for information on health percent). Ignorance of good websites and lack topics in an online health survey by Fox (2006). of confidence or search skills were the major It was found that about 66 percent of health problems faced by respondents in searching seekers began their last online health inquiry e-health information. at a search engine and 27 percent began at a In cross-sectional survey of the Japanese health-related website. Hansen et al. (2003) general population conducted by Takahashi found that school students of Southeast et al. (2011), the prevalence of the Internet use Michigan searching for health information for acquiring health-related information was utilized search engines nearly every time. found to be 23.8 and 6 percent via personal Similarly, in a study conducted by computers and cell phones, respectively. More Eysenbach and Kohler (2002) in Germany none than two-thirds of Internet users felt an of the participants used medical portals or the improvement in understanding symptoms, sites of medical societies or libraries as a conditions or treatments of interested diseases starting point. Rather, they used search and the nearly same share of respondents felt engines in an attempt to find relevant pages, a change in the way they eat or exercise. except in two cases where for a given question However, only 23 percent felt an improvement participants tried to guess a web address. In in their ability to manage health care needs an experiment by Pang et al. (2014) to without visiting a doctor. understand different search approaches of Shaikh et al. (2008) conducted a study to students and staff of University of Melbourne examine the access and utilization pattern of it was found that 19 out of 20 participants the Internet for seeking healthcare information preferred to use Google search engine to by university students in Islamabad. The search health information. In addition to study revealed that out of 304 students who normal keyword search, respondents used reported having access to the Internet in the Google as a web directory. past three months, 43 percent students were The present study was undertaken to using it for seeking healthcare information and determine the use of the Internet especially 78.4 percent of them considered this for seeking health information with the information as reliable. About 25.2 percent following objectives: students discussed health information i. to ascertain the intensity and attitude of obtained from the Internet with their doctor/ use of the Internet for seeking health physician. Similarly, a study was carried out related information, by Carter et al. (2012) in 2 general pediatric ii. to know about the searched health topics, clinics in Gainesville, Florida to ascertain how and the adolescents use the Internet for finding iii. to identify the change experienced in health information. It was found that 51 percent health behavior with the use of the out of a total of 41 respondents used the Internet. Internet to search health information. Of those METHODOLOGY who used the Internet to get health To ascertain the usage of the Internet for information, only 7 percent had ever initiated seeking health related information, data were discussion with doctors about the information collected from the students of Guru Angad Dev they had found from the Internet. Further, Veterinary and Animal Sciences University nearly 34 percent respondents felt change in (GADVASU), Ludhiana (India) (2015). The the way they care for themselves based on University has a well-established Local Area

953 Network (LAN) with the Internet connectivity health to be good and about 18 percent @100mbps under the National Knowledge excellent. While 9 percent of the respondents Network (NKN) of the Government of India. had average health and the remaining 3 percent The survey was conducted using could not say about their state of health. questionnaire as a tool. Relevant literature was Diseases and Health Problems carefully reviewed to design the questionnaire. About 36 percent respondents were A pilot study of 15 students was suffering from the problem of low vision. A conducted to test the questionnaire. These significant proportion of the respondents (14 students were excluded from population percent) had skin problems and the same considered for the study. The suggestions of proportion of respondents were suffering from participants of pilot study further helped to allergies. Simlarly, 13 percent students had refine the questionnaire. To maintain uniformity dental problems. Nearly, six percent in the knowledge level of population under respondents were affected by sleep disorders study, only the postgraduate students were and four percent by migraine. Depression had considered for this study. All of them had done affected three percent respondents. The a bachelor’s degree in veterinary science viz. problems of high blood pressure and chronic Bachelor of Veterinary Science and Animal ulcers were being faced by two percent Husbandry (B.V.Sc. & A.H.), which is 5-year respondents each. Only one respondent each degree programme including 6 months was suffering from lung diseases (like asthma compulsory training with the aim to enrich and bronchitis) and heart disease. Amongst students’ knowledge to be professionally other diseases one respondent was suffering competent and face professional challenges. from anemia and another one from hair fall. There were about 200 students/researchers None of the respondents was suffering from pursuing post-graduation in various other serious ailments. disciplines of veterinary science in the Sources of Health Information University and it was planned to cover 50 Majority of the respondents (71 percent) percent of them. Accordingly, questionnaires consulted doctors for health related were randomly distributed to 130 postgraduate information which strongly supports the students/researchers sitting in the reading findings of Kam et al. (2010), Maraziene et al. halls of the university library during August (2012), and Renahy et al. (2008). The results 25-30, 2014. The students were asked to submit revealed that 16 percent of the students the completed questionnaire at the circulation discussed health related issues with family counter of the library. In total 102 members to get relevant information. questionnaires, with a response rate of 78.46 Newspaper, a popular source of current percent, were received back and 100 of them information, was being used by 15 percent of were found relevant for analysis. To analyze the respondents to get health information. the responses manual calculations were made. Similarly, 15 percent of the respondents sought Out of 100 respondents, 56 comprised of male advice from friends to get health information. students/researchers and the remaining 44 As many as 13 percent of the respondents were female. As such 82 respondents were obtained information on health from television, pursuing master’s degree and 18 doctorate a popular entertainment source. degrees in various disciplines in GADVASU. About 12 percent respondents said that RESULTS AND DISCUSSION they consulted books, the most popular printed State of Health source of information to get health information. Most of the students were enjoying Magazines and medical encyclopaedias were healthy life with a major chunk of the being used by 9 users each to get information respondents (70 percent) perceiving their related to health. Journals, newsletters and

954 chemists were consulted by meager who used the Internet for seeking health respondents. information were using the Internet for this Use of the Internet for Seeking Health purpose for four or more years and nearly Information 26.26 percent respondents for the last 2-3 Ninety nine out of a total of 100 veterinary years. The remaining 33.33 percent of the students used the Internet as a source for respondents were using the Internet for seeking health care information, whereas seeking health information for less than two according to PMLive (2012) only 34 percent of years. the European Union’s total population was Frequency of using the Internet found to browse the Internet for health Only 7.07 percent of the respondents information in 2010. Almost, three time use of accessed the Internet on daily basis to get the e-health information by students as health related information. Parallel to the study compared to the general public of European of Pletneva et al. (2011), about 33.33 percent Union showed that they were more concerned of the veterinary students accessed the about their health than general public. The Internet upto 3 times a week for searching perusal of Table 1 shows the pattern of use of health information. the Internet for seeking e-health information Another 15.15 and 18.18 percent by veterinary students. respondents browsed the Internet once in Time since using the Internet fifteen days and once in a month respectively About 40.40 percent of the respondents for this purpose. Around 25.00 percent

Table 1: Use of the Internet for seeking health related information (n=100) Variables Percentage Time since using the Internet Less than 2 years 33.33 2-3 years 26.26 4 or more years 40.4 Frequency of using the Internet Daily 7.07 1-3 times a week 32.32 Once in fifteen days 15.15 Once a month 18.18 Rarely 27.27 Person who advised to use the Internet At own 69.7 Doctors 11.11 Friends 13.13 Family members 6.06 Any other - Reasons of using the Internet Time saving 25.25 24 X 7 access 31.31 No space barrier 4.04 Information from various sources 29.29 Vast amount of information 32.32 Easy to use 23.23 Search strategy followed Searching in search engines 61.61 Searching in health care websites 37.37 Directly entering URLs of health related websites 11.11

955 respondents rarely searched the Internet to findings of Jimenez-Pernett et al. (2010), who retrieve health care information. also found ease of use, lots of information and Person who advised to use the Internet speed amongst the top most reasons for using Availability of information through the Internet by health information seekers. handheld devices has made the common man Search Strategy skillful in retrieving the required information. Search engines, the easiest gateway to People have started to explore all types of information on the web, become guide of every information on the Internet including health one to find e-health information. Information care at their own through trial and error method seeker is just required to be able to describe without any kind of hesitation. The results of the required information into keywords and the study of Akerkar et al. (2005), who the rest of the work is done by search engines. concluded that 70 percent users looked up the No one, unless experts, is expected to have Internet to access e-health information at their knowledge & skills to get pin pointed e-health own, also holds good for veterinary students information by directly entering the Uniform as same percent of respondents (69.70 percent) Resource Locators (URLs) of specific health started accessing health information from the related websites. Similar is the case with Internet at their own. About 13.13 percent veterinary students, as about 62 percent users respondents were advised by their friends to made use of keywords to search in search look for health information on the Internet. engines for getting e-health information, Doctors advised 11.11 percent respondents to strongly supporting the research conducted surf the Internet to get health information. The by Fox (2006), Hansen et al. (2003), Eysenbach remaining 6.06 percent respondents started and Kohler (2001) and Pang et al. (2014). About using the Internet for getting health one third (37.37 percent) students searched information on the recommendation of their for desired information by searching in health family members. care websites. Only 11.11 percent respondents Reasons of using the Internet directly entered the URLs of specific health Most of the veterinary students accessed related websites to get e-health information. the Internet for seeking e-health information Health Topics Searched because of its inherent characteristics for In order to enjoy healthy life, people have which it is known. Availability of vast amount become conscious about diet and fitness world of information on the Internet was considered over. They are tracking the Internet to get as the main reason of using it to get health instant health tips to remain fit. The trend of information by about one third of the users. high usage of the Internet for seeking Nearly 31.31 percent users said that they use information related to diet, weight & exercise, the Internet because of round the clock seen in the studies of Pew Reseach Center availability of information. Around 29 percent (2014) and Hanauer et al. (2003) was also of the respondents preferred the feature of noticed in the present study of postgraduate availability of information from various veterinary students. A large share of the sources. Nearly one fourth users said that the respondents (44.44 percent) was sensitive Internet saves time to get information, about their eating habits, therefore, searched therefore, they made its use to access speedy for information related to food and nutrition to health care information. About 23.23 percent take healthy diet (Table 2). respondents said that they searched the Fitness was also amongst the top most Internet as it was easy to use. Only four searched topics with nearly 42.42 percent of percent of the users said that no space barrier users searching the Internet to get information made the Internet to be used for getting health related to fitness including weight and exercise. information. These responses validate the The survey conducted by Pletneva et al. (2011)

956 Table 2: Health topics searched Health topics searched Frequency Percentage Hygiene 22 22.22 Food & Nutrition 44 44.44 Fitness (including weight, exercise, etc.) 42 42.42 Preventive measures 23 23.23 Cause and symptoms of particular disease 35 35.35 Treatment of particular disease 31 31.31 Alternate medicines for particular disease 9 9.09 Surgeries 5 5.05 Doctors/Specialists of particular disease 5 5.05 Mental illness 4 4.04 Alcohol/Drug abuse 2 2.02 Any other 0 0 also endorses these results of high usage of social networking sites (Table 3). the Internet for seeking information related to Websites of the expertise (doctors) was healthy lifestyle and nutrition. Nearly 1/3rd given preference by 16.16 percent respondents veterinary students searched the Internet to to get e-health information. Less than 10 find cause, symptoms and treatment of percent users gave preference to websites of particular diseases. Similarly, 23.23 percent hospitals, non-government organizations and respondents searched the internet to take drug manufacturing companies. preventive measures and 22.22 percent users Searching the Internet and Discussion with for information related to hygiene. Less than the Doctor 10 percent of the respondents each accessed Of the 99 respondents using the Internet information related to alternate medicines for to get health information, 71 respondents had particular diseases, surgeries, doctors/ discussed the Internet’s findings with doctors specialists of particular disease, mental illness ruling out the findings of Carter et al. (2012). and alcohol/drug abuse. More than half of these users (57.75 percent) Health Website Preferred had discussed their findings with doctors 1-2 Majority of users (44.44 percent) accessed times. Twenty out of 71 users had triggered a websites of health institutes to get desired discussion 3-4 times and the remaining 10 had information. About one third of the done so 5 or more times. respondents referred to online reference Verification of the Doctor’s Diagnosis or sources like dictionaries, encyclopaedias, etc. Prescription to get pin pointed information. Nearly 17.17 Nearly two third of the respondents (65) percent users obtained health information from relied on the Internet to verify diagnosis made

Table 3: Health website preferred Health website preferred Frequency Percentage Health institutes 44 44.44 Hospitals 6 6.06 Societies/Associations 12 12.12 Non-Government organizations 2 2.02 Government organizations 13 13.13 Doctors 16 16.16 Social networking sites 17 17.17 Drug manufacturing companies 4 4.04 Online reference sources 33 33.33 Any other 0 0.00

957 Table 4: Change experienced in health behavior Change experienced Frequency Percentage Improvement in understanding of symptoms and conditions of the diseases 29 33.72 Improvement in understanding of treatment of the diseases 27 31.39 Improvement in ability to manage health care needs 15 17.44 Led to seek care from doctors/specialists or other health care providers 7 8.14 Improvement in eating habits 41 47.67 Improvement in exercising habits 39 45.34 or medicine prescribed by the doctors. Nearly understanding about eating and exercising 60 percent of these users had done verification habits of about half of the users. Nearly, one of doctor’s diagnosis or prescription 1-2 times. third of the users reported improvement in Eleven respondents said that they explored understanding symptoms/conditions and the Internet 3-4 times and about one fourth treatment of the diseases. The Internet helped users 5 or more times for their satisfaction 7 respondents to seek care from doctors/ regarding diagnosis of disease or medicine specialists or other health care providers. prescribed. These results strongly support the study of Reliability of e-health Information Takahashi et al. (2011) who also noticed similar Information found on the Internet was said results. Pew Research Center (2014) also to be reliable by more than 90 percent of observed parallel outcomes in the Americans, respondents, supporting the findings of where a high share of respondents experienced Shaikh et al. (2008) who found 78.4 percent of change in their overall approach to maintaining students considering the retrieved information their health or the health of someone for whom as reliable. they provide care. Change Experienced in Health Behavior Obstacles Faced in Accessing Health Related The Internet has transformed our health Information from the Internet information seeking habits and the retrieved The perusal of Table 5 shows that the information is influencing our daily routine & respondents encountered various obstacles behavior. This influence is upto such an extent in accessing e-health information. Problem of that people have started to notice its impact too many junk sites was the biggest problem, on their health. The present study showed the being faced by 37.37 percent respondents. same with majority of users (86) experiencing About one third of the users felt that sites that a change in their health behavior (Table 4). require registration were hurdle in getting to Out of these 86 users, the Internet improved the point information.

Table 5: Obstacles faced in accessing health related information from the Internet Obstacle Frequency Percentage Lack of search skills 11 11.11 Lack of knowledge of good websites 24 24.24 Sites that require registration 35 35.35 Encountering links that do not work 13 13.13 Too many junk sites 37 37.37 Information overload 8 8.08 Electricity failure 8 8.08 Not being able to find the desired information 8 8.08 It takes too long to view/download pages 8 8.08 Encountering sites that need to pay to access information 26 26.26 Internet speed 16 16.16

958 The problems of paid sites and lack of Eysenbach, G. 2001. What is e-health? Journal of knowledge of good websites in health care Medical Internet Research. 3 (2): e20. areas were faced by every fourth user. It was Eysenbach, G. and Kohler, C. 2002. How do noticed that 16.16 percent respondents consumers search for and appraise health information on the World Wide Web? qualitative reported that slow speed of the Internet study using focus groups, usability tests, and interrupted the access to information. Problem in-depth interviews. British Medical Journal. of encountering links that do not work was 324: 573-577. reported by 13.13 percent respondents. Lack Fox, S. 2006. Online health search 2006. Pew of search skills, information overload, Research Center. www.pewinternet.org. electricity failure, not being able to get desired Guru Angad Dev Veterinary and Animal Sciences information and time taken in downloading University. 2015. www.gadvasu.in. pages were some other impediments faced by Hanauer, D.A., Fortin, J., Dibble, E., and Col N.F. the users in accessing e-health information. 2003. Use of the Internet for seeking health care information among young adults. In: AMIA CONCLUSIONS Annual Symposium Proceedings Archive. The Internet has grown as a gigantic www.ncbi.nlm.nih.gov. source of information that provides pin Hansen, D.L., Derry, H.A, Resnick, P.J., and pointed information with just a click of mouse Richardson, C.R. 2003. Adolescents searching from various sources within no time. This for health information on the internet: an study leads to the conclusion that the use of observational study. Journal of Medical Internet the Internet improved the understanding of Research. 5 (4): e25. symptoms, treatment of disease, health care Internet World Stats. 2015. needs, eating and exercising habits of more www.internetworldstats.com (accessed February 21, 2015). than 80 percent respondents. The results Jimenez-Pernett, J., Labry-Lima, A.O.D., revealed that there were too many junk sites Bermudez-Tamayo, C., Garcia-Gutierrez, J.F. which became hurdle in getting the accurate and Salcedo-Sanchez, M.D.C. 2010. Use of the health information. To avoid misleading internet as a source of health information by information provided by junk websites certain Spanish adolescents. BMC Medical Informatics quality checks should be imposed by and Decision Making. 10 (1): 6. associations and societies so that only Kam, J., Stanszus, D., Cheah, J.J., Heerasing, N., authoritative information is published online. and Tie, S.Y. 2010. The Internet as a health To further boost the health information seeking information source for university students. Australian Medical Student Journal. 1 (1): 24- behavior of veterinary students, library staff 26. can organize workshop/seminar on Maraziene, D., Klumbiene, J., Tomkeviciute, J. and information literacy skills viz. introductory Miseviciene, I. 2012. Sources and reasons for information skills, advanced internet searching, seeking health information by Lithuanian adults. use of databases with the aim to make them Medicina (Kaunas). 48 (7): 371-378. able to get pin-pointed e-health information in Oxford Dictionaries. 2014. Cyberchondriac. no time. www.oxforddictionaries.com. REFERENCES Oxford Dictionaries. 2013. Internet. Akerkar, S.M., Kanitkar, M. and Bichile, L.S. 2005. www.oxforddictionaries.com. Use of the Internet as a resource of health Pang, P.C., Chang, S., Pearce, J. and Verspoor, K. information by patients: A clinic-based study in (2014) Online health seeking behaviour: the Indian population. Journal of Postgraduate understanding different search approaches. Medicine. 51 (2): 116-118. www.pacis2014.org. Carter, C.G., Black, E.W. and Saliba, H. 2012. Pew Reseach Center. 2014. Health Fact Sheet. URL Adolescents’ use of the Internet in finding health www.pewinternet.org. information. Retrieved from www.aamc.org. Pletneva, N., Vargas, A., and Boyer, C. 2011. How do general public search online health

959 information? The Health On the Net (4): 153-156. Foundation. www.hon.ch. Takahashi, Y., Ohura, T., Ishizaki, T., Okamoto, S., PMLive. 2012. Europeans and online health Miki K., Naito, M., Akamatsu, R., Sugimori, information. www.pmlive.com. H., Yoshiike, N., Miyaki, K., Shimbo, T. and Renahy, E., Parizot, I. and Chauvin, P. 2008. Health Nakayama, T. 2011. Internet use for health- information seeking on the Internet: a double related information via personal computers and divide? Results from a representative survey in cell phones in Japan: A cross-sectional the Paris metropolitan area, France, 2005-06. population-based survey. Journal of Medical BMC Public Health. 8: 69. Internet Research. 13 (4): e110. Shaikh, I.A., Shaikh, M.A., Kamal, A., and Masood, S. 2008. Internet access and utilization for health information among university students in Received: April 20, 2015 Islamabad. Journal of Ayub Medical College. 20 Accepted: July 27, 2015

960 Indian J Econ Dev DOI: 10.5958/2322-0430.2015.00107.9 Volume 11 No. 4 (2015): 961-965 Research Note

EFFECT OF CONTRACT FARMING ON PRODUCTION AND PRICE OF BARLEY IN RAJASTHAN

Sita Ram* and R.C. Kumawat**

ABSTRACT

The present investigation was undertaken with a view to studying the effect of contract farming on the production and price of barley in the state of Rajasthan. Primary data were collected for the agricultural year 2010-11. The secondary data were collected from the records maintained by the contracting firm and regulated markets of the study area. The results of the study revealed that the production of main product of barley on contract farms was higher by 14.40 per cent and the by-product was lower by 0.02 per cent than that on non-contract farms. The average selling prices of the main product was higher by 7.35 per cent and that of by-product was lower by 3.36 per cent on contract farms than on non-contract farms. The remarkable difference between the system was due to higher prices provided by the contracting firm to contract farmers, on the one hand, and lower prices in the market for non-contract farmers.

Key words: contract farming, barley, production, price, Rajasthan JEL Classification: C81, Q10, Q12, Q18

INTRODUCTION and marginal ones are facing threats to their Barley (Hordeum vulgare L.) is the world’s survival from every quarter. Contract farming fourth most important cereal crop after wheat, could be one of the best solutions for reducing rice and maize. The barley producing countries the polarization of rich and poor farmers in the are China, Russia, Germany, USA, Canada, country as well as encouraging them to India, Turkey and Australia. The major use of compete with the very large, rich and heavily barley is in brewing industries for but indirectly subsidized western farmers. manufacturing malt, which is used to make Corporatization of agriculture through bear, industrial alcohol, whisky, malt syrups, contract farming can have many long term brandy, malted milk, vinegar and yeast. Barley benefits; such as better allocative efficiency, are also used as concentrates for feeding higher private investment, increase in output, livestock and poultry. income and exports and a higher multiplier With the WTO’s demand for trade effect leading to the creation of wealth in rural liberalization and reduction in subsidy to India. In the last couple of decades contract farmers, the Indian farmers especially the small farming is viewed as a tool to provide technology, extension services, credit, etc. to the farmers. *Ph.D. Scholar and **Professor, Department of Agricultural Economics, SKN College of The new agricultural policy of 2000 Agriculture, SK Rajasthan Agricultural University, announced by the Government of India seeks Bikaner Campus Jobner-303 329 to promote growth of private sector Email: [email protected] participation in agribusiness through contract

961 farming and land leasing arrangement to of producer farmers was selected taking 1/4 accelerate technology transfer, capital inflow farmers from each village. These farmers were and assured market for crops. Contract farming selected on random basis and categorized into is a type of contractual arrangement, oral or five size groups namely, marginal farmers (0.51- written, between farmers and companies 2 ha), small (2.01-4 ha), semi-medium (4.01-5 specifying one or more conditions of ha), medium (5.01-7 ha) and large farmers (7.01- production and/or marketing of an agricultural 11 ha) based on the cumulative square root product. frequency method of stratification. An equal Features of contracting firm number of non-contract farmers resembling in The entrepreneurship and resource all other aspects except contractual agreement endowment of the farmers were the main were also selected for comparison. criteria for choosing farmers by the contracting Both primary and secondary data were firm. The firm supplied input such as quality required to arrive at the stated objectives. The seeds and technical know-how and procured primary data were collected from the producers the output at pre decided collection centre, as well as marketing intermediaries through establishing vertical linkages between firm and personal interview method with the help of a farmers. The firm supplied seeds at subsidized pretested schedule specifically prepared rate to the farmers at farm gate on cash (standardized) for the purpose. The primary payment. Firm was given the flexibility to data were collected for the Agricultural year farmers for grows other crops also. Payment 2010-11 for which the information was readily was made at delivery of produce by cash. The available. The secondary data in respect of firm offered higher price of the product than number of centres, number of contract farmers, what realized by the non-contract farms of the quantity of seed sold and procurement of regulated market (market up approach of price barley by contract were collected from the determination) of the area. No compensation office records of the company. Discriptive was given by the firm in the event of crop statistical tools such as simple averages and failure. The specific objective of the study was percentages were used to analyse the data. to examine the effect of contract farming on RESULTS AND DISCUSSION production and price of barley in Rajasthan Production of Barley RESEARCH METHODOLOGY The perusal of Table 1 depicts the Among cereals, contract farming was production of barley on contract and non- prevalent in barley crop only in the state of contract farms. The table indicates that overall Rajasthan. There for Rajasthan state and barley production of main product and by-product crop were selected purposively as study area on contract farms was worked out at 47.03 and study crop, respectively. The company quintal and 43.16 quintal, respectively. These entered in to contract farming in five districts parameters (main product and by-product) of the state through a net work of 15 centres. were higher by 5.92 quintal (14.40 percent) and Out of these 15 centres, three centres namely lower by 0.01 quintal (0.02 percent) than that Udaipuria, Tankarda (in Jaipur district) and Sri on non-contract farms. Category wise the Madhopur (in Sikar district) were selected production of main product varied from 44.86 randomly for the study. A separate list of all quintal on marginal farms to 50.98 quintal on the villages adopting contract farming and large farms and that of by-product from 40.38 falling under these three selected centres were quintal to 46.24 quintal on same size groups prepared in descending order of the number under contract farms. In case of non-contract of contract farmers. Out of the selected centres, farms main product varied from 38.45 quintal nine villages in all were selected purposively. on marginal farms to 44.79 quintal on large From these villages, a representative sample farms and by-product varied from 40.54 quintal

962 Table 1: Category-wise production of barley on contract and non-contract farms, 2010-11 (qha-1) Category of farm Farm size Marginal Small Semi-medium Medium Large Overall Contract farms Main product 44.86 45.63 47.16 48.59 50.98 47.03 By product 40.38 42.20 43.05 45.17 46.24 43.16 Non-contract farms Main product 38.45 40.27 41.17 42.46 44.79 41.11 By product 40.54 42.45 43.72 44.45 45.10 43.17 Difference Main product 6.41 5.36 5.99 6.13 6.19 5.92 (16.67) (13.31) (14.55) (14.44) (13.82) (14.40) By product -0.16 -0.25 -0.67 0.72 1.14 -0.01 -(0.39) -(0.59) -(1.53) (1.62) (2.53) -(0.02) Figures in parentheses are the percent increase in production on contract farms over non-contract farms to 45.10 quintal on same size groups. Category local varieties of seeds which were having wise production difference of main product on higher by-product ratio. The category wise per contract and non-contract farms was highest hectare production of main product and by- on marginal farms (16.67 per cent) followed by product increased with the increase in the size semi-medium (14.55 per cent), medium (14.44 group of farms on contract and non-contract per cent), large (13.82 per cent) and small farms farms due to intensive use of inputs. (13.31 per cent) with an overall difference of Price of Barley 14.40 per cent. In case of by-product difference The category wise average selling prices on contract and non-contract farms was of barley on contract and non-contract farms highest on large farms (2.53 per cent) followed have been depicted in Table 2. The Table by medium farms (1.62 per cent). In case of indicates that the overall selling prices of main marginal, small and semi-medium farms the product and by-product on contract farms were difference was estimated to be negative of the order of `940.57 and `158.30 per quintal indicates that the quantities of by-product on and on non-contract farms of `876.21 and contract farms was less as compared to non- `163.62 per quintal, respectively. On contract contract farms. farms the main product was higher by `64.36 The production of barley was more on per quintal (7.35 percent) and by-product was contract farms probably because of better care lower by `5.32 per quintal (3.36 percent) than by the producer farmers at the time of sowing on non-contract farms. The per quintal selling to harvesting, variation in the input use price of the crop was noted to be the highest particularly quality seeds provided by (`1002.49) on large farms followed by medium contracting firm, investments made in farm (`982.63), semi-medium (`945.36), small assets and technical advice provided by (`910.56) and marginal farms (`882.37) under contracting firm at farmers field. As against contract farms. Similarly, in case of non- this, non-contract farms particularly used local contract farms, it was observed to be the varieties of seeds, along with low level of input highest on large farms (`923.62) and lowest use and low investment. These findings were on marginal farms (`820.39). The category wise in confirmity with Rama (1985), Singh et al. selling price difference of main product on (2006), Singh et al. (2006), Sharma (2008), Swain contract and non-contract farms was highest (2009), Senthilnathan et al. (2010) and Roopa on large farms (8.54 percent) followed by et al. (2013). The production of by-product on marginal (7.55 percent), medium (7.45 percent), non-contract farms was more due to use of semi-medium (7.19 percent) and small farms

963 Table 2: Category-wise sale price of barley on contract and non-contract farms, 2010-11 (`q-1) Category of farm Farm size Marginal Small Semi-medium Medium Large Overall Contract farms Main product 882.37 910.56 945.36 982.63 1002.49 940.57 By product 167.98 165.69 159.05 148.11 140.94 158.30 Non-contract farms Main product 820.39 851.35 881.93 914.48 923.62 876.21 By product 176.79 167.80 161.85 156.24 151.51 163.62 Difference Main product 61.98 59.21 63.43 68.15 78.87 64.36 (7.55) (6.95) (7.19) (7.45) (8.54) (7.35) By product -8.81 -2.11 -2.8 -8.13 -10.57 -5.32 -(4.98) -(1.26) -(1.73) -(5.20) -(6.98) -(3.36) Figures in parentheses are the percent higher in price of barley on contract farms over non-contract farms

(6.95 percent) with an overall difference of 7.35 higher selling price. The category wise selling percent. In the case of by-product selling price price difference of by-product was higher on difference on contract and non-contract farms marginal to large farms because large farms was negatively highest on large farms (6.98 have more quantity of produce and less percent) followed by medium (5.20 percent), storage facility. So, they have selling their by- marginal (4.98 percent), semi-medium (1.73 product at just harvest of the crop when price percent) and small farms (1.26 percent) overall per quintal was lower. being 3.36 percent. The difference was CONCLUSIONS estimated to be negative in all size groups of The overall production of main product and farms indicates that the average price of by- by-product on contract farms was worked out product on contract farms was less as at 47.03 quintal and 43.16 quintal, respectively. compared to non-contract farms. These parameters (main product and by- The selling price of main product of barley product) were higher by 5.92 quintal (14.40 was higher on contract farms than on non- percent) and lower by 0.01 quintal (0.02 contract farms because the contracting firm percent) than that on non-contract farms. offered higher price of the product than what Category wise the production of main product realized by the non-contract farms of the and by-product increased with the increase in regulated market (market up approach of price the size groups both under contract and non- determination) of the area. These findings were contract farms. The overall selling prices of in confirmity with Inovejas and Ortega (1997), main product and by-product on contract farms Obare and Kariuki (2003), and Singh et al. were of the order of `940.57 and `158.30 per (2006). The selling price of by product was quintal and on non-contract farms of `876.21 lower due to more area under cereal crops and `163.62, respectively. On contract farms which they have produce more by-product. In the main product was higher and by-product case of category wise selling price difference was lower than that on non-contract farms. of main product was higher on large to marginal The per quintal selling price of the crop was farms because they have more quantity of noted to increased with the increase in the size produce with quality and sale their product groups under contract and non-contract farms. after harvest of the crop when price per quintal REFERENCES was higher. Therefore, they have a better Inovejas, A.M. and Ortega, J.T. 1997. Tobacco bargaining power which helps them in fetching contract growing programme: A strategy for

964 better farm productivity and income in small Sharma, V.P. 2008. India’s agrarian crisis and tobacco farmers. Philippine Journal of Crop corporate-led contract farming: Socio-economic Science (Philippines). 20 (1) : 41. implications for smallholder producers. Obare, G.A. and Kariuki, I.M. 2003. Production International Food and Agribusiness and productivity effect of informal contract Management Review. 11 (4) : 25-48. farming in Kenya’s small holder horticultural Singh, B., Singh, R.K., and Gupta, R.K. 2006. sub-sector. Eastern Africa Journal of Rural Contract farming in potato production (an Development. 19 (1) : 13-24. alternative of rural marketing) in district of Rama, R. 1985. Do transnational agribusiness firms Farrukhabad, Uttar Pradesh. Indian Journal of encourage the agriculture of development Agricultural Marketing. 20 (3) : 57. countries?. International Social Science Journal. Singh, H., Kaur, M. and Sekhon, M.K. 2006. 37: 331-343. Contract farming in Punjab-A strategy for Roopa, H.S., Nagaraj, N. and Chandrakanth, M.G. diversification. Indian Journal of Agricultural 2013. Comparative economic analysis of baby Marketing. 20 (3): 41-70. corn under contract and non-contract farming in Swain, B.B. 2009. Contract farming and agricultural Karnataka. Agricultural Economics Research development: A case study of Orissa. The IUP Review. 26 (Conference issue): 226. Journal of Agricultural Economics. 6 (1) : 55- Senthilnathan, S., Govindaraj, S. and 63. Chandrasekaran, M. 2010. Economic analysis of production and marketing of cotton under contract and non-contract farming: A case study in Tamilnadu. Madras Agriculture Journal. 97 Received: May 18, 2015 (10-12) : 411-414. Accepted: August 03, 2015

The author is also thankful to Department of Science and Technology, New Delhi for providing INSPIRE fellowship during the study period.

965 966 Indian J Econ Dev DOI: 10.5958/2322-0430.2015.00108.0 Volume 11 No. 4 (2015): 967-974 Research Note

IS MNREGA AFFECTING AVAILABILITY, WAGES AND COST OF LABOUR IN INDIAN AGRICULTURE? DISCERNING QUANTITATIVE EVIDENCES

Pushpa*, Punit Kumar Agarwal**, Bulbul G. Nagrale* and B.S. Chandel***

ABSTRACT

Contemporary economic and social change in the agricultural sector in India has ensured that labour shortages are an increasing reality for many primary producers. Present study is based on secondary data collected from various Government publications. The results show that after the implementation of the programme MNREGA, the labour wages all over the India has increased tremendously, especially after 2010. Due to increase in the wages by MNREGA the labour cost for agricultural operations has increased, which has further increased the total cost of production for almost all the crops in India, especially sugarcane, paddy and cotton, which are the labour intensive crops. Due to availability of employment in near the range of 5 km. now labours migration from the states like Bihar, UP, Jharkhand and Odisha has decreased, which has created a situation of labour scarcity for agriculture operations.

Key words: Agricultural, real, and nominal wages, labour scarcity, MNREGA. JEL Classification: J2, J3, J4, J6, J7. INTRODUCTION total workers has been declining over the years, Even though India has the second largest while the corresponding ratio in the secondary man power in the world, all sectors of the and tertiary sectors is on the rise. Pursuant to economy have been affected by the scarcity this, following impacts have been of labour, and the impact being felt more in the predominantly noticed in agriculture in recent agricultural sector. Labourers constitute a vital years: reduction in crop yield, reduction in input in agricultural production, but they are cropping intensity and changes in traditional migrating to different parts of the country for cropping pattern. Though agricultural research earning a better livelihood, adding to the has evolved-in many crop specific, labour- existing imbalance between labour demand and saving implements and technologies, the supply of labourers (Deshingkar, 2003). problem has not been addressed fully. Another The portion of agricultural workers to the matter of concern is that in the sociological perspective, the vocation of casual agricultural

* ** labour is considered to be the last resort and Ph.D. Scholars, Senior Research Fellow, and hence preferred only by people who have no ***Principal Scientist, Department of Dairy Economics, Statistics & Management, National other means of livelihood. Dairy Research Institute, Karnal-132 001, Haryana Regarding the impact of Rural Employment (India) Guaranty Act on labor market outcomes, three Email: [email protected] recent studies found that rural wages saw an

967 upward shift (Azam, 2012, Berg et al., 2012, implementation of MNREGA and continuous and Imbert and Papp, 2012). According to these increase in wages. studies, between 2004-05 and 2007-08, the Act RESULTS AND DISCUSSION accelerated an increase in rural wages between Impact on MNREGA on Labour Availability for three and five percent with female workers and Agriculture marginalised groups of the SC/ST population Today, agricultural labour has become most being the main beneficiaries. These studies important production component in Indian also underline the fact that demand for labor is Agriculture. A little less than half of the total highly seasonal and that the MNREGA serves cost of production of field crops is of labour. It as a safety net during the lean season when is much more for labour intensive crops such agricultural work opportunities are scarce. as sugarcane, cotton, vegetables, etc. In the While these are large effects given that the rural areas, labour has traditionally been program is India-wide and the rural work force provided by landless and also by small and comprising about 300 million people, critique marginal farmers. But during the past 6-7 years, of researchers has largely addressed the situation with respect to availability of implementation issues and not so much the agricultural labourers has been changing idea as such. This paper outlines the intentions owing to growing urbanization, disinterest of behind this scheme, observes current research younger generation in agriculture and in the area and highlights the need for further attraction of town and cities for batter civic research on the use of temporary migration amenities, good communication and with in the states as well as outside the sates entertainment facilities. and the use of labour saving technology to The implementation of self employment meet labour needs in India’s Agriculture. providing scheme like MNREGA by Therefore, the present study was Government of India has also adversely undertaken to assess the impact of MNREGA affected the availability of labour for on the availability of labour for agricultural agricultural operations. In this context there activity, agricultural labour wages and cost of are so many studies which have already proved cultivation of major crops after the that, due to implementation of wage implementation of the scheme in the country. employment scheme there has been a great METHODOLOGY shift of labourers from farm to non-farm The study is based on secondary data employment. There are various studies which collected from various government sources have already proved that MNREGA has played like NSSO, ILO, Population Census, Labour a vital role in creation of labour shortage for and Employment Bureau of India, MNREGA, agricultural practices. Labour Bureau Shimla and the Department of Is MNREGA Luring Away Labours Farm Agriculture and cooperation. A cross sectional Fields? data from 2005-06 to 2013-14 of agricultural Evidence suggests that MNREGA is wages and MNREGA wages has been taken succeeding as a self-targeting programme, with and these nominal wages are deflated from high participation from marginalised groups consumer price index for agricultural labourer including the SCs and STs. At the national to calculate the real wages per day per person. level, the share of SCs and STs in the work The consumer price indices of agricultural provided under MNREGA has been high at labourers and rural labours have been compiled 40-50 percent across each of the years of the for calculating annual growth rates of wages. Scheme’s implementation. In 2011-12 alone, 40 The cost of cultivation data on major crops percent of the total person-days of employment has been compiled for calculating increase in (84 crores out of 209 crores) were provided to cost of cultivation over the year after SCs and STs as according. In the case of both

968 Table 1: State-wise share of SCs and STs in wages. Literature suggests that workfare total population and MNREGA, 2006-07 to programmes like MNREGA, that can put 2011-12* upward pressure on agriculture wages, are (Percent) likely to be some of the most effective ways of State Share of total Cummulative share population in MNREGA improving the welfare of the poorest. MNREGA Andhra Pradesh 22.8 36.1 entitles every worker to wages at the Assam 19.3 43.2 Government of India notified, the state-wise Bihar 16.6 43.8 wage rate, for each day of work. The MNREGA Chhattisgarh 43.4 52.0 notified wage rates have increased across Gujarat 21.9 55.2 Haryana 19.3 37.6 states over the years, with some states like Himachal Pradesh 28.7 33.5 Maharashtra registering an increase of over Jammu and Kashmir 18.5 28.6 200 per cent. The MNREGA wage is higher Jharkhand 38.1 56.6 than the legal minimum agriculture wage in 19 Karnataka 22.8 26.2 states (Table 3). Kerala 10.9 15.1 This paper has attempted to analyse if this Madhya Pradesh 35.5 60.4 Maharashtra 44.6 43.3 increase is causing an upward pressure in th Odisha 38.6 57.7 market wages. Based on NSSO 64 Round Punjab 28.9 60.0 Survey during agricultural year 2008–09, both Rajasthan 29.8 50.3 male and female workers reported earning an Tamil Nadu 20.0 43.1 average of `79 per day for work under the Act. Uttar Pradesh 21.2 46.3 These earnings are 12 per cent higher than the Uttarakhand 20.9 24.3 West Bengal 28.5 42.5 average daily earnings for casual workers. The All India 24.3 51.0 comprehensive time series of rural wage data Source: Share of population from Census of India 2001 and (both agricultural and MNREGA) put together share of MNREGA work from www.mgnrega.nic.in indicates that the advent of MNREGA has Note: (1) Union Territories and some States are not included in the table. (2) The SC/ST share is cumulative from FY 2006- resulted in a significant structural break in rural 07 to 2011-12 as a percent of total person-days generated in wage increases (Table 2) the State. (3) The All India total share of SCs and STs and % After the implementation of MNREGA, share of person average wage increases almost quadrupled to *Provisional Data: At the time of the preparation of the report data entry for States open for the year 2011-12. 13.0 per cent between 2006 and 2013 (Table 3). The study found that MNREGA boosts the

SCs and STs, the participation rate exceeds Table 2: Nominal wages of agricultural their share in the total population (except in labourers and MNREGA wages -1 Maharashtra where it is only marginally less) (`day ) Year Agriculture wages MNREGA wages in Table 1. This trend is definitely not a positive 2005-06 48.58 56.4 indication for agriculture. The results indicate 2006-07 52.98 63.4 that how MNREGA is luring away farm hand 2007-08 58.9 74.2 from agriculture field especially the 2008-09 79.5 88.3 marginalized rural population those who were 2009-10 86.35 90.2 earlier involved in agricultural works for their 2010-11 130.0 109.8 2011-12 169.6 114.5 livelihood. 2012-13 178.09 121.4 Where are Farm Hands When You Need 2013-14 180.5 137.0 Them? Source: 1. Labour Bureau, Shimla for consumer price indices In assessing the impact of MNREGA on for Agricultural Labourers (AL) and Rural Labourers (RL), labour availability, it is important to also look 2. C.S.O. for consumer price indices for new series (CPI-NS). 3.Ministry of Labour Employment Govt. of India, Labour and at the interplay between MNREGA and market Employment Bureau Chandigarh 2013.

969 nominal daily agriculture wage rate by 13.0 per agricultural wages had increased after the cent. Weather this increase is also true in case implementation of MNREGA. If we compare of real income or real wages. If we deflate the growth rate of MNREGA nominal wages MNREGA nominal income with consumer price and growth rate of agricultural wages we can index then we can see that, real income of the find out that the growth rate for agricultural persons who are employed under MNREGA wages is much higher than the MNREGA wage has not shown a quite fluctuating behaviour growth rate, which indicates that as the wages over the year (Table 3). Although, the nominal for MNREGA is increasing agricultural wages wages for MNREGA is continuously are also increasing in the same magnitude or increasing but when we talk about the greater than the MNREGA growth rate (Table increment in real terms in is not at all 4). increasing. The growth rate of real and nominal In few states the increase in real wages are wages has been calculated to show that how in negative, which indicates that though real wages are decreasing over the year (Table nominal wages are increasing but due to effect 3). of inflation over the year this increase in MNREGA has a great influence on the nominal wages get absorbed and real wages wages of agriculture, as the minimum wages come down (Table 4). Thus, in real term, this for MNREGA is higher than the agricultural increase in wages is just an illusion, which is wages in almost all the states of India except snatching more and more working hands from few like Punjab and Haryana. Due to this farms. The Scheme, by influencing wage rates continuous increase in MNREGA wages in the rural unskilled labour market, has agricultural wages are also affected, as it is provided an additional opportunity for the depicted in Table 4 which show how drastically Government to enforce statutory minimum

Table 3: Real wage rate of MNREGA deflated from CPIAL base year 1986-87 (`day-1) States 2005-06 2006-07 2007-08 2008-09 2009-10 2010-11 2011-12 2012-13 2013-14 Andhra Pradesh 21.56 21.45 19.30 17.05 16.67 20.7 19.32 19.12 17.26 Assam 17.13 17.11 17.58 17.10 16.73 18.89 19.54 21.05 19.00 Bihar 19.60 18.26 19.44 19.08 19.50 18.90 16.64 16.29 18.75 Meghalaya 13.55 13.77 15.14 14.75 16.60 25.19 25 22.86 17.99 Haryana 25.27 23.95 27.79 24.04 25.68 20 28.21 28.15 24.18 Himachal Pradesh 20.41 18.75 20.05 18.25 24.07 26.75 26.30 24.68 19.91 Jammu & Kashmir 12.53 15.31 18.16 19.87 19.08 21.91 20.62 21.26 19.07 Karnataka 18.48 18.12 17.81 17.69 16.65 20 23.23 23.32 20.23 Kerala 35.11 32.30 29.16 26.43 25.95 22.76 25.38 25.53 21.40 Madhya Pradesh 16.76 15.23 15.41 15.95 15.94 19.93 20.06 20.22 19.08 Maharashtra 12.77 25.80 20.76 15.73 16.76 21.08 23.38 24.80 19.08 Manipur 20.12 20.77 21.53 19.90 27.69 26.41 23.11 21.07 19.51 Meghalaya 18.32 17.07 22.78 21.69 21.30 18.42 17.39 18.66 18.26 Orissa 16.47 15.53 19.25 15.82 21.41 19.21 16.75 17.14 18.54 Punjab 26.58 23.98 22.66 22.08 21.08 23.90 29.90 29.62 21.32 Rajasthan 19.36 18.16 17.93 18.02 15.25 18.75 20.52 21.81 17.28 Tamil Nadu 22.54 22.29 19.18 17.52 13.93 19.73 20.06 20.64 18.29 Tripura 16.52 15.17 22.75 23.83 21.35 21.79 19.90 19.76 18.64 Uttar Pradesh 19.68 18.43 18.43 18.04 18.50 20.17 19.40 20.08 18.53 West Bengal 19.59 19.18 20.00 18.10 17.92 16.08 15.02 14.47 19.48 India 15.75 16.34 17.79 18.25 17.02 17.02 18.02 17.81 16.95 Source: 1. Labour Bureau, Shimla for consumer price indices for Agricultural Labourers (AL) and Rural Labourers (RL). 2. C.S.O. for consumer price indices for new series (CPI-NS). Ministry of labour and employment, Govt. of India, Labour and Employment Bureau Chandigarh Oct. 2013. 3. ILO. 4. Population Census of India 2011

970 Table 4: Growth rate of wages in different of agricultural labourers from field to states from 2005-06 to 2013-14 MNREGA. (Percent) Though, in this case, research indicates States MNREGA Agricultural wages wages that benefits from increased wages also extend Real Nominal Real Nominal to the agriculture sector and are significant Andhra Pradesh -7.2 2.4 8.9 20.1 even for those households that do not Assam -0.5 8.6 -1.2 7.8 participate in MNREGA. Further, it is pertinent Bihar 0.3 9.6 -0.4 8.8 to keep in mind that there are several factors Gujarat 4.9 14.6 -1.8 7.3 Haryana 0.4 12.1 0.8 12.6 responsible for inflation in an economy like Himachal Pradesh 3.1 10.2 8.4 15.9 India, and the role of MNREGA should not be Jammu & Kashmir 11.6 22.2 7.1 17.2 over-emphasised. Karnataka -2.3 9.3 -1.0 10.8 Impact of MNREGA on Cost of Cultivation of Kerala -7.7 0.5 3.3 12.6 Field Crops Madhya Pradesh -0.5 9.6 -1.2 8.9 Although over the year cost of cultivation Maharashtra 0.5 11.2 -1.0 9.5 Manipur 6.1 15.5 -0.3 8.4 of field crops is increasing but one cannot say Meghalaya 5.5 15.0 1.5 10.6 that this increase in cost of cultivation is mainly Orissa 5.6 16.3 -0.2 9.9 due to MNREGA scheme. Based on the Punjab -5.3 5.2 0.1 11.1 secondary data we can see that the share of Rajasthan -4.7 5.4 1.5 12.2 labour cost in total operational cost is Tamil Nadu -11.3 -2.6 1.2 11.2 Tripura 10.1 18.0 2.1 9.4 continuously increasing over the year, as it is Uttar Pradesh -1.4 7.5 0.3 9.4 presented in given below tables. Thus on the West Bengal -2.3 7.3 -0.8 9.0 bases of these data we can say that the increase India 2.7 13.0 4.7 15.3 in share of labour cost is due to the continuous increase in minimum wage rate for agricultural wages. This continuous increase in statutory labourers and this increase in wage rate is due minimum wages is pushing the per hectare cost to the hick in per day wages MNREGA, as we of cultivation of field crops in the country, have already discussed earlier that there is a which is again intimating the government to positive correlation between the MNREGA increase MSP and SMP. wage rate and Agricultural minimum wage rate. This makes it difficult to conclude that the In the given below table it is clearly depicted casual (non-public works) wage rate is above that how sharply the share of labour cost in the MNREGA wage rate due to competition total operational cost is increasing almost in with the scheme for workers. If indeed every state of the country. MNREGA is influencing casual wages, other Total Cost of Cultivation of Major Crops in research studies argue, that the upward India (Pre and Post MNREGA Situation) pressure on casual wages may translate into Due to increase in minimum labour wages an overall increase in cost of cultivation of after the implementation of MNREGA, the field crops which may further lead to overall share of labour cost in total cost has increased. increase in prices, which could undermine any Earlier it was little a little half of the total cost gains for the poor; if aggregate price levels of production of field crops, but now it is more increase it would reduce net income gains for than half is of labour cost. It is much more for the poor. As it is clear from the data (Table 6) labour intensive crops such as sugarcane, that how minimum agricultural wages are cotton, vegetables, etc. As we had discussed following MNREGA wage rate and the reason earlier that due labour cost share is increasing for this increase is to attract farm hand in field over the year especially for sugarcane, cotton, crop cultivation to combat the labour demand paddy and wheat, and due to that total cost of in agriculture as well as to stop the migration cultivation is also increasing for these crops,

971 Table 5: Increase in MNREGA notified wages from 2006-07 to 2011-12 (`day-1) States MNREGA Wages MAG 2006-07 2007-08 2009-10 2011-12 2012-13 2011-12 Andhra Pradesh 80 80 100 121 137 168 Arunachal Pradesh 55-57 65-67 80 118 124 135-154 Assam 66 76.35 100 130 136 100.42 Bihar 68 77 100 120 122 120 Chhattisgarh 62.63 62.63 100 122 132 114 Gujarat 50 50 100 124 134 100 Haryana 99.21 135 141.02 179 191 173.19 Himachal Pradesh 75 75 100 120-150 126-157 120-150 Jammu and Kashmir 70 70 100 121 131 110 Jharkhand 76.68 76.68 99 120 122 127 Karnataka 69 74 100 125 155 145.58 Kerala 125 125 125 150 164 200 Madhya Pradesh 63 85 100 122 132 124 Maharashtra 47 66-72 100 127 145 100 Manipur 72.4 81.4 81.4 126 144 122.1 Meghalaya 70 70 100 117 128 100 Mizoram 91 91 110 129 136 170 Nagaland 66 100 100 118 124 - Odisha 55 70 90 125 126 90 Punjab 93-105 93-105 100-105 153 166 153.8 Rajasthan 73 73 100 119 133 135 Sikkim 85 85 100 118 124 100 Tamil Nadu 80 80 100 119 132 100 Tripura 60 60 100 118 124 100 Uttar Pradesh 58 58 100 120 125 100 Uttarakhand 73 73 100 120 125 121.65 West Bengal 69.4 69.4 100 130 136 167 Source: Mahatma Gandhi National Rural Employment Guarantee Act (official website), http://www.mgnrega.nic.in. MAG: Minimum agricultural wages (Wage Act) because these crops are more labour intensive Table 6: Cost of cultivation of major crops than other crops. The results presented in in India before MNREGA -1 Table 6 clearly showed that how drastically (`ha ) Years Paddy Wheat Cotton RM Sugarcane the cost of cultivation for the major crops of Pre-MNREGA the country is increasing year by year? 1996-97 14670 13022 16484 9390 26334 The study on cost of cultivation of major 1997-98 15770 12863 15859 8839 27884 1998-99 17412 13963 14531 9981 35926 crops from the year 1996-97 to 2004-05 indicates 1999-00 19208 15982 17378 11660 44071 that before MNREGA the cost was increasing 2000-01 19649 16499 16738 12677 44450 in arithmetic progression but after 2005-06 it 2001-02 20891 17246 18896 13134 45325 2002-03 22665 18512 21872 12357 47729 was increasing in geometric progression. In 2003-04 22082 19406 22721 14777 44891 2004-05, the cost of cultivation for sugarcane 2004-05 22198 19221 24050 14673 51296 was `51296 per ha which has increased up to Post-MNREGA 2005-06 22376 19371 25902 15503 60452 `993.5 per ha little less than 1 lakh (Table 6). 2006-07 23313 21660 27316 16204 60530 If we talk about the increase in cost of 2007-08 25325 23309 29503 18149 68648 cultivation of field crops in percentage term 2008-09 30699 25148 35213 19879 71184 2009-10 34204 28858 38675 22005 90701 then, we can see that the percentage increase 2010-11 36043 30916 47287 24512 99305 in cost of cultivation, specially Sugarcane Source: Compiled from Department of Agriculture Cooperation, Ministry of Agriculture Govt. of India 2010-11 crop, which is considered as the labour RM: Rapeseed and mustard

972 intensive crop is higher than any other crop Gandhi Rural Employment Guarantee Act (Table 7). (MNREGA), which was primarily designed as Few major crops of our country like wheat, a radical and novel response to combat rural paddy, cotton and sugarcane, where we need poverty, is actually hitting the very more human labour at the time of transplanting, foundations of agriculture. Acute shortage of sowing and harvesting got affected if there is farm labour witnessed across the country at unavailability of labour at the peak time. the peak and crucial time of crop harvesting and sowing is not only playing havoc with Table 7: Increase in cost ofcultivation for food production, but is increasingly forcing major crops post-MNREGA small farmers to abandon agriculture. (Percent) Crop Pre MNREGA Post MNREGA Across the paddy growing northern region, 1999-2005 2005-2011 including Punjab and Haryana, farmlands have Paddy 5.61 11.23 been facing a severe shortage of labourers. Wheat 5.88 10.80 Ubiquitous labourers who poured in from Bihar Cotton 5.99 12.84 and UP, even Jharkhand and Orissa have dried Rapeseed and 6.66 9.88 Mustard up, content to stay home and take advantage Sugarcane 8.13 15.23 of work provided under the National Rural Source: Compiled from Department of Agriculture Employment Guarantee Act (NREGA). Acute Cooperation, Ministry of Agriculture Government of India 2010-11. paucity of farm workers is also among the reasons why more and more farmers are SUMMARY AND CONCLUSION quitting agriculture. Let us not forget that 60 Due to continuous increase in MNREGA percent of those who seek guaranteed wage rate there is scarcity of labours for employment are marginal farmers owning small agricultural operations. Majority of marginal tract of tract of land. So there is the need to and small farmers and landless farmers are freeze MNREGA during peak periods of farm going for job under MNREGA, and the fact is operations. that more than 70 percent of our farmers are REFERENCES marginal and small farmers, so if all will go for Ambasta, P., Shankara, V.P.S., and Shah, M. 2008. working under MNREGA then Who will do Two years of NREGA: The road ahead. agriculture?. Due to this shift it is oblivious Economic and Political Weekly. 43 (8): 42. agricultural production definitely will get Akhtar, J.M. and Abdul, A.N.P. 2012. Rural Employment Guarantee Programme and affected, and various studies had already Migration. Kurukshetra. 60 (4): 11-15. proved that there is a positive correlation Palanichamy, A.P. 2011. A study on Mahatma between agricultural production and poverty Gandhi national rural employment guarantee alleviation. One side MNREGA is snatching program (MGNREGP) in Thuinjapuram block labourers from agriculture due to higher wage Thiruvannamalai district in Tamilnadu. rate on the other side it also increasing the International Multidisciplinary Research cost of cultivation for the crops because due Journal. 1 (3): 37-46. to increment in MNREGA wages agricultural Bardhan, K. 2011. Rural employment wages and wages are also increasing, so farmer has to labour markets in india: A survey of research- III. Economic and Political Weekly. 12 (28): 1101 pay more than MNREGA wages for hiring -1118. labourers for their fields as earlier we have Berg, E., Bhattacharyya, S., Durgam, R., and discussed that how MNREGA wages are Ramachandra, M. 2012. Can rural public works pushing agricultural wages. affect agricultural wages? Evidence from India. Due to this increased wages, the cost of Centre for the Study of African Economies cultivation of major crops across the country (CSAE) Working Paper, WPS/2012-05. is also increasing it strange? The Mahatma Deshingkar, P. and Start, D. 2003. Seasonal

973 Migration for Livelihoods, Coping, livelihood impact. Agriculture Economics Accumulation and Exclusion. Working Paper No. Research Review. 22 (4): 443-450. 220, Overseas Development Institute, London. Labour Bureau, Shimla for consumer price indices Biswas, D. 2012. Performance of Mahatma Gandhi for Agricultural Labourers (AL) and Rural National Rural Employment Guarantee Scheme Labourers (RL), 2. C.S.O. for consumer price with special reference to Jalpaiguri district of indices for new series (CPI-NS). 3.Ministry of West Bengal. Journal of Research in Commerce Labour Employment Government of India, and Management. 1(3): 94-102. Labour and Employment Bureau Chandigarh Government of India, Ministry of Labor and 2013. Employment. Employment and Unemployment Shah, M. 2006. Will employment guarantee deliver? Situation in India 2011-12. July 1, 2012. Not unless the black box of the schedule of rates Government of India, New Delhi. is opened and the rates revised urgently in a Imbert, C. and Papp, J. 2012. Equilibrium transparent manner, The Hindu, Leader Page distributional impacts of government Article, 10-May-2006. employment programs: Evidence of India’s Shah, M. 2008. Governance reform key to NREGA employment guarantee. Retrived from success. The Hindu, Leader Page Article 14- www.parisschoolofeconomics.eu. March-2008. Kareemulla, K., Reddy, S.K., Rama Rao, C.A., Kumar, S., and Vekateshwarlu, B. 2009. Soil and water conservation work through National . Rural Employment Guarantee Scheme Received: June 08, 2015 (NREGA) in Andhra Pradesh-An analysis on Accepted: October 15, 2015

974 ABSTRACTS wholesale market. The rest of the sale was at (Ph.D. Dissertations) the farm, in the village, distant market (Delhi), Apni Mandi, to private companies and to Amritpal Kaur. 2015. An economic analysis organized retailers. The study brought out that of seed management and marketing of potato supply chain III was the most efficient one in in Punjab. Department of Economics and the peak period due to direct sale of the |Sociology, Punjab Agricultural University, produce by producer to consumer, whereas, Ludhiana: 126. in lean season, supply chain VI was the most efficient one. The study suggested that Subject: Agricultural Economics modern market infrastructure may be built up Major Advisor: Dr. M.S. Sidhu with the public-private partnership to bring JEL Classification: Y40 efficiency in the marketing of potato as well as other vegetables. The study suggested that Potato is the principal vegetable crop in potato growers may prefer cooperative/group Punjab. The state’s share in the India’s potato marketing for sale of their produce in the production is about five per cent. Seed is an distant markets. important input for potato crop. The study revealed that area under vegetables and potato in the state had increased from 1981-82 to 2011- Shruti Chopra. 2015. Changing pattern of 12. Due to increase in cold storage capacity consumption and its implications for food along with other technological and institutional security in India. Department of Economics factors, the production of potato has increased and |Sociology, Punjab Agricultural University, from 195 thousand metric tonnes in 1966-67 to Ludhiana: 122. 2104 thousand metric tonnes 2011-12 at an annual compound growth rate of 4.54 percent, Subject: Agricultural Economics which was significant at one per cent level of Major Advisor: Dr. M.S. Toor significance. The study further revealed that JEL Classification: Y40 each sample farmer had 10.45 acres area under potato. Kufri Pukhraj variety of potato was the The issue of household food security has most popular one and occupied about 78 per been one of the major concerns in India which cent of the area with the selected farmers. The depends upon several factors such as growth results regarding the sources of potato seed trends in population, per capita income, revealed that most of the farmers used self- urbanization, changes in taste in the era of retained seed followed by seed from seed globalization and future growth of the bottom- dealers and from cold-stores. The study most section of the population. The present brought out that the SRR of selected farmers study is attempted to highlight the long-term for certified seeds was 12.97 percent. The SRR changes in consumption pattern in India from for potato was 10.04 percent with the selected 1993-94 to 2009-10. The consumption pattern farmers of Jalandhar district as compared to of agricultural commodities was analyzed 15.97 percent for the Hoshiarpur district. The temporally, spatially and across the income yield of the crop planted with certified seed classes using household consumption data was high by about 16 percent and statistically from consumption expenditure survey significant at one per cent level of significance. conducted by National Sample Survey The study revealed that marketed surplus of Organization (NSSO). The sharp decline in potato was about 92 percent. The sale pattern cereal consumption scrutinised by the study of potato brought out that its maximum can be attributed to changes in tastes and quantity was sold by the growers in the preferences of the consumer within the food

975 group from cereals to non-cereal food items brought out that the states (Kerala, and from coarse to fine cereals and in general Maharashtra and Tamil Nadu) with highest from, food to non-food items. The results of female literacy percent have low share of the study revealed a structural shift in consumption expenditure on cereals, pulses, consumption pattern over the past two edible oil, vegetables and total food and high decades. Diminishing share of essential food share of consumption expenditure on egg, fish, commodities (cereals, pulses, edible oil, meat, fruits and non-food items. Looking into vegetables) and increasing share of high value the supply and demand balance for cereals, it agricultural commodities (milk and its products, appeared that demand will be met in future with egg, fish, meat and fruits), with rise in income a surplus of cereals. However, it is highly likely empirically confirms Bennet’s Law of that the pulses grains would be short in supply Consumption. The results further, were also in of demand in the coming years under the third conformity with the Engel’s Law of and fourth scenarios (8 and 9 percent growth Consumption. The study also investigated the rate) of the study. The study suggested relationship of change in household knowledge based agriculture, innovations and consumption with household income, socio- policies, which could provide local solutions economic development and other development by global experiences as a prerequisite in this indicators of the major states. The study changing scenario.

976 ABSTRACT of establishment of guava orchard. The net (M.Sc. Thesis) returns per hectare were `71947 in the seventh year and it was expected that this rate of return Veersain. 2013. An economic analysis of to be more or less same upto to age of 25 years. production and marketing of guava in The net present value per hectare was Haryana. Department of Agricultural calculated at 12 per cent discount rate, which Economics CCS Haryana Agricultural comes to `247962.79 for entire expected life of University, Hisar: 89. orchard. Further, on the basis of benefit-cost ratio 1: 5.26 and internal rate of return (35.44 Subject: Agricultural Economics percent) it may be concluded that guava Major Advisor: Dr. V.P. Luhach orchard is a profitable proposition. While, JEL Classification: Y40 comparing the results for different channels, it was observed that major share of the produce The study was undertaken with the objectives: marketed through Channel-II (Producer DPre- (i) to estimate the cost and returns of guava harvest contractorDCommission agentD production in Haryana, (ii) to study the RetailerDConsumer) while the producer’s marketing channels, market margins and price share in consumer’s rupee was higher in direct spread for guava fruit, and (iii) to identify the sale as compared to other channel due to the constraints of production and marketing of elimination of market intermediaries. Marketing guava fruit. The present study was conducted efficiency worked out in guava marketing in Hisar, Fatehabad and Sirsa district of showed that Channel-V (Producer D Haryana, which was selected purposively on Consumer) was most efficient ma one. Major basis of maximum area and production under problems faced by the guava growers in Guava cultivation. Further, Hisar, Fatehabad production of guava were damage due to and Sirsa market were selected for the market aberrant weather conditions, non-availability study. Finally, 60 growers from randomly of good seedling and lack of technical selected two blocks from each district were knowledge. The problems faced in marketing selected for the present study. On the basis of were lack of support price, market organization the nature of the data, budgeting technique and non-availability of processing facilities. and various economic tools were used for The study emphasized the need to develop estimation of cost of production, economic the proper marketing and processing facilities feasibility, marketing costs and margins. The before its cultivation is popularized on a large study revealed that over all guava growers scale in the state. incurred losses during the initial three years

977 978 Indian Journal of Economics and Development Contents Volume 11 No. 1, January-March, 2015 Conservation Agriculture and Diversification Options for Sustainable Agriculture Agriculture for producing both fuel and food: Optimism and prudence for India 1 Yogesh Bhatt, Nilabja Ghosh and M. Rajeshwor Sustaining rice crop through exploring potentialities of basmati with reference to 15 Indian Punjab Joginder Singh Crop diversification for sustainable agricultural development: A case of Haryana 21 Neeru Gehlot and Navneet Kaur Agrarian crisis and diversification strategy for sustainable agricultural development 31 in Punjab Neeraj Sharma Need of crop diversification to achieve sustainable agriculture in Punjab: A brief 41 review Indpreet Kaur Measuring the technical efficiency of the cotton production: The stochastic frontier 53 production function approach Gurprem Singh Bedi, Sukhjeet K. Saran and Taptej Singh Diversification of Punjab agriculture: Issues and perspectives 61 H.S. Rai, L.S. Brar and P.S. Cheema Direct seeded rice for sustainable agriculture in Punjab 71 Kamalpreet Kaur and Prabhjot Kaur Diversifying cropping system through dairying in Punjab-An approach to sustain 79 livelihood Parminder Singh Cheema and Parminder Kaur Sustainability of agriculture systems: Punjab’s scenario 89 Sanica Abbott, S.S. Chahal, J.L. Sharma and Kamala Sustainable farm income of different farming systems in Western Maharashtra 101 J.T. Dorge, D.B. Yadav and H.R. Shinde Economic impact of front line demonstration on pulses in Punjab-A step towards 111 diversification Pankaj Kumar, Kuldeep Singh and Prabhjot Kaur Status and economics of summer mungbean cultivation for sustainable development 117 in Punjab Gurvinder Singh, Manmeet Kaur and Jasdev Singh Technology Infusion in Agriculture and Impact Assessment of Farm Projects Infusion of farm mechanization technologies in Indian agriculture: Progress and 125 impact Mankaran Dhiman and Jaskaran Dhiman

979 A comparative analysis in resource utilization and yield performance of precision 137 farming technologies in north eastern Karnataka G.A. Shitu, G.N. Maraddi and B.Sserunjogi Micro irrigation for sustainable agriculture: A brief review 147 P. Suryavanshi, G.S. Buttar and A.S Brar Impact evaluation of cropping pattern and production pattern due to watershed 157 development project in Rajasthan Gaurav Choudhary, Vikash Pawariya and Sajjan Jheeba Adoption behaviour of resource conservation technologies in paddy cultivation in 167 Punjab 167 Manjeet Kaur, M.K. Sekhon and Amrit Kaur Mahal Biogas technology infusion in rural Punjab 177 Rohit Sharma and Iqbal Singh Economics of crop production under assured and protective irrigation system of 183 Chhattisgarh Vijay K. Choudhary, Youvraj Singh Rajput and Ajay K. Koshta Conservation agriculture and its impact study: An overview 197 Amanpreet Kaur and D.K. Grover Sustainable agriculture through watershed development programme: A study of 207 Bahirwadi watershed project, Ahmednagar, Maharashtra A.J. Amale, V.G. Pokharkar, S.P. Kalhapure and D.B. Yadav Adoption assessment of production technology for paddy cultivation in Konkan 217 region of Maharashtra R.B. Hile, A.S. Darekar and S.B. Datrkar Infusion of single bud chip planting technique for sugarcane propagation 227 Jasvir Singh Gill and Gurpreet Kaur Role of resource conservation technologies in sustainable development of agriculture 233 in Haryana Dalip Kumar Bishnoi, J.K. Bhatia, Gajender Singh and K.N. Rai Infusion of biogas technology in Punjab: A case of large capacity biogas plant 239 Sarbjit Singh Sooch Shelf stable ready to serve sugarcane juice technology: Development and economic 245 analysis Poonam Aggarwal, Karanvir Gill and Amarjeet Kaur Economic Impact of gram technologies developed by MPKV, Rahuri in Western 251 Maharashtra C.M. Gulave, V. G. Pokharkar and R.R. Nirgude Agro based Industries, Future of Organic Farming and Input Subsidies Experiences, situation and prospects of biofuels production in India 261 Jenny Kapngaihlian and M.S. Toor Role of agro-industries for sustainable agricultural development in India 277 Shivani Verma Study of electricity subsidy in Punjab agriculture 285 Amanpreet Kaur, D.K Grover and Parvinder Jeet Kaur

980 Role of sugar industry in sustainable agricultural development: An overview 295 Taptej Singh, Sumit Bhardwaj and Baljinder Kaur Behavior of input cost and output prices of selected crops of Gujarat: A comparative 303 analysis Ganga Devi, Y.C. Zala, and Vivek Pal Labour banks for sustainable agricultural development-An enquiry into the 311 determinants and barriers of joining M. Anoop, N. Ajjan, and K.R. Ashok Future of organic farming in sustainable agriculture 319 Samandeep Kaur, Surbhi Sharma and Seema Ahuja Role of farm inputs in sustaining Punjab agriculture 325 Vikrant Dhawan and J.M.Singh Organic farming: Status and constraints 333 Arjinder Kaur and M.S.Toor Growth of agro and non-agro based village industries in Punjab 339 Pratibha Goyal, Sukhmani and Mini Goyal Challenges to Punjab agriculture in the globalized world 345 Suresh Kumar Khurana and Rubina Chouhan

Dynamics of Agriculture and Allied Sector Dynamics of agricultural development in Gujarat: A district level analysis 351 Vivek Pal, R.L. Shiyani and N.J. Ardeshna Livestock economy of Punjab: Need to strengthen animal health and veterinary 359 services for sustainable development Hanish Sharma and V.M. Wadhawan Cost effective storage of pulses in rural Punjab: A conservation approach 365 Gurbir Kaur and Iqbal Singh Futures market in mitigating price risk: An explorative analysis of castor market 369 Rachana Kumari Bansal, Y. C. Zala and D.J. Parmar A study on sustainable production measures for wheat and rice of Dayalbagh, 379 Agra with climate variations K. Vasanta and Preetvanti Singh Role of conservation agriculture on productivity of groundnut in scarcity area of 387 Pune district 387 M.N. Waghmare and R.K. Rahane Economic impact of summer groundnut technologies developed by MPKV, Rahuri 393 S.A. Kadam, S.D. Navgire and R.R. Nirgude

Agricultural Development and Sociological Ramification Extent and level of poverty volatility across socio-economic correlates of rural 401 households in Niger state, Nigeria Sadiq Mohammed Sanusi Ill effects of green revolution on the agricultural development in the state of Punjab 411 Gurinder Jit Singh Bhullar and Harinder Mohan

981 Female feticide: Social ramification of development 417 Simran Kang Sidhu and Shalini Sharma Rising dowry expenditure in post Green Revolution era in Punjab 425 Gaganpreet Kaur, Sukhdev Singh and Devinder Tiwari Entrepreneurship development for sustainable agriculture in Punjab-A case of 433 apiculture Narinderpal Singh, Sangeet and Raj Kumar Sustainable agricultural development and pattern of domestic consumption 439 expenditure of Punjab farmers V.K. Sharma, H.S. Kingra, Shruti Bhogal and Sukhpal Singh Author Index 448

Volume 11 No. 2, April-June, 2015 Research Articles An application of positive mathematical programming to the Canadian hog sector in 449 the Canadian Regional Agricultural Model Ravinderpal S. Gill, Robert J. MacGregor, Bruce Junkins, Glenn Fox, George Brinkman and Greg Thomas Multidimensional poverty in India: Has the growth been pro-poor on multiple 457 dimensions? Anupama Discrimination against migrants in the world of work in Punjab 471 P. Kataria and S.S.Chahal Role of microfinance in generating income and employment for rural households in 481 Punjab-An econometric approach Munish Kapila, Anju Singla and M.L.Gupta Inflation-unemployment-poverty nexus in Nigeria, 2000-2013: An empirical evidence 489 Obansa Joseph and Ajidani Moses Sabo Pradhan Mantri Jan Dhan Yojana: A vehicle for financial inclusion 499 Amita Shahid and Taptej Singh Structural shift in the milk composition of cattle with increase in cross-bred species in 509 Punjab -Time to revise milk standards Kushal Bhalla, Varinder Pal Singh, Inderpreet Kaur and Pranav K. Singh Plight of women labourers in rural Punjab 517 Dharam Pal and Gian Singh Implications of privatization of school education in rural areas of Punjab: Some field 533 level observations Sukhdev Singh,Tanu Monga and Gaganpreet Kaur Profit efficiency of Egusi melon (Colocynthis citrullus var. lanatus) production in 543 Bida local government area of Niger state, Nigeria Sadiq Mohammed Sanusi Poverty, inequality and inclusive growth during post-reform period in India 533 Sunil Kumar Gupta, Pyare Lal, Vinod Negi and Karan Gupta

982 An economic analysis of direct marketing of potato and onion in Ludhiana city 563 Moti Arega and M.S.Toor Inter-zonal efficiency differences: Study based on farmers of West Bengal 571 Chandan Kumar Maity and Atanu Sengupta Research Note Marketing of coriander spice in Rajasthan 583 Vinod Kumar Verma and S.S. Jheeba Performance of wheat crop in Punjab: A case study of Amritsar district 589 Narinderpal Singh and Kirandeep Kaur An analysis of growth of productivity of paddy in post-reform period in Odisha 595 Rabindra Kumar Mishra Abstracts of Theses 600

Volume 11 No. 3, July-September, 2015 Review Article Water management for sustainability of irrigated agriculture: An Indian perspective 601 J. Dhiman, J.S. Dhiman, R. Aggarwal and M. Dhiman Research Articles Acreage response of sugarcane to price and non-price factors in Punjab 623 Abujam Anuradha Devi and S.S. Chahal Implementation efficiency of MGNREGA: A study of Indians states using Data 631 Envelopment Analysis Paramita Saha and Soma Debnath Modeling and forecasting of wheat in India and its yield sustainability 637 P. Mishra, P.K.Sahu, B.S. Dhekale and Vishwajith K.P. Sectoral analysis of income convergence: Evidence from India 649 Arfat Ahmad Sofi Growth of horticulture sector in Karnataka-Post reform period 661 Ramappa, K.B., Jyoti Upadhyay and Nagaraju,Y. Consumers’ preferences for traditional and organised fresh food retailing in India: 673 Evidence and implications Ishmeeta Singh and Seema Bathla An intervention approach to enhance vegetable production through growers 685 association in Karnataka Siddayya and S. Vijayachandra Reddy Marketing appraisal of lime (Citrus aurantifolia) in middle Gujarat 693 R.R.Christian, Y.C.Zala and V.K.Gondalia Determinants of firm-level performance: A study of Indian manufacturing and service 701 sector Sandeep Kumar Baliyan and Kavita Baliyan

983 Migration of labour to industries: Critical analysis of ramifications 715 Shruti Bhogal and Gian Singh Disparities in infrastructure as a barrier to attain faster inclusive growth trajectory: An 725 inter-state analysis Srinivasa Rao, Pinamala Economic viability of agro machinery service centers established by the primary 743 agricultural co-operative societies in Punjab Dharvinder Singh, Jasdev Singh and Sanjay Kumar Research Notes Financial viability and problems of manufacturers dealing in waste based business 751 Sukhmani and Sandeep Kapur Gender participation in rural farm household decision making: A case of Vaishali 755 district, Bihar Mamta Mehar and Narayan Prasad Economics of kinnow marketing in Punjab vis-à-vis distant markets: A case study 761 G.S. Romana and Jatinder Sachdeva An economics analysis of eco-fashion accessories developed from different left 767 over fabrics Baljit Kaur and Devinder Kaur A study on behaviour of mobile users at Punjab Agricultural University, Ludhiana: 773 Boarders vis-à-vis nonboarders Harleen Kaur and Seema Sharma General Article Economic growth and rural transformation in Eastern India: Strategies for inclusive 779 growth Ranjit Kumar, Uttam Deb, Cynthia Bantilan, N . Nagaraj and M. Bhattarai Abstracts of Theses 799

984 List of Referees Indian Journal of Economics and Development Volume 11, 2015

Dr. A. Anuradha Devi, CU, Manipur Dr. Manjeet Kaur, PAU, Ludhiana Dr. A. Rohini, TNAU, Coimbatore Dr. Manmeet Kaur, PAU, Ludhiana Dr. Ajmer Singh, NDRI, Karnal Dr. Mini Goyal, PAU, Ludhiana Dr. Amritpal Kaur, PAU, Ludhiana Dr. Mohit Gupta, PAU, Ludhiana Dr. Anil Kumar Chauhan, NDRI, Karnal Dr. Munish Kapila,GNIMT,Ludhiana Dr. Anupama, PU, Patiala Dr. Naresh Singla, CUP, Bathinda Dr. Arjinder Kaur, PAU, Ludhiana Dr. Narinder Pal Singh, FASS (PAU), Amritsar Dr. Baljinder Kaur Sidana, PAU, Ludhiana Dr. Navdeep Aggarwal, PAU, Ludhiana Dr. Bhupinder Singh, COA, Gurdasspur Dr.N.D.Singh, Khalsa College, Amritsar Dr. B.P. Singh, UH, Hyderabad Dr. Pankaj Kumar, PAU, Ludhiana Dr. B.S.Chandel, NDRI, Karnal Dr. Parminder Kaur, PAU, Ludhiana Dr. C.O.Mohan CIFT, Kochin Dr. Poonam Kataria, PAU, Ludhiana Dr. Dalip Kumar Bishnoi, CCSHAU, Hisar Dr. Prabhjot Kaur, PAU, Ludhiana Dr. Darmender Singh Kalsi, PAU, Ludhiana Dr. Pradeep Kumar, KU, Kurukshetra Dr. Dharampal, GGDSD College, Banur Dr. Pratibha Goyal, PAU, Ludhiana Dr. Deshmukh R.G.., PDKV Akola Dr. Punit K. Aggarwal, RMVU, Ranchi Dr. Devinder Kaur, PAU, Ludhiana Dr. P.S. Chawla, GADVASU, Ludhiana Dr. Dinesh Kumar, IVRI, IZATNAGAR Dr. Rachna Bansal, AAU, Anand Dr. Gian Singh, PU, Patiala Dr. Rahul Panwar, SRM University, Sonepat Dr. Girish Jha, IARI, NewDelhi Mr. Rajesh Digamberrao Shelke Dr.G.K.Sodhi, FASS, PAU, Patiala Dr. Ramappa, K.B., ISEC, Bangaluru Dr. G..S. Romana, PAU, Ludhiana Dr. Ravinder Malhotra, NDRI, Karnal Dr. Gulshad Mohammed, CRCCMF, Calicut Ms. Rinshu Dwivedi, NIT, Rourkela Dr. H.K.Mavi, PAU, Ludhiana Dr. Rupinder Kaur, PU, Patiala Dr. I.P. Singh, SKRAU, Bikaner Dr. S.K. Mehta, PAU, Ludhiana Dr. Inderpal Singh, Australia Dr. S. Praveena, RAU, TNAU, Perambalur Dr. Inderpreet K. Kular, GADVASU, Ludhiana Dr. S.S.Bururk, SKRAU, Udaipur Dr. J.K. Bhatia, CCSHAU, Hisar Dr. S.S. Guledgudda, UAS, Dharwad Dr. J.L. Sharma, EU, Baru Sahib (HP) Dr. S.S. Kalamkar, SPU, Vallabh Vidyanagar Dr. J.M. Singh, PAU, Ludhiana Dr. Sangeeta Shroff, GIPE, Pune Dr.J.S.Chhina, FASS (PAU), Gurdaspur Dr. Sanjay Kumar, PAU, Ludhiana Dr. J.S. Dhiman, PAU, Ludhiana Dr. Sandeep Kumar, H.P.U, R.C., Khanyara Dr. J.S. Toor, PU, Patiala Dr. Seema Bathla, JNU, New Delhi Dr. Jasbir Singh, JU, Jammu Dr. Selvanayaki S, TANU, Coimbatore Dr. Jasdev Singh Sidhu, PAU, Ludhiana Dr. Shalini Sharma, PAU, Ludhiana Dr. Jatinder Sachdeva, PAU, Ludhiana Dr. Shruti Bhogal, PAU, Ludhiana Dr. Joginder Singh, PAU, Ludhiana Dr. Siddayya, NIRD &PR, Hyderabad Dr. K.Prabhakar, NIRDPR, Hyderabad Dr. Simran K. Sidhu, PAU, Ludhiana Dr. Karan Gupta, Bahra University, Solan Dr. Sukhdev Singh, PAU, Ludhiana Dr. Khusal Bhalla, GADVASU, Ludhiana Dr. Sukhmani Virk, PAU, Ludhiana Dr. M. Javed, PAU, Ludhiana Dr. Sukhpal Singh, PAU, Ludhiana Dr. M.K.Sekhon, PAU, Ludhiana Dr. V.K.Singh, CCSHAU, Hisar Dr. M.S. Sidhu, PAU, Ludhiana Dr. V.P. Lahauch, CCSHAU, Hisar Dr. Mamta Mehar, IRRI India, New Delhi Dr. Vinod Negi, Bahra University, Waknaghat

985 DECLARATION Form IV (See Rule 8) Statement about ownership and other particulars of Indian Journal of Economics and Development

Place of publication : Ludhiana-141004 (Punjab) India Periodicity of publication : Quarterly Printer`s Name : PrintVizion Nationality : Indian Address : 1766/1, Street No. 2, Maharaj Nagar Ludhiana-141004 (Punjab) Publisher`s Name : Dr. Parminder Kaur Nationality : Indian Address : General Secretary Society of Economics and Development Department of Economics and Sociology Punjab Agicultural University Ludhiana-141004(Punjab) Chief Edior`s Name : Dr. S.S. Chahal Nationality : Indian Address : Chief Editor 70-Pink Park Barewal Road Ludhiana-141 012 Name and address of owner : Society of Economics and Development Department of Economics and Sociology Punjab Agicultural University Ludhiana-141004(Punjab) I, Dr. Parminder Kaur, hereby declare that particulars given are true to the best of my knowlegde and belief.

Dated: 25th October, 2015 (Parminder Kaur)

986 Indian Journal of Economics and Development Guidelines for Submission of Manuscripts

1. The research articles, review articles, book reviews, general articles, research notes, and short communications in basic and applied research in economics, agricultural economics, management and development are published in Indian Journal of Economics and Development. 2. The journal is managed by the eminent economists under the domain of Society of Economics and Development and published quarterly. 3. The authors submitting papers to Indian Journal of Economics and Development should be members of this Society. 4. Two copies of manuscript typed in double space should be sent to the Editor, Indian Journal of Economics and Development, Department of Economics and Sociology, Punjab Agricultural University, Ludhiana-141004 and a soft copy to [email protected] simultaneously. All articles must include an abstract not more than 100 words. 5. The length of papers should not be more than 20 typed pages of B5 size in Times New Roman font of size 11 including tables, diagrams and appendices. 6. Name(s) and affiliation(s) of the author(s) with email addresses should be provided on a separate page along with the title of the article. 7. Only essential mathematical notations may be used. All statistical formulae should be neatly typed. Footnotes should be numbered consecutively in plain Arabic superscripts. 8. References: Only cited works should be included in reference list. The reference list should be alphabetized and not numbered. Authors should uniformly follow the reference citation strictly in accordance to examples given under: i. Research Paper: Dhillon, B.S., Kataria, P. and Dhillon, P.K. 2010. National food security vis-à-vis sustainability of agriculture in high crop productivity regions. Current Science. 98 (1): 33-36. ii. Book: Samuelson, P. and Nordhaus, W. 2010. Economics. Tata McGraw Hill Education Private Limited, New Delhi iii. Chapter in a Book or Paper in published proceedings: Sharma, J.L. and S.S.Gill. 2009. Sustainability of agriculture development in Punjab. In: Jain, P.K., B.S.Hansra, K.S.Chakraborty and J.M.Kurup (ed.) Food Security and Sustainable Agriculture. U-Day Publishers and Advertisers, New Delhi: 278-290. iv. Paper in a Conference/Symposium: Singh, S., Park, J. and Litten-Brown, J. 2011. The economic sustainability of cropping systems in Indian Punjab: A farmer’s perspective. In: International Congress of European Association of Agricultural Economists, Switzerland: 9-13. v. Thesis: Balaji, M.N. 2004. Marketing system of potato in Punjab vis-à-vis Karnataka. M.Sc. Thesis, submitted to Punjab Agricultural University, Ludhiana. 9. Units: Use SI units; a few examples are given below: Hectare (ha), Milligram (mg), Rupees (`), Million hectares (Mha), Litre (l), Tonne (t), Millilitre (ml), Million tonnes (Mt), Gram (g), Meter (m), Kilogram (kg) and Centimeter (cm). Please note that no full stop is used after the abbreviation of units. 10. All articles will be referred in anonymity. The authors should comply with the comments of the referee within 20 days’ time, beyond which the papers will be removed from the files of the Society. 11. Papers submitted for publication should be exclusively written for this journal and should not have been published or sent for publication elsewhere. 12. General: The Editorial Board reserves the right to remove the material considered irrelevant. It assumes no responsibility for the views and statements expressed by the authors in their articles. Indian Journal of Economics and Development

______Society of Economics and Development Technology Marketing and IPR Cell Punjab Agricultural University