Comparison on the Farm and Household Income Before

IAR Journal of Agriculture Research and Life Sciences ISSN Print : 2708-5090 | ISSN Online : 2708-5104 Frequency : Monthly Language : English Origin : Kenya Website : https://www.iarconsortium.org/journal-info/iarjals Review Article

Comparison on the farm and household income before and after the adoption of IPM practice: An example from Banke and Surkhet District of Nepal Article History Abstract: Integrated pest management (IPM) technology, a package of practices that utilizes natural predators and careful timing of right doses, is one of the most Received: 21.10. 2020 important measures to cut the use of pesticides. It is not surprising evidence that Accepted: 15. 11 .2020 the application of pesticides during the periods has increased substantially along Revision: 22. 11. 2020 with incredible amount of subsidies. A study on comparison on farm income and Published: 25. 11. 2020 household income before and after adoption of IPM technology was conducted in the Banke and Surkhet districts of Nepal. For assessing the comparison, farmers Author Details were asked a series of questions during the survey to determine the income before Arjun Khanal1 Punya Prasad Regmi2 and after IPM adoption. For the comparison Lorenz curve and Gini coefficient Gopal Bahadur KC3 Dilli Bahadur KC4 were used. This study revealed that that large proportion of the members of the sampled households (27.74%) has attained secondary level. Majority of the & Kishor Chandra Dahal respondents were Janajati/Indigenous followed by Dalit in the study area 68.40% Authors Affiliations 1 population were of economically active age. Similarly, 30% of the household has PhD student at Institute of Agriculture and cultivated tomato followed by cauliflower (27%), bitter gourd (17%), cucumber Animal Science (16%) and eggplant (10%). Cucumber is found to be the most profitable vegetable 2Vice Chancellor at Agriculture and Forestry crops in the study area using IPM technology. The farm income of sample University, Rampur, Chitwan households before IPM practice ranged from Rs. 2,150 to Rs. 466,000 and after the adoption of IPM practice, the farm income ranged from Rs. 5,500 to Rs. 3 Professor at Institute of Agriculture and 499,000. Similarly, the household income of sample households before adoption Animal Science, Department of Plant of IPM practice ranged from Rs. 23,000 to Rs. 647,000 and after the adoption of Pathology IPM practice, the household income ranged from Rs. 25,000 to Rs. 696,000. The

4Project Manager at CIMMYT Gini coefficients were higher for farm income (0.55) and household income (0.37) before adoption of IPM practice compared to those for farm income (0.49) and 5Department of Horticulture, Institute of household income (0.31) after adoption of IPM practice. Since the Lorenz curves Agriculture and Animal Science, Kirtipur, for both farm and household incomes after IPM practice lie close to the line of Kathmandu,Nepal equality compared to those before IPM practice, disparity of income was reduced Corresponding Author* after the provision of IPM practice. Disparity of farm income was reduced Arjun Khanal significantly compared to household income. Thus, identified comparison of farm How to Cite the Article: and household income before and after adoption of IPM practice could be a Arjun Khanal, Punya Prasad Regmi, Gopal source for Agriculture Knowledge Centers, policy makers, researchers and other Bahadur KC, Dilli Bahadur KC & Kishor extension agents to disseminate the IPM technology. Chandra Dahal (2020); Comparison on the farm and household income before and after Keywords: IPM Technology, Vegetable, Farm Income and Household Income. the adoption of IPM practice: An example from Banke and Surkhet District of Nepal. IAR INTRODUCTION J Agri Res Life Sci, 1(6) 164-171. Agriculture is the major sector of Nepalese economy. It provides Copyright @ 2020: This is an open-access article distributed under the terms of the Creative employment opportunities to around 65 percent of the total population and Commons Attribution license which permits contributes about 27 percent in the GDP. Nepalese agriculture has low unrestricted use, distribution, and reproduction productivity, depriving farmers of a sustainable livelihood. Especially in the in any medium for non commercial use mountains, people who survive by cultivating cereals on mountain slopes, river (NonCommercial, or CC-BY-NC) provided the basins, and small valleys to meet their basic needs, often have low income and original author and source are credited. suffer from food deficits. Therefore to reduce farm-poverty, the country, through various plans and policies (NPC, 1995; NPC, 1998; NPC, 2003; NPC, 2007; MOAC, 2004; MOICS, 1992), identified 'vegetables' as one of the leading sub- sectors to harness the advantages of agro ecological diversity in Nepal. The use of synthetic pesticides in agriculture has increased exponentially after the introduction of high-yielding varieties and hybrids in the late 1940s. At the beginning of the current millennium the world pesticide use exceeded 2.5 million tons and the world pesticide expenditures were around $ 32.0 billion (EPA, 2002).

Integrated pest management (IPM) technology, a package of practices that utilizes natural predators and careful timing of right doses, is one of the most important measures to cut the use of pesticides. It is not surprising evidence that the application of pesticides during the periods has increased substantially along with incredible amount of subsidies (Irham and Mariyono, 2002). According to Norton and Mullen (1994), IPM is an approach which uses increased information to make pest control decisions, and also uses multiple tactics to manage pest populations in a way that is both economically efficient and ecologically sound.

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IPM practices natural, environmentally friendly began its program in Nepal in 2004. The main aim of approaches that increase agricultural productivity. the study is to compare the farm and household income Examples of IPM practices are adoption of pest- before and after the adoption of IPM practice. This resistant varieties of crops; biological and physical understanding can help in comparing the income of control methods; environmental modification; bio- farm and household and help sustain vegetable pesticides; and when absolutely necessary, non- production through IPM in Nepal. residual, environmentally-friendly and low mammalian toxic chemical pesticides (IPM CRSP, 2008a). The IPM ETHODOLOGY CRSP, began in September 1993 with the objective of M developing and implementing IPM practices that can Selection of the study area help increase the standard of living and improve the The study was conducted in the mid-western environment in various countries around the world. The region of Nepal. The study focuses to compare the farm objectives areachieved through IPM research, education and household income before and after the adoption of for behavioral changes, policy and institutional reforms, IPM technology in, so that, two districts namely Banke and the development of sustainable, resource-based and Surkhet were purposively selected. local enterprises (IPM CRSP, 2008b); The IPM CRSP

Map of Study Area

Research district

Figure 1. Map of Nepal showing study district

The study was conducted in the mid-western farmers from each VDCs were selected randomly which development region of Nepal in Banke and Surkhet include farmers and marginalized people. district. Six Village Development Committees (VDCs) Various sources and techniques were used for collection were collected from each district i.e. Banke district: of necessary information. In this study, both primary i.Bageshwori, ii. Kamdi, iii. Naubasta, iv. Sitapur, v. and secondary data were collected and analyzed. Bankatawa and vi. Basudevpur and in Surkhet districts: Secondary information were collected from the various i. Chhinchu, ii. Dahachaur, iii. Dashrathpur, iv. published materials like journals, research articles, Mehlkuna, v. Sahare and vi. Malarani. Altogether 500 proceedings of various NGOs and INGOs, reports of households were taken, as the sample comprising 42 District Agriculture Development Office (DADO), District Development Committee (DDC), National

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Agriculture Research Council (NARC), Central Bureau of Statistics (CBS), Village Development Committee Lorenz curve (VDC), Community Development Organizations Lorenz curve shows the relationship between the (CDO), Stattistical year book of Nepal. The local cumulative percentage of aggregate income and the political leaders, working agencies were also the cumulative percentage of the population receiving that sources of primary information. An interview income. It indicates the degree of variability of a questionnaire was prepared to collect primary frequency distribution in a graphical manner. If every information from farmers member of the population receives the same income, the Lorenz curve will coincide with the line of equal distribution. It means 25 percent of the population receives 25 percent of the aggregate income, 50 percent of the population receives 50 percent of aggregate income and so on (Riston, 1977).

Y % of income P 100% -

80% - Line of equality Lorenz Curve

60% -

40% - A G= A/ (A+B)

20% - B % of household X 0% 40% 80% 100% Figure: TheFigure Lorenz 2. The Curve Lorenz curve

To display the disparity in income distribution, Lorenz The range of Gini coefficient varies from 0 to curve was used. In the figure 1, the curve below the line 1; where 0 indicates perfect equality and 1 indicates of inequality indicates the existence of inequity. The perfect inequality. more unequal the income distribution, the further below the diagonal (i.e. equalitarian line) will lay the Lorenz ESULTS Curve. R Gini coefficient Fresh vegetable crops: district wise area, production Gini coefficient was invented by Corrado Gini & yield in 1913 and it is being used with increasing frequency According to statistical information on as a measure of relative distributional inequality. The Nepalese Agriculture (2075/76), in Surkhet district ratio is approximated from the Lorenz curve and production of tomato (18.71 mt/ha) was high followed represents the portion of area under the egalitarian line by cauliflower (17.25 mt/ha), cabbage (16.46 mt/ha), that lies between Lorenz curve and egalitarian. It can be cucumber (16.25 mt/ha), brinjal (16.25 mt/ha) and clearly defined from the figure as: bittergourd (15.24 mt/ha). Similarly, in Banke district G = A/ (A+B) production of tomato (18.68 mt/ha) was high followed Mathematically it can be expressed as by cabbage (17.15 mt/ha), cucumber (16.04 mt/ha), cauliflower (14.94 mt/ha), bittergourd (11.08 mt/ha) G= 1/100(∑XiYi+1 – ∑Xi+1Yi) % Where, and brinjal (10.38 mt/ha). The detail of area and G= Gini coefficient production vegetable crops in the study area districts is X = Cumulative percentage of frequency given in table 1. Y = Cumulative percentage of income

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Table 1. District wise area, production and yield of fresh vegetable crops District Crop Area (Ha) Production (mt) Yield (mt/ha) Surkhet Brinjal 87 1412 16.25 Cucumber 213 3454 16.25 Bittergourd 110 1678 15.24 Cauliflower 163 2809 17.25 Cabbage 143 2350 16.46 Tomato 206 3855 18.71 Banke Brinjal 156 1624 10.38 Cucumber 521 8359 16.04 Bittergourd 313 3464 11.08 Cauliflower 120 1793 14.94 Cabbage 140 2401 17.15 Tomato 90 1681 18.68 Source: Statistical Information on Nepalese Agriculture (2075/76)

Educational status of the study population status of the family members of the sampled Education is one of the important human households. The survey revealed that large proportion capitals, which is the main factor of socio-cultural and of the members of the sampled households (27.74%) economic change in a society seven categories, illiterate have attained secondary level education and lower (who cannot read and write) vocational training proportion of the population have attained University (capable of read and write without school), primary post graduate education (1.89%). The illiterate (formal education up to five class), secondary (formal population was higher (18.16%) in the Banke district as education up to ten class), higher secondary (formal compared to the Surkhet district (17.06). Lower education up to twelve class), university undergraduate illiterate population in the Surkhet district may be due (formal education up to bachelors level) and university to the geographical remoteness. The educational postgraduate (formal education more than bachelors attainment of the members of sampled households was level) were devised in order to assess the educational represented in Table 2.

Table 2. Educational status of the study population in the study area (2017) Educational Level Name of the District Total Banke Surkhet Illiterate 258(18.16) * 217(17.06) 475(17.64) Vocational Training 132(9.29) 112(8.81) 244(9.06) Primary level 435(30.61) 301(23.66) 736(27.33) Secondary level 365(25.69) 382(30.03) 747(27.74) Higher Secondary level 142(9.99) 182(14.31) 324(12.03) University undergraduate 63(4.43) 53(4.17) 116(4.31) University postgraduate 26(1.83) 25(1.97) 51(1.89) Total 1421(100) 1272(100) 2693(100) *figures in parentheses indicate percentage Ethnicity of the respondent Majority of the respondents were Janajati/Indigenous followed by Dalit in the study area. In total, 48.6 percentage of the respondent were Brahmin/Chhetri followed by Janajati/Indigenous (41.6%), Dalit (9.8%). In the Banke district majority of the respondents were Brahmin/Chhetri (50.80%) and in Surkhet district majority of the respondents were also Brahmin/Chhetri (46.4%). The distribution of the respondents in the study area was presented in Table 3. Table 3. Ethnicity of the respondent in the study area (2017) Name of the District Total Ethnicity Banke Surkhet Brahmin/Chhetri 127(50.80) * 116(46.4) 243(48.6) Janajati/Indigenous 105(42.00) 103(41.2) 208(41.6) Dalit 18(7.20) 31(12.4) 49(9.8) Others 0(0.00) 0(0.00) 0(0.00) Total 250(100.00) 250(100.00) 500(100.00) *Figure in parenthesis indicate the percentage Distribution of the economically active population in the study area Age of the family members were categorized in to three classes, less than 15 years, economically active age (15- 59 year) and more than 59 year. Majority of the population (68.40%) was of economically active age. The percentage of economically active population was higher (69.42%) in Surkhet district than Banke district (67.49%). Similarly, the 167

Arjun Khanal et al., IAR J Agri Res Life Sci; Vol-1, Iss-6 (Nov -2020):164-171 percentage of economically active population was higher (57%) in Banke district than Surkhet district (53%) (CBS, 2011). The distribution of the economically active population in the study area was presented in Table 4. Table 4. Economically active population in the study area District Total Age group(Years) Banke Surkhet >15 348(24.49) * 298(23.43) 646(23.99) 15-59 959(67.49) 883(69.42) 1842(68.40) >59 114(8.02) 91(7.15) 205(7.61) Total 1421(100) 1272(100) 2693(100) *Figure in parenthesis indicate the percentage Cultivation of vegetables In the study area it has been found that 30% of the household has cultivated tomato followed by cauliflower/cabbage (27%), bitter gourd (17%), cucumber (16%) and eggplant (10%) respectively. The study revealed that tomato is the major vegetable crops in the study area.

Figure 3. Cultivation of vegetables

Calculation of Benefit Cost (B:C) ratio using IPM technology in the study area In the study area it has been revealed that the benefit cost ratio of cucumber (2.14) was higher followed by tomato (2.11), eggplant (1.92), cauliflower (1.92) and bitter gourd (1.9) in Surkhet district by adopting IPM technology. Similarly, the benefit cost ratio of cucumber (2.01) was higher followed by cauliflower (1.97), tomato (1.93), bitter gourd (1.75) and eggplant (1.29) in Banke district. Hence, cucumber is the most profitable vegetable crops in the study area using IPM technology. Table 5. Benefit cost ratio of different vegetable crops District crops Area/ha Production/mt Yield (mt/ha) B:C Tomato 14.12 209.85 14.86 2.11 cucumber 9.88 125.78 12.73 2.14 Surkhet eggplant 1.5 22.69 15.17 1.92 Bitter gourd 4.96 80.49 16.22 1.9 cauliflower 9.04 104.77 11.6 1.92 Tomato 7.71 92.59 12 1.93 cucumber 3.57 54.62 15.28 2.01 Banke eggplant 1.85 17.22 9.3 1.29 Bitter gourd 3.26 47.71 14.61 1.75 cauliflower 8.48 101.04 11.92 1.97

Income distribution of farm and households before percent of the households had access to only 1.1 percent and after adoption of IPM practice of the cumulative farm income and the richest 0.6 Distribution of farm incomes before and after percent had earned 4.8 percent of the cumulative farm adoption of IPM practice income. Likewise, lower 58.4 percent of the households The figure presented below is the farm income earned only 17.4 percent whereas upper 41.6 percent distribution of sample households before and after earned 82.6 percent of the cumulative farm income adoption of IPM practice. Lorenz curve (Figure 4) before adoption of IPM practice. After the adoption of presents the disparity of farm income distribution IPM practice, the farm income ranged from Rs. 5,500 to before and after adoption of IPM practice. The farm Rs. 499,000. The poorest 16.2 percent of the households income of sample households before IPM practice had access to only 1.5 percent of the cumulative farm ranged from Rs. 2,150 to Rs. 466,000. The poorest 19 income and the richest 1.2 percent had earned 8 percent

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Arjun Khanal et al., IAR J Agri Res Life Sci; Vol-1, Iss-6 (Nov -2020):164-171 of the cumulative farm income. Likewise, lower 53.8 whereas upper 46.2 percent earned 81.6 percent of the percent of the households earned only 18.4 percent cumulative farm income after IPM practice adoption.

Figure 4. Lorenz curve for distribution of farm incomes of households

4.11.2 Distribution of household incomes before and after adoption of IPM practice lower 53.8 percent of the households earned only 27.4 The figure presented below shows the percent whereas upper 46.2 percent earned 72.6 percent household income distribution of sample households of the cumulative household income before IPM before and after IPM practice adoption. Lorenz curve practice adoption. After the adoption of IPM practice, (Figure 5) presents the disparity of household income the household income ranged from Rs. 25,000 to Rs. distribution before and after IPM practice adoption. The 696,000. The poorest 3.6 percent of the households had household income of sample households before access to only 0.2 percent of the cumulative household adoption of IPM practice ranged from Rs. 23,000 to Rs. income and the richest 7.8 percent had earned 17.6 647,000. The poorest 5 percent of the households had percent of the cumulative household income. Likewise, access to only 0.4 percent of the cumulative household lower 50 percent of the households earned only 27.7 income and the richest 7.4 percent had earned 21.9 percent whereas upper 50 percent earned 72.3 percent percent of the cumulative household income. Likewise, of the cumulative household income after IPM practice adoption .

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Figure 5. Lorenz curve for distribution of household incomes of households

Gini coefficient Gini coefficients explain the equality and those for farm income (0.49) and household income inequality in farm and household income distributions (0.31) after adoption of IPM practice. Since the Lorenz of the households in the study area. It was revealed that curves (Figures 4 and 5) for both farm and household the disparity of farm and household income prevailed incomes after IPM practice lie close to the line of both before and after IPM practice adoptions in the equality compared to those before IPM practice, farms of the households. The Gini coefficients were disparity of income was reduced after the provision of higher for farm income (0.55) and household income IPM practice. Disparity of farm income was reduced (0.37) before adoption of IPM practice compared to significantly compared to household income . Table 6. Gini coefficients for farm and household income disparities Gini coefficient for Before IPM practice After IPM practice Farm income 0.55 0.49 Household income 0.37 0.31 UMMARY S Banke and Surkhet districts of Nepal. For assessing Integrated pest management (IPM) the comparison, farmers were asked a series of technology, a package of practices that utilizes questions during the survey to determine the income natural predators and careful timing of right doses, before and after IPM adoption. For the comparison is one of the most important measures to cut the use Lorenz curve and Gini coefficient were used. This of pesticides. It is not surprising evidence that the study revealed that that large proportion of the application of pesticides during the periods has members of the sampled households (27.74%) has increased substantially along with incredible amount attained secondary level. Majority of the of subsidies. A study on comparison on farm respondents were Janajati/Indigenous followed by income and household income before and after Dalit in the study area 68.40% population were of adoption of IPM technology was conducted in the economically active age. Similarly, 30% of the 170

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household has cultivated tomato followed by http://www.epa.gov/pesticides/ipm/brochure/whati cauliflower (27%), bitter gourd (17%), cucumber pm.htm (16%) and eggplant (10%). Cucumber is found to be 3. IPM CRSP. (2008a). What do we do. Retrieved the most profitable vegetable crops in the study area 02/23/2008, from using IPM technology. The farm income of sample http://www.oired.vt.edu/ipmcrsp/IPM_2008/What_ households before IPM practice ranged from Rs. We_Do/what_is_ipm.htm 2,150 to Rs. 466,000 and after the adoption of IPM 4. IPM CRSP. (2008b). IPM CRSP IPM Goals and practice, the farm income ranged from Rs. 5,500 to Objectives. Retrieved 09/08/08, from Rs. 499,000. Similarly, the household income of http://www.oired.vt.edu/ipmcrsp/IPM_2008/What_ sample households before adoption of IPM practice We_Do/goals.htm ranged from Rs. 23,000 to Rs. 647,000 and after the 5. Irham & Mariyono, J. (2002). The impact of adoption of IPM practice, the household income Integrated Pest Management on pesticide demand ranged from Rs. 25,000 to Rs. 696,000. The Gini for soybean farming in Yogyakarta, Indonesia. In: coefficients were higher for farm income (0.55) and Proc. Int. Workshop on Environmental Risk household income (0.37) before adoption of IPM Assessment of Pesticides and Integrated Pesticide practice compared to those for farm income (0.49) Management in Developing Countries 6-9 Nov. and household income (0.31) after adoption of IPM 2001 Kathmandu, Nepal. Landschaftsökologie und practice. Since the Lorenz curves for both farm and Umweltforschung 38, (eds. A. Herrmann, and S. household incomes after IPM practice lie close to Schumann) Braunschweig: 62-72. the line of equality compared to those before IPM 6. MOAC. (2004). National Agricultural Policy. practice, disparity of income was reduced after the Ministry of Agriculture and Cooperative, Nepal provision of IPM practice. Disparity of farm income 7. Norton, G.W., & Mullen, J. (1994). “Economic was reduced significantly compared to household Evaluation of Integrated PestManagement income. Thus, identified comparison of farm and Programs: A Literature Review.” MS thesis, household income before and after adoption of IPM Virginia PolytechnicInstitute and State University. practice could be a source for Agriculture 8. NPC. (1995). Nepal agriculture perspective plan. Knowledge Centers, policy makers, researchers and National Planning Commission and Asian other extension agents to disseminate the IPM Development Bank TA No. 1854-Nep, Nepal. technology. 9. NPC. (1998). The Ninth Plan. National Planning Commission, Nepal. EFERENCES 10. NPC. (2003). The Tenth Plan. National Planning R Commission, Nepal. 1. CBS. (2011). Nepal living standards survey 11. NPC. (2007). Three Years Interim-Plan. National 2010/11. Statistical Report- Volume 1. Central Planning Commission, Nepal. Bureau of Statistics. National Planning 12. Riston, C. (1977). Agricultural economics: Commission Secretariat (NPCS), principles and policy. Granada Publishing Limited, Kathmandu,Nepal.Available:http://siteresources.wo London. pp. 30-38. rldbank.org/INTLSMS/Resources/3358986- 13. Statistical information on Nepalese Agriculture 1181743055198/38773191329489437402/Statistica 2075/76. Government of Nepal, Ministry of l_Report_Vol1.pdf. Retrieved: 21 August 2012. Agriculture and Livestock Development; Planning 2. EPA, Environmental Protection Agency. (2002). and Development Cooperation Coordination What is IPM. Retrieved 02/25/08, from Division; Statistics and Analysis Section, Singha Durbar, Kathmandu, Nepal: 2020.

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