Asian Journal of Agriculture and Rural Development
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Comparison of Technical Efficiency and Socio-economic Status in Animal-crop Mixed Farming Systems in Dry Lowland Sri Lanka
Upul Yasantha Nanayakkara Vithanage Postgraduate Institute of Agriculture, University of Peradeniya, Peradeniya, Sri Lanka Lokugam H. P. Gunaratne Professor; Department of Agricultural Economics and Business Management, Faculty of Agriculture, University of Peradeniya, Sri Lanka Kumara Mahipala M. B. P. Senior Lecturer; Department of Animal Science, Faculty of Agriculture, University of Peradeniya, Sri Lanka Hewa Waduge Cyril Professor; Department of Animal Science, Faculty of Agriculture, University of Peradeniya, Sri Lanka Abstract Pre-tested, structured questionnaires covered management aspects, inputs, outputs, socio- economic situations and constraints in dairy farming among Semi-intensive (SIFS) and Extensive farming systems (EFS) in dry-lowland Sri Lanka. Parametric data were analyzed using two- tailed‘t’ and ‘Z’ tests, and non-parametric values were analyzed using Chi-square and Fisher’s extract tests. Cobb-Douglas model was used to calculate meta-frontier and system-specific frontiers. Returns in SIFS are lower than EFS. Labor costs are 91.72% and 87.26% in EFS and SIFS respectively. Counting family labor, SIFS has no comparative surplus. Excluding this, dairying is profitable even in SIFS. Dairying provides EFS family insurance where selling animals increases income. Discouragement of this in SIFS impacts negatively on sustainable income. Integration is comparatively minimal in EFS. Established with the best practices and technologies available, SIFS requires external resources to enhance efficiencies. If all EFS farmers achieved best farmer TE, output could increase by 45.09%. Similarly, SIFS output could increase by 57.08%. Farmer education and training programs contribute to improved production efficiency. Grassland scarcity and low productivity affect output adversely; poor veterinary and extension services are major constraints. Farmers consider dairying as profitable, which secures its future. Contrastingly, 35.19% of farmers believe it is low status, preferring professional jobs despite lower comparative incomes. Keywords: Buffalos, cattle, farming systems, technical efficiency
Introduction1 respectively (CBSL, 2011). National milk production in 2011 was estimated as 256.78 Agriculture and livestock in Sri Lanka million litres with approximately 201.17 account for 11.2% and 0.84% of total GDP million litres supplied from neat cattle. It is estimated that total milk production grew by 2.3% from 250.99 million litres in 2010 Corresponding author’s (CBSL, 2012). In 2011, the value of the Name: Upul Yasantha Nanayakkara Vithanage total milk and milk products importation Email address: [email protected] was 38,182 LKR millions, a 30.69%
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increment since 2010 (CBSL, 2011). In would thereby ensure long-term 2007, the total milk consumption of Sri sustainability. This finding may have Lanka was around 612,000 MT (32 kg per regional application for countries with capita consumption). Compared to the per similar tropical farming systems. The paper capita consumption of developed countries, is organized as follows. The following Sri Lankan per capita milk consumption is section presents the methodology adopted among the lowest in the world (Ranawana, which will be followed by results, and 2008). Obviously, the dependency on milk discussion sections. The conclusions and powder importation and low liquid milk implications are presented in the last consumption due to low supply will section. probably continue. Methodology In Sri Lanka there are four cattle based farming systems namely up and mid Data collection country, coconut triangle, wet lowland and The study was conducted in dry-lowland dry lowland (Ibrahim et al., 1999). Dairy dairy farming systems in Polonnaruwa and (cattle and buffalo) in the dry lowland zones Baticaloa districts of Sri Lanka. This area is an important capital asset for the peasant accounts 23.41% of dairy population and farmers. Moreover, dairy farming is 30.02% of milk production out of total in predominantly a smallholder mixed crop- dry-lowland in 2011 (CBSL, 2012). Prior to livestock farming operation (Ibrahim et al., the survey, fifteen in-depth discussions 1999; Bandara, 2000; Premarathne and were held with selected small and large Premalal, 2005). scale farmers, officers of major large-scale dairy farming companies (i.e. CIC In 2011, the dairy population of dry lowland Holdings), officers of formal milk was 1,009,390 (63.21% of the total collecting networks (i.e. Milk Industries of population) and the total average daily milk Lanka Company Ltd., Nestle Lanka Ltd.), production of dry lowland was 320, 850 L small-scale milk collectors, veterinary (45.6% of total production) (CBSL, 2012). surgeons, livestock development instructors Total land extent of the dry lowland is and artificial insemination technicians. around 37, 600 km2 and limited land for Approximate numbers of dairy farmers fodder and forage production is a critical were found from the different 10 issue in dairy production (Ranaweera, administrative divisions. Then, stratified 2007). Therefore, national milk self random sample was drawn, probability sufficiency could be achieved only by proportion to size thus end up with a sample enhancing the production in dry lowland. of 96 farms. This information supplemented The development of the dairy industry in the conceptual framework of the survey the country has stagnated mainly for instrument. The structured questionnaire political, technical and socio-economic was pre-tested twice. The questionnaire reasons (Perera and Jayasuriya, 2008). covered dairy management aspects, inputs Further, a poor understanding of socio- used, outputs, socio-economic situations, economic and cultural factors was the main constraints and involvement of households reason for unsatisfactory outcomes of the in dairy farming. dairy development programs in the country (Zemmelink et al., 1999). Against this Analytical framework background, the objective of this study is to The stakeholder discussions as well as field identify the farm-level socio-economic and observations reveled that there are two technical efficiency in the above systems distinct dairy farming systems existed in the that would provide further support to study area that could be named as Extensive increase national dairy production, which farming system (EFS) and Semi-intensive
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farming system (SIFS). The socio-economic independently proposed by Aigner et al. and production details were analyzed for (1977) and Meeusen and Van den Broeck two systems separately and compared if (1977) is used where the error term has two needed. components that are systemic random error and one sided technical inefficiency terms. Dairy production economics - A cost The stochastic frontier model is described benefit analysis was carried out considering as below, the expenditure and revenue on dairy farm activities for a period of one month. The Yi = f (Xi; β) exp (Vi - Ui) cost of labor was calculated based on the duration (hours/day) spent on dairy farming Where i = 1, …, n observations in the activities and the daily labor wage sample; Yi is the observed output level of i- following Mahipala et al., 2006. All data th farm; f (Xi; β) is a production functional were analysed using SAS (1998) statistical form with respect to input vector, Xi, and package. Parametric data were analysed unknown parameter vector β; Ui is a non- using the two-tailed ‘t’ test and ‘Z’ test. negative random variable representing Non parametric values were analysed using technical efficiency; and Vi is the random Chi-square and Fisher’s extract tests. error term which is assumed to be distributed independently and identically as 2 Technical efficiency - TE shows the N (0, σv ) and independent of Ui. The * farmer’s ability to produce the maximum frontier output (Yi ) is represented by f (Xi; level of output by using an existing set of β) exp (Vi) (Gunerathne and Leung, 1997). inputs (i.e. output oriented) or produce the same level of output using fewer inputs (i.e. Maximum likelihood estimate of the input oriented) (Lawson et al., (2004); parameters are measured in order to 2 2 2 2 Pierani and Rizzi (2003). Furthermore, TE parameterization, σv + σ = σs and γ = σ / 2 2 is the ability of a decision-making unit (e.g. (σv + σ ) where σ and σv are indicated farm) to produce maximum output by using standard deviation of Ui and Vi respectively. a given set of inputs and technology (Thiam Also, the ratio of the observed output to the et al., 2001). The analysis based on the corresponding frontier output refers the TE concept of TE in terms of deviation from of i-th farm. The Cobb-Douglas model is best-practice frontier as described by Farrell used here due to its simplicity, absence of (1957). Further, TE is defined as the multicolinearity and homogeneity. capability of the farm to reach its maximum * possible production level by using a given Therefore, TEi = Yi/Yi exp (-Ui) set of inputs and available technology. According to Farrell (1957) there are two The milk production is affected by several ways to quantify technical efficiency either variables (Singh et al., 2006). The number by estimating econometric frontiers such as of cows and all other costs including labor production or cost frontier or by estimating were considered as independent variables, a free disposal convex hull of the observed while milk production represented the input-output ratios using mathematical dependent variable. programming techniques. Since most of the agriculture production systems analysis The Cobb-Douglas stochastic frontier in used the stochastic frontier analysis (Bauer, this study is specified as; 1990), this study deals with the parametric approach.
In the parametric approach, the stochastic frontier production function approach
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1997). In the second level, six other factors (size of the farm family, farmer’s ethnicity, age, education, knowledge of dairy farming and farming systems) were analysed to Where Yi is the value of milk production determine their effect on technical th for the i farm in the sample [in Sri Lankan efficiency of dairy farming. rupees (LKR)/farm]; X1 is the total number of cows of the herd; X2 is the total cost of Constraints and other social issues - The the farm (LKR/farm/month); and β1, β2 and three main production constraints as well as β3 are output elasticity of total cows of the service constraints were identified using herd, total cost of the farm and other factors open-ended questions. Other than that, respectively. social issues related to dairy farming i.e. reasons for being engaged in dairy farming, Technical efficiency was calculated by two recognition as a dairy farmer, children’s methods using Frontier 4.1. Meta-frontier willingness of continuation and the future of and system specific frontiers were estimated dairy farming were also identified using for each farming system. When TE is open-ended questions. measured among different groups of famers, TE should be measured with respect to a Results common frontier (meta-frontier) instead of depending on separate frontiers for each Dairy production economics group (system specific frontier) (Victor et The direct costs and revenues of dairy al., 2010). A major assumption in meta- farming were considered in calculating the production function is that all considered profit of the dairy enterprises. Costs account groups have similar access to technology for expenditure on feeding, breeding, health (Gunaratne and Leung, 1997). System care, labor and so on. Revenues were specific frontier was calculated relatively to generated from selling milk and live all farms belonging to a particular farming animals for meat purposes. system (either EFS or SIFS) whereas meta- frontier was calculated considering all Expenditure - The cost of concentrate and farms as a pool. The outputs obtained from mineral feeding was significantly higher the two models were compared. Non- (p<0.05) in SIFS than EFS (Table 1). compatibility of the data across the system Furthermore, the cost of family labor and is one of the major limitations when using hired labor was significantly greater meta-production function. The surveyed (p<0.05) in EFS than SIFS. The cost of data was gathered by using same drugs and total cost including family labor questionnaire over the same period for dairy farming was found to be throughout different systems. Therefore, it significantly higher (p<0.05) in EFS than greatly eliminates the non-compatibility SIFS. among the data (Gunaratna and Leung,
Table 1: Monthly expenditure on dairy farming Expenditure LKR/farm/month P values EFS1 SIFS1
Dairy animal purchasing 416.67 622.58 0.5838 (2165.08) (1404.07) 31.67 1081.40 Concentrate and mineral supplement 0.0206 (161.55) (1892.00) 209.25 47.84 Drugs 0.0006 (340.45) (103.83)
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19.60 17.33 Veterinary service 0.8597 (81.11) (43.53) 0.00 23.59 Artificial insemination 0.0523 (0.00) (62.13) 0.00 0.60 Natural breeding (stud bull) 0.5344 (0.00) (4.98) 20995.74 12375.00 Family labor 0.0001 (8534.82) (5103.97) 1203.7 0.00 Hired labor 0.0043 (3442.55) (0.00) 0.00 10.43 Equipment 0.3280 (0.00) (54.91) 15.43 2.66 Other 0.2061 (80.18) (15.53) 1896.33 1806.44 Total cost excluding family labor 0.9013 (4026.13) (2797.59) 19003.97 13662.05 Total cost including family labor 0.0057 (11301.48) (6835.27) 1Mean, Values in the parentheses are standard deviations Means significantly different between systems (p<0.05)
Revenue - Dairy farming was the major total dairy farm income between the two income generating enterprise in EFS and it farming systems, and it was greater in EFS accounted for 63.91% of total farm income. than SIFS. Moreover, the profit of the dairy The revenue from milk and meat (selling farm including or excluding family labor culled animals) was significantly (p<0.05) was significantly greater (p<0.05) in EFS higher in EFS than SIFS (Table 2). There than SIFS. was a significant difference (p<0.05) in
Table 2: Monthly revenue from dairy farming Revenue LKR/farm/month P values EFS1 SIFS1
Milk 25888.89 3913.04 0.0001 (21967.52) (4132.46) 23725.31 1077.29 Meat (sell cattle/buffalo) 0.0001 (23066.40) (2361.07) 49614.20 4990.34 Total dairy farm income 0.0001 (43222.12) (5269.97) 47717.86 3183.90 Profit excluding family labor cost 0.0001 (42727.55) (4701.47) 30610.23 -8598.32 Profit including family labor cost 0.0001 (40081.09) (5712.65) 1Mean, Values in the parentheses are standard deviations Means significantly different between systems (p<0.05)
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Technical efficiency years only and that age group was more The technical efficiencies in EFS varied efficient than other farmers in the system. from 0.05 to 0.8847 with respect to system The majority of farmers who achieved TE specific frontier whereas it ranged from level of >75% belong to the age category of 0.0211 to 0.9316 in SIFS. The system >55years in SIFS. Meanwhile, TE increased specific values are significantly different with farmers’ age. The majority of the between two farming systems. The mean farmers in EFS who were educated up to TE in EFS (system specific frontier) was primary level show the highest TE level greater than the mean TE in all farms (meta- compared to others. However, the TE did frontier) (Table 3). The mean TE in system not increase along with education. In SIFS, specific frontier was lower than the mean the highest TE level was achieved by the TE in meta-frontier in SIFS. There was an greater part of farmers who have a basic association between the level of TE in dairy education level. In addition, TE increased farming and variables such as farmers’ age, along with farmers’ education. Only the education, ethnicity and knowledge (Table farmers having a ‘satisfactory’ level of 4). When considering Sinhala/Tamil knowledge indicated >75% TE level in farmers in both farming systems, the EFS. In SIFS, the majority of the farmers majority belongs to the TE level of 51-75%, who reached the >75% TE level have whereas, majority of Muslims belongs to ‘moderate’ knowledge of dairy farming. the TE levels of 25-50% in EFS and <25% Furthermore, in both systems the level of in SIFS. In EFS >75% TE level was TE increased with farmers’ knowledge of achieved by the farmers between 31-55 dairy farming.
Table 3: Mean technical efficiencies in EFS and SIFS based on system specific models and meta-production frontier models Mean TE System specific Meta-frontier 0.5491 0.4129 Extensive farming system1 (0.2045) (0.1907) 0.4292 0.4719 semi-intensive farming system1 (0.2638) (0.2425) P values 0.0453 0.2804 1Mean, Values in the parentheses are standard deviations Means are significantly different between systems at p<0.05 level
Table 4: Level of TE in dairy farming Frequency (%) Variables TE in Extensive farming system TE in Semi-intensive farming system <25% 25-50% 51-75% <75% <25% 25-50% 51-75% <75% Sinhala/Tamil 30.00 20.00 40.00 10.00 16.33 26.53 40.82 16.33 Ethnicity* Muslims 12.50 68.75 18.75 0.00 80.00 20.00 0.00 0.00 15-30 years 22.22 55.56 22.22 0.00 100.00 0.00 0.00 0.00 Age* 31-55 years 7.14 57.14 28.57 7.14 19.51 29.27 36.59 14.63 > 55 years 66.67 0.00 33.33 0.00 18.18 18.18 45.45 18.18 No formal 0.00 100.00 0.00 0.00 100.00 0.00 0.00 0.00 education Basic education 33.33 66.67 0.00 0.00 25.00 50.00 0.00 25.00 (<5 grade) Education* Primary education (5-9 7.69 61.54 30.77 0.00 29.03 16.13 38.71 16.13 grade) Ordinary level 0.00 100.00 0.00 0.00 5.56 38.89 44.44 11.11 (10-11 grade) Knowledge* Very poor 0.00 50.00 50.00 0.00 100.00 0.00 0.00 0.00
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Poor 25.00 62.50 12.50 0.00 25.93 25.93 33.33 14.81 Moderate 20.00 20.00 60.00 0.00 16.67 38.89 27.78 16.67 Satisfactory 0.00 0.00 0.00 100.00 12.50 0.00 75.00 12.50 *There is an association among TE and variables (p<0.05)
Constraints and other social issues of grasslands was the major production In EFS, the major production constraint constraint identified among the majority of among the majority of the farmers was lack the farmers in SIFS as well. Low of grasslands (Table 5). Low productivity productivity of existing animals and of existing animals and water scarcity were unavailability of well-structured sheds were identified second as production constraints. the second and third production constraints Unavailability of well-structured sheds was in SIFS respectively. the third production constraint in EFS. Lack
Table 5: Constraints among different farming systems Frequency (%) First Second Third Issues Production Production Production Constraint* Constraint* Constraint* EFS SIFS EFS SIFS EFS SIFS
Lack of grasslands 81.48 68.12 3.70 17.39 7.41 13.04 Low productivity of existing animals 7.41 10.14 33.33 33.33 25.93 21.74 Unavailability of well-structured sheds 7.41 15.94 11.11 15.94 37.04 26.09 Water scarcity 3.70 5.80 33.33 23.19 3.70 15.94 Lack of stud bulls 0.00 0.00 3.70 4.35 0.00 5.80 Scarcity of high yielding animals 0.00 0.00 0.00 4.35 3.70 13.04 Rules and regulations of the 0.00 0.00 0.00 1.45 0.00 4.35 Mahaweli authority Diseases 0.00 0.00 14.81 0.00 22.22 0.00 First Service Second Service Third Service Issues Constraint* Constraint* Constraint* EFS SIFS EFS SIFS EFS SIFS
Poor extension 18.52 27.54 62.96 36.23 3.70 23.19 Poor veterinary service 48.15 33.33 22.22 27.54 25.93 23.19 Lack of subsidies 0.00 2.90 3.70 4.35 3.70 7.25 Lack of market facilities 18.52 24.64 3.70 13.04 7.41 20.29 Lack of trainings 3.70 8.70 7.41 17.39 40.74 20.29 Poor security 11.11 2.90 0.00 1.45 18.52 5.80 *There is an association among farming systems and variables
Poor veterinary service, poor extension In both farming systems, a majority of the service and lack of training were identified farmers engaged in dairy farming with the as the first, second and third service purpose of increasing their income (Table constraints respectively of the majority of 6). A negligible number of famers rear dairy the farmers in EFS. In SIFS, poor veterinary animals, mainly for the purpose of service and poor extension service were collecting cow dung. The status of the dairy identified as the first and second service farmer was well recognized in both farming constraints respectively. Poor veterinary and systems; however a considerable number of extension services were identified as the farmers stated that there was no satisfactory third service constraint among majority of recognition (‘bad’) for dairy farmers in the the farmers in SIFS. society.
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Table 6: Social issues among different farming systems Frequency (%) Frequency (%) Reasons for doing dairy Recognition for dairy EFS SIFS EFS SIFS farming* farming* Increased income 88.89 78.26 Good 51.85 57.97 Home consumption 3.70 7.25 Bad 37.04 33.33 Preference 3.70 10.14 Neutral 11.11 7.25 Tradition 3.70 2.90 No idea 0.00 1.45 In order to get cow dung 0.00 1.45 Frequency (%) Frequency (%) Children’s willingness of EFS SIFS Future of cattle farming EFS SIFS continuation* Yes 85.19 81.16 Good 92.59 94.20 No 14.81 15.94 Bad 3.70 2.90 No idea 0.00 2.90 No idea 3.70 2.90 *There is an association among farming systems and variables
In both farming systems, the majority of Generally, farmers in the dry zone do not children (next generation) were interested in invest more in purchasing dairy animals. carrying out dairy farming as a source of For instance, they try to breed animals income in future. Also, majority of the themselves via natural breeding or artificial farmers stated that ‘the future of dairy insemination (AI). One the interesting farming is secured’. observation is that the farmers in the EFS generally do not spend money on breeding Discussion their animals. Because of the large herd size and the free grazing system, it is difficult to Dairy production economics – The practise AI. On the other hand, it is not majority of the farmers in dry zone (50% in required to hire stud bulls as the herd the EFS and 51.18% in the SIFS) are already contains stud bulls. Farmers do not educated up to primary level (grades 5-9). have knowledge on breeding to select stud And, they mainly depend on dairy farming bulls therefore; this situation leads to or crop cultivation, especially paddy inbreeding and low production (88.37% in the EFS and 86.72% SIFS) performances of the cows in the EFS. which is passed on over generations. Reproductive and productive performances Moreover, a lack of alternative sources of of dairy cows could be improved by income or employment opportunities in the adapting proper management practices, dry zone restricts many people to the only improved nutrition and new reproductive available option – livestock keeping technologies (Kollalpitiya et al., 2012). (Ratnayaka et al., 1992). In these mixed Farmers in the SIFS usually practise both farming systems, dairy acts as insurance. De natural breeding and AI. However, their Silva and Sandika (2012) have reported this cost of natural breeding is closer to zero as especially for poor farmers if there is a loss they borrow bulls from others without any by crop cultivation, especially paddy, the payment and this social exchange farmers try to compensate for it by selling mechanism could minimize the trend of animals such as bulls, male calves and old inbreeding. Feeding rice straw is common cows. Further, diversification in agriculture in the dry zone. Generally, concentrates are can act as a mitigating factor against crop not fed (Ibrahim et al., (1999). Compared failure. Dairying is emerging as a better to the EFS, farmers in the SIFS usually alternative in diversification especially spend more on concentrates and mineral under mixed farming systems (Bharadwaj et supplements, around 7.63% of the total cost. al., 2006). There is a common trend in foot and mouth disease, mastitis and worm problems in the
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EFS as the animals are enclosed densely in respect to system specific frontier, it a paddock during night time. Therefore, the dropped by 0.136 when farms were drug cost is significantly higher in the EFS. compared with meta-frontier. The results The labor cost is the major component of revealed that the EFS farms have more the dairy expenditure at 91.72 % and 87.26 capability to increase their production by % in the EFS and SIFS respectively. using the best resources and technologies Moreover, 89% of the total expenditure on available in the area. Comparatively, SIFS dairy farming was accounted for by labor in where majority of the farmers are doing the dry zone Ratnayaka et al. (1992) and their farming efficiently, perform better those figures concur with these research than EFS. On the other hand, greater meta- findings. Farmers require more labor (13.61 frontier value in the SIFS implies the hr/day) on dairy farming in the EFS as system has already adopted the best farmers have to look after their animals practices and technologies available in the during the whole grazing period. Some area. In the light of that, the system required farmers require hired labor only during external resources and technologies to cultivating and harvesting periods of paddy, enhance their efficiencies. Paris (2002) when they are unable to dedicate their time reported that farm productivity and to dairy farming. household incomes could be increased by introducing specific technologies. However, Revenue is generated via on-farm or off- poor technology intervention could be farm activities in both farming systems. On- observed among Sri Lankan smallholding farm revenue included the income generated dairy farmers because of a lack of by animal husbandry and crop cultivation. understanding of the complexity of the The income generated by selling milk is 6.6 systems (Bandara, 2000). Farmers who times higher in the EFS than the SIFS as the are in both farming systems carry out dairy total cow unit (CU) (Lactating/Milking/Dry farming with crop farming (upland and cow = 1CU; Heifer = 0.75 CU; Calf = 0.50 lowland crops). Therefore, new CU; Bull = 1.5CU) is 11.86 times higher in interventions should be introduced after the EFS than the SIFS. Income generated considering animal and crop farming and via selling animals for meat purposes was their interactions. Further, in small-scale 22 times higher in the EFS than the SIFS for mixed farming systems in South Asia, low two reasons. One is the large herd size productivity of the animals could be (51.33±8.46) in the EFS, and the other is the observed because introduced technologies socio-religious background where the have not been adopted or their update has majority of the EFS population is Muslim. not been sustainable. Also, crop-livestock There is no religious barrier to dairy animal systems were considered as single slaughtering in Islamic culture. production units and new technologies Comparatively, in the SIFS, the herd size is applied accordingly. Nevertheless, before smaller (4.64±0.43) and the majority is applying new technologies, complexity and Buddhist, and animal slaughtering is not diversity of the farming systems should be encouraged. The SIFS has not a surplus if considered (Thomas et al., 2002). The mean family labor is taken into account compared TE of farmers in the EFS was found to be to the EFS. If family labor is not 54.91% which indicates that the output considered, dairy farming is a reasonably could be increased by 45.09% if all farmers profitable enterprise even in the SIFS. achieved the TE level of the best farmer. In Parallel studies have also reported that Sri the same way, the output of SIFS farmers Lankan small scale dairy farmers earn a could be increased by 57.08%. Some farms marginal profit by their dairy farming performed well, whereas others did not, activities (Navaratne and Buchenrieder, even with the same system. This interesting 2003; DAPH, 2009). observation could be found in both farming systems and this situation was created due Technical efficiency - Even though the to other factors such as farmers’ culture, EFS showed the highest performances with
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Asian Journal of Agriculture and Rural Development, 4(1)2014: 128-141 age, education and knowledge, which relationships and interactions provided that correlated with farm business management. an output from one enterprise would be an input for another (Ibrahim and Schiere, Some farms owned by Muslims are usually 2002). In contrast, there is no proper managed by herdsmen. At the end of each integration among different enterprises in year the owner earns a large sum of money either farming system. Moreover, paddy by selling animals for slaughtering. The straw and crop residues (by-products of herdsmen look after the herds throughout crop cultivation) are used as dairy animals’ the year and practise milking in order to feed in the SIFS during drought periods fulfil their daily necessities, but they do not when fresh roughage becomes limited. Cow try to manage the business in an efficient dung is used in upland crop cultivation, but way. Therefore, those farms are relatively not for paddy in the SIFS. Poultry manure is less efficient than the others. On the other used neither in upland crop cultivation nor hand, Sinhalese and Tamils manage their paddy farming in the SIFS. Only paddy farms efficiently by themselves, by using straw is used as dairy feed and crop residues available resources and technologies. Dairy are not fed to dairy animals in the EFS. farming has been carried out by farmers Neither cow dung nor poultry manure is over generations. For instance, a particular used for upland crop or paddy cultivation in farmer engaged in rearing dairy animals the EFS. Therefore, the integration among since his childhood would have his farming different enterprises is minimal in the EFS experiences enhanced over his life time. compared to the SIFS. Proper livestock- Yeamkong et al. (2010) showed that farm crop integration ensures increasing farm production and revenue is increased with TEs in both farming systems. Furthermore, farmers’ experiences. Therefore, there was a farm productivity could be improved by positive relationship between TE and the integrating crop and livestock enterprises. age of the farmers in the SIFS. Moreover, In both farming systems, farmers are rearing positive relationships were found between dairy and poultry. Even though there is a education and knowledge of the farmers high potential in rearing goats, farmers did with TE in the SIFS. However, such an not take the opportunity. Therefore, farm association could not be observed in the efficiency could be increased by introducing EFS where the majority consists of goats into both farming systems. Further to Muslims, because those farms were this, it has been shown that mixed livestock managed by the herdsmen. Farm could be farming is a very important method to changed by farmer education Ondersteijn et increase farm efficiency (Gaspar et al., al. (2003) because there is a stronger 2009). influence of education on farm productivity in a modernizing environment (Phillips, Constraints and other socially-related 1994). As well, there is a positive issues - Major production constraints were relationship between farmer education and almost similar in both farming systems. farm production (Yeamkong et al., 2010) Grasslands became limited over time due to and farm efficiency (Latruffe et al., 2005). the population pressure. Moreover, the Therefore, well planned farmers’ training grassland of Sri Lanka has low productivity programs (Wubeneh and Ehui, 2006) and and has deteriorated due to mismanagement policy interventions such as farmer under the existing socio-economic position education and agricultural extension (Fuwa in the country (Premaratne et al., 2003). et al., 2007) contribute to improve farm Therefore, the lack of grasslands was the production efficiency. major constraint to expanding dairy farming today and in future. The best solution for Integrated farming is a method in which two the scarcity of grasslands was having a or more enterprises (crops, livestock, fish) lower number of highly productive animals are combined to get maximum efficiency instead of having a larger number of from resources used, considering their unproductive animals. Other than that, an intensive or semi-intensive management
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Asian Journal of Agriculture and Rural Development, 4(1)2014: 128-141 system was more viable than extensive animals. Selling animals for meat purposes management practices. Feed scarcity was strongly discouraged due to cultural become a severe problem during the dry and religious ethics and barriers which have season and farmers should use feeding had a direct negative impact on monthly technologies such as urea treated straw income as well as the financial security of feeding, urea molasses mineral block the farm family. Appropriate livestock-crop feeding etc. especially, during drought integration and mixed livestock farming periods. Similar recommendations are ensure increasing farm production and suggested for dairy farms in Kurunagala efficiency in both systems. Established with district of Sri Lanka and the study revealed the best practices and technologies that proper feeding technology is required available, SIFS required external resources for the dry season. Further, farmers should and technologies to enhance its efficiencies. be trained in feed management (Kubota et In both systems, improved farm production al., 2010). Extension officers and efficiency was achieved through well- veterinary surgeons were reluctant to work planned farmer education and training in these rural areas due to poor programs. Lack of grasslands, low infrastructure and other facilities. Therefore, productivity, water scarcity and the lack of extension and veterinary services unavailability of well-structured sheds has become a major barrier to expanding the affected production adversely, whereas poor dairy sector. Premarathna and Premalal veterinary services, poor extension services (2005) reported that access to extension and lack of training were major service services in dry lowland is very difficult. constraints. The future of dairy farming Most of the studies revealed that efficient seemed secure because most farmers and effective extension services for the considered it to be profitable, and their dairy farmers are a paramount requirement children were willing to continue that way in many parts of the country (Bandara et al., of life. However, policy implementers and 2011). social workers should work cooperatively to enhance the social status of dairy farmers. A majority of the farmers consider dairy farming as a profitable venture, which Acknowledgement secured its future as their children were The authors gratefully acknowledge the willing to carry out dairying in future. In invaluable support given by Dr. H.L.J. contrast, there was another group of farmers Weerahewa, professor, Department of (35.19%) who believe it is a low status they Agricultural Economics and Business occupation in their society. They prefer Management, Faculty of Agriculture, professional jobs even though they may University of Peradeniya; Audrey Cornish, generate a lower income compared with English language advisor; dairy farmers, dairying. officers from Milk Industries of Lanka Company Ltd., Nestle Lanka Ltd., CIC Conclusion holdings, veterinary surgeons, livestock development instructors and artificial Although dairy farming maintenance cost insemination technicians who have given per animal was relatively high in SIFS, their invaluable support to make this study a system returns were lower in comparison success. with EFS, which used low external inputs. Labor cost is considerably high in both References systems. If family labor inputs are considered, SIFS has no surplus compared Aigner, D. J., Lovell, C. A. K., & Schmidt, to EFS. However, if family labor is not P. J. (1977). Foundation and considered, dairy farming is reasonably estimation of stochastic frontier profitable. Dairying acts as insurance for the production function models. Journal family if farmers are willing to sell culled of Econometrics, 6, 21-37.
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