Introduction The demand for processed food has gone up due to an increase in urban population with relatively higher income levels. This has opened up opportunities for various participants in the value chain. High value agriculture (HVA) is composed of agricultural crops and other non-livestock farm-based commodities such as fruits and vegetables and other horticultural commodities which give a higher rate of return to farmers per unit of land, labour and capital as compared to staple crops. It is assumed that when HVA commodities enter well developed value chains, better knowledge and capacities help to increase the value of the product at every link of the chain thereby increasing profitability. This can be achieved only through an integrated approach with the collective efforts of farmers, processors and traders. |
Chillies are mostly consumed and exported as chilli powder, dried chillies, pickled chillies and chilli oleoresins. There are many post-harvest operations like drying, cleaning, processing, packaging etc. The study covered three major chilli growing districts in Tamil Nadu namely Thoothukudi, Ramnathapuram and Virudhunagar. Chillies produced from irrigated area had higher quality than rainfed area. Processing firms procured chillies from contract farmers to maintain the required quality standards. This linkage enabled to change the method of farming by adoption of good agricultural practices (GAP). The adoption of GAP had led to increase in production and price. The expected overall benefits of appropriate adoption and monitoring of good agricultural practices enabled improvement in safety and quality of food and other agricultural products, promotion of sustainable agriculture and contribution to meeting national and international environment and social development objectives. |
The study was conducted to understand the productivity differences between contract and non-contract farmers. The study may be further useful for all the stakeholders especially the processors and government institutions for framing suitable policy measures. |
Top Methodology In southern Tamil Nadu, Ramanathapuram, Thoothukudi and Virudhunagar districts were purposively selected for the study. These districts were selected based on the area under cultivation of Sannam variety of chillies and trading and processing activities. Four blocks from three districts, where both contract and non-contract system of chillies cultivation was existing exclusively, were selected for the study. |
Random sampling technique was used to select 160 sample farmers from eight villages located in four blocks and three districts. In each block two villages were selected and from the selected villages 20 contract and 20 non-contract farmers were selected at random. The farmers were contacted individually for collection of data with the help of well-structured and pre-tested interview schedule. The data thus collected were processed using tabular analysis, multiple regression/production function and decomposition analysis. |
Sources of productivity difference between contract and non-contract farmers Farmers under contract system followed GAP. In order to identify the contribution of GAP technology among the various inputs, decomposition analysis was undertaken. It revealed the total productivity difference between contract and non-contract chilli cultivation system. The output decomposition model developed by Bisaliah (1977) was used for investigating the contribution of various constituent sources to the productivity difference between the contract and independent farmers. For any two production functions, the total change in the productivity could be brought out by shifts in the production parameters that defined the production function itself and by the changes in the input-use levels. Therefore the production function was considered as the convenient econometric model for decomposing the productivity difference. Functions of the form of Equation (1) were fitted for contract and non-contract farmers separately. where Y= Gross return (Rs/ha), L= Land preparation (machine time/ha), F= Farmyard manure (tonnes/ha), P= Plant protection chemicals (Rs/ha), H= Human labour (man days/ ha), I= Irrigation (number of h/crop duration/ha), T= Technology cost (Rs/ha), bj= Regression coefficients (j= 0, 1, 2…, k) (k= 7) The output decomposition model used in this study was:
where Y, S, F, C, P, H, B, M, bj and ui are as denoted in Equation (1). However Equations (2), (3) and (4) represent contract, non-contract and pooled regression functions respectively In other words The decomposition Equation (5) gives an approximate measure of the percentage change in output in contract farming system in the production process. The first flower bracketed expression on the right hand side of Equation (5) is the measure of percentage change in output due to shift in scale parameter of the production function. The second flower bracketed expression is the difference between output elasticities each weighted by natural logarithms of the volume of that input used under non-contract farmer category, a measure of change in output due to shift in slope parameters (output elasticities) of the production function. The third flower bracketed expression is the sum of the natural logarithm of the ratio of each input of contract to non-contract farmers each weighted by the output elasticity of that input. This expression is a measure of change in output due to change in per ha quantities of inputs used in the production process. Top Results and Discussion Response functions for analyzing the yield difference of contract and non-contract chilli farms Value addition at farm level was analysed in terms of increase in returns and higher price for good quality produce. The average return of contract production function analysis was employed to analyse the yield differences between contract and non-contract farming systems. It was also used to estimate the yield responses of contract and non-contract farms to various factors of production. The average return to contract farmer was Rs 1,20,174.37 per hectare and non-contract farmer was Rs 80,561.25 per hectare of chillies. The production function with intercept dummy for contract system was a ‘good fit’ with all the explanatory variables included in the model collectively explaining nearly 55 per cent of the variation in the production (Table 1). The regression coefficient for the intercept dummy variable (0.551) was positive and significant. This implied that the parameters governing the input-output relations in the case of contract farms were different from those of non-contract farms. Thus the results provided the necessary proof for decomposing the total change in per ha output with the adoption of contract farming. These findings are in conformity with those of Hugar et al (2009) wherein the regression coefficient (0.24) for intercept dummy variable was significant. Similar results were obtained by Bisaliah (1977) for Punjab wheat economy, Kunnal (1978) for sorghum economy in the Hubli Taluka of Dharwad district in Karnataka and Hugar et al (2000) for IPM technology in cotton. Decomposition of the outturn difference in contract and non-contract farmers Decomposition analysis was used to estimate the contribution of various sources to the outcome difference between contract and non-contract farmers. Maheswari et al (2008) also identified the sources of productivity difference between the precision and conventional farming by decomposing the productivity change. The outcome difference resulted by adoption of GAP technology between the contract and non-contract productions was decomposed into its constituent sources and results are presented in Table 2. The decomposition analysis showed that the per acre return of contract farming was 55.14 per cent higher than that of non-contract cultivation. The GAP technology component alone contributed 10.99 per cent to the total increase in output while the contribution of all other inputs was 44.15 per cent. The major contributor amongst all the inputs to the difference in return was the cost incurred by the farmers for adopting the GAP technology (27.42%) followed by farmyard manure (19.00%). Irrigation and the chemical fertilizers were found to be positively contributing but at a lower level ie 1.59 and 0.14 per cent respectively. Land preparation and human labour (man days) were found to reduce the gross return. It could be suggested that the usage of the human labour and land preparation were on higher side leading to inefficiencies. Maheswari et al (2008) reported that the total productivity difference between the precision and non-precision farming for tomato was 63.86 per cent and for brinjal 28.14 per cent and contribution of technology was higher for tomato (33.71%) and brinjal (20.48%). Kiresur and Ichangi (2011) indicated that the per ha production of Bt cotton was 26.38 per cent higher than that of non-Bt cotton. The Bt technology component alone contributed 26.56 per cent to the total change in output. Top Conclusion The results of the study showed that per acre return of contract farming was 55.14 per cent higher than that of non-contract cultivation. The GAP technology component alone contributed 10.99 per cent to the total increase in output while the contribution of all other inputs was 44.15 per cent. The major contributor amongst all the inputs to the difference in returns was the cost incurred by the farmers for adopting the GAP technology (27.42%) followed by farmyard manure (19.00%). |
Top Tables | Table 1.: Production function estimates of contract and non-contract system of chillies cultivation
| | Particulars | Contract farming | Non-contract farming | Pooled | | Number of observations | 80 | 80 | 160 | | Intercept | 2.7166 (1.3960) | 4.5328 (1.2104) | 5.0201 (6.7891) | | Land preparation cost (Rs) | -0.0518 (0.9735) | -0.0008 (0.9863) | 0.1313 (0.0138) | | Farmyard manure (tonnes) | 0.3928 (0.0104)** | 0.0960 (0.0134) | 0.245 (6.1515) | | Chemical fertilizers (kg) | 0.2362 (0.5645) | 0.1317 (0.4931)** | 0.0926 (0.5901) | | Human labour (man days) | 0.0454 (0.4025) | 0.0502 (0.4860) | -0.1764 (0.0004) | | Irrigation (number of h/crop duration/ac) | 0.0713 (1.3276) | 0.9988 (5.1174) | 0.4129 (3.789) | | GAP technology adoption cost (Rs) | 0.4653 (0.0189)** | 0.0949 (0.02138)** | 0.2600 (2.189) | | Intercept dummy | - | - | 0.5513*** | | R2 | 0.6098 | 0.8679 | 0.8057 | | Adjusted R2 | 0.5777 | 0.8570 | 0.7981 |
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| Significant at 1% level of significance, | Significant at 5% level of significance | | | Table 2.: Decomposition of productivity in chillies cultivation among contract and non-contract farmers
| | Particulars | Percentage | | Total observed difference in output | 55.14 | | Technology component | 10.99 | | a. Neutral Technology | -181.61 | | b. Non-neutral Technology | 192.60 | | Contribution of inputs | 44.15 | | Land preparation | -1.80 | | FYM | 19.00 | | Manures and manuring | 0.14 | | Human labour (man days) | -2.20 | | Irrigation | 1.59 | | GAP technology cost | 27.42 | | Total estimated difference in output | 55.14 |
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