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Small Farmers and Organised Retail Chains in India

This study compares farmers selling vegetables to Mother Dairy, an organised retail chain, with those selling to the local mandi in Haryana to find out the drivers and constraints determining their participation in these two types of marketing chains, particularly for the small farmers. The findings suggest the significance of farm size in determining farmers’ participation in organised retail chains. Using Heckman selection–correction model, the study found that though the income of participating farmers increases, the increase depends on farm size, while the Ginni coefficient shows that the inequality in income distribution is more among the participants than the non-participating farmers.

The world over rapid rise of organised retail chains (ORCs)1 has been transforming the agricultural food marketing system progressively (Dries et al 2004).2 However, this transformation in India has been slow, both at the upstream and downstream of the supply chain. The size of the food retail market in India was estimated to be large, where the share of agricultural food retailing is growing faster (GoI 2007; NABARD 2011). On the one hand, ORCs are seen as an ­effective channel to link farmers with markets, while, on the other, concerns have also been raised about its impacts on the farmers, particularly the smallholders.

One of the concerns is that agribusiness firms deal mostly with relatively large farmers and exclude the smaller ones. The exclusion of the small farmer from relatively liberalised markets and contract farming can lead to more concentrated landownership and displacement of the rural poor (Key and Runsten 1999).3 The benefits distribution within the rural community by new marketing channels like contract farming can have important implications for their economic and social differentiations (Korovkin 1992). This concern is particularly relevant in case of India where, number of small farmers is on the rise and their farm sizes are shrinking,4 which is resulting in decline of marketable surplus capacity. The small and marginal farmer households earn less than what they spend,5 and half of them are indebted, and most of them live in severe poverty (Kumar et al 2011). Although farmers’ participation neither guarantees its benefits nor insures them against its risks, non-­participation excludes them from its potential benefits, thus increasing inequalities.

Further, the issues, such as high quality standards, high ­rejection rate, procurement of few crops, partial procurement of produce, and delay in payments, may affect income of the participating farmers. The counter-view is that higher prices for better quality, low waste, diversification towards high ­value crops and reduction in marketing cost may improve income of the participating farmers. Besides, inputs and other services by ORCs to farmers can improve their productivity, thus improving their income.

Some of the agriculture produce marketing challenges in ­Indian agriculture have been listed in government reports, ­including the inter-ministerial committee of the government of India and the working groups for the Twelfth Five Year Plan. Inadequate provisioning of regulated markets forces farmers to bear disproportionately higher marketing cost.6 Long distance travels and poor logistic support create huge wastage of agricultural produce. Large number of rural markets are still deprived of weighting, measuring, sorting and packaging ­facilities.7

Moreover, the tax and licencing has increased the trans­action cost and put barriers to entry for market agents.8 The undue regulation of markets has also prevented private investment in marketing infrastructure, post-harvest management, grading and packaging. Regulation has also hampered development of alternative marketing channels in India (Patnaik 2011). Overall, the price efficiency in India has also been low, especially in case of the vegetables.9 The agriculture markets in India are also not well integrated, regionally, vertically and temporally (Acharya 2004). Reforms in agriculture marke­ting in India have been slow.

Since 1950, agriculture and agriculture marketing being a states subject, states have enacted Agricultural Produce Market Committee (APMC) Acts to regulate agriculture ­markets. Its objective was to protect farmers from the exploitation of intermediaries and traders to ensure better prices and timely payment for their produce. The National Com­mission on ­Agriculture, 1976 reviewed the performance of regulations of agriculture markets and found that regulated markets (70% of secondary or terminal markets) have benefitted farmers by preventing trading malpractices, such as unautho­rised market charges, falsification of weights and measures. The commission therefore inter alia recom­mended: (i) establishing a market within a radius of 5 kilometres (km); (ii) bringing unregulated assembly, terminal and even primary markets under regulation; (iii) constitution of a market committee to supervise the market as per rules and regulation; (iv) providing ­facilities of weighting, grading and storing in each regulated market; and (v) licensing market functionaries, like commission agents and trader. But, over the year regulated markets have failed to yield the desired results. As per the Shankerlal Guru Committee, 2001, the regulated agriculture market has res­tricted marketing in India. Later on, efforts have been made to reform the APMC Act, in 2003, and also to promote the direct marketing as an alternative marketing structure. Recently, many organised retail chains, both private and public, have started operations. Of these, Mother Dairy, is a government enterprise, working since 1985.

Against this backdrop, this study compares farmers selling vegetables to Mother Dairy, an organised retail chain, with those selling to local mandi in Haryana, to find out the drivers on their participation in the Mother Dairy chain and consequently the impact on their income, particularly for the small farmers. The study provides evidences that may help in policy decisions on providing institutional mechanisms to make the supply chains inclusive. ­Besides, it contributes in the debate on impact of ORCs on farmers’ income, which may help in policy decisions to devise appropriate institutional framework to restrain the exploitative character of the ORCs. Moreover, the study, unlike many studies in the case of India, uses improved methods to remove the possible selection bias in the sample. The study also discusses the sources of the ­income impact along with a comparison of income distribution between the farmers participating in the farmers market association (FMA farmers henceforth) and those not participating therein (non-FMA farmers henceforth).

Background

Kumar (2006) observed that private agribusiness firms in Punjab operated contract farming more effectively, with positive outcomes for the farmers irrespective of the farm size, that while the state corporation-led contract farming seems to ­favour only those farmers with larger farm who do not benefit as much as direct contract farmers. In absence of representative farmer organisations, contract farming has limited regional and local impact in terms of the inclusion of small farmers (Porter and Howard 1997; Key and Runsten 1999).

The participation of the small farmers in the supply chain depends on their relative advantage or disadvantages. Among advantages, a small farmer operating predominantly with family labour can save on the cost of labour supervision, cost of monitoring, screening of hired labour, cost of contract ­enforcement and cost of negotiation (Key and Runsten 1999). On the other hand, there are disadvantages for small farmers that arise out of their low marketable surplus, low bargaining power and low capacity to invest. Ghezan et al (2002) argued that the factors affecting a small farmer’s access to new marketing channels include low marketable surplus, difficulties in meeting volume, quality and delivery requirements, lack of ­liquidity to withstand the long payment delays and lack of ­access to market information.

The small farmer would be interested in contract farming because it facilitates modern inputs, which are normally unavailable or are more expensively obtained through other sources (Porter and Howard 1997). On the other hand, a firm would prefer dealing with large growers to avoid the complexities of dealing with a large number of small farmers (Glover and Kusterer 1990) and by looking at the large farmers’ investment capacity, risk bearing ability and relatively uniform quality of land.

Ghezan et al (2002) found that in Argentina, supply chains dominated by multinational firms producing frozen French fries, tended to favour medium and large potato farmers, ­excluding the smallholders. High quality standards imposed on the suppliers work as an entry barrier for small growers (Gutman 2002). Deshingkar et al (2003) found that the benefits of government-sponsored schemes in horticulture are reaching the bigger farmers rather than the smaller farmers and landless households. Similar observations about the ­challenges for the small farmers have been made in Costa Rica by Alvarado and Charmel (2002). It has also been witnessed in Africa that producers faced challenges in meeting the tough quality and safety standards, and the requirements to make investments and adopting new practices (Weatherspoon and Reardon 2003; Faiguenbaum et al 2002).

Meeting high quality standards set by ORCs hampers participation of the small producer. The rise in the fixed cost component of the cost of exchange also works as an entry barrier for the small farmer. The exclusion becomes more pronounced when the credit market is imperfect and the cost of borrowing is high for the small farmers (Page and Slater 2003). But new institutions, for example, fair trade companies and cooperatives are helpful in improving the participation of the small producer (Page and Slater 2003).

Reardon and Swinnen (2004) argue that the rise of ORCs brings opportunities for small farmers because these offer a path into high-quality and high-value markets. Their observation also hints that the transformation in the agricultural food system is inclusive of more small farms than it was expected. The exclusion was expected based on the arguments of transaction costs and requirement conditions. The assistance by processing firms to large and small suppliers is overcoming the obstacles in investing and improving quality because few farms can deliver the required quality, which is forcing the ­retail chains to integrate vertically to secure a high-quality supply base (Reardon and Swinnen 2004).

Glover (1984) surveys literature on contract farming to ­examine its bearing on farmers’ welfare, including the issue of participation of the farmer. The study remains inconclusive and argues that in general agribusiness firms prefer large farmers, but most deal with whoever is available, while some look for small farmers. Neven and Reardon (2004) found that supermarkets were not excluding small farmers from supplying to the markets in Kenya in the initial stages of inception. Sutradhar (2014) found that farm size was not a significant entry ­barrier in the participation in Reliance Fresh retail chain in ­Rajasthan in 2011. Miyata et al (2009) conducted a survey of 162 farmer households in Shandong province in China during 2005 to study the impact of contract farming on income of small farmers. They found little evidence to support the ­hypothesis that firms prefer larger farmers over small ones.

Minot (1986) found positive impact of contract farming on income of farmers. Similar observations have also been made by Porter and Howard (1997), in a review of studies conducted on contract farming in Africa. In the Indian context also, a study by Birthal et al (2005) for dairy products found significant improvement in the gross margin of those farmers who participated in ORC. Singh (2002) studied models of contract farming in Punjab and highlighted that despite problems in the models of contract farming, the income of the participants has improved. Studies by the Joseph et al (2008) and Chengappa and Nagraj (2005) provide some leading observations on the impact of ORC on income. In a more rigorous analysis, Sutradhar (2014), found that cauliflower farmers in Rajasthan selling through Reliance Fresh have been able to raise their net revenue per acre significantly, while no such impact was seen for other crops.

The literature on participation of small farmers in ORCs broadly indicates that contracting firms/ORCs prefer to deal with relatively large farmers. However, studies have also indicated that farm size is not a significant barrier in participation. Similarly, there is a broad consensus that income of the ­far­mers participating in the ORCs would improve, however, evidences to support the case for small farmers are not ­prominent.

Data and Methodology

A field survey of 398 farmer households255 linked to Mother Dairy and the rest dependent on local mandifrom 19 villages from Haryana was conducted through structured questionnaire during the summer of 2009 (see Table 1 for details).10 The surveyed districts—Sonipat, Panipat, Karnal and Kurukshetra—are mostly connected to Delhi through National Highway 1 (NH-1), where most of the retail outlets of Mother Dairy are ­located. The surveyed villages are mostly located around 5 km to 30 km distance from NH-1, and in proximity of a town, ­having a vegetable mandi within a maximum radius of 20 km. The state, districts, and villages were purposively selected keeping the procurement operation of Mother Dairy into consideration. The farmer households linked to Mother Dairy and the other farmer households were selected randomly from list of farmer households. Mother Dairy was preferred for the study because of its wide network and long-standing and ­stabilised operations.

A non-FMA village is a nearest located village to FMA village where farmers were supplying vegetables to local mandi. For the selection of the non-FMA farmers, farmers are listed in each non-FMA village recording their basic characteristics and a sample of farmer households was drawn randomly.

Income is defined comprehensively, as net household income (NHI), which includes not only farm business income11 but also subsidiary income or non-farm income.12 Because growing more of contracted crops may result in withdrawal of resour­ces from other crops or non-farm activities, which could result in income forgone. Moreover, it is the overall household ­income which determines expenditure of household on food, clothing, etc, which determines the level of poverty. Since, the expenditure by a household increases with the household size, thus, to pin down the impact of income on poverty, the per capita income of a household is preferred over per acre or per household income.

The econometrics procedure of estimation includes estimation of PROBIT, ordinary least square (OLS) regression and Heckman selection–correction model. The PROBIT model is estimated to identify the factors determining the partici­pation of farmers in ORC. Thereafter, OLS regression and Heckman selection–correction model estimated to know the impact of participation on income of the farmers. The Heckman selection–correction model is used to know the bias, if any, in the results, as the sample is not random. Besides, to overcome the possibility of bias in impact arising out of some unobservable characteristics of the farmers Heckman selection–correction model is used along with regression. The model is specified as follows:

Yi = Xiβ + μ1i outcome equation ... (1)

Ti = (Ziγ + μ2i>0) participation equation .. (2)

where Y is the outcome (per capita income) and X is a vector of the independent variables, while in participation equation Ti is the binary variable take value 1 if participated and 0
otherwise; while Z includes variables that predict whether or not a farmer would participate in ORC. It may be noted that the Z and X may include common variable, and which are taken identical in some studies (Gronau 1974). The selectivity problem is ­defined as:

E[Yi | Xi ,Ti = 1] = Xi β +E[μ1i2i > - Ziγ] ... (3)

Expected value of Yi for observations where farmers have participated into Mother Dairy is defined above. The joint ­distribution of random disturbance term of outcome (μ1i) and participation equation (μ2i) can be written as follows:

μ1i = (σ21/ σ22)* μ2i + υi ... (4)

where σ21 is the covariance of the unobservables of the outcome and participation equations (σ22) is the variance of the unobservable in the participation equation, and υi is assumed to be uncorrelated with the unobservable of participation equation (μ2i). Now since we know the unobservable for outcome equation (μ1i), we can also calculate its expected value which is defined as follow:

E[μ1i | μ2i> - Ziγ] = (σ21/ σ22) E[ (μ2i 22) | (μ2i / σ22 )> - Ziγ/ σ22] = (σ21) ф (Z/ σ22)/ σ22Ф (Ziγ/ σ22) ... (5)

where ф(.) is the standard normal density and Ф(.) is its
cumulative distribution function. The selectivity bias is said to occur wherever σ21 is not zero. The presence of this bias in the models arises due to presence of omitted variables into the original model (1), where the quantity is the omitted variables, also called the Inverse Mills Ratio (IMR), which is defined as:

IMR = ф (Z/ σ22)/ Ф (Z/ σ22) ... (6)

The treated equation, or Heckman selection–correction model, is defined as

Yi = Xiβ + [ф (Ziγ/ σ22)/ Ф (Ziγ/ σ22)]σ ... (7)

where

σ = (σ21/ σ22) which is coefficient of IMR

The estimated coefficients are consistent in Heckman selection–correction model. The Stata software reports lambda, sigma and rho. Rho is correlation coefficient between the unobservable that determines selection equation and the unobservable that determines outcome in outcome equation. Sigma is the adjusted standard error for the outcome equation and lambda is the selection coefficient = sigma * rho. The Average Treatment Effect (ATE) is computed as lambda *average IMR [or exp (ATE) -1)*100 if variable in log form] which is interpreted as how much conditional outcome is shifted up (or down) due to selection or truncated effect. The ATE depends on the statistically significant value of the Chi-square.

The inequality of income distribution is measured using Gini coefficients and Lorenz curve. The value of Gini coeficent ranges between zero and one, where zero shows perfect equality, while one means the most unequal distribution of the variable.

Results and Discussion

Mother Dairy Fruit & Vegetable is a wholly owned subsidiary of National Dairy Development Board (NDDB). It procures large a number of seasonal fruits and vegetables from thousands of farmers across a number of states in India. In Haryana, fruits and vegetables are procured through farmers’ marketing associations (FMA) at the upstream level of the chain, which are sold through Safal outlets spread across National Capital Region (NCR) at downstream. Mother Dairy has distribution centres at Pallabakhtavarpur and Mangolpuri in NCR, which are main coordinating locations having installed a huge infrastructure for storage, processing and logistic facilities.

Most of the procurement centres in villages are maintained by the FMAs in Haryana. Any farmer who has land (no restriction of size), grows fruits or vegetables and is ready to supply, can become member of the association. The objectives of the association are to enhance productivity of fruits and vegetables by provi­ding modern techniques, machines, access to inputs, information, crop protection and crop production programmes. It orga­nises farmers, takes decisions, monitors their actions, enables procurement operations, builds trust and ensures quality. The association is also responsible for procurement of fresh and quality vegetables from growers and transporting it to Mother Dairy. The member farmer of the association elects one president, whereas the secretary, who oversees all procurement operations and maintains records, is appointed by Mother Dairy. On daily basis, the produce brought by farmers is loaded in a vehicle after quality check, weighing and packaging, and then transported to the distribution centre of Mother Dairy. The final quality check is carried out by Mother Dairy at its distribution centre, and the status about rejected percentage and price assigned to the consignment is conveyed to the farmers usually next day of the procurement. Payments are made through the bank account, and usually take more than a week’s time.

Characteristics of surveyed households: The household characteristics are presented in Table 2 (p 17). There are about six persons in an average household; the difference between fma and non-fma farmers is statistically significantly. Proportion of adult (more than 18 years) members is also significantly larger in non-fma group than fma. Average age of fma farmers’ household head is than less compared to the non-fma group. However, education in both the groups is low and does not differ much. These groups also do not differ in terms of agricultural fixed assets (other than land), ownership of cattle and ­vehicle. The fma farmers have some advantages in terms of net operated area, leased in land and area under vegetables. The leased-in area seems to be playing a role in increasing operated area for fma farmers. The cropping intensity is significantly higher for non-fma farmers. The use of inputs such as family labour and biochemicals is higher in fma farmers, while machine labour, irrigation is higher in non-fma farmers, the differences are statistically significant. Marketing cost is lower for fma farmers than the compared group. The value of output is higher and statistically significant for the fma farmers. However, their productivity does not differ significantly from non-fma farmers. The area and value share of vegetables is significantly higher in the case of fma farmers than others. Similarly, the net household income and farm business income are higher for fma farmers, and so is the net household per capita income, and these are statistically significant, too. Off-farm income, however, is higher in non-fma farmers.

The above analysis shows that there are some characteristics which are statistically different between the two groups, especially in terms of household size, land profile, etc. These differences in characteristics between fma farmers and non-fma farmers may play a role in determining the participation.Probit model is estimated to find out the factor determining the participation.

Econometric analysis of participation: The participation of small farmer in ORCs has been a big challenge. This section ­using PROBIT model examines the questions: what are the factors determining participation of the farmers in ORC; and does the farm size work as a barrier for entry in Mother Dairy? The depen­dent variables include household characteristics such as family size, age and education of household head, proportion of adults in family, proportion of leased-in area, farm size and a dummy for vehicles.13 The results of PROBIT model are ­presented in Table 3.

The explanatory power of the PROBIT model is low but statistically significant. Result shows that the farm size is one of the strong predictors of participation in ORCs, which means farmers with large farm size are preferred by the ORCs. ­Although owning a large plot of land is not a condition for participation in the FMA, the dominance of large farmers in ­the associations might have played a role in their selection. Further, leasing-in relatively larger percentage of area is another important predictor of participation in the ORCs. Probably because these farmers diversify more towards vegetables in order to maximise their profits, thus improving their chances of participating in the ORCs. Besides, availability of labour, as reflected in terms of household size and proportion of adult members, tend to ­decrease probability of participation. Probably low ­opportunity cost of a member in larger family may enable to depute a member to market the produce to local mandi.

Younger household heads are more receptive to change, as also reflected in the results, where relatively younger heads are more likely to participate in the ORCs. Low education of household head seems to have no significant impact on participation decision. Owning a vehicle reduces the chances of a farmer to participate, because its use can enable farmers to deal with higher transportation cost and enable farmers to access information about the local market, however, it is not found statistically significant. The above analyses show that labour availability, young household heads, contract in land market and size of operation have played play a crucial role in participation of farmers in the ORCs. This also indicates the possibility of selection bias in the sample.

Determinants of income: Table 2 records that an FMA farmer earns significantly higher per capita income than a non-FMA farmer, and most of their income is contributed by crop income. Within FMA farmers’ crop income, about 42% is contributed by vegetables. During the survey, farmers reported that about 60% of their vegetable produce is supplied to Mother Dairy. In view of presence of selection bias in the data, Heckman sample correction model results, as in Table 5, have also been presented along with OLS regression results (Table 4). As per Heckman’s model, the value of “Rho” is estimated at 0.88, which is high but not statistically significant, and indicates the absence of selection bias in the sample.

The coefficient of dummy FMA is found positive and statistically significant, which suggests that participation of a farmer contributes `1,094 in per capita income of household for every additional acres of land. The comparable value of the coefficient in treatment regression estimates is `1,520. For additional one acre, the income increases by 3%–4% for FMA-farmers compared to non-FMA farmers.

The impact on income is translated through price and non-price channels. The price channels include price efficiency and marketing cost. Among non-price channels, crop diversification, farm productivity and profitability are the prominent ones. The net farmer prices14 between Mother Dairy and local market are compared and found that Mother Dairy paid higher net price to farmers for selected vegetables than the local markets. Along with the price, Mother Dairy also saves marketing cost15 for the farmers. The farm productivity16 is higher for the FMA farmers than the other group. The diversification of FMA farmers, too, is double than the other group and the difference is statistically significant (Table 2).17

Inequality in income and land distribution: Key and Runsten (1999) and Korovkin (1992) indicated implication of exclusion of small farmers from new marketing channels inter alia on
inequalities. The skewed participation in the ORCs may accentuate income inequalities. The Gini coeficient is calculated for land and income distribution for FMA and non-FMA farmers saparately and for all sample hosueholds (Table 6). The result shows that the land is relatively unequally distributed among FMA farmers than the non-FMA farmers. Further, the income is also found relatively unequally distributed among the FMA farmers than the other group. This indicates that participation is likely to worsen the income distribution, however, there is a need for rigorous analysis to establish a cause and effect relation. One can also observe the same looking at the Lorenz curves presented in Figures 1, 2 and 3 (p 18).

 

Conclusions

This study compares farmers selling vegetables to Mother Dairy with those selling to local mandi in Haryana to find out constraints on their participation in the chain and impact on their income, particularly on small farmers’ income. The study also compares the income distribution of the participant and non-participant farmers. The two groups, FMA and non-FMA, differ significantly in terms of characteristics, where FMA has an advantage in land and its utilisation profile, while non-FMA has an advantage in terms of demographic characteristics such as availability of family labour.

The participation of farmers in Mother Dairy is mainly determined by labour availability, age of household head, contract in land market and size of operations. Despite a farmer marketing ­association at upstream of the Mother Dairy supply chain in Haryana, the participation is determined by the size of operational holdings. This may be because of dominance of large farmers in the association. Apart from this, contract in land market, age and availability of family labour explains participation of farmers in Mother Dairy. Further, their participation contributes `1,094–`1,520 (3%–4% of average income) in per capita income for every additional acres of land. This increase in income is probably on account of better price, diversification towards vegetables and reduced marketing cost. In a preliminary examination, the distribution of the income is found more unequal in the case of the farmer, who participated in Mother Dairy compared to the others. The causes for worsening income distribution may need further examination.

These results have important policy implications. First, the farmers’ association is an important institutional innovation for inclusion of farmers in new marketing channels. Having far­mers’ associations in supply chain, Mother Dairy is probably a more inclusive chain than those operating without associations. However, it is equally important that the association ensures equal ­opportunities to farmers irrespective of their size of operation, and is able to counter the influence of a few. Second, the results support the policy of promotion of direct marketing chains to improve the income of farmers and indicate that schemes like Mother Dairy have the potential to yield benefits if scaled up.

1 An ORC consolidates the whole supply chain from procurement to retailing under a single management.

2 In general, based on socio-economic factors and a degree of advancement in policy reforms, waves of development are visible in northern half of the Central Europe, most of the southern Central Europe, and all of Eastern Europe, including the Russian Federation. See IFPRI (2008) also. Schwentesius and Cruz (2002), examines the case of supply chains procuring fruit and vegetables in Mexico over the decade through contractual arrangements with growers for agro-export and agro industry firms.

3 Shah (2011) raises a question on exclusion of the poor from the chain. See also Cacho (2003).

4 In India, 85% of landholding is either small or marginal categories—marginal (0.01−1 ha), small (1.01−2.00 ha).

5 As reflected in the survey by National Sample Survey Office (NSSO) in 2003.

6 The number of regulated markets in India increased to 7,157 in 2010 from 286 in 1950 along with about 21,221 rural primary markets being recorded in the same year. Out of these, 284 regulated markets and 189 rural primary markets were in Haryana. On an average, each market serves 460 sq km area in India compared to 155 sq km in Haryana. However, the density of regulated markets remains highly inadequate compared to 80 sq km (or within 5 km) the norm set by National Commission on Farmers, 1976.

7 As per the Working Group of the Twelfth Five Year Plan, only around 7% of the total quantity sold by farmers is graded before sale. The grading facilities were available only in 1,368 out of about 7,157 regulated markets. The storage capacity was estimated to be only 30% of the required capacity and cold storage was available only for 10% of the fruits and vegetables.

8 As per the Working Group of the Twelfth Five Year Plan, market fee ranging from 0.50% to 2.0%, commission charges vary from 1% to 2.5% in foodgrains and 4% to 8% in fruits and vegetables, other charges, such as, purchase tax, weighing charges of the sale value of the produce. In some states, total charges increased upto 15% which is excessive.

9 Price efficiency in vegetables such as tomato, cauliflower, capsicum and peas in Himachal Pradesh was estimated between 46% and 52% during 1991−92 (Thakur et al 1994). In another study of Karnataka during 1985–86 by Kiresur et al (1989) estimated the farmers’ share ­between 36% and 51% in tomato and brinjal, while in case of potato and onion, which are relatively durable, the price efficiency ranges between 60% and 67%.

10 The questionnaire recorded the household’s characteristics such as demographic, land, crop, assets, cost and income, and terms and condition of the contract, experience of farmers with Mother Dairy, etc.

11 Farm business income is equal to gross agricultural output minus all paid-out costs. The paid-out costs are costs paid to buy inputs such as fertilisers, pesticides, hired machinery, hired labour, irrigation charges, seeds, etc, net value added is calculated deducting intermediate costs from gross value of output like seeds, fertiliser, pesticides, irrigation and transaction cost. The gross agricultural output includes output from all crops and their by products. By-product income includes stalks, straw, etc, at their market price.

12 Non-farm income computed as income/receipts from animal husbandry, agriculture wage employment, non-agriculture wage employment, salary and pension, other household enterprise income and rent on leased-out land. Animal husbandry income includes income from milk, milk products and poultry. Agriculture wage employment income includes the receipt from wage employment in agriculture. Non-agriculture wage employment income includes wage from non-employment income. Other household income includes income from self-employment. Some of the income sources are monthly such as pension and salary, etc, so to arrive at annual figures these are multiplied
by 12.

13 Vehicle dummy, a farmer having vehicles such as four-wheeler, three-wheeler, motorcycle, etc, takes value one and zero otherwise. It has also been used in literature as it indicates reduction in transaction cost by facilitating transport of produce to mandi. In this study, the vehicle dummy is included for identifying the model as well as a variable to show impact of vehicle.

14 Mother Dairy price net of marketing cost.

15 Marketing cost includes transportation cost, loading and unloading charges, packaging, commission of agents and taxes. As reflected in Table 2, the marketing cost is significantly lower in FMA farmers compared to non-FMA farmers.

16 The difference in productivity, value of output per acre of net operated area, between FMA and non-FMA is `3,127, which is not statistically significant.

17 Diversification is defined as percentage of area of vegetables in gross cropped area.

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