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This study comprise of minimizing farm risk through diversification in tomato based farming systems (TBFS). Primary data was collected from 3

Farmers having high and medium degree of diversification faced lower income risk as compared to those farmers having lower degree of diversification.

As Agriculture is a gamble with nature, risk and uncertainty become part and parcel of agriculture. Agricultural production is susceptible to a number of risks such as climatic change, price volatility, pests and disease infestation to mention a few (

Tomato crop is being cultivated since long time in Kolar district, Eastern dry zone of Karnataka contributing to a major part of tomato production to the state and the country. The district had highest area (6618 ha) and production (66553 tons) under tomato cultivation in Karnataka during 2003–04. Tomatoes grown here are marketed to Maharashtra, Tamil Nadu, Madhya Pradesh, Andhra Pradesh, Telangana and many other states (

Tomato based farming system is a type of farming system in which farmers along with tomato as their base crop had diversified to the production of other crops and subsidiary occupations such as sericulture, livestock and others. Farmers who were growing tomato for minimum of one year or more and having a major or substantial proportion of farm income derived from tomato cultivation were considered for the study. Such farmers growing tomato in one or two or in all the three seasons along with other crops and allied farm and/or non-farm activities were selected randomly. A study was conducted with an objective to analyze how diversification minimizes farm risk under tomato based farming systems using quantile regression analysis.

Primary data through personal interview was collected from 3

The Herfindahl index (also known as Herfindahl-Hirschman Index or HHI) was named after economists Orris C. Herfindahl and Albert O. Hirschman. Herfindahl Index (

Where, ^{th} crop in total cropped area, it is given by dividing the area under ^{th} crop by the total cropped area,

Where

The degree of crop diversification was measured in order to know how farmers were minimizing their production or income risk through diversification, the Crop Diversification Index (CDI) used as mentioned below (

The zero value indicates specialization and a movement towards one shows an increase in the extent of crop diversification.

To infer about the magnitude of risk associated with the tomato growers, the coefficient of variation (CV) was estimated. The CV was used as a measure to study the variability in area, production, productivity and income from various sources of enterprises under tomato based farming system. It was computed using the following formula,

Quantile regression is used as an alternative to ordinary least squares (OLS) regression and related methods, which typically assumes that association between independent and dependent variables are the same at all levels. It is applied in the present study as the data finds heteroscedasticity and deviates from the assumptions of OLS regression. This may be because of presence of too many outliers in the data. Quantile regression is not a regression estimated on a quantile, or subsample of data. In OLS regression, the goal is to minimize the distances between the values predicted by the regression line and the observed values. In contrast, quantile regression differentially weighs the distances between the values predicted by the regression line and the observed values, and then tries to minimize the weighted distances. In the present study, the objective of examining effect of diversification in minimizing farm risk is studied using quantile regression. The co-efficient of variation of income is considered as a proxy for the income risk faced by the farmers. The variables considered for quantile regression and the method followed for the analysis is as given below:

Let ^{th} quantile of _{T}(Y)

A linear quantile regression model,

Where the sample estimate β(τ) is the marginal change in the τ^{th} quantile due to the marginal change in x. Quantile regression allows us to study the impact of predictors on different quantiles of the response distribution, and thus provides a complete picture of the relationship between

The error function for minimization is transformed into the standard “Linear Programming” type of dual problems for minimization and maximization. Then, linear programming algorithms are applied to solve the parameters for quantile regression. STATA was the software used to run the quantile regression model. Breusch-Pagan / Cook-Weisberg test for heteroscadasticity was applied to check for the presence of heteroscedasticity in the data. The variables considered in the study are:

On an average, highest average area under dry land was 4.87 acres in

Average land holding size of the sample farmers

In the aggregate, highest landholding size was observed in Malur

The highest average leased-in land was 2.52 acres in Srinivaspura

Tomato was grown as a major crop in all the three

Based on the CDI, the sample farmers from all the three

Classification of farmers based on CDI across different

The average annual income of the farmers was higher for those having higher and medium degree of diversification compared to those farmers with lower degree of diversification. This was because farmers having higher diversification earned income from more number of enterprises than those having less diversification. Farmers having high and medium degree of diversification faced lower risk as compared to those farmers having lower degree of diversification as shown by the figures of CV in

Tomato was the major commercial crop for farmers in the study area. Farmers grew other crops in addition to tomato to minimize variations in the income as tomato prices fluctuated more violently than any other crop. As the proportion of area under tomato to the total cultivated area decreased, the variability or risk measured in terms of co-efficient of variation (CV) increased. This showed that as the extent of land allocated to tomato increased, the risk decreased considerably perhaps due to the economies of scale operating in all the three

The variability in income was highest among livestock enterprise in Malur and contrasting results were seen in the case of Mulbagal where the variability in livestock income was lowest and lower in the case of Srinivaspura

The analysis of risk associated with different types of tomato based farming systems (TBFS) revealed that tomato + other crops + livestock systems in the case of Malur had lowest risk of 63.20 per cent which was a predominant TBFS prevailing in the

The effect of diversification on variability in average annual income of the sample farmers was analysed using quantile regression and the results are presented in the ^{th}, (

The variability in income was considered as the dependent variable as influenced by variables listed in ^{th} quantile. It was negative at this quantile to the farmers who had higher income from tomato and less income variability, exhibited lower degrees of variability in their total income. This result was in contrast with those farmers having higher variability at higher quantiles. This may be due to the fact that the farmers at lower quantiles have less income derived from tomato and contributed less to CV vis-à-vis farmers who were already at higher risk with high variability in income at higher quantiles.

Further increase in income from tomato would increase their income risk. Farmers at lower quantiles were mostly those who were growing hybrid tomatoes with low variability in their annual income compared to those with upper quantiles who were growing HYVs of tomatoes who had comparatively higher CV of income. Increase in the income from other crops led to increase in variability of total income at upper quantiles where farmers had higher income variability. Income from livestock had positive and significant effect on variability in total income for farmers having low income variability at lower quantiles and in OLS regression. It also reduced the risk among the farmers who had higher income variability at upper 0.90^{th} quantile because livestock income was more stable income for farmers growing HYVs of tomatoes who were prone to higher variability in their total income. Income from sericulture reduced the income risk at lower 0.25^{th} quantile for farmers who had low income variability, but increased the risk in their total income for those farmers having higher income variability at upper 0.50^{th} and 0.75^{th} quantiles (

Tomato was the major crop grown in the study area often facing the risks of price volatility. Diversification at different levels reduced the farm income risk for the tomato growers. Farmers having high and medium degree of diversification faced lower risk as compared to those farmers having lower degree of diversification. The results showed that, the inclusion of livestock and sericulture as subsidiary farm enterprises led to stability to farm income of tomato growers. Further, the effect of diversification on variability in average annual income of the sample farmers was analysed using quantile regression analysis. The results of quantile regression showed that as the degree of farm diversification increased, the variability in the total annual income of the farmers decreased. The extent of contribution of diversification on minimizing income risk was highest at the lower quantiles for farmers who had less variability in income compared to those who had higher variability representing upper quantiles. The effect of diversification in reducing risk was lower at upper quantiles because, the farmers at upper quantiles had higher income variability and were those with lower degree of diversification.

Thankful to Department of Agricultural Economics, University of Agricultural Sciences, Bengaluru, Karnataka as this paper is a part of Ph. D. thesis research submitted.