Risk Attitudes and Financial Pro�?tability of Tomato Farmers - A Study in Bangladesh

Purpose: Tomato production in the world has increased tremendously within last few decades. Nevertheless, tomato farmers are facing production, market, price and �?nancial uncertainty. Management of these risks is greatly inﬂuenced by their attitudes towards risk. So, the objective of this study is to determine the risk attitude and pro�?tability of tomato farmers in Bangladesh. Research Method: Sixty sample respondents of tomato farmers were selected from Mymensingh district. The Safety-First principle was used to estimate risk attitude coeﬃcient while tobit regression model was utilized to estimate the factors aﬀecting risk attitudes of tomato farmers. In addition, �?nancial pro�?tability was analyzed from diﬀerent points of view. Findings: Most of the tomato farmers were risk averse. Results �?nd that only 18% of farmers were risk preferring while 42% of farmers were risk averse. Risk preferences of farmers increase with training and education while risk preference decrease with age and experience. Training and education help to understand the importance of receiving newly introduced technology, timely application of seed, irrigation and fertilizer. Education assists to earn from diversi�? ed sources that make them risk preferred. The bene�? t cost ratio (BCR) of tomato farming was 2.31 indicating that tomato farming is pro�? table. Research Limitation: A small sample size was used for this study. Therefore, policy makers should be cautious to generalize the results to a wide context of tomato farmers in developing countries. Originality/Value: Productivity and pro�?tability of tomato farmers can be improved if farmers can manage diﬀerent risks and uncertainty associated with production practices.


INTRODUCTION
Vegetable production in the world has achieved tremendous growth in last 50 years because of new technology adoptions such as modern production practices, new variety seeds and mechanization of farming. Vegetable production is about 290.13 million tons in 2016 (FAOSTAT, 2018). Among diff erent vegetables, tomato captures about 60% of total fresh vegetables produced in the world. Tomato is a great source of diff erent vitamins that reduce the risk of diseases such as gastritis, prostate, breast cancer, skin erythema and coronary heart disease (Levy and Sharoni, 2005). Apart from its nutritional signifi cance, huge productivity of tomato is the blessing for landless farmers, laborers, distressed women of developing countries because it assists to increase income and reduce inequality (Mitra andYunus, 2018). Bangladesh, a developing country, has witnessed about 6.5 times growth in tomato production after its independence. Tomato is one of the major vegetables produced in Bangladesh that occupies about 25% of total fresh vegetables produced in Bangladesh. Farming system is shifting from substantial to commercial level that is responsible for increasing tomato productivity (Mitra and Prodhan, 2018). Although tomato is known as a winter vegetable, it is also available in summer season because of the development of new varieties by Bangladesh Agricultural Research Institute (BARI) and Bangladesh Institute of Nuclear Agriculture (BINA). Tomato is the second most important vegetable export commodity after potato (ITC, 2016).
Nevertheless, tomato farmers are facing production, market, price and fi nancial uncertainty. These uncertainties are created by weather change, disease and pest infestation, market demand, supply and price variation. Increasing supply of tomato reduces market price. Although tomato is available in summer season, its supply increases in winter. Farmers face a huge fi nancial risk when price decreases due to increasing supply . Financial profi tability is greatly infl uenced by diff erent risk factors. Weather in Bangladesh is changing day by day. Untimely rain, drought, prolonged summer and curtailed winter severely damage tomato productivity. Management of these risks is greatly infl uenced by their attitudes towards risk. Businessmen and policy makers will be benefi tted if they are well acquainted with the farmer's risk attitude. Risk management practices and educational programs can be tailored according to farmer's risk attitude (Sulewski and Kloczko-Gajewska, 2014). Insurance providers, seed companies and fi nancial institutions get benefi t from farmers risk attitude when developing insurance policy and marketing new seed varieties. For example; if tomato farmers are risk lovers, they easily adopt newly introduced seed varieties. Although a new variety generally provides a huge production, these varieties may be vulnerable to diseases and pests. Risk averse farmer do not want to buy newly introduced seeds. So, seed companies must be aware about the risk attitude of farmers. Again, risk lover farmers do not want to make insurance for their farms while risk averse farmers usually have an insurance policy (Amaefula et al., 2012). Risk attitude of farmers may be infl uenced by diff erent socioeconomic characteristics of farmers such as age, experience, education, family members, training and extension services.
There are some studies on risk attitude of fruits, vegetables, crops and aquaculture farmers in the world but very few works on tomato. John et al., (2016) found that inter croppers are more risk averse than the mono croppers in Nigeria. Risk attitude infl uences the decisions farmers make in the agricultural production process. Vassalos and Li (2016) got the fi nding that younger farmers with large farm size are more likely to participate in marketing contract agreements in USA. Asci et al., (2014) found that growers in Florida choose fi eld-grown tomato due to high values and risk aversion. Fakayode et al., (2012) got the result that pest and disease attack, traditional methods of farming and weather dependency were the most perceived risk in Nigeria. Maintaining good relationship with traders, selling at low price due to perishability, off -farm income and selling at local market are the major risk management strategies for fruits and vegetables. Lucas and Pabuayon (2011) found that resourcepoor farmers are more likely to consider rice farming as not risky. Probably, they have little to lose or gain compared to resource endowed farmers. In addition, both resources endowed and resource poor farmers think that farming is relatively risky because of the increasing price of fertilizer and environmental factors such as unfavorable weather. Farmers also found that rice and corn are relatively risky than other crops in Philippines. Lee and Cheong (2010) perceived price and production are the most signifi cant risks. Farm management and technical measures are perceived to be more eff ective than other risk management strategies in Vietnam. Jin et al., (2007) found that under uncertainty the traditional NPV rule for aquaculture investment decision should be modifi ed. As far as the author's awareness, risk attitude of tomato farmers in Bangladesh has not carried out. Therefore, the objective of this study is to determine the relationship between risk attitude and profi tability of tomato farmers in a selected area of Bangladesh.

Sampling Procedure and Data Description
Mymensingh is one of the largest districts of Dhaka division. This district covers an area of 4363.48 km 2 which lies between 24°15´ and 25°12´ of northern latitudes and between 90°04´ and 90°49´ of eastern longitudes. Temperature of mymensingh is ranging from 12 to 33 0 C and average annual rainfall is about 2174 mm. This is a promising area of Bangladesh for tomato production that witnessed about 98% increase in the last 8 years. In 2015, about 6800 MT tomato was produced in Mymensingh (BBS, 2015-16). Two upazillas of mymensingh district named Mymensinghsadar and Muktagacha were selected purposively based on the production of tomato. These two upazillas occupy about 60% of total tomato production of Mymensingh (District Statistics, 2013). In this study, sixty sample respondents of tomato farmers were selected from four villages namely, Uzancahier chor, these two upazillas using stratifi ed random sampling techniques ( Figure 01). A pre-tested interview schedule and direct interview method are used for the collection of cross-sectional data. Data is collected on input used, output produced, credit accessibility, socio-economic characteristics of farmers.

Analytical Techniques
Both inferential statistics and econometric techniques are utilized for data analysis. Inferential statistics such as mean and standard deviation are used while ordinary least squares technique is used for estimation of equations.

Estimation of Risk Attitude
The Safety-First principle is used to estimate risk attitude coeffi cient. Safety-First principle was fi rst introduced by Kataoka (1963). This model was modifi ed by Moscardi and de Janvry (1977) and utilized by Amaefula et al., (2012). This principle assumes that the objective of a farmer is to minimize the probability of experiencing variability in tomato production. The Cobb-Douglas production function is used for functional analysis of the data. It is a homogeneous function that assists to measure the return to scale and interpret the elasticity coeffi cients. The productivity of tomato is likely to be infl uenced by diff erent factors like human labor, seed, fertilizer, pesticide, irrigation etc. The empirical form of the Cobb-Douglas regression equation is as follows:  The risk aversion coeffi cient for each producer is obtained from equation (5). Fertilizer is selected among other inputs in estimating risk attitude coeffi cient because it is the main input of tomato production. Moreover, it is the most signifi cant input because of its importance in increasing yield of tomato. The elasticity of fertilizer which is similar to the coeffi cient of fertilizer in the production function is used for calculating this coeffi cient. Coeffi cient of variation of output, output and input prices are used to estimate a value of K for each farmer.
The coeffi cient of variation of productivity (θ) is calculated from summary statistics of tomato productivity in equation (7).
The risk attitude coeffi cient (K) is computed in equation (8): The farmers are classifi ed into four groups based on the risk parameter k following the work of Moscardi and de Janvry (1977

Factors Determining Risk Attitude of Tomato Farmers
Tobit regression model, developed by James Tobin, is utilized to estimate the factors aff ecting risk attitudes of tomato farmers. This is also known as censored regression model. Several previous studies used tobit regression model for investigating the factors aff ecting risk attitude (Yusuf et al., 2015). Risk attitude is ranged from k < 0 to k < 2.0 and this model is shown in equation (9): Where, K = Risk parameter; β= Coeffi cient; X 1 =Age (years); X 2 = Education (Years of schooling); X 3 = Family member (number); The Journal of Agricultural Sciences -Sri Lanka , 2019, Vol.14, No. 3 X 5 = Training (dummy) and e = error term.
Risk parameter of tomato farmers is the dependent variable in the tobit regression model. Age, education, family members, training and experience are independent variables and these variables are hypothesized to have a positive infl uence on risk attitude of tomato farmers. In addition, STATA-14 was used for estimating Cobb-Douglas production function and tobit regression.

Profi tability Analysis of Tomato Production
Return was calculated by multiplying yield with its price. The gross return was estimated as follows in equation 1: Gross return, GR = Σ QP (1) Where, GR = Gross return from tomato (Tk. / hectare); Q =Quantity of the tomato; P = Average price of the tomato.
Gross margin was calculated by subtracting the total variable costs from the gross return , showed in the following equation 2: Where, GM = Gross margin; GR = Gross return; and TVC = Total variable cost.
Net return was calculated by deducting total costs from gross return as shown in the equation.
The formula for calculating net return of tomato production is as follows in equation 3: Where, GR = Gross return; and TC = Total cost.
The Benefi t-Cost Ratio (BCR) is a relative measure which is used to compare benefi t per unit of cost. BCR was estimated as a ratio of gross returns to total costs  showed in the following equation 4: (4)

Summary Statistics
Summary statistics of productivity, input used in production and socio-economic characteristics of tomato farmers are presented in Table 01. Average yield of tomato was found 21067 kg per hectare ranging from a minimum of 9000 kg to as maximum as 35000 kg. Mean yield of BARI hybrid tomato was found 32780 kg per hectare (Baree, 2012). Labor was the main input for producing tomato. Quantity of labor per hectare was 16 man days and a period of eight hours was considered as one man days. Irrigation cost was the second most important cost (Tk. 23057/ha) item of tomato farmers. Timely irrigation helps to increase tomato productivity (Djidonou et al., 2013). Most of the farmers had much experience on tomato farming and they are middle aged. Result also found that farmers' family size was much higher than average family size of Bangladesh (BBS, 2015-16). Young members of the family can work as a family labor that will reduce the hired labor cost. Older farmers had a larger family size and young members of this family contributed to the farm as a labor (Peter and Susan, 2014). In addition, average education (years of schooling) of tomato farmers was 9 years. Education assists to adopt new production techniques and increase productivity.

Determination of Risk Attitude Coeffi cient
The Safety-First principle assumes that the farmer's objective is to minimize the probability of experiencing variability (a shortfall) in output or income below a certain initial level (Amaefula et al., 2012). Assume that, fi rst principle holds and the degree of risk aversion is derived from an observed behavior. Observed behavior can be found from the production technology of tomato farmers. The observed level of inputs use exposes the risk attitude of tomato farmers. First stage of method involves the estimation of production function that shows the direct relationship between input vector (X) and output (Y). Then the most signifi cant input variables are determined from the estimated function based on signs and coeffi cients of signifi cant variables. Table 02 presents the estimation results from the Cobb-Douglas production function. In addition, few goodnessof-fi t measures are reported. One measure is the coeffi cient of multiple determination (). The value of is 0.79 that means 79 percent of the variation in tomato productivity is explained by the explanatory variables included in the model. A second measure is the overall fi t of the estimated regression. F-value (42.9) is signifi cant at 1 percent level of signifi cance implies that all explanatory variables included in the model are important for explaining the variations in productivity of tomato.
Quantity of fertilizer is the most signifi cant input with the coeffi cient of 0.36. In addition, irrigation water per hectare is also signifi cant but its coeffi cient is a little bit lower than fertilizer. Elasticity of scale can be calculated from the estimated coeffi cients. The sum of estimated coeffi cients for tomato farmers is 0.66 that implies farms are operating under decreasing returns to scale. Inputs used in production process can be divided into modern and traditional inputs. Modern inputs are fertilizer, irrigation and pesticide while traditional inputs are seed and labor. Modern inputs have larger share in total output elasticity. Factor share for modern and traditional inputs are 0.65 and 0.02 that shows the importance of modern inputs in production process. Alene and Manyong (2007) also found that modern inputs capture large share of output elasticity.  The Journal of Agricultural Sciences -Sri Lanka , 2019, Vol.14, No. 3 The elasticity of quantity of fertilizer (x2), coeffi cient of variation (θ), output price per kg and factor price per kg are utilized in determining the risk attitude coeffi cient. The risk attitude coeffi cient is specifi c for each farmer. In Figure 02, the calculated coeffi cients were classifi ed into four groups based on their risk attitudes named risk preferring, low risk averse, intermediate risk averse and high risk averse. Results fi nd that only 18% farmers are risk preferring while 42% farmers are risk averse. Therefore, most of the farmers are not interested to take risk. Because of their risk averseness, they may not easily accept newly introduced technology such as hybrid seeds or machinery. Amaefula et al., (2012) found the similar result for poultry birds.

Determinants of risk attitude of tomato farmers
Risk attitude of tomato farmers are aff ected by diff erent socioeconomic factors such as age, education, experience, training and family size. Age, education and training are signifi cant at 1% while experience is signifi cant at 10% (Table 03). Negative coeffi cient of education (-0.04) shows the negative eff ect of education on the risk preference of tomato farmers. That is the higher the years of schooling, the lower is the incidence of risk averseness. Educated farmers can easily attend any kind of training programs and workshops. They can understand the importance of receiving newly introduced technology. Moreover, educated farmers can earn from diversifi ed sources that assist farmers to adopt newly invented technology. Amaefula et al., (2012) found the opposite result. In addition, training programs is negatively related with farmers' risk attitude coeffi cients. Negative coeffi cient (-0.05) means the higher the training received, the lower the risk averseness. Diff erent government and non-government organizations arrange training for tomato farmers. Training on tomato farming introduces the farmers with new variety seeds, pesticides and machinery (Schreinemachers et al., 2016). Furthermore, farmers become aware about the timing of input application that reduces the production risk. Therefore, training facility increases the risk preference.
Positive coeffi cient of Age (0.02) implies that risk aversion increases with the age of tomato farmers. Generally, aged farmers are risk averse. They are not interested to adopt new farming techniques with associated risk (Albert and Duff y, 2012). Amaefula et al., (2012) found similar result. In addition, tomato farmers' experience has positive relation with farmers' risk attitude that is shown by the positive coeffi cient (0.03). So, risk aversion of tomato farmers increases with their experience. Sometimes, experienced farmers apply their own experience in their farm instead of receiving new farming techniques. They think that modern technology may not congenial for tomato farming. Amaefula et al., (2012) found the similar result. In addition, family size is positively related with risk attitude coeffi cient but it is insignifi cant.

Financial Profi tability of Tomato Production
This study also focused on the fi nancial profi tability analysis of tomato farmers. All costs and returns were calculated for the duration of one year operation. The cost of using variable inputs and fi xed inputs were calculated. Variable inputs were human labor, seed, fertilizer, manure, land preparation, pesticide, irrigation, loading and transportation cost while fi xed inputs were depreciation of fi xed assets, rent on land. Table 04 revealed that the total variable cost of tomato cultivation was Tk. 99258.34 per hectare which was 36.41 percent of total cost of tomato production. Fixed cost of production was slightly higher than the total variable cost which was 47.25 percent of total cost of tomato production. Among the diff erent cost items, labor and irrigation cost were the major variable cost items which accounted for about 26.55 and 23.22 percent of the total variable cost. Average gross return of tomato production was Tk. 526663.80 per hectare while gross margin was observed to be Tk. 427405.50. The benefi t cost ratio (BCR) of tomato farming was 2.31 indicating that tomato farming is profi table. Karim et al., (2009) found that benefi t cost ratio for small, medium and large farmers was 4.22, 4.16 and 4.19 respectively. Kushwaha et al., (2018); Parvin (2017) and Samshunnahar et al., (2016) also found the similar result. So, tomato cultivation is profi table.

CONCLUSION
This study examines tomato farmers' attitudes towards risk and its determinants. About 42% tomato farmers are high risk averse. They are not interested to take risk in tomato production. Moreover, tomato farmers risk attitude is infl uenced by diff erent socioeconomic factors. Risk preferences of farmers increase with training and education while risk preference decrease with age and experience. As tomato farmers are risk averse, seed marketing companies should be careful before marketing new variety seeds. Although crop insurance is not practiced in large scale, insurance companies can formulate new policies. Tomato farmers may be interested to open a crop insurance policy. Moreover, machinery importers need to be more aware about the farmers risk attitude. Education of tomato farmers should be ensured. Already government of Bangladesh has taken many steps to increase farmers' education.
Further steps are needed to be taken for increasing farmers' education and their risk preference. Training facility for farmers should be enhanced. Availability of quality training for tomato farmers increases their risk preference. Agriculture offi cers can encourage aged farmers to receive new technology that reduces farmers' risk aversion. In addition, tomato farming in the selected area of Bangladesh is profi table. This study will enrich the literature of risk aversion and help to enhance productivity and profi tability of tomato in Bangladesh.

Data Availability Statement
The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.