A Farm Level Analysis of Risk Attitude, sources and risk measurement strategies among Farmers in Trans Nzoia County, Kenya.

Abstract

Understanding farmers’ attitudes and responses to agricultural risks is important for designing risk management strategies and effective extension activities. Farmer’s risk attitude is a critical barrier to adoption of new agricultural technologies, investment and production decisions in agriculture. The objectives of this study were; to examine the socioeconomic characteristics of the farmers; to elicit the risk attitudes of the farming households; to determine the socioeconomic characteristic affecting risk attitude and to document the risk sources and risk management strategies among the farming households in Trans Nzoia County. The data used in this study were collected by use of a structured questionnaire administered to 167 farming households randomly selected by a multi stage sampling technique. To determine the socioeconomic characteristics, a descriptive analysis was done (frequencies, percentages and means). To elicit responses of farm households towards risk attitude, Equally Likely Certainty Equivalent with Purely Hypothetical risky prospect (ELCE-PH) model was used. A logit regression analysis was further used to determine the influence of risk attitude on socioeconomic characteristics of the household. Data analysis was done using computer programme SPSS 20.0. The study revealed that 74.25% of the households were risk averse while 8.98% and 16.77% were risk seekers and risk neutral respectively. The results further revealed that socioeconomic characteristics may in turn affect the respondent’s attitude towards risk. The coefficient for age, education, off-farm income and membership to farmers’ groups were statistically significant at 1% while gender was significant at 5% in explaining farmers risk attitude. However, the coefficients for household size, access to credit and extension service were statistically insignificant at both 1% and 5% level of significance. Using factor analysis, seven factors explaining 65.53% of the variation were extracted for risk source while five factors explaining 64.53% were extracted for risk management. Factor analysis revealed that weather risks, market and price risks, biological risks, labor bottlenecks, financial risks, land bottlenecks and personal/human risks to be the most prevalent risks while enterprise diversification, risk sharing and cooperatives, off-farm investment, buffer stock index, and financial management were the most adopted risk management strategies among farmers. The study recommends on the need for policy formulation to address agricultural risk, development of agricultural insurance and forward agricultural markets in the study area.