STOCHASTIC FRONTIER TECHNICAL EFFICIENCY ANALYSIS OF WATERMELON (Citrullus lenatus) PRODUCTION IN NIGERIA

The study analysed the efficiency of Watermelon (Citrullus lenatus) Production in Nigeria. A multi-stage sampling technique was used in selecting three hundred and sixty (360) respondents. Selection was done with purposive and simple random sampling, and data collected with a structured questionnaire. The objectives of the study were to identify the socio-economic characteristics of the respondents, determine the technical efficiency and measure the total resource productivity of watermelon production in the study area. The data were analyzed using descriptive statistics and quantitative analytical tool of stochastic frontier model (Cobb Douglas production function). Socio-economic attributes like age, farm size, educational status and farm experience were described to show their relationship with watermelon production in the study area. Results of the stochastic frontier model showed that all the estimated coefficients of the variables of the production function were positive except fungicide. They included: farm size (0.0795), labour (0.0201), number of seed grown (0.926) and fertilizer (0.0207). This implied that watermelon output increases with increase in these variables. It was also shown that labour (0.441), fertilizer (0.475) and fungicide (-1.662) did not exert any significant effect on watermelon output as shown by their t-ratio values. For the factors affecting technical inefficiency of watermelon farmers, age of farmers and farm size were negative and significant at 0.05 levels of probability, while household size, educational qualification and farming experience were all positive and significant at 5% levels of significance and type of cropping was positive and significant at 10% level of significance. Non-farm income was positive and significant at 5% level of probability. This means that one unit increase in these variables would increase technical inefficiency of the farmers and hence decreasing their technical efficiency. Finally, the return to scale parameter returned the value 0.967 which indicated that watermelon production in the study area was in the Stage II of the production surface. Based on the results of the analysis the following were recommended. Watermelon farmers should be provided and encouraged to take loans, be assisted with extension services and become members of farmer associations, in order to boost their production. Also inputs such as farm size, labour, seeds, fertilizer and fungicide should be increased for optimum production.