PRODUCTION INPUTS ALLOCATION AND PERFORMANCE BENCHMARK OF RICE FARMS IN ANAMBRA STATE, NIGERIA

Abstract

The study analyzed the production input allocation and the performance benchmark of rice farms in Anambra State, Nigeria. Efforts at improving farm management and production notwithstanding, more is yet to be achieved in that regard in Anambra State and Nigeria at large.  Past studies concentrated on areas like profitability, resources and production efficiency with little or no focus on benchmarking the production and the pattern of input mix. To achieve the broad objective, four (4) specific objectives were outlined thus: (i) categorized production input patterns based on farm performance and based on expenses (iii) determined a performance standard/ benchmark from the category after identifying the existing standard, (iii) identified the diverse effect of production inputs cost on rice farm output for different production levels, and (v) identify the causal factors of difference in farm performance in the study area.  A multi-staged sampling technique was employed in the selection of 120 farms. The study utilized primary data generated by the researcher using structured and pretested questionnaire. Descriptive statistics, net farm income, quantile regression and ANOVA were used for the analysis of the primary data gotten through the administration of a structured questionnaire. The 120 farms were grouped as top performers (40), average performers (40) and below average performers (40). The top performing farms had an average net farm income of N340,305 while the average farms and below average farms had N207,567 and N92,258, respectively. The benchmark standard for rice farms in Anambra State was N390, 500. The basic effects of the cost variables on the farm performance as identified with the quantile regression was that the higher the net farm income, the better the opportunities available to increase performance. The major causal factor of differences in farm outcome were, financial constraints, managerial ability, level of input utilization at the farm level, size of farm, high cost of inputs, pests and diseases, and seasonal variation or climate change. The ANOVA result showed that mean expenditures was not significantly different. The study therefore, recommended that cost management and better production instructions should be taken seriously by the extension agents and government should subsidize agricultural inputs for farmers.