This paper analysed the technical efficiency of rice farmers in Ebonyi State, Nigeria. It specifically analysed the levels and determinants of technical efficiency. A multistage sampling technique was adopted for sample selection. A total of 150 well-structured questionnaires were distributed but only 91 were returned. The Cobb-Douglas production function model was used to predict the farm level technical efficiency. Results from the Cobb- Douglas Regression showed a sigma square (σ2) of 0.06584 which was statistically significant at 1 percent. The technical efficiency scores among the rice farmers ranged from 0.012 to 1.000 with a mean of 0.350. Farm size, quantity of rice seedlings, quantity of fertilizer and quantity of agrochemical all were showed positive correlation with coefficients of 0.93511, 0.08310, 0.11200 and 0.14345 respectively while farm labour showed a negative correlation with technical efficiency. The determinants of technical inefficiency were age, household size and extension visits. This paper advocates the strengthening of existing extension system for timely sourcing of highly improved rice varieties and subsidized fertilizer. There is the need to enhance farmers’ technical efficiency through adequate training on optimum input mix. Keywords: Rice production, technical efficiency, cobb-douglas production function
The increased incidences of farming risks have been a challenge among smallholder farmers in Nigeria. This study set out to assess risk management strategies among arable crop farmers in Owerri West Local Government Area of Imo State, Nigeria. The proportion of risk in arable crop production and the factors that influence risk in arable crop production in the study area were specifically estimated. Primary data used for the study were collected with the aid of well-structured questionnaire from eighty-four farmers in the study area. Data were analyzed using descriptive and inferential statistics. The result revealed that majority of the arable crop farmers (52.4%) were females. The average age and household size were 54 years and 5 persons respectively. Educational level, age, farming experience, farm size, household size and farm income were the factors influencing the estimated output of the farmers due to the prevalence of risk in arable crop production. The study recommended the continuous education of arable crop farmers in the area so as to increase their capacity to deal with risk on their farms.
Estimation of household food demand patterns and elasticity is often conceived as an important prerequisite for designing, predicting and analysing agricultural policy impacts. Based on this fact, this study sets out to estimate the food demand of rural households in the study area with a view to identifying its determinants and responsiveness to price and household food expenditure. The study employed a multi-stage sampling technique in the selection 254 rural households in Enugu state and the primary data collected were analysed using Descriptive statistics and Quadratic Almost Ideal Demand System (QUAIDS) model. The result of the descriptive statistics showed that the mean age, household size, and years of education of the sampled rural households were 49. 7±13.30, 7.0 ± 3.02, and 13.96±3.96. The result of the QUAIDS model revealed that all the expenditure elasticity for the selected staple food items were positive and hence, regarded as normal goods and In terms of magnitude, all the staple food items except rice and yam have expenditure elasticity less than one and are therefore, regarded as necessities. Own price elasticity were all negative as expected in both uncompensated and compensated price elasticity estimates. The Marshallian cross price elasticity estimates also revealed that almost all the selected staple food items have positive cross-price elasticity values indicating that they are net substitutes while the Hicksian/compensated cross price elasticity revealed that majority of the selected staple food items have negative cross-price elasticity values indicating that they are complements. However, the study further revealed that price, total food expenditure, sex, age, marital status, years of education, household size and household head income were the key determinants of the rural household demand for the selected staple food items in the study area. Based on the foregoing, the result of this study should inform the design of food security related policies aimed at improving the nutritional status of the poor and vulnerable households in the country.
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