Determinants of Technical Efficiency in Urban Food Crop Production in Ibadan Metropolis, Oyo State, Nigeria

Abstract

This study analysed the level and the determinants of technical efficiency among urban crop farmers in Ibadan metropolis. The random sampling technique was used in selecting 85 farmers from the following three areas where urban food crop production is prominent: Ajibode, Ijokodo, and Eleyele. The data were collected through structured questionnaires and analysed using both descriptive statistics and stochastic production frontier based on the Cobb-Douglas production function. The results reveal that farmland, family labour, and plantlings are statistical determinants of urban food crop production in the study area. Fertilizer and hired labour are, however, not statistically significant in influencing urban food crop production. The result of the determinants of technical efficiency model reveals that full-time occupation in urban farming and sole-cropping pattern were positively related to technical efficiency. An increase in the age, experience, and years of formal education of the urban farmers increases the level of technical efficiency. The policy implication is that there are still more opportunities to raise the present level of technical efficiency of urban crop production in the study area. Educating B. T. Omonona is Lecturer,

Urban-Rural Disparities in Food Security: An Analysis of Household Survey Data in Plateau State, Nigeria

“…Methodologically, the high predictive accuracy (89% correct classification) of the discriminant model in distinguishing urban vs. rural households based on food security indicators demonstrates the value of multivariate analytical techniques for segmentation and policy analysis. Combining discriminant analysis with spatial mapping and regression approaches can provide greater nuance into drivers of food insecurity across geographical and socioeconomic divides (Omonona et al, 2019). While these findings are consistent with existing literature, the relatively small localized sample limits generalizability.…”

“…These results align with previous studies showing substantial disparities in food security between urban and rural areas, especially in developing countries like Nigeria. For example, Omonona et al (2019) in their analysis of national household data in Nigeria also found income, education, and household size differentiated the food security status of urban and rural households. Similarly, a study in India by Bharati et al (2022) identified income, household size, and food prices as key determinants of the urban-rural food security differential.…”

“…However, the study also found that urban households had higher levels of food waste, with higher incomes leading to increased food consumption and waste. A study by Omonona et al (2019) analyzed nationwide household data from the General Household Survey Panel in Nigeria to compare food security status between urban and rural areas. Using discriminant analysis, they found that income, education level, and household size were the most significant variables differentiating the food security of urban versus rural households.…”