Farm Level Productivity and Profitability of Records Keeping Cassava Farmers in Udenu Local Government Area, Enugu State, Nigeria

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Abstract

Record keeping plays a crucial role in various aspects of organizational functioning. Maintaining accurate records is essential for effective financial management and serves as a valuable resource for operational management and decision-making. This study assessed the farm-level productivity and profitability of cassava farmers who keep records in Udenu Local Government Area of Enugu State, Nigeria. The specific objectives were to analyze the productivity of these cassava farmers, evaluate their profitability, and describe the constraints they face. A multistage sampling procedure, incorporating simple random sampling, was used to collect data from 77 cassava farmers who keep records. The data were analyzed using descriptive statistics, ordinary least squares regression, and gross margin analysis. The findings indicated that labor significantly increased cassava productivity, while the amount of credit used, stem cuttings, and farm size negatively impacted productivity. Additionally, cassava production was found to be profitable, with a gross margin of ₦115,331.95 per hectare of land cultivated and a return on investment of ₦1.47 for every Naira invested. The constraints faced by cassava farmers included high labor costs, limited farmland, a lack of improved varieties, poor road networks, and insufficient access to extension agents. To address these issues, it is recommended to tailor credit products to meet the specific needs of farmers, such as offering seasonal loans, working capital loans, or loans for specific farming activities. Furthermore, efficient land use planning, considering factors like crop rotation and diversified crops, could help maximize the land's potential.

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How to Cite
Ukpe, U. H., & Eze, M. U. (2024). Farm Level Productivity and Profitability of Records Keeping Cassava Farmers in Udenu Local Government Area, Enugu State, Nigeria. African Multidisciplinary Journal of Sciences and Artificial Intelligence, 1(1), 472-485. https://doi.org/10.58578/amjsai.v1i1.3629