Determinants of farm profit among cassava-based farming households in Osun State Nigeria: robust estimation approach
The Ordinary Least Squares (OLS) method is widely adopted in studies estimating net profit generation among farmers, despite its strong tendency to produce misleading estimates, particularly when one or more fundamental assumptions are violated. This study examined the determinants of farm income am...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2025-12-01
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| Series: | Cogent Food & Agriculture |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/23311932.2024.2449577 |
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| Summary: | The Ordinary Least Squares (OLS) method is widely adopted in studies estimating net profit generation among farmers, despite its strong tendency to produce misleading estimates, particularly when one or more fundamental assumptions are violated. This study examined the determinants of farm income among smallholder cassava farmers in Osun State, Nigeria correcting for various assumptions violations of the classical regression model. Aside from the usual OLS estimator, we employed robust estimators such as Maximum Likelihood-type estimator (M-estimator), Monotone M-estimator (MM-estimator), and Scale estimator (S-estimator) to account for outliers. One hundred and one smallholder farmers in Osun state were randomly selected for the study. Analyses revealed that the major socioeconomic factors affecting farming household profit were access to credit, farm size, years spent in farmers’ associations, years of experience, and distance to the nearest market, which are the major determinants of net farm profit among the cassava-based farming households in the study area. On average, the return on investment (ROI) among cassava-based farming households was found to be ₦1.32 per naira invested. The research findings indicated that cassava cultivation is a profitable venture within the investigated area and recommended robust methodology for effectiveness and accurate information in future analysis. |
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| ISSN: | 2331-1932 |