LOGISTIC REGRESSION AND STATISTICAL INFERENCE FOR EVALUATING THE ECONOMIC BENEFITS OF CROP DIVERSIFICATION
DOI:
https://doi.org/10.69980/4g8zjq43Keywords:
Crop Diversification, Logistic Regression, Agricultural Income, Economic Benefits, Statistical InferenceAbstract
Crop diversification is gaining popularity as an approach to agricultural production that can benefit the profitability of the farm, the income stability of farmers, and sustainable resource use. The study employs logistic regression and statistical inference to assess the economic benefits of crop diversification. The quantitative research design involved secondary agricultural data with 405 observations and 23 variables, including monocropping, diversified cropping, income outcomes, and agricultural characteristics. Income differences and factors were compared using descriptive statistics, chi-square tests, and multivariable logistic regression with study-clustered standard errors. There was a positive income benefit for diversified cropping in 69.4% of the observations after provisional classification of the inconsistent outcome types. While there was considerable variability and income measurements were not harmonised, the mean income was higher for diversified cropping (115,790.87) than monocropping (56,693.30). The chi-square analysis revealed significant associations between soil fertility, agricultural inputs, water use efficiency, labour engagement, and price-related factors. Labour engagement was the only statistically significant predictor after adjustment by multivariable logistic regression (OR = 3.50; p = 0.038). The model showed an apparent ROC-AUC of 0.726 and a study-grouped cross-validation of 0.338, demonstrating poor predictive generalizability. In general, the economic gains of crop diversification were evident, though there were differences between agroecosystems. The results highlight the significance of the agricultural management conditions, reliable income measures, and additional validation for evidence-based agricultural diversification decisions.
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