Evaluating logistic regression and geographically weighted logistic regression models for predicting orange-fleshed sweet potato adoption intention in Benin

Abstract

Abstract The low adoption rate of biofortified crops, like orange-fleshed sweet potatoes (OFSP), by farmers remains a major food security concern. Accurate forecasting models for OFSP adoption intention are essential for breeding and introduction projects. This study aims to (i) identify key predictors of OFSP adoption intention among farmers in Benin, integrating various factors, and (ii) investigate regional variations in these predictors through different modeling approaches. We used a diverse set of predictors, including social, geographical, and psychological constructs, to model adoption intention in different sweet potato production areas in Benin. Both logistic regression (LR) and geographically weighted logistic regression (GWLR) models were developed and assessed. The GWLR model significantly outperformed the LR model, achieving a validated result of 94.2%, compared to 87% for the LR model. The GWLR model accurately identified areas with medium and high adoption propensities, mainly in northern Benin, aligning closely with observed data. Driving factors showed robust spatial heterogeneities, influencing OFSP adoption intentions differently across regions, with correlations ranging from positive to negative. The GWLR model excels in elucidating the spatial nuances of diverse factors, offering a promising avenue for more reliable predictions for OFSP adoption.

Keywords

• Adoption
• Orange-fleshed sweet potato
• Biofortified varieties
• Geospatially weighted logistic regression
• Psychological constructs
• Predictive modeling