Hybrid DEA-Machine Learning Methodology for Evaluating and Modeling Technical Efficiency of Rice Farming in Niger

This study evaluates the technical efficiency of family rice farms in Niger, using a hybrid framework that integrates Data Envelopment Analysis (DEA) with Machine Learning. Primary data were collected from 103 farms in Gaya, Niger, during the 2025 dry season. To account for structural heterogeneity, a typology of production systems was established using Principal Component Analysis (PCA) and Hierarchical Ascendant Classification (HAC). Technical efficiency was assessed through an input oriented Variable Returns to Scale (VRS) DEA model, and the resulting scores were subsequently modeled using a Random Forest algorithm to capture nonlinear relationships and interaction effects. The analysis revealed a mean efficiency score of 0.76, ranging from 0.42 to 1.00, with 18% of farms operating on the efficiency frontier (θ = 1). Commercial farms exhibited the highest performance (median = 0.83), while small family farms recorded lower efficiency (median = 0.71). The Random Forest model demonstrated high predictive accuracy (R2 = 0.9168; MSE = 0.0027). The main determinants of efficiency were: Seed quantity, Farm size and Fertilizer use with respectively a positive effect. Other influential factors included herbicide use and irrigation, while socio demographic variables such as education, gender, and marital status had negligible impact. This hybrid DEA–Machine Learning framework provides both a rigorous benchmark for measuring technical efficiency and a predictive tool for identifying drivers of performance. The findings offer evidence based insights to guide targeted policies aimed at improving resource use, strengthening resilience, and enhancing the sustainability of rice farming systems in Niger.