Determinants of Fish Farming Production in N’Zérékoré, Guinea: A Weighted Logistic Regression and Random Forest Approach

Abstract

This study examines the determinants of annual fish production in the prefecture of N’Zérékoré (Guinea) using data from a representative sample of 212 fish farmers, corresponding to a weighted population of approximately 530 producers. A weighted logistic regression model and a survey-weighted random forest were jointly applied to 1) identify the main factors associated with high annual fish production and 2) assess out-of-sample predictive performance under a complex multi-stage sampling design. The results show that sexing practice, the number of fingerlings stocked per 100 units, and total pond surface area per 100 m 2 were significantly and positively associated with high annual fish production. Farmer experience showed a positive but non-significant association in the final model. The weighted logistic regression model achieved a survey-weighted AUC of 0.861, indicating excellent discriminatory ability, while the weighted random forest reached an accuracy of 0.817 and a survey-weighted AUC of 0.856 on the test set. Despite methodological differences, both approaches converge on the same key determinants, particularly stocking intensity, pond surface area, and sexing practice, confirming the robustness of the findings. By combining an interpretable statistical model with a flexible non-parametric method, this study provides a coherent and complementary analytical framework for analyzing weighted agricultural data. Beyond its contribution to understanding annual fish production in Guinea, the study proposes a reproducible methodological approach that can be applied to similar agricultural surveys in West Africa.