Modeling farmer behavior in adopting agroecological practices using AI tools in shea butter agroforestry systems - D.S.J.C. Gbemavo; J. Laly; V.N. Adjahossou (Heliyon)
This study examines the performance of machine learning algorithms for identifying importance features for agroecological practices adoption in shea agroforestry systems. Primary data were collected from 272 representative and randomly selected farmers in two regions of northern Benin. Four machine learning algorithms (Naïve Bayes, Neural Network, Support Vector Machine and Bagging Decision Trees) were compared using four statistical performance metrics: accuracy, balanced accuracy, recall, and the area under the receiver operating characteristic curve (AUC), as well as calibration plots. The results indicated that the Naïve Bayes model performed best for predicting animal parking practices, and biopesticide while the Support Vector Machine outperformed other models in predicting cultural associations, improved seeds, and organic fertilizers adoption. Feature importance analysis revealed that the most significant feature influencing the adoption of animal parking and organic fertilizers practices was the farmers' region of origin, with the highest importance value (100%). When considering features with at least 50% importance, main occupation and means of transport for manure were also relevant for the adoption of animal parking practice. The farmers' ethnic group, village, and age class were the most influential characteristics for biopesticides, cultural association, and improved seeds practices, respectively, each with a 100% importance value. For improved seeds practice, farm assets and secondary occupation were also relevant when considering features with at least 50% importance. Therefore, providing comprehensive training on agroecological practices such as animal parking, crop diversification, biopesticide management, organic fertilization, and improved seeds use to farmers in Northern Benin, particularly those managing shea agrosystems, could contribute to the sustainable development of this vital resource and enhance food security.