Gbemavo D S J C, Laly J, Adjahossou V N
Biostatistics and Modelling Unit (UBM)/Laboratory of Genetics, Biotechnology and Applied Botany (GEBBA)/ National School of Biosciences and Applied Biotechnology (ENSBBA), National University of Sciences, Technologies, Engineering and Mathematics (UNSTIM Abomey), BP 14 Dassa Zounmé, Dassa Zounmé, Benin.
Laboratory of Genetics, Biotechnology and Applied Botany (GEBBA), National School of Applied Biosciences and Biotechnologies (ENSBBA) of Dassa Zoumé/ National University of Sciences, Technologies, Engineering and Mathematics (UNSTIM), PO Box 14, Dassa-Zoumé, Benin.
Heliyon. 2024 Nov 22;10(23):e40600. doi: 10.1016/j.heliyon.2024.e40600. eCollection 2024 Dec 15.
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.
本研究考察了机器学习算法在识别牛油果农林复合系统中采用农业生态实践的重要特征方面的表现。主要数据来自贝宁北部两个地区的272名具有代表性且随机挑选的农民。使用四种统计性能指标（准确率、平衡准确率、召回率以及受试者工作特征曲线下面积（AUC））以及校准图，对四种机器学习算法（朴素贝叶斯、神经网络、支持向量机和装袋决策树）进行了比较。结果表明，朴素贝叶斯模型在预测动物圈养实践和生物农药方面表现最佳，而支持向量机在预测文化关联、改良种子和有机肥料采用方面优于其他模型。特征重要性分析表明，影响动物圈养和有机肥料实践采用的最显著特征是农民的原籍地区，重要性值最高（100%）。当考虑重要性至少为50%的特征时，主要职业和粪便运输方式对于动物圈养实践的采用也很重要。农民的族群、村庄和年龄组分别是生物农药、文化关联和改良种子实践的最具影响力特征，重要性值均为100%。对于改良种子实践，当考虑重要性至少为50%的特征时，农场资产和次要职业也很重要。因此，为贝宁北部的农民，特别是那些管理牛油果农业系统的农民，提供关于动物圈养、作物多样化生物农药管理、有机施肥和改良种子使用等农业生态实践的全面培训，可能有助于这一重要资源的可持续发展并加强粮食安全。
