Integrating climate–crop statistics, machine learning, and household surveys for sustainable agricultural practices

Among the models tested, random forest (RF) outperformed support vector machine (SVM) and multiple linear regression (MLR) and multiple linear regression (MLR), demonstrating its robustness for predicting nonlinear crop–climate relationships.