A Data-driven Model for Sustainable Deployment of Climate Smart Agriculture Practices Among Smallholder Farmers in Kakamega

Abstract

Kenya’s agriculture is dominated by millions of smallholder farmers who produce over 75 percent of the national agricultural production. The smallholder farmers, however, are the most vulnerable to climate change because various socioeconomics, demography, and policy trends limit their capacity to adapt to change. To mitigate against the negative effects of climate change on smallholder farmers’ numerous interventions, in the form of Climate Smart Agriculture Technologies have been developed and promoted by development partners and government departments. Not all the targeted smallholder farmers, however, participate in and adopt the technologies at the ideal rates and intensity leading to their dis-adoption and abandonment. This study, therefore, sought to develop a data-driven model for the sustainable deployment and adoption of CSA practices among smallholder farmers in Kakamega County. The study employed a mixed methods research design. Through a quantitative survey of 428 smallholder Climate Smart Agriculture Technology adopters and dis-adopters, this study reviewed and investigated the major socio-economic and biophysical characteristics of the different smallholder farmer categories. Supervised Machine Learning using the Scikit-Learn library of Python Programming language to build, pilot, and review Decision Tree and Random Forest Classifier models for the sustainable deployment and adaptation of CSA practices among Kakamega County's smallholder farmers. 19 key variables were identified for developing a predictive model for CSA Technology adoption. A predictive tool was developed and piloted among 15 smallholder CSA farmers. The classifier model produced a Mean Squared Error of 0.16. The proposed model predicted smallholder farmer adoption at an accuracy of 89.53 percent and 90.0 percent with test data and pilot data, respectively. This study, therefore, proposes a new model for the optimal selection of Climate Smart Agriculture intervention beneficiaries.