Machine learning models for predicting decisions to be made by small scale dairy farmers in Eastern Africa

In dairy, lack of decision support tools for identifying farmers' needs and demands have caused many programs, strategies, and projects to fail. This has led to the inefficient and fragmented allocation of scarce development resources. This study demonstrated how machine learning (ML) can be used as a tool to bridge this gap; by developing ML models to be used in identifying factors that can influence farmers decisions, predicting decision to be made by a farmer and forecast on farmers demands regarding to their specific need or service. Four countries: Ethiopia, Kenya, Tanzania and Uganda were selected for this study. In the course of the study four models were developed one for each country with regard to the usage of animal supplements, keeping of exotic animals, use of Artificial insemination (AI) as breeding methods and animal milk productivity. Data was collected through face to face interviews, from a total of 16 308 small scale dairy farmers in Ethiopia (n = 4679), Kenya (n = 5278), Tanzania (3500) and Uganda (n = 2851). The decision tree algorithm was used to model categorical problems (use of supplement and breeding decision), which attained the accuracy of 78%-90%. Moreover, K-nearest neighbor was employed for numeric problems (keeping of exotic animals and animal milk productivity) with an accuracy of 0.78-0.96 Adjusted R The use of ML techniques assisted in classifying farmers based on their characteristics and it was possible to identify the key factors that can be taken then prioritized to improve the dairy sector among countries. Also, the results of this study offer a number of practical implications for the dairy industry where the proposed ML models can enable decision-makers in developing the National Dairy Master Plan and design policies that promote the growth of smallholder dairy farming. Moreover, these models shade light to potential service providers and investors who want to invest in dairy to identify potential areas or groups of farmers to focus with. 2 value.