Modelling the Maturity Levels of Farmer Groups Using Artificial Neural Networks

Hanningtone Simiyu, Joseph Tanui, Anthony Waititu, Jeremias Mowo

Abstract: This paper sets to develop an artificial neural networks model that predicts the performance of farmer groups i. e. whether beginner, intermediate or mature depending on the groups level of performance. Five broad classes of variables were used namely governance, management, leadership, resilience and capacity development. Data was collected through random sampling from farmer groups in East Africa. Two districts were involved from each country, mainly targeting districts where the International Funding for Agricultural Development had ongoing activities. A model with an overall accuracy 97 % was developed and was effective in prediction of various farmer organizations

Keywords: performance levels, farmer groups, neural networks, Capacity needs assessment