Climate-informed agronomic advisories for maize in Colombia: Progress report for the Excellence in Agronomy (EiA) initiative Latin America Use Case
Decision making in agriculture has been based on general (blanket) recommendations made by technicians, the farmer's own knowledge or local practices that are adopted as customary for generations. Recognizing the need to generate information to help make site-specific decisions based on traditional agronomic research, this study uses Machine Learning (ML) models and a Global Harmony Search (GHS) methodology to find an optimal solution to the combination of practices that a farmer could implement according to his soil and climate conditions specific to his land.