Operations research and machine learning to manage risk and optimize production practices in agriculture: good and bad experience
The potential for operations research with farmer supplied data coupled with machine learning to improve crop management is explored through a series of case studies from developing countries. The information provided by the farmers ranged from solely yield to a description of the management of the crop and some details of the growth environment. The climate or weather conditions of the georeferenced farms were estimated from publicly available data bases. Two principle analytical approaches were used.