Yield prediction in agriculture a comparison between regression kriging and random forest

Crop yield forecasting plays a crucial role in farming planning and management, international food trade, ecosystem sustainability, and so on. There are many factors that have an influence on crop yield, such as plantation area, efficiency of irrigation systems, variations in rainfall and temperature, quality of crop seeds, topographic attributes, soil quality and fertilisation, and disease occurrence. In the recent years, many researches support that data mining techniques algorithms can be profitable applied to a wide range of agriculture problems and, in particular, to have reasonable crop yield predictions. The aim of this chapter to compare the potentiality of two hybrid spatial interpolation techniques for the spatial estimation of the crop yield: regression kriging (RK) and random forest (RF). As a case study, we interpolated the winter wheat yields in the Southern U.S. Great Plains.