Fecha de publicación: 01/08/2023 Fuente: Wipo "precision agriculture" A system and method for interpolating soil chemistry variables to different plots of land is described. A first interpolation training model includes a machine learning model that receives soil composition information. A distance field training model generates spatial predictors that are applied to the machine learning model. The first interpolation training model prioritizes spatial smoothing over accuracy. A second interpolation training model is also applied that includes a distance weighting training model that more greatly weighs interpolated soil composition information closer to a point of interpolation than interpolated soil composition information that is further away to the point of interpolation. The second interpolation training model prioritizes accuracy over spatial smoothing. The illustrative crop prediction engine estimates soil chemistry values at different locations with the first interpolation training model and the second interpolation training model.