A data partitioning scheme for spatial regression

Precision agriculture data consisting of crop yield and topographic features are examined with the objective of explaining yield variability as a function of topographic attributes in order to extrapolate this knowledge to unseen agricultural sites. It is demonstrated that random data partitioning into training, validation and test subsets is not appropriate when dealing with agricultural problems characterized with strong spatial data correlation. A simple spatial data partitioning scheme that leads to significantly faster neural network training and slightly better generalization is proposed. Also, integration of predictors formed from spatially partitioned data led to improved generalization over a bagging integration procedure in experiments. The margin between the best spatial model and a trivial predictor for our precision agriculture problem was small indicating that topographic features alone could explain only a small amount of the yield variability.