Using Ancillary Data to Improve Prediction of Soil and Crop Attributes in Precision Agriculture

Abstract

This chapter describes three geostatistical methods to incorporate secondary information into the mapping of soil and crop attributes to improve the accuracy of their predictions. The application of the methods is illustrated in two case studies. Cokriging is the multivariate extension of the well known ordinary kriging. It does not require ancillary data to be available at all nodes of the interpolation grid, whereas kriging with external drift and simple kriging with local means do. Cokriging, however, is more demanding in terms of variogram inference and modelling. The other two methods use ancillary data to model the spatial trend of the primary variable. Kriging with an external drift can account for local changes in the linear correlation between primary and secondary variables. Simple kriging with local means, which applies kriging to regression residuals and adds the kriged residual to the regression estimate, is the most straightforward of these methods to implement. The prediction performance of each technique was evaluated by cross-validation. As the results are site-specific, the choice of technique for a given site should be guided by the results of cross-validation.