Generalized analysis of spatial variation in yield monitor data

Abstract

Australian lupin and cotton yield monitor data were analysed using spatial models from the Matérn class of spatial covariance functions. Despite difficulties with the spatial disposition of the data, the analysis supports the statistical model in which the variation is a linear combination of white noise and the de Wijs process. The de Wijs process, also called the logarithmic covariance function, is a generalized covariance function that is conformally invariant and suggests that there is variation at all spatial scales. The present work also indicates that anisotropy and convolution are properties of yield monitor data and that it is hard to distinguish the two. The degree and causes of anisotropy require further investigation. Fitting this model is relatively easy for small, precision-agriculture datasets and open source software is available to this end. Comparing the de Wijs model with more general models in the Matérn class is computationally intensive for precision-agriculture datasets.