Field-scale experimental designs for detecting spatial variability of crop response to input application

Abstract

Precision agriculture assumes the presence of spatial variability in crop response to input application. Field-scale experiments allow for exploring such variability. However, the interaction between the spatial variability of factors controlling crop response and the applied experimental design conditions the results. It is necessary to identify experimental designs that optimize the acquisition of reliable information on crop response. Field-scale experimental designs with different spatial resolutions were evaluated to estimate the spatial variability of crop response to input application. Spatial response patterns were simulated as an underlying process to generate yield maps. Geographically weighted regression (GWR) was used to estimate the crop response patterns, which were compared with the underlying stochastic field. The results indicate that designs with high spatial resolution better capture spatial variability patterns across a wide range of considered spatial structures. Additionally, chessboard-type plot designs outperform strip designs as they allow for detecting spatial variability in both directions. The results are sensitive to the parameterization of GWR, kernel, and bandwidth.