The importance of modeling the effects of trend and anisotropy on soil fertility maps

Abstract

• Restricted Maximum Likelihood has a higher accuracy in Fields with higher anisotropy. • Kriging with Method of Moments has higher accuracy in areas with lower anisotropy. • Spatial variability in soil chemical fertility used in Precision Agriculture must be modeled. • Directional effects in geospatial data must be considered in variogram modeling. • Synthetic data with variable levels of trend and anisotropy were simulated. Precision Agriculture (PA) commonly uses interpolation to generate maps for site-specific management. Semivariogram modeling with kriging interpolation considerers several parameters such as trend and anisotropy, which require proper estimation to return reliable maps. Often present in agricultural fields, these directional effects can also account for machine traffic and crop/soil management. Despite modeling trend and anisotropy being desirable for creating soil fertility maps, these effects are often disregarded during semivariogram modeling. Hence, this study evaluates whether semivariogram modeling considering anisotropy and trend influences the improvement of maps used in precision agriculture. Predicted performance and trends identified in the data when modeling anisotropy were evaluated considering two sampling grid densities, using the method of moments (MoM) and restricted maximum likelihood (REML) to estimate semivariogram parameters. Different levels of trend and anisotropy were tested on four types of virtual fields with 100 repetitions, and two experimental fields. Data were processed in an automated manner for virtual field generation, sampling extraction, semivariogram modeling, kriging, and cross-validation. Metrics were then subjected to bootstrapping and the differences were compared using confidence intervals. Results indicate that modeling directional effects improved the accuracy of kriging-generated maps. REML resulted in the best variability estimation in strong anisotropy, whereas MoM was more efficient in fields with weaker anisotropy. Modeling anisotropy was particularly useful in experimental fields, where trend was considered a function of spatial covariates. Consequently, semivariogram modeling must consider both directional effects to provide accurate soil fertility maps for precision agriculture.