Unveiling spatial variability in herbicide soil sorption using Bayesian digital mapping

Regional mapping herbicide sorption to soil is essential for risk assessment. However, conducting analytical quantification of adsorption coefficient (K_d ) in large-scale studies is too costly; therefore, a research question arises on goodness of K_d spatial prediction from sampling. The application of a spatial Bayesian regression (BR) is a newer technique in agricultural and natural resources sciences that allows converting spatially discrete samples into maps covering continuous spatial domains. The objective of this work was to unveil herbicide sorption to soil at a landscape scale by developing a predictive BR model. We integrated a large set of ancillary soil and climate covariables from sites with K_d measurements into a spatial mixed model including site random effects. The models were fitted using glyphosate and atrazine K_d s, determined in 80 and 120 sites, respectively, from central Argentina. For model assessment, measurements of global and point-wise prediction errors were obtained by cross-validation; residual variability was estimated by bootstrap to compare BR with regression kriging. Results showed that the BR spatial predictions outperformed regression kriging. The glyphosate K_d model (root mean square prediction error, 13% of the mean) included aluminum oxides, pH, and clay content, whereas the atrazine K_d model strongly depended on soil organic carbon and clay and on climatic variables related to water availability (root mean square prediction error, 27%). Spatial modeling of a complex edaphic process as herbicide sorption to soils enhanced environmental interpretations. An efficient approach for spatial mapping provides a modern perspective on the study of herbicide sorption to soil.