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 ) in large-scale studies is too costly; therefore, a research question arises on goodness of K 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.