2021
DOI: 10.1002/jeq2.20254
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Unveiling spatial variability in herbicide soil sorption using Bayesian digital mapping

Abstract: 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 object… Show more

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