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_ds, 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.

中文翻译：

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使用贝叶斯数字绘图揭示除草剂土壤吸附的空间变异性

除草剂吸附到土壤的区域绘图对于风险评估至关重要。然而，在大规模研究中对吸附系数 ( K _d )进行分析量化成本太高；因此，出现了一个关于从采样进行K _d空间预测的优劣性的研究问题。空间贝叶斯回归 (BR) 的应用是农业和自然资源科学中的一项新技术，它允许将空间离散样本转换为覆盖连续空间域的地图。这项工作的目的是通过开发预测性 BR 模型，在景观尺度上揭示除草剂对土壤的吸附。我们将来自站点的大量辅助土壤和气候协变量与K _d测量到一个空间混合模型，包括站点随机效应。这些模型使用草甘膦和莠去津K _d s拟合，分别在阿根廷中部的 80 个和 120 个地点测定。对于模型评估，通过交叉验证获得全局和逐点预测误差的测量值；通过 bootstrap 估计剩余变异性，以比较 BR 与回归克里金法。结果表明，BR 空间预测优于回归克里金法。草甘膦K _d模型（均方根预测误差，平均值的 13%）包括氧化铝、pH 值和粘土含量，而莠去津K _d模型强烈依赖于土壤有机碳和粘土以及与可用水量相关的气候变量（均方根预测误差，27%）。复杂土壤过程的空间建模作为除草剂对土壤的吸附增强了环境解释。一种有效的空间制图方法为研究除草剂对土壤的吸附提供了现代视角。