Spatial predictive modelling essential to assess the environmental impacts of herbicides

Abstract

The precise prediction of adsorption coefficient (Kd) of herbicides retention in soil requires a careful and robust assessment of alternative statistical methods for predictive modelling. In this work, Kd was modelled as function of soil variables from a regional soil survey using various frameworks: Ordinary and Partial Least Squares regression, Random Forests, Generalized Boosted regression (GB), and Bayesian regression with INLA (INLA). Each approach is applied with and without spatial coordinates included in the covariates for the mean structure. Further, the residuals from the mean structure are either assumed independent or assumed spatially correlated and kriged. For model validation, measurements of pointwise and global predictive ability were assessed. All methods showed good performance (prediction error < 20%). GB without spatial coordinates in the mean structure, nor residuals kriged (i.e. the raw GB predictions based on the covariates) gave a small pointwise classification rate, but INLA with spatial constraints yielded the best fit (the smallest mean squared prediction errors) which resulted suitable for both, process understanding and mapping. The modelling has been illustrated for mapping glyphosate retention, with aluminum oxides, pH, sand, and clay percentages identified as master variables. Results may be extended to other herbicides and dynamic parameters using georeferenced data.