Spatial predictive modelling essential to assess the environmental impacts of herbicides

GIANNINI KURINA, F.; HANG, S.; MACCHIAVELLI, R.; BALZARINI, M.

GEODERMA

ELSEVIER SCIENCE BV

Año: 2019 vol. 354

0016-7061

he 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