BALZARINI MONICA GRACIELA
Artículos
Título:
Unveiling spatial variability in herbicide soil sorption using Bayesian digital mapping
Autor/es:
GIANNINI-KURINA, FRANCA; HANG, SUSANA; RAMPOLDI, ARIEL E.; PACCIORETTI, PABLO; BALZARINI, MÓNICA
Revista:
JOURNAL OF ENVIRONMENTAL QUALITY
Editorial:
AMER SOC AGRONOMY
Referencias:
Año: 2021 p. 1 - 1
Resumen:
egional mapping herbicide sorption to soil is essential for risk assessment. However, conducting analytical quantification of adsorption coefficient (Kd) in large-scale studies is too costly; therefore, a research question arises on goodness of Kd 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 Kd measurements into a spatial mixed model including site random effects. The models were fitted using glyphosate and atrazine Kds, determined in 80 and 120 sites, respectively, from central Argentina. For model assessment, measurements of global and point-wise prediction errors were obt