Skip navigation

Prediction of the glyphosate sorption coefficient across two loamy agricultural fields

Prediction of the glyphosate sorption coefficient across two loamy agricultural fields

Paradelo Perez, Marcos ORCID: 0000-0002-2768-0136, Nogaard, Trine, Moldrup, Per, Ferré, T.P.A., Kumari, K.G.I.D., Arthur, Emmanuel and de Jonge, Lis W. (2015) Prediction of the glyphosate sorption coefficient across two loamy agricultural fields. Geoderma, 259-60. pp. 224-232. ISSN 0016-7061 (Print), 1872-6259 (Online) (doi:https://doi.org/10.1016/j.geoderma.2015.06.011)

[img]
Preview
PDF (Author's Accepted Manuscript)
26602 PARADELO_PEREZ_Prediction_Of_The_Glyphosate_Sorption_Coefficient_(AAM)_2015.pdf - Accepted Version
Available under License Creative Commons Attribution Non-commercial No Derivatives.

Download (989kB) | Preview

Abstract

Sorption is considered one of the most important processes controlling pesticide mobility in agricultural soils. Accurate predictions of sorption coefficients are needed for reliable risk assessments of groundwater contamination from pesticides. In this work, we aim to estimate the glyphosate sorption coefficient, Kd, from easily measurable soil properties in two loamy, agricultural fields in Denmark: Estrup and Silstrup. Forty-five soil samples in Estrup and 65 in Silstrup were collected from the surface in a rectangular grid of 15 × 15-m from each field, and selected soil properties and glyphosate sorption coefficients were determined. Multiple linear regression (MLR) analyses were performed using nine geo-referenced soil properties as variables to identify the parameters related with glyphosate sorption. Scenarios considered in the analyses included: (i) each field separately, (ii) both fields together, and (iii) northern and southern sections of the field in Silstrup. Considering correlations with all possible sets of the same nine geo-referenced properties, a best-four set of parameters was identified for each model scenario. The best-four set for the field in Estrup included clay, oxalate-extractable Fe, Olsen P and pH, while the best-four set for Silstrup included clay, OC, Olsen P and EC. When the field in Silstrup was separated in a northern and southern section, the northern section included EC, and oxalate-extractable Fe, Al and P, whereas the southern part included pH, clay, OC and Olsen P. The best-four set for both fields together included clay, sand, pH and EC. Thus, the most common parameters repeated in the best-four sets included clay and pH as also reported previously in the literature, but in general, the composition of the best-four set differed for each scenario, suggesting that different properties control glyphosate sorption in different locations and at different scales of analysis. Better predictions were obtained for the best-four set for the field in Estrup (R2 = 0.87) and for both fields (R2 = 0.70), while the field in Silstrup showed a lower predictability (R2 = 0.36). Possibly, the low predictability for the field in Silstrup originated from opposing gradients in clay and oxalate-extractable Fe across the field. Also, whereas a lower clay content in Estrup may be the limiting variable for glyphosate sorption, the field in Silstrup has a higher clay content not limiting the sorption, but introducing more variability in Kd due to changes in other soil properties.

Item Type: Article
Uncontrolled Keywords: sorption, glyphosate, field scale, multiple linear regression
Subjects: S Agriculture > S Agriculture (General)
Faculty / Department / Research Group: Faculty of Engineering & Science
Faculty of Engineering & Science > Natural Resources Institute
Faculty of Engineering & Science > Natural Resources Institute > Agriculture, Health & Environment Department
Last Modified: 15 May 2020 14:58
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
Selected for GREAT 2019: None
Selected for REF2021: None
URI: http://gala.gre.ac.uk/id/eprint/26602

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics