Real-time forecasting of pesticide concentrations in soil

Gavan McGrath; P Suresh C Rao; Per-Erik Mellander; Ivan Kennedy; Michael Rose; Lukas van Zwieten

doi:10.1016/j.scitotenv.2019.01.401

Real-time forecasting of pesticide concentrations in soil

Sci Total Environ. 2019 May 1:663:709-717. doi: 10.1016/j.scitotenv.2019.01.401. Epub 2019 Jan 31.

Authors

Gavan McGrath¹, P Suresh C Rao², Per-Erik Mellander³, Ivan Kennedy⁴, Michael Rose⁵, Lukas van Zwieten⁵

Affiliations

¹ Ishka Solutions, Nedlands 6009, Australia; Teagasc, Environment, Soils and Water Department, Johnstown Castle, Wexford, Ireland; School of Agriculture and Environment, University of Western Australia, 6009 Perth, Australia. Electronic address: gavan@ishkasolutions.com.au.
² Lyles School of Civil Engineering, Purdue University, West Lafayette, IN 47907, USA; Agronomy Department, Purdue University, West Lafayette, IN 47907, USA.
³ Teagasc, Environment, Soils and Water Department, Johnstown Castle, Wexford, Ireland.
⁴ Sydney Institute of Agriculture, University of Sydney, Sydney 2006, Australia.
⁵ Wollongbar Primary Industries Institute, NSW Department of Primary Industries, Wollongbar, 2477, Australia.

PMID: 30731416
DOI: 10.1016/j.scitotenv.2019.01.401

Abstract

Forecasting pesticide residues in soils in real time is essential for agronomic purposes, to manage phytotoxic effects, and in catchments to manage surface and ground water quality. This has not been possible in the past due to both modelling and measurement constraints. Here, the analytical transient probability distribution (pdf) of pesticide concentrations is derived. The pdf results from the random ways in which rain events occur after pesticide application. First-order degradation kinetics and linear equilibrium sorption are assumed. The analytical pdfs allow understanding of the relative contributions that climate (mean storm depth and mean rainfall event frequency) and chemical (sorption and degradation) properties have on the variability of soil concentrations into the future. We demonstrated the two uncertain reaction parameters can be constrained using Bayesian methods. An approach to a Bayesian informed forecast is then presented. With the use of new rapid tests capable of providing quantitative measurements of soil concentrations in the field, real-time forecasting of future pesticide concentrations now looks possible for the first time. Such an approach offers new means to manage crops, soils and water quality, and may be extended to other classes of pesticides for ecological risk assessment purposes.

Keywords: Agronomy; Forecasting; Herbicide; Model; Pesticide; Rainfall.