Real-time forecasting of pesticide concentrations in soil

Empirical model to assess leaching of pesticides in soil under a steady-state flow and tropical conditions

Abstract

An empirical model of leaching of pesticides was developed to simulate the concentration of fungicides throughout unsaturated soil. The model was based on chemical reactions and the travel time of a conservative tracer to represent the travel time required for water to flow between soil layers. The model's performance was then tested using experimental data from dimethomorph and pyrimethanil applied to the soil under field and laboratory conditions. The empirical model simulated fungicide concentration on soil solids and in soil solution at different depths over time (mean square error between 2.9 mg2 kg-2 and 61mg2 kg-2) using sorption percentages and degradation rates under laboratory conditions. The sorption process was affected by the organic carbon, clay, and the effective cation exchange capacity of the soil. The degradation rate values of dimethomorph (0.039 d-1-0.009 d-1) and pyrimethanil (0.053 d-1-0.004 d-1) decreased from 0 to 40 cm and then remained constant in deeper soil layers (60-80 cm). Fungicide degradation was a critical input in the model at subsurface layers. The model was determined to be a reliable mathematical tool to estimate the leachability of pesticides in tropical soil under a steady-state flow. It may be extended to other substances and soils for environmental risk assessment projects.

Graphical abstract

Supplementary Information

The online version contains supplementary material available at 10.1007/s13762-023-05038-w.

Herbicide residues in Australian grain cropping soils at sowing and their relevance to crop growth

Herbicides are used extensively in Australian grain cropping systems. Despite occasional observations of herbicide-induced phytotoxicity, there is little information on the persistence and carryover of multiple herbicide classes in cropping soils and the risk to subsequent crops. Two soil surveys were conducted, in 2015 (n = 40) and 2016 (n = 42), across different Australian grain cropping fields prior to sowing of winter crops, and soil samples analysed for herbicide residues (16 analytes in 2015 and 22 analytes in 2016). Samples in 2015 were taken at two depths (0-10 cm and 10-30 cm), whilst samples in 2016 were taken in topsoil (0-10 cm) only, but from two discrete locations in each field. Our research in both years found at least one herbicide (or herbicide metabolite) residue at all sites, with a median of 6 analytes detected in 2015 and 7 analytes detected in 2016. The most frequently detected residues were glyphosate and its primary breakdown product aminomethylphosphonic acid (AMPA), in 87 and 100%, respectively, of topsoil (0-10 cm) samples in 2015, and 67 and 93% of samples in 2016. The median concentration of glyphosate in 2015 was 0.12 mg kg^-1, while AMPA was 0.41 mg kg^-1. In 2016, median concentrations of glyphosate and AMPA were 0.22 mg kg^-1 and 0.31 mg kg^-1. Residues of 2,4-dichlorophenoxyacetic acid, trifluralin and diflufenican were also detected in >40% of topsoil samples in both seasons, but with median concentrations of <0.05 mg kg^-1. A literature review found limited availability of phytotoxicity thresholds for major grain crops exposed to soilborne herbicide residues. A risk assessment using available thresholds suggested that although up to 29% of fields contained trifluralin residues that could constrain cereal crop growth, and 24% of fields contained residues of phenoxy or sulfonylureas that could affect dicotyledonous crops, the majority of these fields when planted with tolerant crops would be unlikely to be affected by herbicide residues. More work is required to ascertain the spatial distribution, bioavailability and phytotoxicity of residues and residue mixtures to enable a more accurate agronomic risk assessment.

An assessment of potential pesticide transmission, considering the combined impact of soil texture and pesticide properties: A meta-analysis

Pesticides are widely employed as a cost-effective means of reducing the impacts of undesirable plants and animals. The aim of this paper is to develop a risk ranking of transmission of key pesticides through soil to waterways, taking into account physico-chemical properties of the pesticides (soil half-life and water solubility), soil permeability, and the relationship between adsorption of pesticides and soil texture. This may be used as a screening tool for land managers, as it allows assessment of the potential transmission risks associated with the use of specified pesticides across a spectrum of soil textures. The twenty-eight pesticides examined were differentiated into three groups: herbicides, fungicides and insecticides. The highest risk of pesticide transmission through soils to waterways is associated with soils containing <20% clay or >45% sand. In a small number of cases, the resulting transmission risk is not influenced by soil texture alone. For example, for Phenmedipham, the transmission risk is higher for clay soils than for silt loam. The data generated in this paper may also be used in the identification of critical area sources, which have a high likelihood of pesticide transmission to waterways. Furthermore, they have the potential to be applied to GIS mapping, where the potential transmission risk values of the pesticides can be layered directly onto various soil textures.

A Review on Prediction Models for Pesticide Use, Transmission, and Its Impacts

The lure of increased productivity and crop yield has caused the imprudent use of pesticides in great quantity that has unfavorably affected environmental health. Pesticides are chemicals intended for avoiding, eliminating, and mitigating any pests that affect the crop. Lack of awareness, improper management, and negligent disposal of pesticide containers have led to the permeation of pesticide residues into the food chain and other environmental pathways, leading to environmental degradation. Sufficient steps must be undertaken at various levels to monitor and ensure judicious use of pesticides. Development of prediction models for optimum use of pesticides, pesticide management, and their impact would be of great help in monitoring and controlling the ill effects of excessive use of pesticides. This paper aims to present an exhaustive review of the prediction models developed and modeling strategies used to optimize the use of pesticides.