Modeling the Ability of a Maize–Olive Agroforestry System in Nitrogen and Herbicide Pollution Reduction Using RZWQM2 and Comparison with Field Measurements

Abstract

Agricultural pollution models are a valuable tool for researchers and managers to predict and assess the potential contamination from the use of fertilizers and pesticides in the field. RZWQM2 is a comprehensive software package developed by the US EPA to predict environmental pollution after agrochemical application. The aim of the present study was to predict, using RZWQM2, the nitrogen and pesticides contents in soil of a monocrop and a tree-crop agroforestry system, and evaluate the effect of trees in reducing pollutants. Soil, weather, and agrochemical parameters for each setup were used as inputs in the model. Soil samples were collected at various depths and distances from the olive trees and were analyzed in the laboratory for nitrogen and pesticide contents. From the analysis of the results, it can be concluded that the model could identify the positive impact of the tree-crop agroforestry system in pollution reduction. Comparing the estimates with the relevant field data, the model presented some overestimation of the pesticide levels, particularly for the high-adsorptive and persistent pendimethalin herbicide, and slightly underestimated the concentrations of nitrates in the soil profile, while ammonium concentrations were well described. Overall, the model can be considered a useful and powerful tool for assessing the positive impacts of agroforestry systems in reducing soil pollution.