Influence of input uncertainty on prediction of within-field pesticide leaching risks

Previous research has suggested that pesticide losses at the field scale can be dominated by a small proportion of the field area. The objective of this study was to investigate whether site-specific applications (i.e. avoiding high-risk areas) at the field scale can contribute to a reduction of pesticide leaching despite uncertainty in the underlying model-based leaching risk map. Using a meta-model of the dual-permeability model MACRO, the annual average pesticide leaching concentrations were estimated for 162 sample sites on a 47 ha field. The procedure was repeated for different scenarios describing different patterns of spatial variation of degradation half-lives and the partition coefficient to soil organic carbon. To account for interpolation uncertainty, maps of predicted pesticide leaching risk were produced by the method of sequential Gaussian simulation. The results of the case study show that larger reductions of predicted leaching were achieved by site-specific application than by that of a comparable uniform dose reduction. Hence, site-specific-applications may be a feasible method to reduce pesticide leaching at the field-scale providing that the model approach gives reasonable estimates of the spatial pattern of pesticide leaching.