Sensitivity analyses for four pesticide leaching models

Abstract

Sensitivity analyses using a one-at-a-time approach were carried out for leaching models which have been widely used for pesticide registration in Europe (PELMO, PRZM, PESTLA and MACRO). Four scenarios were considered for simulation of the leaching of two theoretical pesticides in a sandy loam and a clay loam soil, each with a broad distribution across Europe. Input parameters were varied within bounds reflecting their uncertainty and the influence of these variations on model predictions was investigated for accumulated percolation at 1-m depth and pesticide loading in leachate. Predictions for the base-case scenarios differed between chromatographic models and the preferential flow model MACRO for which large but transient pesticide losses were predicted in the clay loam. Volumes of percolated water predicted by the four models were affected by a small number of input parameters and to a small extent only, suggesting that meteorological variables will be the main drivers of water balance predictions. In contrast to percolation, predictions for pesticide loss were found to be sensitive to a large number of input parameters and to a much greater extent. Parameters which had the largest influence on the prediction of pesticide loss were generally those related to chemical sorption (Freundlich exponent nf and distribution coefficient Kf) and degradation (either degradation rates or DT50, QTEN value). Nevertheless, a significant influence of soil properties (field capacity, bulk density or parameters defining the boundary between flow domains in MACRO) was also noted in at least one scenario for all models. Large sensitivities were reported for all models, especially PELMO and PRZM, and sensitivity was greater where only limited leaching was simulated. Uncertainty should be addressed in risk assessment procedures for crop-protection products.

Analysis of parameter uncertainty and sensitivity in PCPF-1 modeling for predicting concentrations of rice herbicides

“…[52][53][54] It thus improves the quality of the regression and increases the R 2 adj . 44,55,56) In this analysis, the rank-transformed parameters were used instead of the raw parameter values, and the standardized rank regression coe cient (SRRC) was then calculated via regression analysis as a measure of variable importance. 57) Together with the stepwise regression analysis, statistical assumptions regarding the data used in the regression were veried in order to assure the reliability of the analysis.…”

“…Other pesticide fate models have also been reported to be sensitive to sorption and soil degradation inputs. 20,55,62) As rice pesticide fate and transport models are very sensitive to sorption coecients, 19) and since paddies adsorption values have been reported to be very di erent in upland elds and even to vary between paddy elds, 63) it seems crucial to devote time acquire accurate soil organic carbon data.…”

Mobilidade do ametryn em latossolos brasileiros

“…Há grande variedade de modelos para avaliar o destino ambiental de substâncias e, em particular, sua transferência para águas superficiais e subterrâneas após serem aplicados em áreas agrícolas (Dubus et al, 2003). Um método simples e que requer pouco investimento em equipamentos e recursos consiste no bioensaio, no qual espécies indicadoras são cultivadas em solo previamente contaminado.…”

Automation and evaluation of three pesticide fate models for a national analysis of leaching risk in Canada

“…Morgan (2002) used Visual Basic and Unix batch files to automate the running of PRZM to classify land units in the Columbia River basin. The computer program SENSAN was used by Dubus et al (2003) to automate PRZM, MACRO PELMO and PESTLA in determining their sensitivity to various input parameters. RunMACRO (Hansen 2001) was developed to automate sensitivity analysis of MACRO.…”