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Ring test with the models LEACHP, PRZM-2 and VARLEACH: Variability between model users in prediction of pesticide leaching using a standard data set

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Publication details

JournalPesticide science
DatePublished - Jul 1996
Issue number3
Number of pages10
Pages (from-to)249-258
Original languageEnglish


A ring test was carried out with three mathematical models for pesticide leaching to compare predictions from a number of modellers for a single field experiment when using the same model. The exercise sought to investigate the level of variation, if any, in model output introduced by user-dependent subjectivity during selection of input parameters. Five modellers were given a full description of a field experiment carried out in the UK to determine the leaching potential of a novel pesticide and then used the models LEACHP, PRZM-2 and VARLEACH to predict concentrations of pesticide in soil water at 1 m depth and in soil for a 1 m profile 220 days after application. Agreement with observed results was generally best for LEACHP and worst for VARLEACH, but no two sets of predicted results for a given model were exactly the same, even for the simple model VARLEACH. Differences between simulations with the same model were attributed to a number of input parameters which could not be derived from the experimental information provided and thus introduced subjectivity into the modelling process. The parameters identified included dispersivity, initial soil conditions and factors determining the rate of pesticide degradation. Differences between output data with the same model were of a similar order of magnitude to the variation associated with held measurements and were generally smaller than the discrepancies between observed and predicted data. User-dependence of modelling has not previously been considered, but should be an important component in assessing model output and in evaluating the validity and use of a given programme. Model development should seek to reduce subjectivity in selection of input parameters and improve the guidance available to users where subjectivity cannot be eliminated.

    Research areas

  • mathematical models, pesticide leaching, user-dependence, parameter selection

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