Fit of Four Curve−Linear Models to Decay Profiles for Pest Control Substances in Soil

Abstract

Experiments that investigate the pattern of degradation of pest control substances in soil are often undertaken to estimate the persistence of compounds in the environment. Mathematical models are typically fit to decay data to facilitate the interpretation of the results and make predictions concerning the environmental fate of xenobiotics in soil. Four mathematical models were fit to 61 data sets to compare their performance in conforming to empirical patterns of degradation of pest control substances in soil. The use of composite residual plots allowed comparisons of the performance of the different models over many data sets. While an exponential model, estimated using nonlinear regression, fit many data sets very well, a shift-log, biexponential, and Monod equation appears superior in many cases, and systematic deviations from data sets are often less evident with the latter models. A knowledge of the patterns of bias typically exhibited by each model across many data sets may be useful for selecting models with reduced bias when fitting individual data sets.