Comparison of linear and nonlinear regression for modeling the first-order degradation of pest-control substances in soil.

First-order kinetic models are often used to profile the degradation of pest-control compounds in soil. This approach is based on enzyme theory and is often favored due to its simplicity and its requirement by regulatory agencies. Here, linear and nonlinear regression approaches to modeling first-order decay are compared. Composite residual plots of many soil degradation data sets are presented on a normalized scale. These plots illustrate the general error structure for the data and are useful for detecting common mis-specifications of the models. Results indicate that a nonlinear regression approach to modeling first-order decay of compounds in soil more accurately describes most data sets when compared with a linear approach. Specifically, the observed error structure does not support the broad use of a logarithmic transformation to stabilize the variance. In addition, models generated using the linear approach generally exhibit more dramatic systematic deviations from the observations as compared with models generated using the nonlinear approach. The analysis methods described here may be useful for comparing alternative models in this and other research areas.