Modeling spatial variation in microbial degradation of pesticides in soil.

Currently, no general guidance is available on suitable approaches for dealing with spatial variation in the first-order pesticide degradation rate constant k even though it is a very sensitive parameter and often highly variable at the field, catchment, and regional scales. Supported by some mechanistic reasoning, we propose a simple general modeling approach to predict k from the sorption constant, which reflects bioavailability, and easily measurable surrogate variables for microbial biomass/activity (organic carbon and clay contents). The soil depth was also explicitly included as an additional predictor variable. This approach was tested in a meta-analysis of available literature data using bootstrapped partial least-squares regression. It explained 73% of the variation in k for the 19 pesticide-study combinations (n = 212) in the database. When 4 of the 19 pesticide-study combinations were excluded (n = 169), the approach explained 80% of the variation in the degradation rate constant. We conclude that the approach shows promise as an effective way to account for the effects of bioavailability and microbial activity on microbial pesticide degradation in large-scale model applications.