Pesticide degradation rate coefficient, k is a very sensitive parameter and often highly variable. The application of pesticide fate models at larger scales would benefit from an improved knowledge and understanding of the relationships between degradation rates and easily measured soil properties that are widely available from soil surveys. Supported by some mechanistic reasoning, we propose a simple multiplicative model to predict k from a compound-dependent reference rate coefficient, surrogate or proxy soil variables i.e. clay and soil organic carbon (SOC) for microbial activity and bioavailability. The model was tested in a meta-analysis of available literature data comprising degradation rates measured for a wide range of substances in strongly contrasting soils, using partial least squares regression (PLSR) bootstrapping re-sampling technique. Literature review revealed that the activity or efficiency of microbial biomass decreases with depth in the soil profile, so depth was included as additional predictor. Further, the model was benchmarked against the pure intercept model (no explanatory variable) i.e. the average k values. For the limited dataset for which microbial biomass was measured, the model reduced the overall RMSE by 31% (from 0.29 to 0.20). Replacing microbial biomass with proxy variable e.g., SOC, the model is found to work for 15 compounds, reduced the RMSE from 0.26 for the pure intercept model to 0.20 (i.e. by 23%) and explained 80% of the variation in degradation rate of these selected Pesticides. The results suggested that a general multiplicative model (eq 5, Ghafoor et al., 2011) shows promise as an effective approach to predict degradation compared with the use of average values for k in the context of risk assessment of plant protection products. Reference  : Ghafoor, A; Moeys, J; Stenstr�m, J; Tranter, G; Jarvis, N. 2011. Modeling spatial variation in microbial degradation of pesticides in soil. Environ. Sci. & Technol. 45:6411-6419.

Improved methodologies to relate degradation kinetics to site or environmental variables

248th National Meeting (2014)

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