Epsilon Open Archive

Abstract

Intensive agricultural practices and use of pesticides, essential to achieve high crop yields, present particular risks to soil and water resources which sustain life. Degradation and sorption of pesticides in soils are both spatially variable and also among the most sensitive factors determining losses to surface water and groundwater. Currently, no general guidance is available on suitable approaches for dealing with spatial variation in pesticide degradation and sorption in catchment or regional scale modelling applications.

This thesis investigated sorption and degradation of three pesticides (bentazone, isoproturon, and glyphosate) in the cultivated soils of an agricultural catchment in Sweden with the aim to develop and test simple model approaches that could support large-scale modelling.

In the case of sorption, an extended partitioning model improved upon the koc concept for all three compounds studied: inorganic sorbents dominated sorption in sub-surface soils and their effects were only masked by organic matter in surface soils with organic carbon contents larger than ca.2%. Interactions between organic and inorganic sorbents affected glyphosate sorption, but apparently not that of bentazone or isoproturon. It was concluded that information on clay, fclay, iron and aluminum oxides and soil pH, in addition to organic carbon, foc, is needed to accurately predict pesticide sorption. The variables foc, fclay and pH are generally available, whereas measurements of oxides of Al and Fe are rarely reported.

The degradation rate constant (k) was highly variable with coefficients of variation ranging between 42 and 64% for the three herbicides. This variability could be attributed to variations in microbial biomass and pesticide bioavailability. A simple modelling 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) was successfully tested in a meta-analysis of available literature data using bootstrapped partial least squares regression (PLSR). In conclusion, this 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.