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Person working in lab on agrochemical product.

Improving Environmental Fate Models for Agrochemicals


Before making a pesticide or other agrochemical available on the marketplace, agrochemical companies need to fully understand the potential fate of the chemical in the environment.  In the case of pesticides, the potential for the chemical to leech into groundwater is of particular concern. Currently, computer simulations utilizing data about soil characteristics and weather conditions at various locations are used to predict pesticide concentrations under different conditions.  Lab data, including information about the rate of degradation and sorption properties, is used to build the computer models.  However, inaccurate assumptions in the model will lead to incorrect conclusions about the environmental fate of the pesticide, potentially unnecessarily jeopardizing registration, or resulting in unsafe application recommendations.


The standard models assume that sorption is due only to binding to the organic matter in the soil.  In lower horizons of soil, the organic matter is approximately zero, and thus no sorption is assumed.  In reality this may not be entirely correct when other mechanisms of sorption are taken into consideration. 

Battelle looked at one of the standard scenarios used to assess pesticide leaching, the “Borstel” model.  This is considered to be one of the more critical scenarios, as it is a very sandy soil and represents a greater risk for leaching into groundwater.  To provide data to refine the simulation, Battelle collected “Borstel” soil as a core down to 1m.  The core was segmented and each horizon analyzed to confirm its similarity to the soil characterization data used in the computer simulation.  Each soil horizon was then used in an adsorption/desorption study to determine the actual sorption of the chemical.  The measured sorption in the deeper soil horizons was found to be greater than predicted by assuming organic matter binding as the sole mechanism, leading to a more favorable leaching risk assessment. 


Using the new lab data, we were able to refine the assumptions used in the computer model to provide a more realistic risk assessment of potential leaching.  The refined modelling can be used to make better decisions about application recommendations, and provide critical data necessary to maintain product registration or move into new market areas.