A fuzzy knowledge-based model for assessing risk of pesticides into the air in cropping systems

Pesticide use in current cropping systems has become a key input to improve productivity. However, their potential risk to nature demands tools for designing a sustainable use. In this work, a fuzzy knowledge-based model was developed for assessing risk of pesticides into the air. The model was based on fuzzy logic theory which provides a means for representing uncertainty by including knowledge about different processes related to pesticide dynamics using functions, control rules and logical inference systems. All these elements were built through a literature review. Results from the sensitivity analysis on the final model structure showed that the Henry's law constant was the most influential input variable related to the active ingredient identity, while the most influential management and environmental input variables on the pesticide air risk values were the droplet size together with the application method and the current wet bulb temperature depression value, respectively. Results from an independent model validation showed a significant goodness-of-fit between the simulated risk of drift and volatilization and the observed values under experimental conditions. Long-term simulations in a real soybean production system in Argentina showed results of drift reduction in post-emergence conditions of the crop under aerial application condition, and a significant effect of the identity of the active ingredient in the risk values. Simulated risk values from the developed model allow to identify ex ante the combination of agronomic decisions, together with environmental conditions that can reduce the risk of pesticides in the air in real production systems. Further combination with ecotoxicological classification tools should improve pesticide use assessment in agricultural systems.

Historical trends of the ecotoxicological pesticide risk from the main grain crops in Rolling Pampa (Argentina)

We showed the results of the first long-term analysis (1987-2019) of pesticide (herbicides, fungicides and insecticides) impact in the Rolling Pampa, one of the main agricultural areas of Argentina. Using a clear and meaningful tool, based not only on acute toxicity but also on scaling up the results to total sown area, we identified time trends for both pesticide impact and the ecoefficiency of modal pesticide profiles. By the end of the time series, soybean showed a pesticide impact four times greater than maize crop in the studied area. However, the time trend in the subperiod (2012-2019) showed a sustainable pattern of pesticide use in soybean crop, with an improvement in its ecoefficiency. Oppositely, maize showed a relatively constant ecoefficiency value during most of the time series, suggesting a possible path towards an unsustainable cropping system. Findings from this study suggest that some efforts have to be made to improve the pest management decisions towards a more efficient pesticide profiles in maize crop and to keep improving the ecotoxicity pesticide profile in soybean crops because of its large sown area in the studied area.