K. E. Tollefsen

This study uses a Bayesian network model to predict pesticide effects on aquatic ecosystems in rice fields, finding azoxystrobin and MCPA pose higher community risk than acetamiprid.

This paper uses Bayesian networks to assess pesticide risk under future climate and agricultural scenarios, finding that increased pesticide use, not climate variables, drives higher risk.

This paper develops a Bayesian network for probabilistic risk assessment of pesticides, demonstrating its application to azoxystrobin, metribuzin, and imidacloprid.