Exploration of a comprehensive versus a regulatory-oriented modeling framework for field pesticide transport assessment.

Sci Total Environ

Agricultural and Biological Engineering, Purdue University, 225 South University Street, West Lafayette, IN 47907, USA. Electronic address:

Published: January 2024

Numerous computer models have been developed for simulating pesticide fate and transport. It is usually hard to choose which model is the best for a particular research or regulatory purpose. Currently, the PRZM (Pesticide Root Zone Model) model is widely used for regulatory purposes regarding runoff and erosion. However, it simplifies many hydrological processes and management practices which affect pesticide fate simulations. In this study, the APEX (Agricultural Policy / Environmental eXtender model) model, which is more comprehensive and may provide a more realistic representation of pesticide fate, was compared with the PRZM model regarding methods and capabilities of characterizing hydrology, management, and pesticide transport. Four case studies were used to compare the performances of the two models for simulating surface runoff, sediment yield, pesticide in runoff, and pesticide in sediment. Results showed that the APEX model performed better than the PRZM model for simulating surface runoff and sediment yield, and performed similarly to the PRZM model for simulating pesticide loads in runoff and erosion. Both models have limitations for capturing the runoff events caused by high intensity rainfall. APEX is superior to PRZM in simulating detailed management operations, considering more hydrological processes, and achieving spatially distributed simulation, but it requires a higher number of inputs and user-selected parameters compared to PRZM. With further validations of the capabilities of APEX in pesticide modeling and the development of web-based platforms to facilitate the set up and use of comprehensive models, a more accurate and reliable pesticide assessment scheme is anticipated by using comprehensive models like APEX.

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http://dx.doi.org/10.1016/j.scitotenv.2023.167487DOI Listing

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