Project Number: 8042-12630-008-24
Start Date: Jul 01, 2014
End Date: Jun 30, 2019
Microbial submodels have to be compatible with the ARS environmental quality model APEX. Transfer functions have to be developed to eliminate non-publicly available site-specific input for those models. Testing datasets have to encompass a range of different climatic, soil, and management conditions. BARC natural settings, including creeks and ponds, can be used in field experiments to fill existing knowledge gaps. The sampling optimization algorithms have to provide the maximum predictive capacity to establish the site-specific risk of exceeding the microbial standards of irrigation water quality. Artificial intelligence methods will be utilized. The tool developed will gather publicly available data from USGS, USDA, NOAA, and EPA databases for applying the model and optimizing sampling schedule for a specific farm or watershed condition.