Project Number: 8042-12630-011-24-S
Project Type: Non-Assistance Cooperative Agreement
Start Date: Jul 1, 2014
End Date: Jun 30, 2019
The purpose of this project is to (1) develop submodels of microbial fate and transport processes affecting microbial quality of irrigation water sources; (2) create testing datasets for those models using literature sources, and if needed, carry out experiments at BARC, (3) develop algorithms to optimize the sampling schedule based on obtaining maximum information content for a given number of yearly observations and local agricultural management practices, and (4) develop the decision support tool to determine site-specific best practices on monitoring microbial quality of surface water.
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.