Location: Livestock Nutrient Management Research
Project Number: 3090-31630-006-059-N
Project Type: Non-Funded Cooperative Agreement
Start Date: Sep 15, 2025
End Date: Sep 14, 2030
Objective:
Artificial intelligence has aided in the identification of promising, novel feed additives which could improve beef cattle efficiency of production. This project aims to continue the on-going research and to further develop a pipeline where novel feed additives are identified through leveraging artificial intelligence by the Cooperator and then rapidly screened using in vitro techniques in house.
Approach:
The cooperator, in collaboration with ARS has developed a computer modeling pipeline, which leverages artificial intelligence paradigms, to screen large numbers of compounds for their capacity to enhance ruminal fermentation patterns, which may translate to improved feed efficiency in cattle (i.e., altered volatile fatty acid profiles, reduced gaseous energy loss). Once the Cooperator's lab has identified promising candidates, an in vitro screening process will occur where fermentation profiles will be assessed by ARS. The data obtained from the in vitro trials will then be used to train the AI model to make refinements to result in enhanced estimates for optimal feed additives in subsequent trials. We plan to continue this effort with 1000 molecules to hopefully obtain a fully trained AI model.