I-Corps: Predictive algorithms to determine individual feed intake in beef cattle.

Award ID: 2348526

The broader impact/commercial potential of this I-Corps project is the development of an on-farm tool to allow animal agriculture producers to determine how much their animals are eating. Approximately 70% of an animal agriculture operations variable cost of production is the cost of feed. However, there are few approaches that allow producers to measure their animals feed intake, and the limited number of locations that have that capacity are expensive. The proposed low cost, on-farm tool would allow farmers to identify potential replacement animals that are more efficient, improve how they manage animals in the feedlot and to quantify intakes of animals grazing pasture. Currently there is no way for pasture animal intake to be determined when animals are grazing at scale. If 5% of US beef producers made use of this tool that would be 40,000 operations and likely improve the management decisions related to upwards of 500,000 to a million cattle. This I-Corps project is based on the development of a predictive algorithm to make use of daily animal weight and water intake, along with weather data, to predict daily feed intake. The proposed tool has been trained using data from a specialized feeding barn that has equipment to measure feed intake as well as animal weight and water intake. Currently, state-of-the-art systems significantly over- or under-estimate the actual feed intake. The proposed tool intends to work in situations where either there is not an expensive feed intake system or in extensive grazing pasture situations where weighing feed is not possible. The system has been validated on ~2200 animals fed in the barn and almost 100 animals grazing small plots where a ground truth can be determined for grazing feed intake. Results have shown predictions of individual daily feed intake to within 92-95% accuracy. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Program Director: Jaime A. Camelio