In animal agriculture, passive monitoring technology has the potential to lead to needed innovations as we look for solutions to make global food production more resilient. Here, we use passive intake systems to measure daily weight, water intake, and climatic variables to accurately predict dry matter intake. Such an approach, if it can be successfully applied for grazing animals would dramatically improve the ability of animal agriculture to reduce the ecological footprints of food production. Two hundred and five animals were studied in a drylot setting (152 bulls for 88 d and 53 steers for 50 d). We used both traditional statistical and modern machine learning approaches to test the ability to predict dry matter intake. Although all approaches had success in predicting dry matter intake, the best prediction came from a machine learning approach which was able to predict the average daily dry matter intake during a test to within 0.75 kg/d. Evaluation and refining of algorithms used to predict dry matter intake in the drylot by adding more representative data will allow for future extrapolation to controlled small plot grazing and, ultimately, more extensive grazing animal intakes at a production scale.

Read the full article in the Journal of Animal Science.