Predicting dry matter intake in beef cattle.

Technology that facilitates estimations of individual animal dry matter intake (DMI) rates in group-housed settings will improve production and management efficiencies. Estimating DMI in pasture settings or facilities where feed intake cannot be monitored may benefit from predictive algorithms that use other variables as proxies. This study examined the relationships between DMI, animal performance, and environmental variables. Here we determined whether a machine learning approach can predict DMI from measured water intake variables, age, sex, full body weight, and average daily gain (ADG). Two hundred and five animals were studied in a drylot setting (152 bulls for 88 d and 53 steers for 50 d). Collected data included daily DMI, water intake, daily predicted full body weights, and ADG using In-Pen-Weighing Positions and Feed Intake Nodes. After exclusion of 26 bulls of low-frequency breeds and one severe (>3 standard deviations) outlier, the final number of animals used for modeling was 178 (125 bulls, 53 steers). Climate data were recorded at 30-min intervals throughout the study period. Random Forest Regression (RFR) and Repeated Measures Random Forest (RMRF) were used as machine learning approaches to develop a predictive algorithm. Repeated Measures ANOVA (RMANOVA) was used as the traditional approach. Using the RMRF method, an algorithm was constructed that predicts an animal's DMI within 0.75 kg. Evaluation and refining of algorithms used to predict DMI in drylot by adding more representative data will allow for future extrapolation to controlled small plot grazing and, ultimately, more extensive group field settings. 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.

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