Water is a vital nutrient for beef cattle, but we still lack accurate tools to predict how much water an individual animal needs each day. This matters because cattle are raised in a wide range of climates, and water availability is becoming less predictable with climate change. In this study, we used a machine learning model to predict daily water intake for individual beef cattle using information available in intake-monitored herds and structured performance-test environments, such as animal weight, dry matter intake, and local weather. We trained the model using data from over 2,200 animals raised in different production systems, including both pasture and feedlot settings. The model learned to detect patterns over time, such as how animals respond to changes in heat and humidity. When tested on new animals and environments, the model made highly accurate predictions and outperformed the standard equation currently used by the beef industry. These results show that artificial intelligence can be used to predict water needs in real time, which could help producers manage cattle more effectively during hot weather and identify animals that use water more efficiently.

Read the full article in the Journal of Animal Science.