PSXIV-11 Predictive modeling of dry matter intake, feed, and water efficiency in pasture-based cattle systems.

Abstract Accurate prediction of dry matter intake (DMI) in cattle grazing on pasture is essential for optimizing nutritional management, enhancing production efficiency, and reducing feed costs for the beef industry. In pasture-based systems, DMI is influenced by factors such as forage quality, environmental conditions, individual animal body weight and stage of production. We have developed predictive that model uses key factors such as, forage composition, climactic conditions, body weight, age, breed, sex, and individual water intake to determine an animal’s individual daily DMI. From this individual DMI we can calculate individual pasture feed and water efficiency. This study aims to use a robust predictive model for DMI, RFI and RWI in cattle on pasture using a data-driven approach. Data was collected from a representative sample of yearling crossbred (n = 31) and purebred heifers (n = 33) managed on diverse pasture systems over two 60-day periods during the summer and early fall. A rotational grazing approach was used, giving a thirty-day rest period with an average pasture of 1.2 ha. Key variables measured included forage nutrient composition, pasture biomass, climactic conditions (such as temperature and humidity), animal-specific characteristics (including body weight and breed), and a total individual animal water intake. Using this data, individual predicted residual water and feed efficiency were calculated, as well as a pasture dry matter intake. Crossbred heifers had an average DMI of 7.54 Kg/day (6.73 kg to 12.0 kg) and purebred heifers averaged a DMI of 9.40 Kg/day (8.53 kg to 10.85 kg). Crossbred heifers’ RWI ranged from -6.67 L to +3.99 L and the purebreds RWI ranged from -6.6 L to +10.62 L/day. Crossbred heifers displayed a pasture RFI between -0.51 kg and 0.47 kg and the purebred heifers’ pasture RFI ranged from -0.75 kg to +0.75 kg. The analysis revealed that factors such as water intake, and body weight gain were significant predictors of individual animal dry matter intake. This model, which integrated both environmental and animal specific variables, explains 97% of the variation in dry matter intake. These results show a multidimensional approach to capturing the animal to environment interactions that determine feed intake in cattle. In conclusion, the predictive model used in this study provides a promising tool for estimating DMI in pasture-based cattle operations. Its implementation can assist producers in fine-tuning feeding strategies, thereby improving animal performance and reducing feed waste. Future research will focus on incorporating additional data sources and refining the model to enhance its predictive capabilities across different management systems and environmental conditions.