Modeling the forage–animal interface for estimating the daily gain of stocker cattle grazing bermudagrass

Résumé fourni par la source

Beef cattle production is a significant economic activity in the southeastern United States (US). Managing grazing systems efficiently requires understanding of the complex forage growth-defoliation-animal performance system and its components, such as forage crop plants, grazing animals, and the interface between them. Due to this complexity, the individual system components must be studied in relation to the system as a whole. This necessitates the use of mathematical modeling. Modeling a grazing system includes evaluating the forage crop system and the animal system via a coupled forage-animal interface (FAI) and mathematically representing all the interactions among the plants, animals, and the environment. Because of the continuous changes in weather, soil conditions, forage maturity, animal maturity, and diet selectivity, the production and nutritive value of forage plants vary daily, and so do animal nutrient requirements, herbage intake, and animal performance. Models that can account for these daily phenomena are needed for realistically simulating dynamic animal performance. However, there are currently no computer systems for estimating the daily performance of stockers that are grazing bermudagrass (Cynodon dactylon [L.] Pers.) pasture, a widely used warm-season perennial grass in the southeastern US. Thus, we developed a daily gain estimation system for stockers (DGESS) grazing bermudagrass by coupling the CROPGRO Perennial Forage Model to a stocker daily gain model through a simple FAI module. Then, we tested the performance of the DGESS by using stocker average daily gain (ADG) and body weight (BW) and bermudagrass forage mass (FM) data collected from 33 trials conducted at Overton, Texas, during 1987-2020. Using these data and the corresponding values predicted by the DGESS, the values of various goodness-of-fit measures, including the mean absolute error (MAE), the root mean square error (RMSE), and the Willmott Index (WI), were computed for each variable. The small values of MAE (0.36 kg hd-1 for ADG, 22 kg hd-1 for BW, 1,859 kg ha-1 for FM) and RMSE (0.44 kg hd-1 for ADG, 31 kg hd-1 for BW, 2,204 kg ha-1 for FM) and the large values of WI (0.77 for ADG, 0.91 for BW, 0.73 for FM) indicated that the performance of the DGESS was strongest for predicting stocker BW, while predictions of ADG and FM exhibited greater variability. By predicting the daily gain of stockers grazing bermudagrass, the DGESS can significantly contribute to forage-beef modeling for the scientific community and economic implications for stakeholders. The DGESS can also predict the daily nutritive value of bermudagrass forage and daily FM and forage allowance.

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