Example：10.1021/acsami.1c06204 or Chem. Rev., 2007, 107, 2411-2502
Modifying the National Research Council weight gain model to estimate daily gain for stockers grazing bermudagrass in the southern United States Journal of Animal Science (IF3.159), Pub Date : 2022-01-13, DOI: 10.1093/jas/skac011 Prem Woli, Francis M Rouquette, Charles R Long, Luis O Tedeschi, Guillermo Scaglia
The energy requirements, feed intake, and performance of grazing animals vary daily due to changes in weather conditions, forage nutritive values, and plant and animal maturity throughout the grazing season. Hence, realistic simulations of daily animal performance can be made only by the models that can address these changes. Given the dearth of simple, user-friendly models of this kind, especially for pastures, we developed a daily gain model for large-frame stockers grazing bermudagrass sCynodon dactylon (L.) Pers.], a widely used warm-season perennial grass in the southern United States. For model development, we first assembled some of the classic works in forage-beef modeling in the last 50 yr into the National Research Council (NRC) weight gain model. Then, we tested it using the average daily gain (ADG) data obtained from several locations in the southern United States. The evaluation results showed that the performance of the NRC model was poor as it consistently underpredicted ADG throughout the grazing season. To improve the predictive accuracy of the NRC model to make it perform under bermudagrass grazing conditions, we made an adjustment to the model by adding the daily departures of the modeled values from the data trendline. Subsequently, we tested the revised model against an independent set of ADG data obtained from eight research locations in the region involving about 4,800 animals, using 30 yr (1991–2020) of daily weather data. The values of the various measures of fit used, namely the Willmott index of 0.92, the modeling efficiency of 0.75, the R2 of 0.76, the root mean square error of 0.13 kg d−1, and the prediction error relative to the mean observed data of 24%, demonstrated that the revised model mimicked the pattern of observed ADG data satisfactorily. Unlike the original model, the revised model predicted more closely the ADG value throughout the grazing season. The revised model may be useful to accurately reflect the impacts of daily weather conditions, forage nutritive values, seasonality, and plant and animal maturity on animal performance.