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Submitted to: Florida Ruminant Nutrition Symposium
Publication Type: Proceedings Publication Acceptance Date: 1/31/2017 Publication Date: 2/13/2017 Citation: Gunter, S.A. 2017. Predicting forage intake by grazing beef cattle. Florida Ruminant Nutrition Symposium, 28th Annual Meeting University of Florida, Institute of Food Agriculture Science, Gainsville, FL. Pages 125-136 Interpretive Summary: Voluntary intake by cattle is controlled by a complex mix of physical and physiological factors that interact with a variety of environmental, geo-spatial, and experiential influences external to the animal. These factors are intensified in grazing ruminants, where selective grazing and variability in dietary options also affect eating decisions. Because of the complexity of intake control and associated interacting factors, developing methods that yield accurate and precise predictions of voluntary intake by grazing cattle has been a challenge for animal scientists. Nonetheless, reliable estimates of intake are necessary to make informed management decisions related to sustainable management of grazing lands and to provide economically sustainable quantities of supplemental nutrients. Currently available empirical regression equations to predict intake include independent variables like body weight and energy concentration (or digestibility). Adding production variables [e.g., calf average daily gain/weaning weight for cows] seems to improve the accuracy and precision of these regression models, but to be applied in practice, these production variables must be estimated from historical data, which adds another source of variation. Similarly, intake can be predicted from estimates of energy requirements and energy concentration of the diet selected [the dry matter intake required approach], but this method also involves forecasting of requirements. For all empirical methods, estimates of forage digestibility or energy concentration are essential, but obtaining accurate estimates is difficult. Because our knowledge base of how, in a quantitative sense, intake control mechanisms and external factors influence voluntary intake by grazing cattle is limited, development of tools for predicting intake is likely to be a continuing long-term process. Technical Abstract: Voluntary intake by cattle is controlled by a complex mix of physical and physiological factors that interact with a variety of environmental, geo-spatial, and experiential influences external to the animal. These factors are intensified in grazing ruminants, where selective grazing and variability in dietary options also affect eating decisions. Because of the complexity of intake control and associated interacting factors, developing methods that yield accurate and precise predictions of voluntary intake by grazing cattle has been a challenge for animal scientists. Nonetheless, reliable estimates of intake are necessary to make informed management decisions related to sustainable management of grazing lands and to provide economically sustainable quantities of supplemental nutrients. Currently available empirical regression equations to predict intake include independent variables like body weight and energy concentration (or digestibility). Adding production variables [e.g., calf average daily gain/weaning weight for cows] seems to improve the accuracy and precision of these regression models, but to be applied in practice, these production variables must be estimated from historical data, which adds another source of variation. Similarly, intake can be predicted from estimates of energy requirements and energy concentration of the diet selected [the dry matter intake required approach], but this method also involves forecasting of requirements. For all empirical methods, estimates of forage digestibility or energy concentration are essential, but obtaining accurate estimates is difficult. Because our knowledge base of how, in a quantitative sense, intake control mechanisms and external factors influence voluntary intake by grazing cattle is limited, development of tools for predicting intake is likely to be a continuing long-term process. |
