Predicting forage intake in extensive grazing systems

Authors: GALYEAN, MICHAEL - Texas Tech University; Gunter, Stacey

Submitted to: Journal of Animal Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 5/20/2016
Publication Date: 11/9/2016
Publication URL: https://handle.nal.usda.gov/10113/5695338
Citation: Galyean, M.L., Gunter, S.A. 2016. Predicting forage intake in extensive grazing systems. Journal of Animal Science. 94(6):26-43.

Interpretive Summary: Voluntary intake by grazing cattle and other ruminants is controlled by a complex mix of factors that interact with a variety of influences external to the animal. These factors are intensified in grazing ruminants, where selective grazing and potential variability in food options also affect dietary decisions. As a result of the complexity of intake control and associated interacting factors, developing methods that yield accurate and precise predictions of voluntary intake has been a long-standing challenge for animal scientists. Nonetheless, reliable estimates of intake are necessary to make informed management decisions related to the sustainable management of grazing lands and to provide economically sustainable quantities of supplemental nutrients. Currently available regression equations to predict intake include independent variables like body weight and digestibility. Adding production variables like average daily gain for growing cattle or 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. Similarly, intake can be predicted from estimates of energy requirements and digestibility of the diet selected, but this method also involves forecasting of requirements. For all empirical methods, estimates of forage digestibility are essential, but obtaining accurate estimates is difficult with grazing livestock. More complex mechanistic or quasi-mechanistic models have been developed, but the application of these models has been too limited to determine whether they offer significant advantages over traditional empirical models. Because our knowledge base of how, in a quantitative sense, both intake control mechanisms and external factors influence voluntary intake by grazing ruminants is limited, development of tools for predicting intake is likely to be a long-term process.

Technical Abstract: Voluntary intake by cattle and other ruminants 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 potential variability in dietary options also affect eating decisions. As a result 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 long-standing 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 to maintain desired production levels. Currently available empirical regression equations to predict intake include independent variables like body weight and energy concentration (or digestibility). Adding production variables (e.g., average daily gain for growing cattle or 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 and could decrease the predictive ability of these equations. 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 with grazing livestock. More complex mechanistic or quasi-mechanistic models have been developed, but the application of these models has been too limited to determine whether they offer significant advantages over traditional empirical models. Because our knowledge base of how, in a quantitative sense, both intake control mechanisms and external factors influence voluntary intake by grazing ruminants is limited, development of tools for predicting intake is likely to be a long-term process.