Difficulties associated with predicting forage intake by grazing beef cows

Authors: Gunter, Stacey; FAULKNER, D. - University Of Arizona; MEYER, A. - University Of Wyoming; SCHOLLJEGERDES, E. - New Mexico State University; SPRINKLE, J. - University Of Arizona; SOTO-NAVARRO, S. - New Mexico State University; Coleman, Samuel

Submitted to: American Society of Animal Science Proceedings
Publication Type: Abstract Only
Publication Acceptance Date: 3/12/2013
Publication Date: 7/8/2013
Citation: Gunter, S.A., Faulkner, D.B., Meyer, A.M., Scholljegerdes, E.J., Sprinkle, J.E., Soto-Navarro, S.A., Coleman, S.W. 2013. Difficulties associated with predicting forage intake by grazing beef cows. Journal Animal Science. 91(E-Supplementation 2):440. (Abstract)

Interpretive Summary:

Technical Abstract: The current National Research Council (NRC) model is based on a single equation that relates dry matter intake (DMI) to metabolic size and net energy density of the diet offered and was a significant improvement over previous models. However, observed DMI by grazing animals can be conceptualized by a function that includes animal demand, largely determined by metabolic or linear size, physiological state, genetics, or any combination. Forage DMI is in reality modified by its nutritive value and nutrient balance, herbage mass and structure, locomotion, climate (or environment), profitability of bites, the interaction of genetics with the environment, and level and type of supplementation. Even in the database used to generate the current NRC equation, DMI by cows is poorly predicted at the extremes. In fact, across the range of actual DMI, predicted DMI is rather flat indicating an insensitivity so further refinement of the model is needed. Also, it may be necessary to construct multiple models designed for various rangeland and pasture types. We would suggest that future models be based on multiple equations including functions for physiological state, previous plane of nutrition, animal suitability to the environment, and activity to modify the predicted DMI. Further, the model could possibly account for imbalances of protein to energy, particularly as it relates to ruminal function, and herbage distribution and accessibility as it influences grazing behavior and selectivity. The inclusion of some of these functions may render the model inputs too complex for many users, hence models must be evaluated for complexity as well as how well the model fits under multiple situations. Further, the issue of how reference data was collected (pen vs. pasture) and how the methods or constraints influence DMI must be evaluated. For instance, if DMI is greater under grazing, is it because of greater metabolic demand due to activity and climatic conditions, or to differences in direct measurement of DMI compared to indirect methods (e.g., internal and external markers). Overall, the new NRC model needs to be more robust in its ability to account for the wide variation in the environment, dietary characteristics, and metabolic demands.