|SPRINKLE, J. - University Of Arizona|
Submitted to: Journal of Animal Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 11/25/2013
Publication Date: 7/1/2014
Publication URL: http://handle.nal.usda.gov/10113/59372
Citation: Coleman, S.W., Gunter, S.A., Sprinkle, J.E., Neel, J.P. 2014. Beef Species Symposium: Difficulties associated with predicting forage intake by grazing beef cows. Journal of Animal Science. 92:2775-2784.
Interpretive Summary: The current model used by beef cattle nutritionist and land managers to predict the forage intake by grazing cattle provide unsatisfactory estimates that do a poor job of predicting animal performance and herbage removal from the landscape. Improving the predictive ability of intake models for grazing cattle is an important research mission because intake not only determines animal performance and profitability of a ranch, its prediction can be used to determine the impact of grazing cattle on wildlife habitat and other ecological services. The reason current models perform poorly with grazing cattle is they were constructed with data from cattle in feedlots; hence, they work well with feedlot cattle. This research examines the environmental conditions and impact of differing physiological states, such as growing, non-lactating, or lactating have on forage intake by grazing cattle. The new forage intake models are currently being constructed by a committee organized by the National Research Council and should be published in 2015. It is our hope that this research will provide guidance and assistance to the committee in constructing the new models so their predictive quality is improved.
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 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. 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. We developed a broad based database that includes pasture studies with growing animals and dry and lactating cows on pasture and in confinement. New equations are presented for consideration in the new model. We found that the premise behind earlier NRC equations based on diet digestibility and body weight (BW) are sound, but that for cows, additional drivers based on milk production or calf performance were stronger than BW. We would suggest that future models be based on multiple equations, including functions for physiological state, 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. Further, the issue of how reference data was collected (pen vs. pasture) and how the methods or constraints influence DMI must be evaluated. 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.