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United States Department of Agriculture

Agricultural Research Service


item Coleman, Samuel

Submitted to: Florida Cattleman
Publication Type: Trade Journal
Publication Acceptance Date: 7/17/2006
Publication Date: 8/3/2006
Citation: Coleman, S.W. 2006. Evaluating forage quality of grazing cattle. Florida Cattleman. August 70(11): p. 16, 18, 20, 22, 24, 26, 28.

Interpretive Summary:

Technical Abstract: Providing mother cows with an adequate supply of high quality forage is a difficult task in much of Florida and the Gulf Coast despite the rather mild winters. Many folks in the north, including the author before he came to Florida, have the idea that cattlemen in the region have abundant green grass year-round. In South Florida it is possible with some careful planning, but in much of the state both abundance and quality is compromised at some time during the year. Some knowledge about what your animals are grazing is important to properly manage the total nutrition package that includes forage available for grazing, supplemental forage (e.g., hay) or supplemental feed (e.g., molasses or grain). Determination of the amount eaten and characteristics of the diet of grazing ruminants remains one of the most difficult tasks in research. While several lifetimes have been devoted to developing techniques to ‘measure’ intake and diet quality, they are laborious, expensive, and often lack both precision and accuracy. Hence, there has been little effort on the part of producers or consultants to attempt these determinations in commercial operations. The result has largely been a reliance on guesswork to assess the nutritional status and potential needs for supplementing grazing animals. Most consultants and some producers have in turn relied on laboratory analyses to determine feed value. The problem with predicting in vivo measurements with routine chemistry or NIRS has been in obtaining sufficient numbers of samples for which reference data were obtained under carefully defined conditions. More rigorous statistical procedures and larger sample sets may help overcome problems of developing broadly-based robust equations.

Last Modified: 05/26/2017
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