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item Whetsell, M
item Rayburn, E
item Neel, James - Jim
item Fontenot, J
item Clapham, William

Submitted to: American Society of Animal Science
Publication Type: Proceedings
Publication Acceptance Date: 6/30/2005
Publication Date: 7/24/2005
Citation: Whetsell, M.S., Rayburn, E.B., Neel, J.P., Fontenot, J.P., Clapham, W.M. 2005. Evaluating the prediction of dmi and adg in backgrounding cattle. #M164, J. Anim. Sci., Vol. 83, Suppl. 1, p. 49.

Interpretive Summary:

Technical Abstract: On a farm, historical experience is used to develop a forage-livestock system considered the optimum for the farm by the farmer. Computer models allow extension staff and farmers to evaluate alternative farming systems or system components without investment of capital or exposure to risk. In this study we evaluated the accuracy of the 2000-update National Research Council’s “Nutrient Requirements of Beef Cattle” computer model for predicting calf performance during backgrounding. Seventy-two British or British cross stocker steers were used to measure performance during the fall and spring of 2001-2002, 2002-2003 and 2003-2004 in Morgantown, West Virginia. The calves were randomly assigned to one of the three forage based wintering diets, designed to provide three distinct production levels (0.25, 0.50 and 0.75 kg per animal). The treatments were timothy hay supplemented with soybean meal or soybean meal and soybean hulls at two different levels. The National Research Council model was used to predict average daily gain (ADG) of the cattle using dry matter intake (DMI) calculated using the NRC 2000 calf DMI equation and also the observed DMI. Model DMI error was measured by calculating the model DMI residuals, the difference between predicted DMI minus observed DMI. Model error was also measured for ADG. Results showed that the predicted DMI was similar to that observed and slightly lower than that predicted by the calf DMI equation based on ration NEM (P<0.001). However, ADG using either equation (estimated DMI or observed DMI) was about 0.3 kg animal-1 day-1 lower than observed ADG (P<0.001). Results indicate that the model did not accurately predict animal performance of the cattle on feed but it did predict the increase in performance across treatments as energy content of the diet increased.