Application of Mathematical Models to Individually Allocate Feed of Group-fed Cattle
Academic Article
Overview
Research
Identity
Additional Document Info
Other
View All
Overview
abstract
2007 American Registry of Professional Animal Scientists. A data set of group-fed growing and finishing steers with individual feed access was used to evaluate predictions of required individual DM by 2 mathematical models (Cornell Value Discovery System, CVDS; and beef NRC) to allocate feed of group-fed, commingled cattle. Forty-eight crossbred steers (BW = 296 kg) were assigned to 1 of 6 pens and fed 1 of 4 growing diets formulated to have different energy concentrations for restricted or ad libitum intake regimen for 56 d. The diets were a low-starch diet fed ad libitum, a high-starch diet fed ad libitum, a high-starch with restricted intake, and an intermediate diet fed ad libitum with an average energy intake between ad libitum low-starch and ad libitum high starch diets. On d 57, all steers (BW = 401 kg) were placed on the ad libitum high-starch diet for finishing until d 140. The CVDS was able to account for 61% of the variation in the observed DMI (oDMI) of steers during the growing period, and for 71% of the variation in oDMI during finishing, with an average overprediction of 3.76%. In the same fashion, the NRC model was able to explain 59% of the variation in oDMI after adjustment for known performance during the growing period with no bias (P > 0.10), and 57% of the variation in oDMI during the finishing period, with an average underprediction of 4.40%. Our overall evaluation suggested that the CVDS was more precise and accurate than the NRC model when predicting DMI for individual animals. Both models were sensitive to the previous level of nutrition of the cattle, suggesting that more variables are necessary to increase the prediction precision for cattle growing systems. The results from a risk analysis suggested that an amount of approximately $17.00/animal may be either over- or under-charged in the billing process of a commercial feedlot growing and finishing periods. Therefore, mathematical models could assist commercial feedlots to improve the accuracy of the billing process while maintaining the same income per pen.
