Submitted to: Journal of Animal Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: March 15, 2010
Publication Date: July 1, 2010
Repository URL: http://hdl.handle.net/10113/43315
Citation: Williams, C.B. 2010. Application of Biological Simulation Models in Estimating Feed Efficiency of Finishing Steers. Journal of Animal Science. 88(7):2523-2529. Interpretive Summary: Feed represents the greatest input in beef production, accounting for approximately 66% and 77% of the total cost of gain in cow/calf and yearling beef cattle finishing systems, respectively (Anderson et al., 2005). This suggests that increased efficiency of converting feed into a marketable product would have a large positive impact on profitability. Characterizing animals for feed efficiency requires a measurement of individual animal feed intake which is expensive and limited to the capacity of the recording equipment. On the other hand, biological simulation models are capable of predicting the feed intake required for cattle to achieve their observed performance and provide a vehicle through which predictions of individual animal feed intake can be obtained for large numbers of cattle at very low cost. This study evaluated the feasibility of using predicted feed intake in place of measured feed intake to characterize individual animals for feed efficiency. Results showed that it would not be feasible to replace measured feed intake with predicted feed intake. However, animals were more accurately characterized for residual differences in feed intake when these differences were obtained as the difference between measured and predicted feed intake, rather than with a linear statistical model. The use of biological simulation models as in this latter application would allow for the combining of data sets from geographical regions and also obtaining accurate estimates of feed efficiency in small data sets. This use, however, still requires the measurement of individual animal feed intake; hence, it would apply more to researchers and breeders rather than individual producers in the beef industry.
Technical Abstract: Data on individual daily feed intake, bi-weekly BW, and carcass composition were obtained on 1,212 crossbred steers. Within animal regressions of cumulative feed intake and BW on linear and quadratic days on feed were used to quantify initial and ending BW, average daily feed intake (OFI) and ADG over a 120-d finishing period. Feed intake was predicted (PFI) with 3 biological simulation models (BSM), a) Decision Evaluator for the Cattle Industry (DECI), b) Cornell Value Discovery System (CVDS), and c) National Research Council update 2000, (NRC2) using observed growth and carcass data as input. Residual feed intake (RFI) was estimated using OFI (RFIEL) in a linear statistical model (LSM) and feed conversion ratio (FCR) was estimated as OFI/ADG (FCRE). Output from the BSM was used to estimate RFI by using PFI in place of OFI with the same LSM, and FCR was estimated as PFI/ADG. These estimates were evaluated against RFIEL and FCRE. In a second analysis, estimates of RFI were obtained for the 3 BSM as the difference between OFI and PFI, and these estimates were evaluated against RFIEL. The residual variation was extremely small when PFI was used in the LSM to estimate RFI, and this was mainly due to the fact that the same input variables (initial weight, days on feed and ADG) were used in both the BSM and LSM. Hence the use of PFI obtained with BSM, as a replacement for OFI in a LSM to characterize individual animals for RFI was not feasible. This conclusion was also supported by low correlations (< 0.4) between RFIEL and RFI obtained with PFI in the LSM, and very low correlations (< 0.13) between RFIEL and FCR obtained with PFI. In the second analysis, correlations (> 0.89) for RFIEL with the other RFI estimates suggest little difference between RFIEL and any of these RFI estimates. In addition, results suggest that the RFI estimates calculated with PFI would be better able to identify low OFI, low ADG animals as inefficient compared with RFIEL. These results may be due to the fact that computer models predict performance on an individual animal basis in contrast to a LSM which estimates a fixed relationship for all animals; hence, the BSM may provide RFI estimates that are closer to the true biological efficiency of animals. In addition, BSM would facilitate comparisons across different data sets, and provide more accurate estimates of efficiency in small data sets where errors would be greater with a LSM.