USDA, ARS, US Meat Animal Research Center, Clay Center, NE 68933, USA.
J Anim Sci. 2010 Jul;88(7):2523-9. doi: 10.2527/jas.2009-2655. Epub 2010 Mar 26.
Data on individual daily feed intake, BW at 28-d intervals, 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 observed 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, b) Cornell Value Discovery System, and c) NRC update 2000, using observed growth and carcass data as input. Residual feed intake (RFI) was estimated using OFI (RFI(EL)) in a linear statistical model (LSM), and feed conversion ratio (FCR) was estimated as OFI/ADG (FCR(E)). 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 RFI(EL) and FCR(E). 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 RFI(EL). 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 BW, days on feed, and ADG) were used in 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 weak correlations (<0.4) between RFI(EL) and RFI obtained with PFI in the LSM, and very weak correlations (<0.13) between RFI(EL) and FCR obtained with PFI. In the second analysis, correlations (>0.89) for RFI(EL) with the other RFI estimates suggest little difference between RFI(EL) and any of these RFI estimates. In addition, results suggest that the RFI estimates calculated with PFI would be better able to identify animals with low OFI and small ADG as inefficient compared with RFI(EL). 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 may 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.
对 1212 头杂交阉牛进行了个体日采食量、28 天间隔 BW 以及胴体组成的数据收集。利用累积采食量和 BW 在直线和二次线性日采食量上的动物内回归，来量化 120 天育肥期的初始和结束 BW、平均日观察采食量（OFI）和 ADG。使用 3 种生物模拟模型（BSM）对采食量进行预测（PFI）：a）牛业决策评估器、b）康奈尔价值发现系统和 c）NRC 更新 2000，将观测到的生长和胴体数据作为输入。使用线性统计模型（LSM）中的 OFI（RFI（EL））估计残留采食量（RFI），并将采食量转化率（FCR）估计为 OFI/ADG（FCR（E））。BSM 的输出用于通过用 OFI 代替 PFI 并使用相同的 LSM 来估计 RFI，而 FCR 则估计为 PFI/ADG。将这些估计值与 RFI（EL）和 FCR（E）进行比较。在第二次分析中，对于 3 种 BSM，通过将 OFI 与 PFI 之间的差值来获得 RFI 的估计值，并将这些估计值与 RFI（EL）进行比较。当 PFI 用于 LSM 中估计 RFI 时，残差变化非常小，这主要是因为在 BSM 和 LSM 中使用了相同的输入变量（初始 BW、线性日采食量和 ADG）。因此，使用 BSM 获得的 PFI 来替代 OFI 在 LSM 中对个体动物进行 RFI 特征描述是不可行的。这一结论也得到了以下事实的支持：LSM 中 RFI（EL）与 PFI 获得的 RFI 之间的相关性较弱（<0.4），以及 PFI 获得的 RFI（EL）与 FCR 之间的相关性非常弱（<0.13）。在第二次分析中，与 RFI（EL）相关的其他 RFI 估计值的相关性（>0.89）表明，RFI（EL）与这些 RFI 估计值之间几乎没有差异。此外，结果表明，与 RFI（EL）相比，使用 PFI 计算的 RFI 估计值能够更好地识别出采食量低、ADG 小的动物，因为它们效率较低。这些结果可能是由于计算机模型根据个体动物预测性能，而不是 LSM 为所有动物估计固定关系；因此，BSM 可能会提供更接近动物真实生物学效率的 RFI 估计值。此外，BSM 可以促进不同数据集之间的比较，并在 LSM 中误差较大的小数据集提供更准确的效率估计值。
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