Beef Cattle Institute, Kansas State University, Manhattan, KS, USA.
Department of Animal Science, Texas A&M University, College Station, TX, USA.
J Anim Sci. 2021 Feb 1;99(2). doi: 10.1093/jas/skaa406.
Nutrition models are important tools in management decisions, but improvements are needed for cow-calf producers to accurately predict nursing calf performance. Therefore, the objective of this study was to assess the ability of published milk yield (MY) and forage intake equations to predict calf feed intake and weaning weight (WW) using an independent, multi-breed dataset. A dataset with 406 nursing calves was used to evaluate two MY equations: 1) National Academies of Sciences, Engineering, and Medicine (2016) (NASEM) and 2) Wood (1967) (WOOD) and five feed intake equations: 1) equations from Table 9.1 in Tedeschi et al. (2006) (TED06), 2) equations 2 to 7 in Baker et al. (1976) (BAK76), 3) equation 25 in Tedeschi and Fox (2009) (TED09A), 4) equations 17, 19, and 24 in Tedeschi and Fox (2009) (TED09B), and 5) equation from Holloway et al. (1982) (HOL82). MY was measured at 14-d interval by hand milking, and individual feed intake of nursing calves was determined during a 240-d nursing period. Calf birth and WW were measured on days 0 and 240, respectively. Each combination of MY and feed intake equation was used to predict calf feed intake and WW from observed MY, calf birth weight, and calf slaughter weight. Predicted and observed values were compared using concordance correlation coefficient (CCC) and mean bias (MB). Factors affecting the deviation between observed and predicted values were analyzed using regression, and a revised equation was developed. Feed intake equations poorly predicted observed feed intake with CCC < 0.4 and MB ranged from -108% to 69%. However, statistics were slightly improved when using WOOD rather than the NASEM MY equation. BAK76 and TED09B feed intake equations were considerably more accurate (MB = -14.4% to 13.0%) in predicting feed intake but still not precise (CCC < 0.30). Predictions of WW had CCC ranging from 0.19 to 0.71 and MB ranging from -25.9% to 41.8% and were not significantly affected by the MY equation. TED06 and BAK76 feed intake equations were the most precise (CCC > 0.60) and accurate (MB = 1.7% to 8.5%) in predicting WW. Sire breed accounted for significant variation in the deviation between observed and predicted values of feed intake and in a revised equation to predict total feed energy intake from total milk energy intake. In conclusion, refinements of feed intake equations for nursing calves need to account for breed to improve current nutrition models.
营养模型是管理决策中的重要工具,但需要改进,以便奶牛场生产者能够准确预测哺乳犊牛的性能。因此,本研究的目的是使用独立的多品种数据集评估已发表的产奶量(MY)和饲料摄入量方程预测犊牛采食量和断奶体重(WW)的能力。使用包含 406 头哺乳犊牛的数据集评估了两种 MY 方程:1)美国国家科学院、工程院和医学院(2016 年)(NASEM)和 2)伍德(1967 年)(WOOD)和五种饲料摄入量方程:1)泰德西等人(2006 年)表 9.1 中的方程(TED06),2)贝克等人(1976 年)方程 2 至 7(BAK76),3)泰德西和福克斯(2009 年)的方程 25(TED09A),4)泰德西和福克斯(2009 年)的方程 17、19 和 24(TED09B),以及 5)霍洛韦等人(1982 年)的方程(HOL82)。通过手工挤奶每隔 14 天测量 MY,在 240 天的哺乳期间确定哺乳犊牛的个体饲料摄入量。犊牛出生和 WW 分别在第 0 天和第 240 天测量。用观察到的 MY、犊牛出生体重和犊牛屠宰体重,每个 MY 和饲料摄入量方程的组合用于预测犊牛的饲料摄入量和 WW。使用一致性相关系数(CCC)和平均偏差(MB)比较预测值和观察值。使用回归分析影响观察值与预测值之间偏差的因素,并开发修订后的方程。饲料摄入量方程对观察到的饲料摄入量的预测精度较差,CCC<0.4,MB 范围为-108%至 69%。然而,使用 WOOD 而不是 NASEM MY 方程时,统计数据略有改善。BAK76 和 TED09B 饲料摄入量方程在预测饲料摄入量方面更准确(MB=-14.4%至 13.0%),但仍不精确(CCC<0.30)。WW 的预测值 CCC 范围为 0.19 至 0.71,MB 范围为-25.9%至 41.8%,不受 MY 方程的显著影响。TED06 和 BAK76 饲料摄入量方程在预测 WW 方面最精确(CCC>0.60)和准确(MB=1.7%至 8.5%)。父本品种解释了观察值与预测值之间的饲料摄入量偏差和修订后的从总奶能量摄入量预测总饲料能量摄入量的方程的显著变化。总之,需要改进哺乳犊牛的饲料摄入量方程,以考虑品种,从而改进当前的营养模型。