Abstract：
Large datasets allow estimation of feed required for individual milk components or body maintenance. Phenotypic regressions are useful for nutrition management, but genetic regressions are more useful in breeding programs. Dry matter intake records from 8,513 lactations of 6,621 Holstein cows were predicted from phenotypes or genomic evaluations for milk components and body size traits. The mixed models also included DIM, age-parity subclass, trial date, management group, and BW change during 28- and 42-d feeding trials in mid lactation. Phenotypic regressions of DMI on milk (0.014 ± 0.006), fat (3.06 ± 0.01), and protein (4.79 ± 0.25) were much less than corresponding genomic regressions (0.08 ± 0.03, 11.30 ± 0.47, and 9.35 ± 0.87, respectively) or sire genomic regressions multiplied by 2 (0.048 ± 0.04, 6.73 ± 0.94, and 4.98 ± 1.75). Thus, marginal feed costs as fractions of marginal milk revenue were higher from genetic than phenotypic regressions. According to the ECM formula, fat production requires 69% more DMI than protein production. In the phenotypic regression, it was estimated that protein production requires 56% more DMI than fat. However, the genomic regression for the animal showed a difference of only 21% more DMI for protein compared with fat, whereas the sire genomic regressions indicated approximately 35% more DMI for fat than protein. Estimates of annual maintenance in kilograms DMI/kilograms BW per lactation were similar from phenotypic regression (5.9 ± 0.14), genomic regression (5.8 ± 0.31), and sire genomic regression multiplied by 2 (5.3 ± 0.55) and are larger than those estimated by the National Academies for Science, Engineering, and Medicine based on NEL equations. Multiple regressions on genomic evaluations for the 5 type traits in body weight composite (BWC) showed that strength was the type trait most associated with BW and DMI, agreeing with the current BWC formula, whereas other traits were less useful predictors, especially for DMI. The Net Merit formula used to weight different genetic traits to achieve an economically optimal overall selection response was revised in 2021 to better account for these estimated regressions. To improve profitability, breeding programs should select smaller cows with negative residual feed intake that produce more milk, fat, and protein.
摘要：
大型数据集允许估计单个牛奶成分或身体维护所需的饲料。表型回归对营养管理很有用，但是遗传回归在育种计划中更有用。根据表型或基因组对牛奶成分和体型性状的评估，预测了6,621头荷斯坦奶牛的8,513次泌乳的干物质摄入量（dryi）记录。混合模型还包括牛奶中的天数，年龄均等子类，审判日期,管理组，哺乳期中期28天和42天喂养试验中的体重变化。DMI对牛奶的表型回归（0.014±0.006），脂肪（3.06±0.01），和蛋白质（4.79±0.25）远低于相应的基因组回归（0.08±0.03、11.30±0.47和9.35±0.87）或父系基因组回归乘以2（0.048±0.04、6.73±0.94和4.98±1.75）。因此，边际饲料成本作为边际牛奶收入的分数来自遗传高于表型回归。根据能量校正的牛奶配方，脂肪生产需要比蛋白质生产多69%的STI。在表型回归中，据估计，蛋白质生产需要比脂肪多56%的STI。然而,动物的基因组回归显示，与脂肪相比，蛋白质的MI仅增加21％，而父亲基因组回归表明，脂肪的MI比蛋白质多35%。与表型回归（5.9±0.14）相似，以kgBMI/kg体重/泌乳为单位的年维持量估算值，基因组回归(5.8±0.31)，和父系基因组回归乘以2（5.3±0.55），大于基于NEL方程的NASEM（2021）估计的值。对体重复合物（BWC）中5种类型性状的基因组评估的多元回归表明，强度是与体重和MI最相关的类型性状，同意目前的《生物武器公约》公式，而其他特征是不太有用的预测因子，尤其是对于dmi。用于对不同遗传性状进行加权以实现经济上最佳的整体选择响应的净绩效公式在2021年进行了修订，以更好地解释这些估计的回归。为了提高盈利能力，育种计划应选择较小的母牛，这些母牛具有负的残留饲料摄入量，可以产生更多的牛奶，脂肪，和蛋白质。
