Department of Veterinary Science, Texas Tech University, Lubbock 79409, USA.
Department of Animal and Food Sciences, Texas Tech University, Lubbock 79409, USA.
J Anim Sci. 2023 Jan 3;101. doi: 10.1093/jas/skad230.
Based on principles of the California Net Energy System, the dry matter intake (DMI) by feedlot cattle can be subdivided into DMI required for maintenance and DMI required for gain. Thus, if DMI along with body weight at a compositional endpoint and shrunk weight gain are known, dietary concentrations of net energy for maintenance and gain (NEm and NEg, respectively) can be calculated from growth performance data. Close agreement between growth performance-predicted and tabular NEm and NEg values implies the system can be used to accurately predict growth performance and be used to evaluate marketing and management decisions. We used 747 pen means from 21 research studies conducted at Texas Tech University and South Dakota State University to assess the agreement between growth performance-predicted NEm and NEg values and those calculated from tabular energy values for feeds reported by the 2016 National Academies of Science, Engineering, and Medicine publication on beef cattle nutrient requirements. Regression of growth performance-predicted values on tabular values with adjustment for random effects of study indicated that the intercepts of the two regressions did not differ from zero, and the slopes did not differ from one. Residuals (tabular minus growth performance-predicted values) for NEm and NEg averaged -0.003 and -0.005, respectively. Nonetheless, the precision of growth performance-predicted values was low, with approximately 40.3% of performance-predicted NEm values and 30.9% of NEg values falling within 2.5% of the corresponding tabular value. Residuals for NEm were divided into quintiles to evaluate dietary, growth performance, carcass, and energetics variables that might help explain lack of precision in growth performance-predicted values. Among the variables considered, gain:feed ratio was the most discriminating, with differences (P < 0.05) among each of the quintiles. Despite these differences, however, gain:feed ratio did not explain important percentages of variation in components of growth performance-predicted NEm values like maintenance energy requirements (r2 = 0.112) and retained energy (r2 = 0.003). Further research with large datasets that include dietary composition, growth performance and carcass data, and environmental variables, along with fundamental research on maintenance requirements and energy retention, will be required to identify ways to improve the precision of growth performance-predicted NE values.
基于加利福尼亚净能系统的原理，饲养场牛的干物质采食量（DMI）可细分为维持需要的 DMI 和增重需要的 DMI。因此，如果知道 DMI 以及组成终点时的体重和收缩增重，则可以从生长性能数据中计算维持和增重的净能浓度（分别为 NEm 和 NEg）。生长性能预测的 NEm 和 NEg 值与表列值之间的密切一致性表明，该系统可用于准确预测生长性能，并用于评估营销和管理决策。我们使用了德克萨斯理工大学和南达科他州立大学进行的 21 项研究的 747 个笔均值，以评估生长性能预测的 NEm 和 NEg 值与 2016 年美国国家科学院、工程院和医学院关于肉牛营养需求的报告中报告的饲料表列能量值计算值之间的一致性。用研究的随机效应调整生长性能预测值与表列值的回归表明，两个回归的截距没有差异，斜率也没有差异。NEm 和 NEg 的残差（表列值减去生长性能预测值）平均为-0.003 和-0.005。尽管如此，生长性能预测值的精度仍然较低，大约有 40.3%的性能预测 NEm 值和 30.9%的 NEg 值在相应表列值的 2.5%以内。将 NEm 的残差分为五分位数，以评估可能有助于解释生长性能预测值精度低的饮食、生长性能、胴体和能量学变量。在所考虑的变量中，增重：饲料比是最具区分力的，每个五分位数之间都存在差异（P<0.05）。然而，尽管存在这些差异，但增重：饲料比并不能解释生长性能预测的 NEm 值组成部分的重要百分比变化，如维持能量需求（r2=0.112）和保留能量（r2=0.003）。需要进行更多的研究，使用包括饮食组成、生长性能和胴体数据以及环境变量的大型数据集，以及关于维持需求和能量保留的基础研究，以确定提高生长性能预测 NE 值精度的方法。
