Division of Animal and Dairy Sciences, Chungnam National University, Daejeon 34134, Republic of Korea.
Department of Animal Science, Texas A&M University, College Station, TX 77843-2471, USA.
J Anim Sci. 2021 Jul 1;99(7). doi: 10.1093/jas/skab182.
Understanding the utilization of feed energy is essential for precision feeding in beef cattle production. We aimed to assess whether predicting the metabolizable energy (ME) to digestible energy (DE) ratio (MDR), rather than a prediction of ME with DE, is feasible and to develop a model equation to predict MDR in beef cattle. We constructed a literature database based on published data. A meta-analysis was conducted with 306 means from 69 studies containing both dietary DE and ME concentrations measured by calorimetry to test whether exclusion of the y-intercept is adequate in the linear relationship between DE and ME. A random coefficient model with study as the random variable was used to develop equations to predict MDR in growing and finishing beef cattle. Routinely measured or calculated variables in the field (body weight, age, daily gain, intake, and dietary nutrient components) were chosen as explanatory variables. The developed equations were evaluated with other published equations. The no-intercept linear equation was found to represent the relationship between DE and ME more appropriately than the equation with a y-intercept. The y-intercept (-0.025 ± 0.0525) was not different from 0 (P = 0.638), and Akaike and Bayesian information criteria of the no-intercept model were smaller than those with the y-intercept. Within our growing and finishing cattle data, the animal's physiological stage was not a significant variable affecting MDR after accounting for the study effect (P = 0.213). The mean (±SE) of MDR was 0.849 (±0.0063). The best equation for predicting MDR (n = 106 from 28 studies) was 0.9410 ( ± 0.02160) +0.0042 ( ± 0.00186) × DMI (kg) - 0.0017 ( ± 0.00024) × NDF(% DM) - 0.0022 ( ± 0.00084) × CP(% DM). We also presented a model with a positive coefficient for the ether extract (n = 80 from 22 studies). When using these equations, the observed ME was predicted with high precision (R2 = 0.92). The model accuracy was also high, as shown by the high concordance correlation coefficient (>0.95) and small root mean square error of prediction (RMSEP), <5% of the observed mean. Moreover, a significant portion of the RMSEP was due to random bias (> 93%), without mean or slope bias (P > 0.05). We concluded that dietary ME in beef cattle could be accurately estimated from dietary DE and its conversion factor, MDR, predicted by the dry matter intake and concentration of several dietary nutrients, using the 2 equations developed in this study.
理解饲料能量的利用对于肉牛生产的精准饲养至关重要。本研究旨在评估预测可消化能（DE）与代谢能（ME）比值（MDR）是否比预测 ME 更可行，并开发一种预测肉牛 MDR 的模型方程。我们基于已发表的数据构建了一个文献数据库。通过对 69 项研究中包含的 306 个 ME 和 DE 浓度的平均值进行荟萃分析，以测试 DE 与 ME 之间的线性关系中是否可以排除截距。使用包含研究的随机系数模型作为随机变量来开发预测生长和育肥肉牛 MDR 的方程。选择在田间常规测量或计算的变量（体重、年龄、日增重、采食量和膳食营养素成分）作为解释变量。用其他已发表的方程来评估所开发的方程。结果发现，没有截距的线性方程比具有截距的方程更能代表 DE 与 ME 之间的关系。截距（-0.025±0.0525）与 0 无差异（P=0.638），且无截距模型的赤池信息量和贝叶斯信息量准则均小于具有截距的模型。在我们的生长和育肥牛数据中，在考虑研究效应后，动物的生理阶段并不是影响 MDR 的显著变量（P=0.213）。MDR 的平均值（±SE）为 0.849（±0.0063）。预测 MDR 的最佳方程（n=28 项研究中的 106 个数据点）为 0.9410（±0.02160）+0.0042（±0.00186）×DMI（kg）-0.0017（±0.00024）×NDF（% DM）-0.0022（±0.00084）×CP（% DM）。我们还提出了一个具有正醚提取物系数的模型（n=22 项研究中的 80 个数据点）。当使用这些方程时，观察到的 ME 可以得到高精度的预测（R2=0.92）。模型的准确性也很高，表现为高一致性相关系数（>0.95）和小均方根预测误差（RMSEP），<5%的观察平均值。此外，RMSEP 的很大一部分是由于随机偏差（>93%），而不是平均或斜率偏差（P>0.05）。我们得出结论，使用本研究中开发的 2 个方程，根据饲料 DE 及其转化因子 MDR、干物质摄入量和几种饲料营养素的浓度，可准确估计肉牛的饲料 ME。
