Department of Dairy Science, Virginia Polytechnic Institute and State University, Blacksburg 24061.
Department of Dairy Science, Virginia Polytechnic Institute and State University, Blacksburg 24061.
J Dairy Sci. 2020 Dec;103(12):11285-11299. doi: 10.3168/jds.2020-18372. Epub 2020 Oct 9.
Ruminal pH is a critical factor to regulate nutrient degradation and fermentation. However, it has been poorly predicted in the Molly cow model, and recent improvements in the representation of nitrogen cycling across the rumen wall altered some of the modeled responses to feed nutrients, resulting in some model bias. The objectives of this study were to further improve the representation of pH and to refit parameters related to ruminal metabolism and nutrient digestion in the model to resolve this bias, and to use the improved model to estimate nitrogen and energy fluxes with varying rumen-degradable protein (RDP; 40 vs. 60%) and ruminally degraded starch (RDSt; 50 vs. 75%). A meta data set containing 284 peer reviewed studies with 1,223 treatment means was used to derive parameter estimates for ruminal metabolism and nutrient digestions. Refitting the parameters significantly improved the accuracy and precision of the model predictions for ruminal nutrient outflow [acid detergent fiber (ADF), neutral detergent fiber (NDF), total N, microbial N, nonammonia N, and nonammonia nonmicrobial N], ammonia and blood urea concentrations, and fecal nutrient outflow (protein, ADF, and NDF). The prediction error for body weight was decreased from 19.3 to 6.2% with decreased mean bias (from 76.0 to 11.5%) and slope bias (from 17.2 to 7.7%), primarily due to improved representations of ruminal dry matter and liquid pool size. Adding ammonia concentration as a driver to the pH equation increased the precision of predicted ruminal pH and, thereby, the precision of predicted volatile fatty acid (VFA) concentrations, due to improved representation of pH regulation of VFA production rates. Although minor mean and slope bias were observed for ruminal pH and VFA concentrations, the concordance correlation coefficients indicated that much of the observed variation in these variables remains unexplained. Overall, the biological functions of nutrient degradation and digestion appear to be represented without bias. Simulated results indicated that decreasing RDP and RDSt proportions in an isonitrogenous and isocaloric diet can slightly improve N efficiency, and increasing RDSt proportions can increase energy efficiency.
瘤胃 pH 是调节营养物质降解和发酵的关键因素。然而,在莫利牛模型中,它的预测效果较差,而最近对瘤胃壁氮循环的表示方法的改进改变了一些对饲料养分的模型响应,导致了一些模型偏差。本研究的目的是进一步改进 pH 的表示方法,并重新拟合模型中与瘤胃代谢和营养消化相关的参数,以解决这种偏差,并利用改进后的模型估计不同瘤胃可降解蛋白(40%与 60%)和瘤胃降解淀粉(50%与 75%)比例下的氮和能量通量。使用包含 284 项同行评审研究和 1223 个处理均值的荟萃分析数据集来推导瘤胃代谢和营养消化的参数估计值。重新拟合参数显著提高了瘤胃营养流出物(酸性洗涤纤维(ADF)、中性洗涤纤维(NDF)、总氮、微生物氮、非氨氮和非氨非微生物氮)、氨和血尿素浓度以及粪便营养流出物(蛋白质、ADF 和 NDF)的模型预测的准确性和精密度。通过减少平均偏差(从 76.0%减少到 11.5%)和斜率偏差(从 17.2%减少到 7.7%),体重的预测误差从 19.3%降低到 6.2%,主要是由于瘤胃干物质和液体池大小的表示得到了改善。将氨浓度作为 pH 方程的驱动因素添加,可以提高预测瘤胃 pH 的精度,从而提高预测挥发性脂肪酸(VFA)浓度的精度,因为这可以改善 pH 对 VFA 生成速率的调节作用。尽管瘤胃 pH 和 VFA 浓度存在较小的平均偏差和斜率偏差,但一致性相关系数表明,这些变量的大部分观测变异仍未得到解释。总的来说,营养物质降解和消化的生物学功能似乎没有偏差地得到了体现。模拟结果表明,在等氮和等热量饮食中降低可降解蛋白和瘤胃降解淀粉的比例可以略微提高氮效率,而增加瘤胃降解淀粉的比例可以提高能量效率。