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采用一次改变一个变量的方法对法国国家农业研究院2018年反刍动物饲养系统进行敏感性分析:日粮输入变量对奶牛多种反应预测的影响。

Sensitivity analysis of the INRA 2018 feeding system for ruminants by a one-at-a-time approach: Effects of dietary input variables on predictions of multiple responses of dairy cattle.

作者信息

Jeon Seoyoung, Lemosquet Sophie, Toulemonde Anne-Cécile, Kiessé Tristan Senga, Nozière Pierre

机构信息

UMR Herbivores, INRAE, VetAgro Sup, 63122 Saint-Genès-Champanelle, France.

UMR PEGASE, INRAE, Institut Agro, 35590 Saint-Gilles, France.

出版信息

J Dairy Sci. 2024 Jul;107(7):4558-4577. doi: 10.3168/jds.2023-24361. Epub 2024 Mar 8.

Abstract

In the feeding system for ruminants developed in 2018 by the French National Institute of Agricultural Research (INRA), the prediction of multiple animal responses is based on the integration of the characteristics of the animal and the available feedstuff characteristics, as well as the rationing objectives. In this framework, the characterization of feedstuffs in terms of net energy, digestible protein, and fill units requires information on their chemical composition, digestibility, and degradability. Despite the importance of these feed characteristics, a comprehensive assessment of their impact on the responses predicted by the INRA 2018 feeding system has not been carried out. Thus, our study investigated how variables predicted by the INRA feeding system (i.e., outputs) for dairy cows are affected by variation in feed characterization (i.e., inputs). We selected 5 input variables for the sensitivity analysis: CP, OM apparent digestibility (OMd), gross energy (GE), effective degradability of nitrogen assuming a passage rate of 6%/h (ED6_N), and true intestinal digestibility (dr_N) of nitrogen. A one-at-a-time sensitivity analysis was performed on predicted digestive, productive, and environmental output variables for dairy cows with 6 contrasted diets. These 6 diets were formulated to meet 95% of the potential daily milk production (37.5 kg) of a multiparous cow at wk 14 of lactation. The values of the 5 key input variables of each feedstuff were then randomly sampled around the INRA 2018 feed table values (reference point). The response of the output variable to the variation of the input variable was quantified and compared using the tangent value at the reference point and the normalized sensitivity coefficient. Among the major final output variables, CP and dr_N had the greatest impact on N excretion in urine (as a proportion of total fecal and urinary N excretion; UN/TN); OMd and GE had the greatest impact on N utilization efficiency (NUE; N in milk as proportion of intake N); and ED6_N had the greatest impact on milk protein yield (MPY). Additionally, CP, GE, and dr_N had the least effect on methane emission, OMd had the least effect on UN/TN, and ED6_N had the least effect on NUE. The responses of most output variables to ED6_N and dr_N variations were highly dependent on diet and were related to the ratio between protein truly digestible in the intestine (PDI; i.e., MP) and net energy for lactation (UFL; i.e., NE) at the reference point of each diet. Overall, we were able to analyze the response of output variables to the variations of the input variables, using the tangent and its normalized value at the reference point. The predicted final outputs were more affected by variations in CP, GE, and OMd. The other 2 input variables, ED6_N and dr_N, had a smaller effect on the final output variables, but the responses varied between the diets according to their PDI/UFL ratio. Our present study was conducted using 6 representative diets for dairy cattle fed at their potential, but should be completed by the analysis of more diverse conditions.

摘要

在法国国家农业研究所(INRA)2018年开发的反刍动物饲养系统中,对多种动物反应的预测基于动物特征、可用饲料特征以及配给目标的整合。在此框架下,根据净能量、可消化蛋白质和填充单位对饲料进行表征,需要有关其化学成分、消化率和降解性的信息。尽管这些饲料特性很重要,但尚未对它们对INRA 2018饲养系统预测的反应的影响进行全面评估。因此,我们的研究调查了INRA饲养系统预测的变量(即输出)如何受到饲料表征变化(即输入)的影响。我们选择了5个输入变量进行敏感性分析:粗蛋白(CP)、有机物表观消化率(OMd)、总能(GE)、假设通过率为6%/小时的氮有效降解率(ED6_N)以及氮的真肠道消化率(dr_N)。对6种对比日粮的奶牛预测的消化、生产和环境输出变量进行了一次一个变量的敏感性分析。这6种日粮的配方旨在满足泌乳第14周经产奶牛潜在日奶产量(37.5千克)的95%。然后,围绕INRA 2018饲料表值(参考点)对每种饲料的5个关键输入变量的值进行随机抽样。使用参考点处的切线值和归一化敏感性系数对输出变量对输入变量变化的响应进行量化和比较。在主要的最终输出变量中,CP和dr_N对尿氮排泄(占粪便和尿氮排泄总量的比例;UN/TN)影响最大;OMd和GE对氮利用效率(NUE;牛奶中的氮占摄入氮的比例)影响最大;ED6_N对牛奶蛋白产量(MPY)影响最大。此外,CP、GE和dr_N对甲烷排放影响最小,OMd对UN/TN影响最小,ED6_N对NUE影响最小。大多数输出变量对ED6_N和dr_N变化的响应高度依赖于日粮,并且与每种日粮参考点处肠道中真正可消化蛋白质(PDI;即MP)与泌乳净能(UFL;即NE)之间的比例有关。总体而言,我们能够使用参考点处的切线及其归一化值来分析输出变量对输入变量变化的响应。预测的最终输出受CP、GE和OMd变化的影响更大。另外两个输入变量ED6_N和dr_N对最终输出变量的影响较小,但响应根据它们的PDI/UFL比例在不同日粮之间有所不同。我们目前的研究是使用6种代表泌乳奶牛潜在采食量的日粮进行的,但应该通过分析更多样化的条件来完善。

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