回顾:在奶牛营养和遗传研究中改进剩余采食量建模

IF 4 2区 农林科学 Q1 AGRICULTURE, DAIRY & ANIMAL SCIENCE Animal Pub Date : 2024-07-22 DOI:10.1016/j.animal.2024.101268
R.B. Stephansen , P. Martin , C.I.V. Manzanilla-Pech , G. Giagnoni , M.D. Madsen , V. Ducrocq , M.R. Weisbjerg , J. Lassen , N.C. Friggens
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引用次数: 0

摘要

最近,剩余采食量(RFI)模型在奶牛饲料效率排名中越来越受欢迎。RFI 模型根据奶牛的预期采食量与观察到的采食量进行排序,其中负表型(采食量低于预期)对奶牛有利。然而,解释 RFI 模型得出的回归系数的生物学意义已被证明具有挑战性。此外,在营养和遗传研究中,RFI 的多特征建模已被提出作为最小平方 RFI 的替代方法。为了解决 RFI 回归系数的生物学解释难题,并提出改进 RFI 建模的方法,营养学家和遗传学家之间需要开展跨学科合作。因此,本文旨在探讨传统最小平方 RFI 模型面临的挑战,并提出改进 RFI 建模的解决方案。在传统的最小二乘法 RFI 模型中,一组固定效应用于解决具有不同均值和方差的性状的系统效应(如季节效应和产犊年龄)。因此,测量误差和模型拟合误差会在表型中累积,从而产生不良影响。多变量 RFI 模型由于使用了特定性状的固定效应,可能会减少这一问题。此外,在多性状 RFI 模型中,DM 摄入量对乳能量的回归系数往往具有更多的生物学意义,这表明在最小平方 RFI 模型中,固定效应和回归系数之间存在混杂效应。然而,从 RFI 模型中定义回归系数的精确期望值或寻找精确的饲料标准系数似乎很困难,尤其是当这些系数适用于日粮或管理系统各不相同的广泛牛群时。为了改进 RFI 的多性状建模,我们建议改进能量状态变化的建模。此外,我们还提出了一种推导日粮能量密度和个体消化效率的新方法。消化效率被定义为与消化过程相关的效率部分,主要反映了从总能量到可代谢能量的转化。我们发现该模型对饲料中能量密度的先验值并不敏感,而且消化效率存在个体差异。建议的方法需要进一步开发和验证。总之,使用多性状 RFI 可以提高奶牛饲料效率排名的准确性,从而改善奶牛场的经济和环境可持续性。
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Review: Improving residual feed intake modelling in the context of nutritional- and genetic studies for dairy cattle

The residual feed intake (RFI) model has recently gained popularity for ranking dairy cows for feed efficiency. The RFI model ranks the cows based on their expected feed intake compared to the observed feed intake, where a negative phenotype (eating less than expected) is favourable. Yet interpreting the biological implications of the regression coefficients derived from RFI models has proven challenging. In addition, multitrait modelling of RFI has been proposed as an alternative to the least square RFI in nutrition and genetic studies. To solve the challenge with the biological interpretation of RFI regression coefficients and suggest ways to improve the modelling of RFI, an interdisciplinary effort was required between nutritionists and geneticists. Therefore, this paper aimed to explore the challenges with the traditional least square RFI model and propose solutions to improve the modelling of RFI. In the traditional least square RFI model, one set of fixed effects is used to solve systematic effects (e.g., seasonal effects and age at calving) for traits with different means and variances. Thereby, measurement and model fitting errors can accumulate in the phenotype, resulting in undesirable effects. A multivariate RFI model will likely reduce this problem, as trait-specific fixed effects are used. In addition, regression coefficients for DM intake on milk energy tend to have more biologically meaningful estimates in multitrait RFI models, which indicates a confounding effect between the fixed effects and regression coefficients in the least square RFI model. However, defining precise expectations for regression coefficients from RFI models or sourcing for accurate feed norm coefficients seems difficult, especially if the coefficients are applied to a wide cattle population with varying diets or management systems, for example. To improve multitrait modelling of RFI, we suggest improving the modelling of changes in energy status. Furthermore, a novel method to derive the energy density of the diet and individual digestive efficiency is proposed. Digestive efficiency is defined as the part of the efficiency associated with digestive processes, which primarily reflects the conversion from gross energy to metabolisable energy. We show the model was insensitive to prior values of energy density in feed and that there was individual variation in digestive efficiency. The proposed method needs further development and validation. In summary, using multitrait RFI can improve the accuracy of the ranking of dairy cows’ feed efficiency, consequently improving economic and environmental sustainability on dairy farms.

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来源期刊
Animal
Animal 农林科学-奶制品与动物科学
CiteScore
7.50
自引率
2.80%
发文量
246
审稿时长
3 months
期刊介绍: Editorial board animal attracts the best research in animal biology and animal systems from across the spectrum of the agricultural, biomedical, and environmental sciences. It is the central element in an exciting collaboration between the British Society of Animal Science (BSAS), Institut National de la Recherche Agronomique (INRA) and the European Federation of Animal Science (EAAP) and represents a merging of three scientific journals: Animal Science; Animal Research; Reproduction, Nutrition, Development. animal publishes original cutting-edge research, ''hot'' topics and horizon-scanning reviews on animal-related aspects of the life sciences at the molecular, cellular, organ, whole animal and production system levels. The main subject areas include: breeding and genetics; nutrition; physiology and functional biology of systems; behaviour, health and welfare; farming systems, environmental impact and climate change; product quality, human health and well-being. Animal models and papers dealing with the integration of research between these topics and their impact on the environment and people are particularly welcome.
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