Université Paris-Saclay, INRAE, AgroParisTech, UMR GABI, 78350 Jouy-en-Josas, France.
Université Paris-Saclay, INRAE, AgroParisTech, UMR GABI, 78350 Jouy-en-Josas, France.
J Dairy Sci. 2021 Jun;104(6):6329-6342. doi: 10.3168/jds.2020-19844. Epub 2021 Mar 25.
Residual feed intake (RFI) is an increasingly used trait to analyze feed efficiency in livestock, and in some sectors such as dairy cattle, it is one of the most frequently used traits. Although the principle for calculating RFI is always the same (i.e., using the residual of a regression of intake on performance predictors), a wide range of models are found in the literature, with different predictors, different ways of considering intake, and more recently, different statistical approaches. Consequently, the results are not easily comparable from one study to another as they reflect different biological variabilities, and the relationship between the residual (i.e., RFI) and the underlying true efficiency also differs. In this review, the components of the RFI equation are explored with respect to the underlying biological processes. The aim of this decomposition is to provide a better understanding of which of the processes in this complex trait contribute significantly to the individual variability in efficiency. The intricacies associated with the residual term, as well as the energy sinks and the intake term, are broken down and discussed. Based on this exploration as well as on some recent literature, new forms of the RFI equation are proposed to better separate the efficiency terms from errors and inaccuracies. The review also considers the time period of measurement of RFI. This is a key consideration for the accuracy of the RFI estimation itself, and also for understanding the relationships between short-term efficiency, animal resilience, and long-term efficiency. As livestock production moves toward sustainable efficiency, these considerations are increasingly important to bring to bear in RFI estimations.
残余采食量(RFI)是一种越来越常用于分析家畜饲料效率的特征,在一些领域,如奶牛,它是最常用的特征之一。尽管计算 RFI 的原理始终相同(即使用摄入量对性能预测因子的回归残差),但文献中发现了广泛的模型,具有不同的预测因子、不同的考虑摄入量的方式,以及最近不同的统计方法。因此,由于反映了不同的生物学变异性,并且残余(即 RFI)与潜在的真实效率之间的关系也不同,因此结果不容易在一项研究与另一项研究之间进行比较。在这篇综述中,探讨了 RFI 方程的组成部分,以了解潜在的生物学过程。这种分解的目的是更好地理解在这个复杂特征中,哪些过程对效率的个体变异性有重要贡献。对残差项、能量汇和摄入量项进行了细分和讨论。基于这种探索以及一些最近的文献,提出了 RFI 方程的新形式,以更好地将效率项与误差和不准确性分开。该综述还考虑了 RFI 的测量时间。这对于 RFI 估计的准确性以及理解短期效率、动物弹性和长期效率之间的关系至关重要。随着畜牧业向可持续效率发展,在 RFI 估计中越来越需要考虑这些因素。