Invited review: Disentangling residual feed intake—Insights and approaches to make it more fit for purpose in the modern context

Abstract

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.

Individual Feed Efficiency Monitoring of Charolaise Candidate Young Bulls in Relation to Feeding Behavior and Self-Performance Test Results

“…The conventional basic multiple regression model uses other potential predictors; such as measures of body composition [ 9 , 10 ] which can also be included [ 11 ]. Moreover, different statistical approaches were found in literature, so the results are not easily comparable [ 12 ]. Recently, Esfandyari and Jensen [ 13 ] suggested to avoid the analysis of derived traits as well as the use of a two-step procedure for computing RFI.…”

An alternative interpretation of residual feed intake by phenotypic recursive relationships in dairy cattle

“…Multiple-trait models have been proposed that obtain RFI genetic values indirectly through followup partial regression (Kennedy et al, 1993;Lu et al, 2015;Islam et al, 2020;Tempelman and Lu, 2020). With various methods available, the biological implications for the computed RFI remain to be exploited (Martin et al, 2021).…”

Genetic improvement of resilience in dairy cattle using longitudinal data

“…However, for example persistency of the lactation curve is genetically evaluated in the Netherlands (CRV, 2020g) and better persistency may be related 1 General introduction with a less severe negative energy balance (Sölkner and Fuchs, 1987;Dekkers et al, 1998). Furthermore, data is becoming available on feed intake which is used for selection for feed efficiency (Berry et al, 2014;Pryce et al, 2014;Martin et al, 2021b). Automatic recording of e.g.…”