Invited review: Determination of large-scale individual dry matter intake phenotypes in dairy cattle

Abstract

Feed efficiency has been widely studied in many areas of dairy science and is currently seeing renewed interest in the field of breeding and genetics. A critical part of determining how efficiently an animal utilizes feed is accurately measuring individual dry matter (DM) intake. Currently, multiple methods are used to measure feed intake or determine the DM content of that feed, resulting in different levels of accuracy of measurement. Furthermore, the scale at which data need to be collected for use in genetic analyses makes some methodologies impractical. This systematic review aims to provide an overview of the current methodologies used to measure both feed intake in ruminants and DM content of feedstuffs, current methods to predict individual DM intake, and applications of large-scale intake measurements. Overall, advances in milk spectral data analysis present a promising method of estimating individual DM intake on a herd scale with further validation of prediction models. Although measurements of individual feed intake rely on the same underlying principle, the methods selected are largely dictated by the costs of capital, labor, and necessary analyses. Finally, DM methodologies were synthesized into a comprehensive protocol for use in a variety of feedstuffs.

Genetic mechanisms underlying feed utilization and implementation of genomic selection for improved feed efficiency in dairy cattle

“…To genetically select animals for improved feed efficiency, pedigree information and individual phenotypic records associated with feed intake and production traits are required. The simplest way to record DMI is based on the amount of feed offered and refused by each cow per day, with the associated dry matter percentage (Seymour et al 2019). Other important variables to be recorded for assessment of feed efficiency are milk production and composition, lactation stage, water intake, diet composition, bodyweight and BCS over the course of lactation, health/disease events, and reproductive performance traits.…”

“…In dairy cattle, RFI is estimated by regressing dry matter intake (DMI) on a variety of physiological activities, which commonly include production (milk yield or energy-corrected milk), metabolic body weight, changes in body weight or body condition score (BCS), and stage of lactation (Connor 2015;Byskov et al 2017;Seymour et al 2019). Other residual traits have also been analyzed in place of RFI to obtain an estimate closer to the biology behind feed efficiency, such as RSP (Coleman et al 2010).…”

“…Shetty et al (2017) used a partial least-squares approach to estimate DMI based on MIR spectral data. Although further studies are necessary, such models are a promising way to estimate individual energy intake (Dórea et al 2018;Seymour et al 2019). In summary, multiple alternatives to quantify individual variability of feed efficiency in dairy cattle have been proposed.…”

Grouping crossbred Holstein x Gyr heifers according to different feed efficiency indexes and its effects on energy and nitrogen partitioning, blood metabolic variables and gas exchanges

“…On the other hand, the residual feed intake (RFI), predominantly used in beef cattle [9,10], has also provided relevant data about the efficiency of Bos taurus taurus dairy cows [11,12]. The RFI is calculated by the difference between actual and expected feed intake of a group of animals over a defined period of time [13]. Residual weight gain (RG) is not a usually index for dairy cattle, but may be interesting due to allow a selection of animals with better average daily gain without increasing dry matter intake [14].…”

Infrared Spectrometry as a High-Throughput Phenotyping Technology to Predict Complex Traits in Livestock Systems

“…Given the economic impact of animal feed costs on farmer's profitability, feed efficiency has been widely discussed as a key phenotype to be included in the selection indexes and for management decisions on livestock operations (Berry and Crowley, 2013;Berry, 2015;Seymour et al, 2019). Selecting animal for feed efficiency is highly attractive, but the practical implementation might be challenging, primarily because individual feed intake records on a large-scale are unavailable to date, and secondly some aspect of production, such as milk output or body weight, and energy sinks including maintenance, need to be accounted to determine individual feed efficiency (Berry and Crowley, 2013;Connor, 2015).…”