Genetic variation in residual feed intake is associated with body composition, behavior, rumen, heat production, hematology, and immune competence traits in Angus cattle1

Herd, R. M.; Velazco, J.; Smith, Helen; Arthur, P. F.; Hine, Brad C.; Oddy, V. H.; Dobos, Robin; Hegarty, R. S.

doi:10.1093/jas/skz077

Cited by 29 publications

(20 citation statements)

References 33 publications

(54 reference statements)

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“…Studies to date in growing cattle indicate that the number of feed events per day explained an additional 5.7 to 6.0 percentage points of the variability in DMI over and above that already explained by MBW, ADG, and backfat depth ( Durunna et al, 2011 ; Herd et al, 2019 ); this is less than the 9.2 percentage point improvement observed in the present study for MEI. Additionally, in steers fed a high energy finishing diet, Durunna et al (2011) reported an increase in R 2 from 54% to 64% when both feeding time per day and number of feed events per day were included in a model with DMI as the dependent variable; this is similar to the calculated increase from 53% to 67% for the R 2 of the MEI model in the present study.…”

Section: Discussioncontrasting

confidence: 87%

“…Of the studies that have explicitly reported phenotypic correlations between feeding behavior traits and both performance and efficiency traits in a relatively large cohort of cattle ( Robinson and Oddy, 2004 ; Nkrumah et al, 2007 ; Lancaster et al, 2009 ; Kelly et al, 2010 ; Schwartzkopf-Genswein et al, 2011 ; Lin et al, 2013 ), the present study is one of the largest. Furthermore, there is a paucity of studies ( Kelly et al, 2010 ) that have reported the repeatability estimates for feeding behavior traits in cattle using mixed models methodology, and few studies ( Durunna et al, 2011 ; McGee et al, 2014 ; Herd et al, 2019 ) have calculated the contribution of different feeding behavior traits to the variation in either DMI or MEI. There is also a paucity of studies ( McGee et al, 2014 ) reporting correlations at both the feed event level and meal level with performance and efficiency traits in the same cohort of cattle; moreover, information on correlations between feeding behavior and both RG and RIG are apparently nonexistent.…”

Section: Discussionmentioning

confidence: 99%

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Large variability in feeding behavior among crossbred growing cattle

Kelly

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Murphy

et al. 2020

Journal of Animal Science

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The purpose of this study was to define an extensive suite of feeding behavior traits in growing crossbred cattle and to investigate their phenotypic inter-relationships as well as relationships with other performance and efficiency traits. Time-series feeding behavior data, as well feed intake and live-weight records, were available for 624 growing crossbred cattle, of which 445 were steers and 179 were heifers. Feeding behavior repeatability estimates were calculated using linear mixed models. Additionally, partial Spearman correlations were estimated among 14 feeding behavior traits, as well as between feeding behavior with both performance and feed efficiency traits, using residuals retained from linear mixed models. The marginal contribution of several feeding behavior traits to the variability in metabolizable energy intake (MEI) was also determined. Repeatability estimates of 0.57, 0.36, and 0.48 were calculated for the number of feed events per day, the total time spent feeding per day and feeding rate, respectively. Cattle that ate more frequently each day ate at a faster rate and consumed less energy in each visit to the feed bunk. More efficient cattle fed less often per day and fed for a shorter duration per day; they also had a slower feeding rate and fed for longer in each visit to the feed bunk. Moreover, heavier cattle fed for a longer duration per day, had a faster feeding rate, but fed less often per day; heavier animals also fed first in the pen after fresh feed was offered. The number of feed events per day and feeding time per day together explained an additional 13.4 percentage points of the variability in MEI above that already explained by all of growth rate, live-weight, and backfat depth. The results from the present study suggest that several repeatable time-series related feeding behavior traits, that are less resource intensive to measure, may have a role as useful predictor traits of important but relatively difficult to record traits, such as feed intake and efficiency.

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Section: Discussioncontrasting

confidence: 87%

Section: Discussionmentioning

confidence: 99%

Large variability in feeding behavior among crossbred growing cattle

Kelly

Sleator

Murphy

et al. 2020

Journal of Animal Science

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“…This result was unexpected however we are unable to compare to other studies as to the best of our knowledge this is the first paper to report a relationship between RWLC and temperament traits. We found no relationship between RFI and temperament when measured as chute or exit score, this is consistent with Herd et al (2019) who reported a negative correlation between RFI and temperament when measured as flight score. The presence of a relationship between exit score and RLWC demonstrates a relationship between temperament and some aspect of feed efficiency.…”

Section: Figure 1 Potential Sites Of Action For Phytoestrogens In the Reproductive Processes In Sheepsupporting

confidence: 91%

“…At 48 h incubation, methane production tended be greater (5.0 ± 0.59 v 3.7 ± 0.56 mL, P = 0.07) for Low-RFI-f-EBV than High-RFI-f-EBV. This tendency for greater methane production/unit digesta from more efficient cattle at 48 h incubation is consistent with the results of Herd et al (2019) and greater digestion in the rumen of more efficient animals, but needs to be married with data on rumen volume to provide a fuller picture of RFI effects on daily methane emission. Further studies should include rumen characteristics between cattle varying in RFI-f-EBVs grazing pastures of differing quality and availability and at different stages of the production cycle.…”

Section: Referencesupporting

confidence: 71%

“…The rumen microbiome contributes up to 90% of metabolic needs from ingested feed through impacts on feed fermentation, retention time and digestibility (Mizrahi and Jami 2018). EBVs for residual feed intake determined in feedlot (RFIf-EBV) have been shown to be associated with digestive function on feedlot diets (Herd et al 2019), but it is not known how RFI-f-EBV affects rumen characteristics of young grazing beef cattle. We hypothesized rumen fluid from more efficient cattle would have a greater fermentation capacity.…”

Section: Figure 1 Timeline Of Vaccination Trialmentioning

confidence: 99%

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Abstracts from the Proceedings of the 33rd Biennial Conference of the Australian Association of Animal Sciences

2021

Anim. Prod. Sci.

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During the career of Professor Eric Underwood, the feeding of livestock was closely linked to the availability of local resources. He solved problems by making the connections between the nutrient content of soil and livestock health, but while his research had global impact, I am sure he didn't foresee either the scale of the global feed trade or the environmental impact that livestock systems would have on the world 50 years on from his retirement in 1970.The livestock industry in 2020 is very different from what it was in 1970: global meat production is ~336 million tonnes per year (FAOSTAT averaged over 2016, 17 and 18) compared to the 92 million tonnes produced globally 50 years before. In Australia, the proportional contribution (by weight) of sheep and goat meat to total meat production has decreased from 35 to 16% over that time period and the annual import (mainly from Argentina) of soybean cake in 2016-18 averaged 743,000 tonnes out of a total of 895,000 tonnes of imported feed. Thus, even with Australia's land mass, feeding its livestock sector has a long-range footprint.As we know, livestock production also has global impact through the emission of greenhouse gases (GHGs) into the atmosphere -the relationship also influenced by diet. Gerber et al. (2013) published an extensive assessment of the global contribution of emissions of GHGs from the livestock sector in 2013: beef and cattle milk production accounted for 41 and 20% of the emissions, while pig meat and poultry meat and eggs contributed 9 and 8% respectively. The expansion of pasture and feed crops into forests accounted for 9%.Awareness of the negative environmental impacts of livestock production systems started to become an important topic for research in the 1990s, as policies to limit the environmental impact of agriculture were introduced (e.g. Nitrate Vulnerable Zones in the European Union). The publication of Livestock's Long Shadow in 2006 raised global awareness of the long-range environmental impacts of livestock production systems and since then millions of dollars have been invested in quantifying those impacts. Looking at agricultural production simply through an environmental lens is not sufficient though, since global agreement to the 2030 Agenda for Sustainable Development with 17 Sustainable Development Goals (SDGs). These goals are to be delivered as a package and include 'zero hunger' and 'economic growth', both goals to which livestock production makes a significant contribution (Mehrabi et al. 2020).Research questions thus need to address the potential consequences on all goals, of pathways for making progress towards individual goals. Eric Underwood solved animal health problems through his research, providing solutions based on nutritional knowledge. In the last 50 years animal science has added greatly to that knowledge and supported the development and implementation of technologies which have facilitated the 3.5-fold increase in global meat production. Agriculture and trade policies have also played a significant ro...

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