Advertisement

Invited Review: Genetic decision tools for increasing cow efficiency and sustainability in forage-based beef systems

  • Troy N. Rowan
    Correspondence
    Corresponding author.
    Affiliations
    Department of Animal Science and Department of Large Animal Clinical Sciences, University of Tennessee Institute of Agriculture, Knoxville 37996
    Search for articles by this author
  • Author Footnotes
    * This article resulted from the presentation given at the Bill E. Kunkle Interdisciplinary Beef Symposium, “Forage-Based Beef Management Tools and Their Impacts on Environmental Sustainability,” Southern Section of ASAS Annual Meeting, January 2022 in Ft. Worth, Texas.
      This paper is only available as a PDF. To read, Please Download here.

      ABSTRACT

      Purpose

      The beef industry is experiencing pressure to increase the efficiency and sustainability of forage-based cow-calf production. Dozens of traits affect a cow’s ability to be biologically, economically, and environmentally efficient. Furthermore, in an increasingly volatile climate, an animal’s genetic merit must be considered in the context of its environment. This review discusses the complex problem of cow efficiency and how the industry can leverage genetic selection tools to improve these traits. I review currently available selection tools for increasing cow-calf production efficiency and discuss developing technologies and research that will drive future innovation.

      Sources

      This review draws on primary literature from animal breeding, beef production, engineering, and agricultural sustainability to broadly discuss the challenges of genetically improving cow-calf efficiency and adaptability.

      Synthesis

      Decision support tools allow breeders to select sires that will have more efficient daughters. Historically, these tools have focused on moderating mature cow size and milk production. They have recently expanded to include traits that directly measure feed efficiency and other related phenotypes such as longevity and fertility. Genetic selection tools also exist for adaptive traits to help producers select sires that produce offspring that respond better to environmental stressors such as heat stress or high elevation.

      Conclusions and Applications

      Future work to develop genetic selection tools for forage-based beef systems will rely on integrating genomics, animal breeding, and precision livestock technologies. These developing technologies, coupled with existing tools, will enable producers to more directly make breeding decisions focused on cow efficiency and greenhouse gas emissions.

      Key words

      References

        • Aguerre M.J.
        • Wattiaux M.A.
        • Powell J.M.
        • Broderick G.A.
        • Arndt C.
        Effect of forage-to-concentrate ratio in dairy cow diets on emission of methane, carbon dioxide, and ammonia, lactation performance, and manure excretion.
        J. Dairy Sci. 2011; 94: 3081-3093https://doi.org/10.3168/jds.2010-4011
        • Aherin D.G.
        Stochastic systems model assessment of historical cow-calf biological and economic efficiency for different mature cow weight and peak lactation combinations in the Kansas Flint Hills.
        PhD Diss. Kansas State Univ, Manhattan2020
        • Ahlberg C.M.
        • Allwardt K.
        • Broocks A.
        • Bruno K.
        • Taylor A.
        • Mcphillips L.
        • Krehbiel C.R.
        • Calvo-Lorenzo M.
        • Richards C.J.
        • Place S.E.
        • Desilva U.
        • Vanoverbeke D.L.
        • Mateescu R.G.
        • Kuehn L.A.
        • Weaber R.
        • Bormann J.
        • Rolf M.M.
        Characterization of water intake and water efficiency in beef cattle.
        J. Anim. Sci. 2019; 97: 4770-4782https://doi.org/10.1093/jas/skz354
        • Allen M.R.
        • Shine K.P.
        • Fuglestvedt J.S.
        • Millar R.J.
        • Cain M.
        • Frame D.J.
        • Macey A.H.
        A solution to the misrepresentations of CO2-equivalent emissions of short-lived climate pollutants under ambitious mitigation.
        NPJ Clim. Atmos. Sci. 2018; 1: 1-8https://doi.org/10.1038/s41612-018-0026-8
        • Almeida A.K.
        • Hegarty R.S.
        • Cowie A.
        Meta-analysis quantifying the potential of dietary additives and rumen modifiers for methane mitigation in ruminant production systems.
        Anim. Nutr. 2021; 7: 1219-1230https://doi.org/10.1016/j.aninu.2021.09.005
        • Andresen C.E.
        • Wiseman A.W.
        • McGee A.
        • Goad C.
        • Foote A.P.
        • Reuter R.
        • Lalman D.L.
        Maintenance energy requirements and forage intake of purebred vs. crossbred beef cows.
        Transl. Anim. Sci. 2020; 4: 1182-1195https://doi.org/10.1093/tas/txaa008
        • Arbre M.
        • Rochette Y.
        • Guyader J.
        • Lascoux C.
        • Gómez L.M.
        • Eugène M.
        • Morgavi D.P.
        • Renand G.
        • Doreau M.
        • Martin C.
        Repeatability of enteric methane determinations from cattle using either the SF6 tracer technique or the GreenFeed system.
        Anim. Prod. Sci. 2016; 56: 238-243https://doi.org/10.1071/AN15512
        • Archer J.A.
        • Reverter A.
        • Herd R.M.
        • Johnston D.J.
        • Arthur P.F.
        Genetic variation in feed intake and efficiency of mature beef cows and relationships with post-weaning measurements.
        No. 10-07 in 7th World Congr. Genet. Appl. Livest. Prod. 2002 (Montpellier, France)
        • Archimède H.
        • Eugène M.
        • Marie Magdeleine C.
        • Boval M.
        • Martin C.
        • Morgavi D.P.
        • Lecomte P.
        • Doreau M.
        Comparison of methane production between C3 and C4 grasses and legumes.
        Anim. Feed Sci. Technol. 2011; 166–167: 59-64https://doi.org/10.1016/j.anifeedsci.2011.04.003
        • Asem-Hiablie S.
        • Battagliese T.
        • Stackhouse-Lawson K.R.
        • Alan Rotz C.
        A life cycle assessment of the environmental impacts of a beef system in the USA.
        Int. J. Life Cycle Assess. 2019; 24: 441-455https://doi.org/10.1007/s11367-018-1464-6
        • Basarab J.A.
        • Crowley J.J.
        • Abo-Ismail M.K.
        • Manafiazar G.M.
        • Akanno E.C.
        • Baron V.S.
        • Plastow G.
        Genomic retained heterosis effects on fertility and lifetime productivity in beef heifers.
        Can. J. Anim. Sci. 2018; 98: 642-655https://doi.org/10.1139/cjas-2017-0192
        • Benyshek L.L.
        • Johnson M.H.
        • Little D.E.
        • Bertrand J.K.
        • Kriese L.A.
        Applications of an animal model in the United States beef cattle industry.
        J. Dairy Sci. 1988; 71: 35-53https://doi.org/10.1016/S0022-0302(88)79978-6
        • Berry D.P.
        • Crowley J.J.
        Cell Biology Symposium: Genetics of feed efficiency in dairy and beef cattle.
        J. Anim. Sci. 2013; 91: 1594-1613https://doi.org/10.2527/jas.2012-5862
        • Bertrand J.K.
        • Berger P.J.
        • Willham R.L.
        Sire X environment interactions in beef cattle weaning weight field data.
        J. Anim. Sci. 1985; 60: 1396-1402https://doi.org/10.2527/jas1985.6061396x
        • BIF Guidelines Wiki contributors
        Intake and Feed Efficiency.
        BIF Guidelines Wiki. 2021;
        • Bir C.
        • De Vuyst E.A.
        • Rolf M.
        • Lalman D.
        Optimal beef cow weights in the U.S. southern Plains.
        J. Agric. Resour. Econ. 2018; 43: 103-117
        • Boldt R.J.
        • Speidel S.E.
        • Thomas M.G.
        • Enns R.M.
        Genetic parameters for fertility and production traits in Red Angus cattle.
        J. Anim. Sci. 2018; 96: 4100-4111https://doi.org/10.1093/jas/sky294
        • Boyer C.M.
        • McFarlane Z.M.
        • Mulliniks T.
        • Griffith A.P.
        Investment into developing heifers: When does she become profitable? 2018 Annu.
        Meet. Agric. Appl. Econ. Assoc, Washington, DC2018
        • Boyer C.N.
        • Griffith A.P.
        • DeLong K.L.
        Reproductive failure and long-term profitability of spring-and fall-calving beef cows.
        J. Agric. Resour. Econ. 2020; 45: 78-91
        • Bradford H.L.
        • Fragomeni B.O.
        • Bertrand J.K.
        • Lourenco D.A.L.
        • Misztal I.
        Genetic evaluations for growth heat tolerance in Angus cattle.
        J. Anim. Sci. 2016; 94: 4143-4150https://doi.org/10.2527/jas.2016-0707
        • Braz C.U.
        • Rowan T.N.
        • Schnabel R.D.
        • Decker J.E.
        Genome-wide association analyses identify genotype-by-environment interactions of growth traits in Simmental cattle.
        Sci. Rep. 2021; 11: 13335https://doi.org/10.1038/s41598-021-92455-x
        • Briggs E.A.
        • Holder A.
        • Moore M.F.
        • Lalman D.
        132 ranking beef heifers for residual intake using an unprocessed grass hay diet and its relationship to greenhouse gasses.
        J. Anim. Sci. 2022; 100: 36https://doi.org/10.1093/jas/skac028.069
        • Brito L.F.
        • Schenkel F.S.
        • Oliveira H.R.
        • Cánovas A.
        • Miglior F.
        Meta-analysis of heritability estimates for methane emission indicator traits in cattle and sheep.
        Pages 11–16 in 11th World Cong. Genet. Appl. Livest. Prod. 2018https://doi.org/10.22004/ag.econ.274108
        • Butts W.T.
        • Koger M.
        • Pahnish O.F.
        • Burns W.C.
        • E. J. Warwick.
        Performance of two lines of Hereford cattle in two environments.
        J. Anim. Sci. 1971; 33: 923-932https://doi.org/10.2527/jas1971.335923x
        • Cooprider K.L.
        • Mitloehner F.M.
        • Famula T.R.
        • Kebreab E.
        • Zhao Y.
        • Van Eenennaam A.L.
        Feedlot efficiency implications on greenhouse gas emissions and sustainability.
        J. Anim. Sci. 2011; 89: 2643-2656https://doi.org/10.2527/jas.2010-3539
        • Coppa M.
        • Jurquet J.
        • Eugène M.
        • Dechaux T.
        • Rochette Y.
        • Lamy J.-M.
        • Ferlay A.
        • Martin C.
        Repeatability and ranking of long-term enteric methane emissions measurement on dairy cows across diets and time using GreenFeed system in farm-conditions.
        Methods. 2021; 186: 59-67https://doi.org/10.1016/j.ymeth.2020.11.004
        • Costa R.B.
        • Misztal I.
        • Elzo M.A.
        • Bertrand J.K.
        • Silva L.O.C.
        • Łukaszewicz M.
        Estimation of genetic parameters for mature weight in Angus cattle.
        J. Anim. Sci. 2011; 89: 2680-2686https://doi.org/10.2527/jas.2010-3574
        • Crowley J.J.
        • Evans R.D.
        • Mc Hugh N.
        • Kenny D.A.
        • McGee M.
        • Crews Jr., D.H.
        • Berry D.P.
        Genetic relationships between feed efficiency in growing males and beef cow performance.
        J. Anim. Sci. 2011; 89: 3372-3381https://doi.org/10.2527/jas.2011-3835
        • Cundiff L.V.
        • Gregory K.E.
        • Koch R.M.
        Effects of heterosis on reproduction in Hereford, Angus and shorthorn cattle.
        J. Anim. Sci. 1974; 38: 711-727https://doi.org/10.2527/jas1974.384711x
        • Dagel A.
        • Menendez III, H.
        • Brennan J.R.
        Improving Heifer Development Programs Using Precision Technology and DDGS.
        South Dakota State Univ, 2022
        • Davison C.
        • Michie C.
        • Hamilton A.
        • Tachtatzis C.
        • Andonovic I.
        • Gilroy M.
        Detecting heat stress in dairy cattle using neckmounted activity collars.
        Agriculture. 2020; 10: 210https://doi.org/10.3390/agriculture10060210
        • de Haas Y.
        • Pszczola M.
        • Soyeurt H.
        • Wall E.
        • Lassen J.
        Invited review: Phenotypes to genetically reduce greenhouse gas emissions in dairying.
        J. Dairy Sci. 2017; 100: 855-870https://doi.org/10.3168/jds.2016-11246
        • Dickerson G.E.
        Implications of genetic-environmental interaction in animal breeding.
        Anim. Sci. 1962; 4: 47-63https://doi.org/10.1017/S0003356100034395
        • Dickerson G.E.
        Animal size and efficiency: Basic concepts.
        Anim. Sci. 1978; 27: 367-379https://doi.org/10.1017/S0003356100036278
        • Dinkel C.A.
        • Brown M.A.
        An evaluation of the ratio of calf weight to cow weight as an indicator of cow efficiency.
        J. Anim. Sci. 1978; 46: 614-617https://doi.org/10.2527/jas1978.463614x
        • Doye D.G.
        • Lalman D.L.
        Moderate versus big cows: Do big cows carry their weight on the ranch?.
        Southern Ag. Econ. Assoc. Annu. Meet. 2011; https://doi.org/10.22004/ag.econ.98748
        • Drouillard J.S.
        Current situation and future trends for beef production in the United States of America—A review.
        Asian-Australas. J. Anim. Sci. 2018; 31: 1007-1016https://doi.org/10.5713/ajas.18.0428
        • Duffield T.F.
        • Merrill J.K.
        • Bagg R.N.
        Meta-analysis of the effects of monensin in beef cattle on feed efficiency, body weight gain, and dry matter intake.
        J. Anim. Sci. 2012; 90: 4583-4592https://doi.org/10.2527/jas.2011-5018
        • Dumortier J.
        • Hayes D.J.
        • Carriquiry M.
        • Dong F.
        • Du X.
        • Elobeid A.
        • Fabiosa J.F.
        • Martin P.A.
        • Mulik K.
        The effects of potential changes in United States beef production on global grazing systems and greenhouse gas emissions.
        Environ. Res. Lett. 2012; 7024023https://doi.org/10.1088/1748-9326/7/2/024023
        • Durbin H.J.
        • Lu D.
        • Yampara-Iquise H.
        • Miller S.P.
        • Decker J.E.
        Development of a genetic evaluation for hair shedding in American Angus cattle to improve thermotolerance.
        Genet. Sel. Evol. 2020; 52 (Correction to article: 10.1186/s12711-021-00623-4): 63https://doi.org/10.1186/s12711-020-00584-0
        • Egger-Danner C.
        • Cole J.B.
        • Pryce J.E.
        • Gengler N.
        • Heringstad B.
        • Bradley A.
        • Stock K.F.
        Invited review: Overview of new traits and phenotyping strategies in dairy cattle with a focus on functional traits.
        Animal. 2015; 9: 191-207https://doi.org/10.1017/S1751731114002614
        • Evans J.L.
        • Golden B.L.
        • Hough B.L.
        A new genetic prediction for cow maintenance energy requirements.
        in: Proc. 34th Beef Improve. Fed. Meet., Omaha, NE. 2002
        • Falconer D.S.
        The problem of environment and selection.
        Am. Nat. 1952; 86: 293-298https://doi.org/10.1086/281736
        • Fennewald D.J.
        • Weaber R.L.
        • Lamberson W.R.
        Genotype by environment interaction for stayability of Red Angus in the United States.
        J. Anim. Sci. 2018; 96: 422-429https://doi.org/10.1093/jas/skx080
        • Ferrell C.L.
        • Jenkins T.G.
        Cow type and the nutritional environment: Nutritional aspects.
        J. Anim. Sci. 1985; 61: 725-741https://doi.org/10.2527/jas1985.613725x
        • Fontes P.L.P.
        • Oosthuizen N.
        • Cliff Lamb G.
        Chapter 4—Reproductive management of beef cattle.
        in: Bazer F.W. Lamb G.C. Wu G. Pages 57–73 in Animal Agriculture. Acad. Press, 2020
        • Forni S.
        • Aguilar I.
        • Misztal I.
        Different genomic relationship matrices for single-step analysis using phenotypic, pedigree and genomic information.
        Genet. Sel. Evol. 2011; 43: 1https://doi.org/10.1186/1297-9686-43-1
        • García-Ruiz A.
        • Cole J.B.
        • VanRaden P.M.
        • Wiggans G.R.
        • Ruiz-López F.J.
        • Van Tassell C.P.
        Changes in genetic selection differentials and generation intervals in US Holstein dairy cattle as a result of genomic selection.
        Proc. Natl. Acad. Sci. USA. 2016; 113 (Correction to article: 10.1073/pnas.161157011): E3995-E4004https://doi.org/10.1073/pnas.1519061113
        • Garnsworthy P.C.
        The environmental impact of fertility in dairy cows: a modelling approach to predict methane and ammonia emissions.
        Anim. Feed Sci. Technol. 2004; 112: 211-223https://doi.org/10.1016/j.anifeedsci.2003.10.011
        • Garnsworthy P.C.
        • Difford G.F.
        • Bell M.J.
        • Bayat A.R.
        • Huhtanen P.
        • Kuhla B.
        • Lassen J.
        • Peiren N.
        • Pszczola M.
        • Sorg D.
        • Visker M.H.P.W.
        • Yan T.
        Comparison of methods to measure methane for use in genetic evaluation of dairy cattle.
        Animals (Basel). 2019; 9: 837https://doi.org/10.3390/ani9100837
        • Garrick D.J.
        The nature, scope and impact of genomic prediction in beef cattle in the United States.
        Genet. Sel. Evol. 2011; 43: 17https://doi.org/10.1186/1297-9686-43-17
        • Gerber P.J.
        • Steinfeld H.
        • Henderson B.
        • Mottet A.
        • Opio C.
        • Dijkman J.
        • Falcucci A.
        • Tempio G.
        Tackling Climate Change Through Livestock: A Global Assessment of Emissions and Mitigation Opportunities.
        Food Agric. Org. United Nations (FAO), 2013
        • Giess L.K.
        • Thomas M.G.
        • Speidel S.E.
        • Culbertson M.M.
        • Shafer W.R.
        • McGuire S.C.
        • Enns R.M.
        Whole herd reporting data from the American Simmental Association as a data source for heifer pregnancy phenotypes.
        Transl. Anim. Sci. 2021; 5: S199-S203https://doi.org/10.1093/tas/txab152
        • González-Recio O.
        • López-Paredes J.
        • Ouatahar L.
        • Charfeddine N.
        • Ugarte E.
        • Alenda R.
        • Jiménez-Montero J.A.
        Mitigation of greenhouse gases in dairy cattle via genetic selection: 2. Incorporating methane emissions into the breeding goal.
        J. Dairy Sci. 2020; 103: 7210-7221https://doi.org/10.3168/jds.2019-17598
        • Grainger C.
        • Clarke T.
        • McGinn S.M.
        • Auldist M.J.
        • Beauchemin K.A.
        • Hannah M.C.
        • Waghorn G.C.
        • Clark H.
        • Eckard R.J.
        Methane emissions from dairy cows measured using the sulfur hexafluoride (SF6) tracer and chamber techniques.
        J. Dairy Sci. 2007; 90: 2755-2766https://doi.org/10.3168/jds.2006-697
        • Gray K.A.
        • Smith T.
        • Maltecca C.
        • Overton P.
        • Parish J.A.
        • Cassady J.P.
        Differences in hair coat shedding, and effects on calf weaning weight and BCS among Angus dams.
        Livest. Sci. 2011; 140: 68-71https://doi.org/10.1016/j.livsci.2011.02.009
        • Gregory K.E.
        • Cundiff L.V.
        • Koch R.M.
        Breed effects and heterosis in advanced generations of composite populations for preweaning traits of beef cattle.
        J. Anim. Sci. 1991; 69: 947-960https://doi.org/10.2527/1991.693947x
        • Hayes B.J.
        • Daetwyler H.D.
        • Goddard M.E.
        Models for genome × environment interaction: Examples in livestock.
        Crop Sci. 2016; 56: 2251-2259https://doi.org/10.2135/cropsci2015.07.0451
        • Hayes B.J.
        • Donoghue K.A.
        • Reich C.M.
        • Mason B.A.
        • Bird-Gardiner T.
        • Herd R.M.
        • Arthur P.F.
        Genomic heritabilities and genomic estimated breeding values for methane traits in Angus cattle.
        J. Anim. Sci. 2016; 94: 902-908https://doi.org/10.2527/jas.2015-0078
        • Hazel L.N.
        The genetic basis for constructing selection indexes.
        Genetics. 1943; 28: 476-490https://doi.org/10.1093/genetics/28.6.476
        • Hegarty R.S.
        • Goopy J.P.
        • Herd R.M.
        • McCorkell B.
        Cattle selected for lower residual feed intake have reduced daily methane production.
        J. Anim. Sci. 2007; 85: 1479-1486https://doi.org/10.2527/jas.2006-236
        • Helwatkar A.
        • Riordan D.
        • Walsh J.
        Sensor technology for animal health monitoring.
        Int. J. Smart Sensing Intell. Syst. 2020; 7: 1-6https://doi.org/10.21307/ijssis-2019-057
        • Henderson C.R.
        Best linear unbiased prediction of breeding values not in the model for records.
        J. Dairy Sci. 1977; 60: 783-787https://doi.org/10.3168/jds.S0022-0302(77)83935-0
        • Holt T.N.
        • Callan R.J.
        Pulmonary arterial pressure testing for high mountain disease in cattle.
        Vet. Clin. North Am. Food Anim. Pract. 2007; 23 (vii. 10.1016/j.cvfa.2007.08.001.): 575-596
        • Hristov A.N.
        • Oh J.
        • Giallongo F.
        • Frederick T.
        • Weeks H.
        • Zimmerman P.R.
        • Harper M.T.
        • Hristova R.A.
        • Zimmerman R.S.
        • Branco A.F.
        The use of an automated system (GreenFeed) to monitor enteric methane and carbon dioxide emissions from ruminant animals.
        J. Vis. Exp. 2015; https://doi.org/10.3791/52904
        • Jenkins T.G.
        • Ferrell C.L.
        Productivity through weaning of nine breeds of cattle under varying feed availabilities: I.
        Initial evaluation. J. Anim. Sci. 1994; 72: 2787-2797https://doi.org/10.2527/1994.72112787x
        • Jenkins T.G.
        • Ferrell C.L.
        Matching beef genetics with production environment.
        Pages 41–46 in Proc. Beef Improve. Fed. Annu. Meet. Symp., Chocktaw, MS. 2006
        • Jones F.M.
        • Phillips F.A.
        • Naylor T.
        • Mercer N.B.
        Methane emissions from grazing Angus beef cows selected for divergent residual feed intake.
        Anim. Feed Sci. Technol. 2011; 166–167: 302-307https://doi.org/10.1016/j.anifeedsci.2011.04.020
        • Kaps M.
        • Herring W.O.
        • Lamberson W.R.
        Genetic and environmental parameters for mature weight in Angus cattle.
        J. Anim. Sci. 1999; 77: 569-574https://doi.org/10.2527/1999.773569x
        • Kenny D.A.
        • Fitzsimons C.
        • Waters S.M.
        • McGee M.
        Invited review: Improving feed efficiency of beef cattle—The current state of the art and future challenges.
        Animal. 2018; 12: 1815-1826https://doi.org/10.1017/S1751731118000976
        • Knapp J.R.
        • Laur G.L.
        • Vadas P.A.
        • Weiss W.P.
        • J. M. Tricarico
        Invited review: Enteric methane in dairy cattle production: Quantifying the opportunities and impact of reducing emissions.
        J. Dairy Sci. 2014; 97: 3231-3261https://doi.org/10.3168/jds.2013-7234
        • Koch R.M.
        • Swiger L.A.
        • Chambers D.
        • Gregory K.E.
        Efficiency of feed use in beef cattle.
        J. Anim. Sci. 1963; 22: 486-494https://doi.org/10.2527/jas1963.222486x
        • Koltes J.E.
        • Cole J.B.
        • Clemmens R.
        • Dilger R.N.
        • Kramer L.M.
        • Lunney J.K.
        • McCue M.E.
        • McKay S.D.
        • Mateescu R.G.
        • Murdoch B.M.
        • Reuter R.
        • Rexroad C.E.
        • Rosa G.J.M.
        • Serão N.V.L.
        • White S.N.
        • Woodward-Greene M.J.
        • Worku M.
        • Zhang H.
        • Reecy J.M.
        A vision for development and utilization of high-throughput phenotyping and big data analytics in livestock.
        Front. Genet. 2019; 10: 1197https://doi.org/10.3389/fgene.2019.01197
        • Lalman D.
        • Briggs E.A.
        • Moore M.F.
        39 cow-calf management strategies and their influence on environmental sustainability.
        J. Anim. Sci. 2022; 100: 22-23https://doi.org/10.1093/jas/skac028.043
        • Lassen J.
        • Løvendahl P.
        Heritability estimates for enteric methane emissions from Holstein cattle measured using noninvasive methods.
        J. Dairy Sci. 2016; 99: 1959-1967https://doi.org/10.3168/jds.2015-10012
        • Liu S.
        • Proudman J.
        • Mitloehner F.M.
        Rethinking methane from animal agriculture.
        CABI Agric. Biosci. 2021; 2: 1-13https://doi.org/10.1186/s43170-021-00041-y
        • Lupo C.D.
        • Clay D.E.
        • Benning J.L.
        • Stone J.J.
        Lifecycle assessment of the beef cattle production system for the northern great plains, USA.
        J. Environ. Qual. 2013; 42: 1386-1394
        • MacNeil M.D.
        • Vukasinovic N.
        A prototype national cattle evaluation for sustained reproductive success in Hereford cattle.
        J. Anim. Sci. 2011; 89: 1712-1718https://doi.org/10.2527/jas.2010-3353
        • MacNeil M.D.
        • Lopez-Villalobos N.
        • Northcutt S.L.
        A prototype national cattle evaluation for feed intake and efficiency of Angus cattle.
        J. Anim. Sci. 2011; 89: 3917-3923https://doi.org/10.2527/jas.2011-4124
        • Manafiazar G.
        • Baron V.S.
        • McKeown L.
        • Block H.
        • Ominski K.
        • Plastow G.
        • Basarab J.A.
        Methane and carbon dioxide emissions from yearling beef heifers and mature cows classified for residual feed intake under drylot conditions.
        Can. J. Anim. Sci. 2020; 100: 522-535https://doi.org/10.1139/cjas-2019-0032
        • Meuwissen T.H.
        • Hayes B.J.
        • Goddard M.E.
        Prediction of total genetic value using genome-wide dense marker maps.
        Genetics. 2001; 157: 1819-1829https://doi.org/10.1093/genetics/157.4.1819
        • Meyer A.M.
        • Kerley M.S.
        • Kallenbach R.L.
        The effect of residual feed intake classification on forage intake by grazing beef cows.
        J. Anim. Sci. 2008; 86: 2670-2679https://doi.org/10.2527/jas.2007-0642
        • Misztal I.
        Is genomic selection now a mature technology?.
        J. Anim. Breed. Genet. 2016; 133: 81-82https://doi.org/10.1111/jbg.12209
        • Montaño-Bermudez M.
        • Nielsen M.K.
        • Deutscher G.H.
        Energy requirements for maintenance of crossbred beef cattle with different genetic potential for milk.
        J. Anim. Sci. 1990; 68: 2279-2288https://doi.org/10.2527/1990.6882279x
        • Moore M.F.
        • Briggs E.A.
        • Lalman D.
        • Holder A.
        133 ranking mature beef cows for residual intake using an unprocessed grass hay diet and its relationship to greenhouse gas exchange.
        J. Anim. Sci. 2022; 100: 35https://doi.org/10.1093/jas/skac028.067
      1. National Academies of Sciences, Engineering, and Medicine. 2016. Nutrient Requirements of Beef Cattle. 8th rev. ed. Natl. Acad. Press.

        • Nielsen M.K.
        • MacNeil M.D.
        • Dekkers J.C.M.
        • Crews Jr., D.H.
        • Rathje T.A.
        • Enns R.M.
        • Weaber R.L.
        Review: Lifecycle, total-industry genetic improvement of feed efficiency in beef cattle: Blueprint for the Beef Improvement Federation.
        Prof. Anim. Sci. 2013; 29: 559-565https://doi.org/10.15232/S1080-7446(15)30285-0
        • Northcutt S.
        • Bowerman B.
        Angus feed efficiency selection tool: RADG. By the numbers.
        Angus J. 2010; : 170-172
        • Northcutt S.L.
        • Wilson D.E.
        Genetic parameter estimates and expected progeny differences for mature size in Angus cattle.
        J. Anim. Sci. 1993; 71: 1148-1153https://doi.org/10.2527/1993.7151148x
        • Pang H.
        • Makarechian M.H.
        • Basarab J.A.
        A simulation study on the effects of cow size and milk production on bioeconomic efficiency of range beef cattle.
        J. Appl. Anim. Res. 1999; 16: 119-128https://doi.org/10.1080/09712119.1999.9706273
        • Pauling R.C.
        • Speidel S.E.
        • Thomas M.G.
        • Holt T.N.
        • Enns R.M.
        Evaluation of moderate to high elevation effects on pulmonary arterial pressure measures in Angus cattle1.
        J. Anim. Sci. 2018; 96: 3599-3605https://doi.org/10.1093/jas/sky262
        • Place S.E.
        • Mitloehner F.M.
        Pathway to Climate Neutrality for U.S. Beef and Dairy Cattle Production.
        UC Davis, 2021
        • Prayaga K.C.
        • Corbet N.J.
        • Johnston D.J.
        • Wolcott M.L.
        • Fordyce G.
        • Burrow H.M.
        Genetics of adaptive traits in heifers and their relationship to growth, pubertal and carcass traits in two tropical beef cattle genotypes.
        Anim. Prod. Sci. 2009; 49: 413-425https://doi.org/10.1071/EA08247
        • Reith S.
        • Hoy S.
        Review: Behavioral signs of estrus and the potential of fully automated systems for detection of estrus in dairy cattle.
        Animal. 2018; 12: 398-407https://doi.org/10.1017/S1751731117001975
        • Rhodes J.
        Comparative physiology of hypoxic pulmonary hypertension: Historical clues from brisket disease.
        J. Appl. Physiol. 2005; 98: 1092-1100https://doi.org/10.1152/japplphysiol.01017.2004
        • Richardson C.M.
        • Amer P.R.
        • Quinton C.
        • Crowley J.
        • Hely F.S.
        • van den Berg I.
        • Pryce J.E.
        Reducing greenhouse gas emissions through genetic selection in the Australian dairy industry.
        J. Dairy Sci. 2022; 105: 4272-4288https://doi.org/10.3168/jds.2021-21277
        • Robert B.
        • White B.J.
        • Renter D.G.
        • Larson R.L.
        Evaluation of three-dimensional accelerometers to monitor and classify behavior patterns in cattle.
        Comput. Electron. Agric. 2009; 67: 80-84https://doi.org/10.1016/j.compag.2009.03.002
        • Rotz C.A.
        • Asem-Hiablie S.
        • Place S.
        • Thoma G.
        Environmental footprints of beef cattle production in the United States.
        Agric. Syst. 2019; 169: 1-13https://doi.org/10.1016/j.agsy.2018.11.005
        • Rowan T.N.
        • Durbin H.J.
        • Seabury C.M.
        • Schnabel R.D.
        • Decker J.E.
        Powerful detection of polygenic selection and evidence of environmental adaptation in US beef cattle.
        PLoS Genet. 2021; 17e1009652https://doi.org/10.1371/journal.pgen.1009652
        • Santana Jr., M.L.
        • Eler J.P.
        • Cardoso F.F.
        • Albuquerque L.G.
        • Ferraz J.B.S.
        Phenotypic plasticity of composite beef cattle performance using reaction norms model with unknown covariate.
        Animal. 2013; 7: 202-210https://doi.org/10.1017/S1751731112001711
        • Schaeffer L.R.
        Animal Models. Volumes Direct.
        2019
        • Schiermiester L.N.
        • Thallman R.M.
        • Kuehn L.A.
        • Kachman S.D.
        • Spangler M.L.
        Estimation of breed-specific heterosis effects for birth, weaning, and yearling weight in cattle.
        J. Anim. Sci. 2015; 93: 46-52https://doi.org/10.2527/jas.2014-8493
        • Siberski-Cooper C.J.
        • Koltes J.E.
        Opportunities to harness high-throughput and novel sensing phenotypes to improve feed efficiency in dairy cattle.
        Animals (Basel). 2021; 12: 15https://doi.org/10.3390/ani12010015
        • Snelling W.M.
        • Golden B.L.
        • Bourdon R.M.
        An EPD for stayability of beef cows.
        Pages 169–172 in World Congr. Genet. Appl. Livest. Prod. Vol. 5. 1994
        • Snelling W.M.
        • Golden B.L.
        • Bourdon R.M.
        Withinherd genetic analyses of stayability of beef females.
        J. Anim. Sci. 1995; 73: 993-1001https://doi.org/10.2527/1995.734993x
        • Spangler M.L.
        Decision Support Systems. Beef Sire Selection Manual.
        3rd ed. Pages 29–30. Natl. Beef Cattle Eval. Consort. 2021
        • Speidel S.E.
        • Thomas M.G.
        • Holt T.N.
        • Enns R.M.
        Evaluation of the sensitivity of pulmonary arterial pressure to elevation using a reaction norm model in Angus cattle.
        J. Anim. Sci. 2020; 98: 5https://doi.org/10.1093/jas/skaa129
        • Tedeschi L.O.
        • Abdalla A.L.
        • Álvarez C.
        • Anuga S.W.
        • Arango J.
        • Beauchemin K.A.
        • Becquet P.
        • Berndt A.
        • Burns R.
        • De Camillis C.
        • Chará J.
        • Echazarreta J.M.
        • Hassouna M.
        • Kenny D.
        • Mathot M.
        • Mauricio R.M.
        • McClelland S.C.
        • Niu M.
        • Onyango A.A.
        • Parajuli R.
        • Pereira L.G.R.
        • Del Prado A.
        • Tieri M. Paz
        • Uwizeye A.
        • Kebreab E.
        Quantification of methane emitted by ruminants: A review of methods.
        J. Anim. Sci. 2022; 100skac197https://doi.org/10.1093/jas/skac197
        • Tullo E.
        • Finzi A.
        • Guarino M.
        Review: Environmental impact of livestock farming and precision livestock farming as a mitigation strategy.
        Sci. Total Environ. 2019; 650: 2751-2760https://doi.org/10.1016/j.scitotenv.2018.10.018
        • USDA-APHIS
        Reference of beef cow-calf management practices in the United States, 2007–08.
        USDA, Anim. Plant Health Insp. Serv., Vet. Serv., Natl. Anim. Health Monit. Syst. 2008
        • Visscher P.M.
        • Goddard M.E.
        Fixed and random contemporary groups.
        J. Dairy Sci. 1993; 76: 1444-1454https://doi.org/10.3168/jds.S0022-0302(93)77475-5
        • Wall E.
        • Simm G.
        • Moran D.
        Developing breeding schemes to assist mitigation of greenhouse gas emissions.
        Animal. 2010; 4: 366-376https://doi.org/10.1017/S175173110999070X
        • Wray N.R.
        • Kemper K.E.
        • Hayes B.J.
        • Goddard M.E.
        • Visscher P.M.
        Complex trait prediction from genome data: Contrasting EBV in livestock to PRS in humans: Genomic prediction.
        Genetics. 2019; 211: 1131-1141https://doi.org/10.1534/genetics.119.301859
        • Zimmermann M.J.
        • Kuehn L.A.
        • Spangler M.L.
        • Thallman R.M.
        • Snelling W.M.
        • Lewis R.M.
        Breed and heterotic effects for mature weight in beef cattle.
        J. Anim. Sci. 2021; 99skab209https://doi.org/10.1093/jas/skab209