1932

Abstract

There is sustained growth in the number of tropical cattle, which represent more than half of all cattle worldwide. By and large, most research in tropical areas is still focused on breeds of cattle, their particular advantages or disadvantages in tropical areas, and the tropical forages or feeds that could be usefully fed to them. A consistent issue for adaptation to climate is the heat of tropical environments. Changing the external characteristics of the animal, such as color and coat characteristics, is one way to adapt, and there are several major genes for these traits. However, further improvement in heat tolerance and other adaptation traits will need to use the entire genome and all physical and physiological systems. Apart from the response to heat, climate forcing through methane emission identifies dry season weight loss as an important if somewhat neglected trait in climate adaptation of cattle. The use of genome-estimated breeding values in tropical areas is in its infancy and will be difficult to implement, but will be essential for rapid, coordinated genetic improvement. The difficulty of implementation cannot be exaggerated and may require major improvements in methodology.

Loading

Article metrics loading...

/content/journals/10.1146/annurev-animal-022516-022921
2017-02-08
2024-06-19
Loading full text...

Full text loading...

/deliver/fulltext/animal/5/1/annurev-animal-022516-022921.html?itemId=/content/journals/10.1146/annurev-animal-022516-022921&mimeType=html&fmt=ahah

Literature Cited

  1. Kottek M, Grieser J, Beck C, Rudolf B, Rubel F. 1.  2006. World map of the Köppen-Geiger climate classification updated. Meteorol. Z. 15:259–63 [Google Scholar]
  2. Peel MC, Finlayson BL, McMahon TA. 2.  2007. Updated world map of the Köppen-Geiger climate classification. Hydrol. Earth Syst. Sci. 11:1633–44 [Google Scholar]
  3. Sanders JO. 3.  1980. History and development of zebu cattle in the United States. J. Anim. Sci. 50:1188–200 [Google Scholar]
  4. Hanotte O, Bradley DG, Ochieng JW, Verjee Y, Hill EW, Rege JEO. 4.  2002. African pastoralism: genetic imprints of origins and migrations. Science 296:336–39 [Google Scholar]
  5. Gibbs RA, Taylor JF, Van Tassell CP, Barendse W, Eversole KA. 5.  et al. 2009. Genome-wide survey of SNP variation uncovers the genetic structure of cattle breeds. Science 324:528–32 [Google Scholar]
  6. McTavish EJ, Decker JE, Schnabel RD, Taylor JF, Hillis DM. 6.  2013. New World cattle show ancestry from multiple independent domestication events. PNAS 110:E1398–406 [Google Scholar]
  7. MacHugh DE, Shriver MD, Loftus RT, Cunningham P, Bradley DG. 7.  1997. Microsatellite DNA variation and the evolution, domestication and phylogeography of taurine and zebu cattle (Bos taurus and Bos indicus). Genetics 146:1071–86 [Google Scholar]
  8. Martinez AM, Gama LT, Canon J, Ginja C, Delgado JV. 8.  et al. 2012. Genetic footprints of Iberian cattle in America 500 years after the arrival of Columbus. PLOS ONE 7:11e49066 [Google Scholar]
  9. 9. Food Agric. Organ 2010. 2000 world census of agriculture. Rep., Food Agric. Organ., Rome, 246 [Google Scholar]
  10. Wint W, Robinson TP. 10.  2007. Gridded livestock of the world 2007 Rep., Food Agric. Organ. Rome 141 [Google Scholar]
  11. Bhagwat SA, Nogué S, Willis KJ. 11.  2012. Resilience of an ancient tropical forest landscape to 7500 years of environmental change. Biol. Conserv. 153:108–17 [Google Scholar]
  12. Bhagwat SA, Nogué S, Willis KJ. 12.  2014. Cultural drivers of reforestation in tropical forest groves of the Western Ghats of India. Forest Ecol. Manag. 329:393–400 [Google Scholar]
  13. Chávez AB, Broadbent EN, Zambrano AMA. 13.  2014. Smallholder policy adoption and land cover change in the southeastern Peruvian Amazon: a twenty-year perspective. Appl. Geogr. 53:223–33 [Google Scholar]
  14. Hertel TW, Lobell DB. 14.  2014. Agricultural adaptation to climate change in rich and poor countries: current modeling practice and potential for empirical contributions. Energy Econ 46:562–75 [Google Scholar]
  15. Lobell DB, Tebaldi C. 15.  2014. Getting caught with our plants down: the risks of a global crop yield slowdown from climate trends in the next two decades. Environ. Res. Lett. 9:074003 [Google Scholar]
  16. Renwick AR, Vickery JA, Potts SG, Bolwig S, Nalwanga D. 16.  et al. 2014. Achieving production and conservation simultaneously in tropical agricultural landscapes. Agric. Ecosyst. Environ. 192:130–34 [Google Scholar]
  17. Dinerstein E, Baccini A, Anderson M, Fiske G, Wikramanayake E. 17.  et al. 2015. Guiding agricultural expansion to spare tropical forests. Conserv. Lett. 8:262–71 [Google Scholar]
  18. Holmes ND, Griffiths R, Pott M, Alifano A, Will D. 18.  et al. 2015. Factors associated with rodent eradication failure. Biol. Conserv. 185:8–16 [Google Scholar]
  19. Fordyce G, James TA, Holroyd RG, Beaman NJ, Mayer RJ, O'Rourke PK. 19.  1993. The performance of Brahman-Shorthorn and Sahiwal-Shorthorn beef cattle in the dry tropics of northern Queensland. 3. Birth weights and growth to weaning. Aust. J. Exp. Agric. 33:119–27 [Google Scholar]
  20. Shiota AM, Ferreira dos Santos S, Bueno de Mattos Nascimento MR, Ferreira Moura AR, Visona de Oliveira M, Ferreira IC. 20.  2013. Physiological parameters, hair coat characteristics and thermal gradients in Nellore heifers in summer and winter in tropical environment. Biosci. J. 29:1687–95 [Google Scholar]
  21. Wolcott ML, Johnston DJ, Barwick SA. 21.  2014. Genetic relationships of female reproduction with growth, body composition, maternal weaning weight and tropical adaptation in two tropical beef genotypes. Anim. Prod. Sci. 54:60–73 [Google Scholar]
  22. Porto-Neto LR, Barendse W, Henshall JM, McWilliam SM, Lehnert SA, Reverter A. 22.  2015. Genomic correlation: harnessing the benefit of combining two unrelated populations for genomic selection. Genet. Sel. Evol. 47:84 [Google Scholar]
  23. Façanha DAE, Gomes da Silva R, Campos Maia AS, Guilhermino MM, De Vasconcelos AM. 23.  2010. Annual variation of morphologic traits and hair coat surface temperature of Holstein cows in semi-arid environment. Rev. Bras. Zootec. 39:837–44 [Google Scholar]
  24. Sahagun R, Medina JHV, Ruiz IJ. 24.  2009. Estimates of genetic and environmental parameters in tropical areas that influence the growth beef bovine F1. J. Anim. Vet. Adv. 8:2503–7 [Google Scholar]
  25. Baars RMT, Solano C, Baayen MT, Rojas J, ’t Mannetje L. 25.  1996. MIS support for pasture and nutrition management of dairy farms in tropical countries. Comput. Electron. Agric. 15:27–39 [Google Scholar]
  26. López A, Arroquy JI, Juárez Sequeira AV, García M, Nazareno M. 26.  et al. 2014. Effect of protein supplementation on tropical grass hay utilization by beef steers drinking saline water. J. Anim. Sci. 92:2152–60 [Google Scholar]
  27. Ramírez-Restrepo CA, O'Neill CJ, López-Villalobos N, Padmanabha J, McSweeney C. 27.  2014. Tropical cattle methane emissions: the role of natural statins supplementation. Anim. Prod. Sci. 54:1294–99 [Google Scholar]
  28. Santos SA, de Campos Valadares Filho S, Detmann E, Diniz Valadares RF, de Mendes Ruas JR, de Mello Amaral P. 28.  2011. Different forage sources for F1 Holstein × Gir dairy cows. Livest. Sci. 142:48–58 [Google Scholar]
  29. Magalhães KA, Valadares Filho SC, Detmann E, Diniz LL, Pina DS. 29.  et al. 2010. Evaluation of indirect methods to estimate the nutritional value of tropical feeds for ruminants. Anim. Feed Sci. Technol. 155:44–54 [Google Scholar]
  30. Bahbahani H, Hanotte O. 30.  2015. Genetic resistance: tolerance to vector-borne diseases and the prospects and challenges of genomics. Rev. Sci. Tech. 34:185–97 [Google Scholar]
  31. Chevillon C, de Garine-Wichatitsky M, Barre N, Ducornez S, de Meeus T. 31.  2013. Understanding the genetic, demographical and/or ecological processes at play in invasions: lessons from the southern cattle tick Rhipicephalus microplus (Acari: Ixodidae). Exp. Appl. Acarol. 59:203–18 [Google Scholar]
  32. Weir W, Karagenc T, Gharbi M, Simuunza M, Aypak S. 32.  et al. 2011. Population diversity and multiplicity of infection in Theileria annulata. Int. J. Parasitol. 41:193–203 [Google Scholar]
  33. Jiménez M, Martinez-Urtaza J, Chaidez C. 33.  2011. Geographical and temporal dissemination of Salmonellae isolated from domestic animal hosts in the Culiacan Valley, Mexico. Microb. Ecol. 61:811–20 [Google Scholar]
  34. Szabo MPJ, Olegario MMM, Santos ALQ. 34.  2007. Tick fauna from two locations in the Brazilian savannah. Exp. Appl. Acarol. 43:73–84 [Google Scholar]
  35. Caldwell LC, Chase CC, Riley DG, Coleman SW, Phillips WA. 35.  et al. 2011. The influence of tropical adaptation on plasma concentrations of insulin-like growth factor-I in purebred and crossbred beef cattle. J. Anim. Sci. 89:4017–22 [Google Scholar]
  36. Porto-Neto LR, Jonsson NN, D'Occhio MJ, Barendse W. 36.  2011. Molecular genetic approaches for identifying the basis of variation in resistance to tick infestation in cattle. Vet. Parasitol. 180:165–72 [Google Scholar]
  37. Liao XP, Peng F, Forni S, McLaren D, Plastow G, Stothard P. 37.  2013. Whole genome sequencing of Gir cattle for identifying polymorphisms and loci under selection. Genome 56:592–98 [Google Scholar]
  38. Makina SO, Muchadeyi FC, van Marle-Koster E, Taylor JF, Makgahlela ML, Maiwashe A. 38.  2015. Genome-wide scan for selection signatures in six cattle breeds in South Africa. Genet. Sel. Evol. 47:92 [Google Scholar]
  39. Porto-Neto LR, Reverter A, Prayaga KC, Chan EKF, Johnston DJ. 39.  et al. 2014. The genetic architecture of climatic adaptation of tropical cattle. PLOS ONE 9:e113284 [Google Scholar]
  40. Chan EKF, Nagaraj SH, Reverter A. 40.  2010. The evolution of tropical adaptation: comparing taurine and zebu cattle. Anim. Genet. 41:467–77 [Google Scholar]
  41. Renaudeau D, Collin A, Yahav S, de Basilio V, Gourdine JL, Collier RJ. 41.  2012. Adaptation to hot climate and strategies to alleviate heat stress in livestock production. Animal 6:707–28 [Google Scholar]
  42. Joyce LA, Briske DD, Brown JR, Polley HW, McCarl BA, Bailey DW. 42.  2013. Climate change and North American rangelands: assessment of mitigation and adaptation strategies. Rangel. Ecol. Manag. 66:512–28 [Google Scholar]
  43. Murgueitio E, Calle Z, Uribe F, Calle A, Solorio B. 43.  2011. Native trees and shrubs for the productive rehabilitation of tropical cattle ranching lands. Forest Ecol. Manag. 261:1654–63 [Google Scholar]
  44. Lapola DM, Schaldach R, Alcamo J, Bondeau A, Msangi S. 44.  et al. 2011. Impacts of climate change and the end of deforestation on land use in the Brazilian legal Amazon. Earth Interact 15:1–29 [Google Scholar]
  45. Puig CJ, Greiner R, Huchery C, Perkins I, Bowen L. 45.  et al. 2011. Beyond cattle: potential futures of the pastoral industry in the Northern Territory. Rangel. J. 33:181–94 [Google Scholar]
  46. Bényei B, Barros CCW. 46.  2000. Effect of superovulation on performance of bovine embryo donors imported from temperate zone to tropical climate during the first two years of adaptation. Arq. Bras. Med. Vet. Zootec. 52:366–71 [Google Scholar]
  47. Bo GA, Baruselli PS, Martínez MF. 47.  2003. Pattern and manipulation of follicular development in Bos indicus cattle. Anim. Reprod. Sci. 78:307–26 [Google Scholar]
  48. Hammond AC, Olson TA. 48.  1994. Rectal temperature and grazing time in selected beef-cattle breeds under tropical summer conditions in subtropical Florida. Trop. Agric. 71:128–34 [Google Scholar]
  49. Bertipaglia ECA, da Silva RG, Cardoso V, Fries LA. 49.  2007. Hair coat characteristics and sweating rate of Braford cows in Brazil. Livest. Sci. 112:99–108 [Google Scholar]
  50. Ribeiro ARB, Alencar MM, Freitas AR, Regitano LCA, Oliveira MCS, Ibelli AMG. 50.  2009. Heat tolerance of Nelore, Senepol × Nelore and Angus × Nelore heifers in the southeast region of Brazil. S. Afr. J. Anim. Sci. 39:263–65 [Google Scholar]
  51. McManus C, Castanheira M, Paiva SR, Louvandini H, Fioravanti MCS. 51.  et al. 2011. Use of multivariate analyses for determining heat tolerance in Brazilian cattle. Trop. Anim. Health Prod. 43:623–30 [Google Scholar]
  52. Gautier M, Flori L, Riebler A, Jaffrezic F, Laloe D. 52.  et al. 2009. A whole genome Bayesian scan for adaptive genetic divergence in West African cattle. BMC Genom 10:550 [Google Scholar]
  53. Barris W, Harrison B, McWilliam S, Bunch R, Goddard M, Barendse W. 53.  2012. Next generation sequencing of African and Indicine cattle to identify single nucleotide polymorphisms. Anim. Prod. Sci. 52:133–42 [Google Scholar]
  54. Canavez FC, Luche DD, Stothard P, Leite KRM, Sousa-Canavez JM. 54.  et al. 2012. Genome sequence and assembly of Bos indicus. J. Hered. 103:342–48 [Google Scholar]
  55. Barendse W, McWilliam S, Bunch RJ, Harrison BE. 55.  2015. Adaptive divergence in the bovine genome. bioRxiv doi:10.1101/022764 [Google Scholar]
  56. Riley DG, Chase CC, Coleman SW, Olson TA. 56.  2007. Evaluation of birth and weaning traits of Romosinuano calves as purebreds and crosses with Brahman and Angus. J. Anim. Sci. 85:289–98 [Google Scholar]
  57. Coleman SW, Chase CC, Phillips WA, Riley DG, Olson TA. 57.  2012. Evaluation of tropically adapted straightbred and crossbred cattle: postweaning gain and feed efficiency when finished in a temperate climate. J. Anim. Sci. 90:1955–65 [Google Scholar]
  58. Contreras G, Chirinos Z, Zambrano S, Molero E, Paez A. 58.  2011. Morphological characterization and zoometric indexes of Criollo Limonero cows of Venezuela. Rev. Fac. Agron. Univ. Zulia 28:91–103 [Google Scholar]
  59. Maciel S, Okeyo AM, Amimo J, Scholtz MM, Neser FWC, Martins M. 59.  2013. The effect of geographical region of birth on the reproductive performance of the Nguni in southern Mozambique. S. Afr. J. Anim. Sci. 43:S60–S63 [Google Scholar]
  60. Burrow HM. 60.  2001. Variances and covariances between productive and adaptive traits and temperament in a composite breed of tropical beef cattle. Livest. Prod. Sci. 70:213–33 [Google Scholar]
  61. Burrow HM, Prayaga KC. 61.  2004. Correlated responses in productive and adaptive traits and temperament following selection for growth and heat resistance in tropical beef cattle. Livest. Prod. Sci. 86:143–61 [Google Scholar]
  62. Barwick SA, Johnston DJ, Burrow HM, Holroyd RG, Fordyce G. 62.  et al. 2009. Genetics of heifer performance in “wet” and “dry” seasons and their relationships with steer performance in two tropical beef genotypes. Anim. Prod. Sci. 49:367–82 [Google Scholar]
  63. Barwick SA, Wolcott ML, Johnston DJ, Burrow HM, Sullivan MT. 63.  2009. Genetics of steer daily and residual feed intake in two tropical beef genotypes, and relationships among intake, body composition, growth and other post-weaning measures. Anim. Prod. Sci. 49:351–66 [Google Scholar]
  64. Burrow HM. 64.  2012. Importance of adaptation and genotype × environment interactions in tropical beef breeding systems. Animal 6:729–40 [Google Scholar]
  65. Maia ASC, da Silva RG, Bertipaglia ECA. 65.  2003. Haircoat traits in Holstein cows in tropical environments: a genetic and adaptive study. Braz. J. Anim. Sci. 32:843–53 [Google Scholar]
  66. Ojango JMK, Ducrocq V, Pollott GE. 66.  2005. Survival analysis of factors affecting culling early in the productive life of Holstein-Friesian cattle in Kenya. Livest. Prod. Sci. 92:317–22 [Google Scholar]
  67. Koonawootrittriron S, Elzo MA, Thongprapi T. 67.  2009. Genetic trends in a Holstein × other breeds multibreed dairy population in central Thailand. Livest. Sci. 122:186–92 [Google Scholar]
  68. Flori L, Gonzatti MI, Thevenon S, Chantal I, Pinto J. 68.  et al. 2012. A quasi-exclusive European ancestry in the Senepol tropical cattle breed highlights the importance of the slick locus in tropical adaptation. PLOS ONE 7:e36133 [Google Scholar]
  69. Newman S, Reverter A, Johnston DJ. 69.  2002. Purebred-crossbred performance and genetic evaluation of postweaning growth and carcass traits in Bos indicus × Bos taurus crosses in Australia. J. Anim. Sci. 80:1801–8 [Google Scholar]
  70. Schatz TJ, Ridley PER, La Fontaine DJM, Hearnden MN. 70.  2007. Effects of genotype, sex and stocking rate on postweaning efficiency and value-adding potential at turnoff of weaners grazing improved pasture in the Douglas Daly region of the Northern Territory. Aust. J. Exp. Agric. 47:1272–76 [Google Scholar]
  71. Boerner V, Johnston DJ, Tier B. 71.  2014. Accuracies of genomically estimated breeding values from pure-breed and across-breed predictions in Australian beef cattle. Genet. Sel. Evol. 46:61 [Google Scholar]
  72. Bonsma JC. 72.  1949. Breeding cattle for increased adaptability to tropical and subtropical environments. J. Agric. Sci. 39:204–21 [Google Scholar]
  73. Johnston DJ, Reverter A, Burrow HM, Oddy VH, Robinson DL. 73.  2003. Genetic and phenotypic characterisation of animal, carcass, and meat quality traits from temperate and tropically adapted beef breeds. 1. Animal measures. Aust. J. Agric. Res. 54:107–18 [Google Scholar]
  74. Prayaga KC, Corbet NJ, Johnston DJ, Wolcott ML, Fordyce G, Burrow HM. 74.  2009. Genetics of adaptive traits in heifers and their relationship to growth, pubertal and carcass traits in two tropical beef cattle genotypes. Anim. Prod. Sci. 49:413–25 [Google Scholar]
  75. Turner H, Schleger A. 75.  1960. The significance of coat type in cattle. Aust. J. Agric. Res. 11:645–63 [Google Scholar]
  76. Jian W, Duangjinda M, Vajrabukka C, Katawatin S. 76.  2014. Differences of skin morphology in Bos indicus, Bos taurus, and their crossbreds. Int. J. Biometeorol. 58:1087–94 [Google Scholar]
  77. Behl R, Behl J, Joshi BK. 77.  2010. Heat tolerance mechanisms in cattle status in zebu cattle: a review. Indian J. Anim. Sci. 80:891–97 [Google Scholar]
  78. Blackshaw J, Blackshaw A. 78.  1994. Heat stress in cattle and the effect of shade on production and behaviour: a review. Aust. J. Exp. Agric. 34:285–95 [Google Scholar]
  79. Landaeta-Hernández A, Zambrano-Nava S, Hernández-Fonseca JP, Godoy R, Calles M. 79.  et al. 2011. Variability of hair coat and skin traits as related to adaptation in Criollo Limonero cattle. Trop. Anim. Health Prod. 43:657–63 [Google Scholar]
  80. München Alfonzo EP, Barbosa da Silva MVG, dos Santos Daltro D, Stumpf MT, Dalcin VC. 80.  et al. 2016. Relationship between physical attributes and heat stress in dairy cattle from different genetic groups. Int. J. Biometeorol. 60:245–53 [Google Scholar]
  81. Klungland H, Vage DI, Gomez-Raya L, Adalsteinsson S, Lien S. 81.  1995. The role of melanocyte-stimulating hormone (MSH) receptor in bovine coat color determination. Mamm. Genome 6:636–39 [Google Scholar]
  82. Seitz JJ, Schmutz SM, Thue TD, Buchanan FC. 82.  1999. A missense mutation in the bovine MGF gene is associated with the roan phenotype in Belgian Blue and Shorthorn cattle. Mamm. Genome 10:710–12 [Google Scholar]
  83. Dorshorst B, Henegar C, Liao XP, Almen MS, Rubin CJ. 83.  et al. 2015. Dominant red coat color in Holstein cattle is associated with a missense mutation in the coatomer protein complex, subunit alpha (COPA) gene. PLOS ONE 10:e0128969 [Google Scholar]
  84. Li WB, Sartelet A, Tamma N, Coppieters W, Georges M, Charlier C. 84.  2016. Reverse genetic screen for loss-of-function mutations uncovers a frameshifting deletion in the melanophilin gene accountable for a distinctive coat color in Belgian Blue cattle. Anim. Genet. 47:110–13 [Google Scholar]
  85. Durkin K, Coppieters W, Drögemüller C, Ahariz N, Cambisano N. 85.  et al. 2012. Serial translocation by means of circular intermediates underlies colour sidedness in cattle. Nature 482:81–U103 [Google Scholar]
  86. Oulmouden A, Julien R, Laforet M, Leveziel H. 86.  2005. Use of silver gene for the authentication of the racial origin of animal populations, and of the derivative products thereof WO Patent Appl. No. PCT/FR2004/001,952 [Google Scholar]
  87. Schmutz SM, Dreger DL. 87.  2013. Interaction of MC1R and PMEL alleles on solid coat colors in Highland cattle. Anim. Genet. 44:9–13 [Google Scholar]
  88. Berryere TG, Schmutz SM, Schimpf RJ, Cowan CM, Potter J. 88.  2003. TYRP1 is associated with dun coat colour in Dexter cattle or how now brown cow?. Anim. Genet. 34:169–75 [Google Scholar]
  89. Fontanesi L, Tazzoli M, Russo V, Beever J. 89.  2010. Genetic heterogeneity at the bovine KIT gene in cattle breeds carrying different putative alleles at the spotting locus. Anim. Genet. 41:295–303 [Google Scholar]
  90. Fontanesi L, Scotti E, Russo V. 90.  2012. Haplotype variability in the bovine MITF gene and association with piebaldism in Holstein and Simmental cattle breeds. Anim. Genet. 43:250–56 [Google Scholar]
  91. Littlejohn MD, Henty KM, Tiplady K, Johnson T, Harland C. 91.  et al. 2014. Functionally reciprocal mutations of the prolactin signalling pathway define hairy and slick cattle. Nat. Commun. 5:5861 [Google Scholar]
  92. Karim L, Takeda H, Lin L, Druet T, Arias JAC. 92.  et al. 2011. Variants modulating the expression of a chromosome domain encompassing PLAG1 influence bovine stature. Nat. Genet. 43:405–13 [Google Scholar]
  93. Schmutz SM, Berryere TG, Ciobanu DC, Mileham AJ, Schmidtz BH, Fredholm M. 93.  2004. A form of albinism in cattle is caused by a tyrosinase frameshift mutation. Mamm. Genome 15:62–67 [Google Scholar]
  94. Brenig B, Beck J, Floren C, Bornemann-Kolatzki K, Wiedemann I. 94.  et al. 2013. Molecular genetics of coat colour variations in White Galloway and White Park cattle. Anim. Genet. 44:450–53 [Google Scholar]
  95. Hawken RJ, Zhang YD, Fortes MRS, Collis E, Barris WC. 95.  et al. 2012. Genome-wide association studies of female reproduction in tropically adapted beef cattle. J. Anim. Sci. 90:1398–410 [Google Scholar]
  96. Schleger A, Turner H. 96.  1960. Analysis of coat characters of cattle. Aust. J. Agric. Res. 11:875–85 [Google Scholar]
  97. Olson TA, Lucena C, Chase CC, Hammond AC. 97.  2003. Evidence of a major gene influencing hair length and heat tolerance in Bos taurus cattle. J. Anim. Sci. 81:80–90 [Google Scholar]
  98. Mariasegaram M, Chase CC, Chaparro JX, Olson TA, Brenneman RA, Niedz RP. 98.  2007. The slick hair coat locus maps to chromosome 20 in Senepol-derived cattle. Anim. Genet. 38:54–59 [Google Scholar]
  99. Carlson DF, Lancto CA, Zang B, Kim E-S, Walton M. 99.  et al. 2016. Production of hornless dairy cattle from genome-edited cell lines. Nat. Biotechnol. 34:479–81 [Google Scholar]
  100. Whitworth KM, Rowland RRR, Ewen CL, Trible BR, Kerrigan MA. 100.  et al. 2016. Gene-edited pigs are protected from porcine reproductive and respiratory syndrome virus. Nat. Biotechnol. 34:20–22 [Google Scholar]
  101. Carroll D, Van Eenennaam AL, Taylor JF, Seger J, Voytas DF. 101.  2016. Regulate genome-edited products, not genome editing itself. Nat. Biotechnol. 34:477–79 [Google Scholar]
  102. Grisart B, Coppieters W, Farnir F, Karim L, Ford C. 102.  et al. 2002. Positional candidate cloning of a QTL in dairy cattle: identification of a missense mutation in the bovine DGAT1 gene with major effect on milk yield and composition. Genome Res 12:222–31 [Google Scholar]
  103. Barendse W, Harrison B, Hawken R, Ferguson D, Thompson J. 103.  et al. 2007. Epistasis between calpain 1 and its inhibitor calpastatin within breeds of cattle. Genetics 176:2601–10 [Google Scholar]
  104. Hayes BJ, Bowman PJ, Chamberlain AJ, Goddard ME. 104.  2009. Invited review: genomic selection in dairy cattle: progress and challenges. J. Dairy Sci. 92:433–43 [Google Scholar]
  105. VanRaden PM, Van Tassell CP, Wiggans GR, Sonstegard TS, Schnabel RD. 105.  et al. 2009. Invited review: reliability of genomic predictions for North American Holstein bulls. J. Dairy Sci. 92:16–24 [Google Scholar]
  106. Winkelman AM, Johnson DL, Harris BL. 106.  2015. Application of genomic evaluation to dairy cattle in New Zealand. J. Dairy Sci. 98:659–75 [Google Scholar]
  107. Ma P, Lund MS, Nielsen US, Aamand GP, Su G. 107.  2015. Single-step genomic model improved reliability and reduced the bias of genomic predictions in Danish Jersey. J. Dairy Sci. 98:9026–34 [Google Scholar]
  108. Kemper KE, Reich CM, Bowman PJ, vander Jagt CJ, Chamberlain AJ. 108.  et al. 2015. Improved precision of QTL mapping using a nonlinear Bayesian method in a multi-breed population leads to greater accuracy of across-breed genomic predictions. Genet. Sel. Evol. 47:29 [Google Scholar]
  109. Chen L, Schenkel F, Vinsky M, Crews DH, Li C. 109.  2013. Accuracy of predicting genomic breeding values for residual feed intake in Angus and Charolais beef cattle. J. Anim. Sci. 91:4669–78 [Google Scholar]
  110. Boerner V, Johnston D, Wu XL, Bauck S. 110.  2015. Accuracy of Igenity genomically estimated breeding values for predicting Australian Angus BREEDPLAN traits. J. Anim. Sci. 93:513–21 [Google Scholar]
  111. MacLeod IM, Bowman PJ, vander Jagt CJ, Haile-Mariam M, Kemper KE. 111.  et al. 2016. Exploiting biological priors and sequence variants enhances QTL discovery and genomic prediction of complex traits. BMC Genom 17:144 [Google Scholar]
  112. Bortolussi G, McIvor JG, Hodgkinson JJ, Coffey SG, Holmes CR. 112.  2005. The northern Australian beef industry, a snapshot. 2. Breeding herd performance and management. Aust. J. Exp. Agric. 45:1075–91 [Google Scholar]
  113. Mariasegaram M, Harrison BE, Bolton JA, Tier B, Henshall JM. 113.  et al. 2012. Fine-mapping the POLL locus in Brahman cattle yields the diagnostic marker CSAFG29. Anim. Genet. 43:683–88 [Google Scholar]
  114. Horton BJ, Banks RG, van der Werf JHJ. 114.  2015. Industry benefits from using genomic information in two- and three-tier sheep breeding systems. Anim. Prod. Sci. 55:437–46 [Google Scholar]
  115. Ruddiman WF. 115.  2003. The anthropogenic greenhouse era began thousands of years ago. Clim. Change 61:261–93 [Google Scholar]
  116. Hill J, McSweeney C, Wright ADG, Bishop-Hurley G, Kalantar-zadeh K. 116.  2016. Measuring methane production from ruminants. Trends Biotechnol 34:26–35 [Google Scholar]
  117. Arndt DS, Blunden J, Willett KW. 117.  2015. State of the climate in 2014. Bull. Am. Meteorol. Soc. 96:S1–S267 [Google Scholar]
  118. Fowler D, Pilegaard K, Sutton MA, Ambus P, Raivonen M. 118.  et al. 2009. Atmospheric composition change: ecosystems-atmosphere interactions. Atmos. Environ. 43:5193–267 [Google Scholar]
  119. Lelieveld J, Crutzen PJ, Dentener FJ. 119.  1998. Changing concentration, lifetime and climate forcing of atmospheric methane. Tellus Ser. B Chem. Phys. Meteorol. 50:128–50 [Google Scholar]
  120. Robertson GP, Paul EA, Harwood RR. 120.  2000. Greenhouse gases in intensive agriculture: contributions of individual gases to the radiative forcing of the atmosphere. Science 289:1922–25 [Google Scholar]
  121. Charmley E, Williams SRO, Moate PJ, Hegarty RS, Herd RM. 121.  et al. 2016. A universal equation to predict methane production of forage-fed cattle in Australia. Anim. Prod. Sci. 56:169–80 [Google Scholar]
  122. Hackmann TJ, Spain JN. 122.  2010. Invited review: ruminant ecology and evolution: perspectives useful to ruminant livestock research and production. J. Dairy Sci. 93:1320–34 [Google Scholar]
  123. Halbert ND, Gogan PJP, Hiebert R, Derr JN. 123.  2007. Where the buffalo roam: the role of history and genetics in the conservation of bison on US federal lands. Park Sci 24:22–29 [Google Scholar]
  124. Kolipinski M, Borish S, Scott A, Kozlowski K, Ghosh S. 124.  2014. Bison: yesterday, today, and tomorrow. Nat. Areas J. 34:365–75 [Google Scholar]
  125. Robinson DL, Goopy JP, Hegarty RS, Oddy VH, Thompson AN. 125.  et al. 2014. Genetic and environmental variation in methane emissions of sheep at pasture. J. Anim. Sci. 92:4349–63 [Google Scholar]
  126. de Haas Y, Windig JJ, Calus MPL, Dijkstra J, de Haan M. 126.  et al. 2011. Genetic parameters for predicted methane production and potential for reducing enteric emissions through genomic selection. J. Dairy Sci. 94:6122–34 [Google Scholar]
  127. Goopy JP, Robinson DL, Woodgate RT, Donaldson AJ, Oddy VH. 127.  et al. 2016. Estimates of repeatability and heritability of methane production in sheep using portable accumulation chambers. Anim. Prod. Sci. 56:116–22 [Google Scholar]
  128. Lassen J, Lovendahl P. 128.  2016. Heritability estimates for enteric methane emissions from Holstein cattle measured using noninvasive methods. J. Dairy Sci. 99:1959–67 [Google Scholar]
  129. Lassen J, Poulsen NA, Larsen MK, Buitenhuis AJ. 129.  2016. Genetic and genomic relationship between methane production measured in breath and fatty acid content in milk samples from Danish Holsteins. Anim. Prod. Sci. 56:298–303 [Google Scholar]
  130. Pinares-Patino CS, Hickey SM, Young EA, Dodds KG, MacLean S. 130.  et al. 2013. Heritability estimates of methane emissions from sheep. Animal 7:316–21 [Google Scholar]
  131. Robinson DL, Goopy JP, Donaldson AJ, Woodgate RT, Oddy VH, Hegarty RS. 131.  2014. Sire and liveweight affect feed intake and methane emissions of sheep confined in respiration chambers. Animal 8:1935–44 [Google Scholar]
  132. Waghorn GC, Hegarty RS. 132.  2011. Lowering ruminant methane emissions through improved feed conversion efficiency. Anim. Feed Sci. Technol. 166–67:291–301 [Google Scholar]
  133. Bishop-Hurley GJ, Paull D, Valencia P, Overs L, Kalantar-zadeh K. 133.  et al. 2016. Intra-ruminal gas-sensing in real time: a proof-of-concept. Anim. Prod. Sci. 56:204–12 [Google Scholar]
  134. Beauchemin KA, Kreuzer M, O'Mara F, McAllister TA. 134.  2008. Nutritional management for enteric methane abatement: a review. Aust. J. Exp. Agric. 48:21–27 [Google Scholar]
  135. Doreau M, van der Werf HMG, Micol D, Dubroeucq H, Agabriel J. 135.  et al. 2011. Enteric methane production and greenhouse gases balance of diets differing in concentrate in the fattening phase of a beef production system. J. Anim. Sci. 89:2518–28 [Google Scholar]
  136. Kurihara M, Magner T, Hunter RA, McCrabb GJ. 136.  1999. Methane production and energy partition of cattle in the tropics. Br. J. Nutr. 81:227–34 [Google Scholar]
  137. McCrabb GJ, Hunter RA. 137.  1999. Prediction of methane emissions from beef cattle in tropical production systems. Aust. J. Agric. Res. 50:1335–39 [Google Scholar]
  138. Archimede H, Eugene M, Magdeleine CM, Boval M, Martin C. 138.  et al. 2011. Comparison of methane production between C3 and C4 grasses and legumes. Anim. Feed Sci. Technol. 166–67:59–64 [Google Scholar]
  139. Ash A, Hunt L, McDonald C, Scanlan J, Bell L. 139.  et al. 2015. Boosting the productivity and profitability of northern Australian beef enterprises: exploring innovation options using simulation modelling and systems analysis. Agric. Syst. 139:50–65 [Google Scholar]
  140. Johnson CR, Reiling BA, Mislevy P, Hall MB. 140.  2001. Effects of nitrogen fertilization and harvest date on yield, digestibility, fiber, and protein fractions of tropical grasses. J. Anim. Sci. 79:2439–48 [Google Scholar]
  141. Hill JO, Coates DB, Whitbread AM, Clem RL, Robertson MJ, Pengelly BC. 141.  2009. Seasonal changes in pasture quality and diet selection and their relationship with liveweight gain of steers grazing tropical grass and grass-legume pastures in northern Australia. Anim. Prod. Sci. 49:983–93 [Google Scholar]
  142. Panjaitan T, Quigley SP, McLennan SR, Swain AJ, Poppi DP. 142.  2014. Digestion of forages in the rumen is increased by the amount but not the type of protein supplement. Anim. Prod. Sci. 54:1363–67 [Google Scholar]
  143. Poppi DP, McLennan SR. 143.  1995. Protein and energy utilization by ruminants at pasture. J. Anim. Sci. 73:278–90 [Google Scholar]
  144. Ribeiro GO, Teixeira AM, Velasco FO, Faria WG, Pereira LGR. 144.  et al. 2014. Production, nutritional quality and in vitro methane production from Andropogon gayanus grass harvested at different maturities and preserved as hay or silage. Asian-Aust. J. Anim. Sci. 27:330–41 [Google Scholar]
  145. Sampaio CB, Detmann E, Paulino MF, Valadares SC, de Souza MA. 145.  et al. 2010. Intake and digestibility in cattle fed low-quality tropical forage and supplemented with nitrogenous compounds. Trop. Anim. Health Prod. 42:1471–79 [Google Scholar]
  146. Herrero M, Havlik P, Valin H, Notenbaert A, Rufino MC. 146.  et al. 2013. Biomass use, production, feed efficiencies, and greenhouse gas emissions from global livestock systems. PNAS 110:20888–93 [Google Scholar]
  147. Chilliard Y, Martin C, Rouel J, Doreau M. 147.  2009. Milk fatty acids in dairy cows fed whole crude linseed, extruded linseed, or linseed oil, and their relationship with methane output. J. Dairy Sci. 92:5199–11 [Google Scholar]
  148. Dijkstra J, van Zijderveld SM, Apajalahti JA, Bannink A, Gerrits WJJ. 148.  et al. 2011. Relationships between methane production and milk fatty acid profiles in dairy cattle. Anim. Feed Sci. Technol.166–67590–95 [Google Scholar]
  149. Mohammed R, McGinn SM, Beauchemin KA. 149.  2011. Prediction of enteric methane output from milk fatty acid concentrations and rumen fermentation parameters in dairy cows fed sunflower, flax, or canola seeds. J. Dairy Sci. 94:6057–68 [Google Scholar]
  150. van Engelen S, Bovenhuis H, Dijkstra J, van Arendonk J, Visker M. 150.  2015. Short communication: genetic study of methane production predicted from milk fat composition in dairy cows. J. Dairy Sci. 98:8223–26 [Google Scholar]
  151. Griinari JM, Baumann DE. 151.  2000. Biosynthesis of conjugated linoleic acid and its incorporation into meat and milk in ruminants. Advances in Conjugated Linoleic Acid Research 1 MP Yurawecz, MM Mossoba, JKG Kramer, MW Pariza, GJ Nelson 180–200 Champaign, IL: Am. Oil Chem. Soc. [Google Scholar]
  152. Bousquet P, Ringeval B, Pison I, Dlugokencky EJ, Brunke EG. 152.  et al. 2011. Source attribution of the changes in atmospheric methane for 2006–2008. Atmos. Chem. Phys. 11:3689–700 [Google Scholar]
  153. Yan XY, Ohara T, Akimoto H. 153.  2003. Development of region-specific emission factors and estimation of methane emission from rice fields in the East, Southeast and South Asian countries. Glob. Change Biol. 9:237–54 [Google Scholar]
/content/journals/10.1146/annurev-animal-022516-022921
Loading
/content/journals/10.1146/annurev-animal-022516-022921
Loading

Data & Media loading...

  • Article Type: Review Article
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error