Breeding for a Greener Future: Selective Breeding and Crossbreeding Approaches to Minimize Methane Emissions in Ruminant Livestock

Methane emissions from ruminant livestock systems are a major contributor to agricultural greenhouse gases, intensifying global climate change. To mitigate these emissions, breeding strategies that reduce enteric methane output without compromising productivity must be developed. This review synthesizes the current research on the role of selective breeding and strategic crossbreeding in lowering methane emissions through enhanced feed conversion efficiency, rumen function, and incorporation of low-emission genotypes. The success of such breeding programs hinges on the precision of methane phenotyping techniques, including both direct (respiration chambers and tracer gas methods) and indirect (infrared sensors and milk or fecal biomarkers) measures, alongside the application of advanced quantitative genetic models, such as random regression and reaction norm models. The integration of genomic selection, high-throughput phenomics, and environmental covariates enables the identification of heritable variations in methane traits and facilitates genotype-by-environment interaction (GxE) modeling. Effective mitigation through genetic improvement requires a holistic understanding of the genetic architecture of methane production and its interactions with dietary, microbial, and management factors. Ultimately, incorporating both additive genetic effects and non-genetic influences into selection decisions can significantly accelerate progress toward low-emission ruminant populations with low methane emissions.

Selective Breeding, Crossbreeding, Methane, GxE Interactions, Epigenetics, Genomics, Phenomics, Ruminants

1. Amer, P. R. (2006). Approaches to formulating breeding objectives. In Proceedings of the 8th World Congress on Genetics Applied to Livestock Production, Belo Horizonte, Minas Gerais, Brazil, 13-18 August, 2006 (pp. 31-01). Instituto Prociência. [Google Scholar] [Crossref]

2. Ayalew, W., Wu, X-Y., Tarekegn, G.M., Chu, M., Liang, C-N., Tessema, T.S., Yan, P. (2023). Signatures of positive selection for local adaptation of African native cattle populations: A review. J. Integr. Agric. 22(7), 1967–1984. [Google Scholar] [Crossref]

3. Bačėninaitė, D., Džermeikaitė, K., and Antanaitis, R. (2022). Global Warming and Dairy Cattle: How to Control and Reduce Methane Emission. Animals (Basel). 26, 12(19), 2687. doi: 10.3390/ani12192687. [Google Scholar] [Crossref]

4. Baker, R.L., and Gray, G.D. (2004). Appropriate breeds and breeding schemes for sheep and goats in the tropics. In: Sani, R.A., Gray, G.D., Baker, R.L. (Eds.), Worm Control for Small Ruminants in Tropical Asia, Canberra, ACIAR Monograph No. 113, pp. 63–96. [Google Scholar] [Crossref]

5. Bayer, W., and Feldmann, A. (2003). Diversity of animals adapted to smallholder system. Conservation and Sustainable Use of Agricultural Biodiversity. http://www.eseap.cipotato.org/UPWARD/Agrobio-sourcebook.htm. [Google Scholar] [Crossref]

6. Beauchemin, K.A., Janzen, H.H., Little, S.M., McAllister, T.A., and McGinn, S.M. (2011). Mitigation of greenhouse gas emissions from beef production in western Canada; evaluation using farm-based life cycle assessment. Anim. Feed Sci. Techn. 166, 663–677. [Google Scholar] [Crossref]

7. Bello, A., et al. (2022). Genetic improvement through CBBPs in Nigeria: Emerging models. Journal of Animal Breeding and Genetics, 139(4), 376–388. [Google Scholar] [Crossref]

8. Berry, D. P., & Crowley, J. J. (2019). Cell biology and genetics of feed efficiency and its relation with methane emissions. Animal, 13(s1), s482–s494. https://doi.org/10.1017/S1751731119001947 [Google Scholar] [Crossref]

9. Bett, R. C., et al. (2020). Participatory breeding approaches for dairy goats in Kenya. Tropical Animal Health and Production, 52(3), 867–876. https://doi.org/10.1007/s11250-019-02124-x [Google Scholar] [Crossref]

10. Bilton, T.P., Hickey, S.M., Janssen,.PH., Jonker, A., Hess, M.K., Bryson, B., W., Bain, W, E. et al (2022). Impact of breeding for divergent methane yield on milk composition in breeding ewes. Proc. Assoc. Advmt. Anim. Breed. Genet: 24, 42-45. [Google Scholar] [Crossref]

11. Bird-Gardiner, T., Donoghue, K.A., Arthur, P.F., Herd, R.M., Hegarty, R.F. (2015). Divergent selection for methane yield in beef cattle. In: Proceedings of the 21st Association for the Advancement of Animal Breeding and Genetics, 28-30 September 2015, Lorne, Australia, pp. 122–125. [Google Scholar] [Crossref]

12. Bowen, J.M., Cormican, P., Lister, S.J., McCabe, M.S., Duthie, C.-A., Roehe, R., Dewhurst, R.J. (2020). Link between the rumen microbiota, methane emissions and feed efficiency of finishing steers offered dietary lipid and nitrate supplementation. PLoS ONE. 15, e0231759. doi: 10.1371/journal.pone.0231759. [Google Scholar] [Crossref]

13. Breider, I.S., Wall, E., Garnsworthy, P.C. (2019). Heritability of methane production and genetic correlations with milk yield and body weight in Holstein-Friesian dairy cows. J. Dairy Sci. 102, 7277–7281. [Google Scholar] [Crossref]

14. Britannica, T. Editors of Encyclopaedia (2024), June 14. phenotype. Encyclopedia Britannica. https://www.britannica.com/science/phenotype. [Google Scholar] [Crossref]

15. Brito, L.F., Schenkel, F.S., Oliveira, H.R., Cánovas, A., Miglior, F. (2018). Meta-analysis of heritability estimates for methane emission indicator traits in cattle and sheep. In: Proceedings of the 11th World Congress on Genetics Applied to Livestock Production, Volume Challenges–Environmental, 11–16 February 2018, Auckland, New Zealand, p. 740. [Google Scholar] [Crossref]

16. Broucek, J. (2014). Production of Methane Emissions from Ruminant Husbandry: A Review. J. Environ. Protect. 5, 1482-1493. http://dx.doi.org/10.4236/jep.2014.515141. [Google Scholar] [Crossref]

17. Chagas J.C., Ramin M., Exposito R.G., Smidt H., Krizsan S.J. (2021). Effect of a Low-Methane Diet on Performance and Microbiome in Lactating Dairy Cows Accounting for Individual Pre-Trial Methane Emissions. Anim. 11,2597. doi: 10.3390/ani11092597. [Google Scholar] [Crossref]

18. Chagunda, M.G.G. (2013). Opportunities and challenges in the use of the Laser Methane Detector to monitor enteric methane emissions from ruminants. Anim. 7, 394–400. [Google Scholar] [Crossref]

19. CIEL (2024). Project: Breed for CH4nge – breeding low methane sheep. The front door to innovation for the livestock sector. Leprino X UK Agri-Tech Centre. [Google Scholar] [Crossref]

20. Colditz, I.G., and Brad C. H. (2016) Resilience in farm animals: biology, management, breeding and implications for animal welfare. Anim. Prod. Sci. 56, 1961-1983. [Google Scholar] [Crossref]

21. Conington, J., Keady, R., & Dwyer, C. (2021). Integrating low methane traits into sheep breeding programs. Animal Frontiers, 11(2), 47–55. https://doi.org/10.1093/af/vfab015 [Google Scholar] [Crossref]

22. Coolen, M. W., Statham, A. L., Qu, W., Campbell, M. J., Henders, A. K., Montgomery, G. W., Martin, N. G., and Clark, S. J. (2011). Impact of the genome on the epigenome is manifested in dna methylation patterns of imprinted regions in monozygotic and dizygotic twins. PLoS ONE 6, e25590. doi: 10.1371/journal. pone.0025590. [Google Scholar] [Crossref]

23. Crouch, J., Shvedova, M., Thanapaul, R.J.R.S., Botchkarev, V., Roh, D. (2022). Epigenetic Regulation of Cellular Senescence. Cells. 15, 11(4), 672. doi: 10.3390/cells11040672. [Google Scholar] [Crossref]

24. Cusack, D.F., Kazanski, C.E., Hedgpeth, A., Chow, K., Cordeiro, A.L., Karpman, J., Ryals, R. (2021) Reducing climate impacts of beef production: A synthesis of life cycle assessments across management systems and global regions. Glob. Chang. Biol. 27(9), 1721-1736. doi: 10.1111/gcb.15509. [Google Scholar] [Crossref]

25. Danchin, É., Charmantier, A., Champagne, F.A., Mesoudi, A., Pujol, B., and S. Blanchet, S. (2011). Beyond DNA: Integrating inclusive inheritance into an extended theory of evolution. Nature Review. Genet. 12, 475–486. [Google Scholar] [Crossref]

26. Danielsson, R, Dicksved, J, Sun, L, Gonda, H, Müller, B, Schnürer, A, Bertilsson, J. (2017). Methane Production in Dairy Cows Correlates with Rumen Methanogenic and Bacterial Community Structure. Front. Microbiol. 17, 8, 226. doi: 10.3389/fmicb.2017.00226. [Google Scholar] [Crossref]

27. de Haas, Y., Pszczola, M., Soyeurt, H., Wall, E., Lassen, J. (2017) Invited review: Phenotypes to genetically reduce greenhouse gas emissions in dairying. J. Dairy Sci. 100, 855–870. [Google Scholar] [Crossref]

28. de Haas, Y., Windig, J.J., Calus, M.P.L., Dijkstra, J., de Haan, M., Bannink, A., Veerkamp, R.F. (2011). Genetic parameters for predicted methane production and potential for reducing enteric emissions through genomic selection. J. Dairy Sci. 2011, 94, 6122–6134. [Google Scholar] [Crossref]

29. de Vienne, D. (2022). What is a phenotype? History and new developments of the concept. Genetica 150, 153–158. https://doi.org/10.1007/s10709-021-00134-6. [Google Scholar] [Crossref]

30. Difford, G. F., Plichta, D. R., Løvendahl, P., Noel, S. J., Højberg, O., & Lassen, J. (2018). Host genetics and the rumen microbiome jointly associate with methane emissions in dairy cows. Frontiers in Genetics, 9, 694. https://doi.org/10.3389/fgene.2018.00694 [Google Scholar] [Crossref]

31. Donoghue, K. A., Jonker, A., Hickey, S. M., & Parnell, P. F. (2016). Genetic parameters for methane emissions in sheep. Animal Production Science, 56(3), 572–580. https://doi.org/10.1071/AN15211 [Google Scholar] [Crossref]

32. Donoghue, KA, Bird-Gardiner, T., herd, RM., Hegarty, RS., Arthur, PF., (2020) Genetic variance and covariance components for carbon dioxide production and postweaning traits in Angus cattle. journal of Animal Science, 98(9): 1525-3163.10.1093/jas/skaa253. [Google Scholar] [Crossref]

33. Dunislawska, A., Slawinska, A., Siwek, M., and Bednarczyk. M. (2021). Epigenetic changes in poultry due to reprogramming of the gut microbiota. Anim. Front. 11(6):74–82. doi: 10.1093/af/vfab063. [Google Scholar] [Crossref]

34. Endris, M., Kebede, K., and Abebe, A. (2022). Challenges of community based small ruminant breeding program: A review. Glob. J. Animal Sci. Res. 10, 114–127. Retrieved from: http://www.gjasr.com/index.php/GJASR/article/view/142. [Google Scholar] [Crossref]

35. Endris, M., Tumwasorn, S., Sopannarath, P., and Prasanpanich, S. (2013). Genotype by Region Interaction on Milk Production Traits of Holstein Crossbred Dairy Cows in Thailand. Kasetsart J. (Nat. Sci.) 47: 228 - 237 (2013). [Google Scholar] [Crossref]

36. Falk, R. 2009. Genetic analysis. A history of genetic thinking. Cambridge: Cambridge University Press. [Google Scholar] [Crossref]

37. Fennessy PF, Byrne TJ, Proctor LE, Amer PR. 2019 The potential impact of breeding strategies to reduce methane output from beef cattle. Anim. Prod. Sci. 59, 1598–1610. [Google Scholar] [Crossref]

38. Fresco, S., Boichard, D., Fritz S., Lefebvre R., Barbey S., Gaborit M., Martin P (2023) Comparison of methane production, intensity, and yield throughout lactation in Holstein cows. J. Dairy Sci. 106(Suppl. 1), DOI: 10.3168/jds.2022-22855. [Google Scholar] [Crossref]

39. Friedlingstein, P., Jones, M. W., O'Sullivan, M., Andrew, R. M., Hauck, J., Peters, G. P., Peters, W., Pongratz, J., Sitch, S., Le Quéré, C., Bakker, D. C. E., Canadell, J. G., Ciais, P., Jackson, R. B. , Anthoni, P. , Barbero, L. , Bastos, A. , Bastrikov, V. , Becker, M. , … Zaehle, S. (2019). Global carbon budget 2019. Earth Syst. Sci. Data, 11(4), 1783–1838. 10.5194/essd-11-1783-2019. [Google Scholar] [Crossref]

40. Fu, G., Yun, Y. (2022). Phenotyping and phenomics in aquaculture breeding. Aquaculture and Fisheries, 7, 140–146. https://doi.org/10.1016/j.aaf.2021.07.001. [Google Scholar] [Crossref]

41. Galton, F. (1876). A theory of heredity. Journal of the Anthropological Institute 5: 329–348. [Google Scholar] [Crossref]

42. Garnsworthy, PC, Difford, GF, Bell, MJ, Bayat, AR, Huhtanen, P, Kuhla, B, Lassen, J, Peiren, N, Pszczola, M, Sorg, D, Viske,r MHPW, Yan, T. (2019). Comparison of Methods to Measure Methane for Use in Genetic Evaluation of Dairy Cattle. Anim (Basel). 21, 9(10), 837. doi: 10.3390/ani9100837. [Google Scholar] [Crossref]

43. Garrod, A.E. (2022). Inborn Errors of Metabolism. Henry Frowde and Hodder & Stoughton: London, 1909. [Google Scholar] [Crossref]

44. Gaughan JB, Lees AM, Lees JC (2022) Adaptation of beef cattle to heat stress challenges. Climate Change and Livestock Production: recent advances and future perspectives. Springer Singapore, Singapore, pp 29–38. [Google Scholar] [Crossref]

45. Gaughan, J.B., Sejian, V., Mader, T.L., Dunshea, F.R. (2019). Adaptation strategies: ruminants, Animal Frontiers, Volume 9, Issue 1, January 2019, Pages 47–53, https://doi.org/10.1093/af/vfy029. [Google Scholar] [Crossref]

46. Genesis-Faraday Partnership, (2008). A study of the scope for the application of research in animal genomics and breeding to reduce nitrogen and methane emissions from livestock based food chains. Final Report of Project AC0204 to the Department for Environment, Food and Rural Affairs, April 2008. Retrieved January 30, 2009, from http://randd.defra.gov.uk/Document.aspx?Document=AC0204_7639_FRP.docGoogle Scholar. [Google Scholar] [Crossref]

47. Gerber, P. J., Hristov, A. N., Henderson, B., Makkar, H., Oh, J., Lee, C., et al. (2013). Technical options for the mitigation of direct methane and nitrous oxide emissions from livestock: A review. Anim. 7(s2), 220–234. https://doi.org/10.1017/s1751731113000876. [Google Scholar] [Crossref]

48. Getachew, T. (2018). Overview of Community Based Breeding Program and implementation procedure. ICARDA, at the SmaRT Ethiopia workshop and field day on Small Ruminant Community Based Breeding Program (CBBP), Hosaena, Ethiopia, 27–28 March. https://www.slideshare.net/ILRI/cbbp-overview-2018 94045437. [Google Scholar] [Crossref]

49. Gibney, E.R., and Nolan, C.M. (2010). Epigenetics and gene expression. Heredity. 105:4–13. [Google Scholar] [Crossref]

50. Gizaw, S., van Arendonk, J. A. M., Dessie, T., Mirkena, T., & Mwai, O. (2022). Community-based breeding programs in Africa: a decade of experience and lessons for the future. Animal Genetic Resources, 71, 61–73. https://doi.org/10.1017/S2078633622000062 [Google Scholar] [Crossref]

51. Gonçalves, H. C., Oliveira, P. S. N., & Lobo, R. B. (2022). Genetic and environmental strategies to reduce methane emission in tropical beef cattle. Tropical Animal Health and Production, 54, 209. https://doi.org/10.1007/s11250-022-03141-w [Google Scholar] [Crossref]

52. Gondwe, T. N., & Banda, T. (2018). Community-based goat breeding in Malawi: Lessons from implementation. African Journal of Agricultural Research, 13(12), 621–628. [Google Scholar] [Crossref]

53. González-Recio O, López-Paredes J, Ouatahar L, Charfeddine N, Ugarte E, Alenda R, Jiménez-Montero J.A. (2020). Mitigation of greenhouse gases in dairy cattle via genetic selection: 2. Incorporating methane emissions into the breeding goal. J. Dairy Sci. 103, 7210–7221. (doi:10.3168/jds.2019-17598). [Google Scholar] [Crossref]

54. Goopy, J. P., Robinson, D. L., Woodgate, R. S., Donaldson, A., Oddy, V. H., Vercoe, P. E., et al. (2015). Estimates of repeatability and heritability of methane production in sheep using portable accumulation chambers. Anim. Prod. Sci. 56, 116. doi:10.1071/AN13370. [Google Scholar] [Crossref]

55. Grainger, C., Clarke, T., McGinn, S.M., Auldist, M.J., Beauchemin, K.A., Hannah, M.C., Waghorn, G.C., Clark, H. and Eckard, R.J. (2007). Methane emissions from dairy cows measured using the sulfur hexafluoride (SF6) tracer and chamber techniques. J. Dairy Sci. 90, 2755–2766. [Google Scholar] [Crossref]

56. Guozhong, D., Min, Q., Changjin, A., Jun, Z., Khas-Erdene, Wang, X., Zhang, Z., and Yang, Y. (2014). Feeding a High-Concentrate Corn Straw Diet Induced Epigenetic Alterations in the Mammary Tissue of Dairy Cows, PLoS One, 9(9). [Google Scholar] [Crossref]

57. GWA (Government of Western Australia) 2023. Selective breeding of sheep for reduced methane emissions. Department of Primary Industries and Regional Development's Agriculture and Food. 30 May 2023. https://www.bing.com/ck/a?!&&p=31c5a7491d847dcfJmltdHM9MTcyMDM5NjgwMCZ pZ3VpZD0zZWM0ZTE4Yi03YWI2LTZkN2MtMmIwMS1lZjk1N2I0ZTZjZmImaW5zaWQ9NTAxNg&ptn=3&ver=2&hsh=3&fclid=3ec4e18b-7ab6-6d7c-2b01-ef957b4e6cfb&u=a1aHR0cHM6Ly93d3cuYWdyaWMud2EuZ292LmF1L2NsaW1hdGUtY2hhbmdlL3NlbGVjdGl2ZS1icmVlZGluZy1zaGVlcC1yZWR1Y2VkLW1ldGhhbmUtZW1pc3Npb25z&ntb=1. [Google Scholar] [Crossref]

58. Gwala, P. E., et al. (2019). Farmer-participatory goat breeding in Tanzania. Livestock Research for Rural Development, 31(5). [Google Scholar] [Crossref]

59. Habimana, R., et al. (2023). CBBP model for dairy productivity improvement in Rwanda’s mixed farming systems. East African Journal of Science and Technology, 15(2), 91–102. [Google Scholar] [Crossref]

60. Haile A, Getachew T, Rekik M, Abebe A, Abate Z, Jimma A, Mwacharo JM, Mueller J, Belay B, Solomon D, Hyera E, Nguluma AS, Gondwe T and Rischkowsky B (2023), Howtosucceedin implementing community-based breeding programs: Lessons from the f ield in Eastern and Southern Africa. Front. Genet. 14:1119024. doi: 10.3389/fgene.2023.1119024 [Google Scholar] [Crossref]

61. Haile, A., Getachew, T., Mirkena, T., Duguma, G., Gizaw, S., Wurzinger, M., et al. (2020). Community-based sheep breeding programs generated substantial genetic gains and socioeconomic benefits. Animal 14, 1362–1370. doi:10.1017/S1751731120000269. [Google Scholar] [Crossref]

62. Haile, A., Wurzinger, M., Mueller, J., Mirkena, T., Duguma, G., Mwai, O., ... & Sölkner, J. (2019). Guidelines for Setting up Community-based Sheep Breeding Programs in Ethiopia. ICARDA Manual. [Google Scholar] [Crossref]

63. Hammond, K.; Humphries, D.; Crompton, L.; Kirton, P.; Green, C.; Reynolds, C. (2013). Methane Emissions from Growing Dairy Heifers Estimated Using an Automated Head Chamber (GreenFeed) Compared to Respiration Chambers or SF6 Techniques. Adv. Anim. Biosci. 2013, 4, 391. [Google Scholar] [Crossref]

64. Hayes, BJ, Donoghue, KA, Reich, CM, Mason, BA, Bird-Gardiner, T, Herd, RM, Arthur, PF. (2016) Genomic heritabilities and genomic estimated breeding values for methane traits in Angus cattle. J. Anim. Sci. 94, 902–908. (doi:10.2527/jas.2015-0078) [Google Scholar] [Crossref]

65. Herrero, M., Havlik, P., Valin, H., Notenbaert, A., Rufino, M. C., Thornton, P. K., et al. (2013). Biomass use, production, feed efficiencies, and greenhouse gas emissions from global livestock systems. Proceedings of the National Academy of Sciences of the United States of America, 110(52), 20888–20893. https://doi.org/10.1073/pnas.1308149110 [Google Scholar] [Crossref]

66. Herrero, M.; Henderson, B.; Havlík, P.; Thornton, P.K.; Conant, R.T.; Smith, P.; Wirsenius, S.; Hristov, A.N.; Gerber, P.; Gill, M.; et al. (2016). Greenhouse Gas Mitigation Potentials in the Livestock Sector. Nat. Clim. Chang. 6, 452–461. [Google Scholar] [Crossref]

67. Hill, D.L., and Wall, E. (2017). Weather influences feed intake and feed efficiency in a temperate climate. J. Dairy Sci. 2017; 100:2240 2257. doi: 10.3168/jds.2016-11047. [Google Scholar] [Crossref]

68. Huhtanen, P.; Cabezas-Garcia, E.H.; Utsumi, S., and Zimmerman, S. (2015). Comparison of methods to determine methane emissions from dairy cows in farm conditions. J. Dairy Sci. 2015, 98, 3394–3409. [Google Scholar] [Crossref]

69. Ibeagha-Awemu, E.M., and Khatib, H. (2007). Epigenetics of Livestock Breeding, Handbook of Epigenetics.441-463. [Google Scholar] [Crossref]

70. Ibeagha-Awemu, E.M., and Yu. Y. (2021). Consequence of epigenetic processes on animal health and productivity: Is additional level of regulation of relevance? Anim. Front. 11(6):7–18. doi: 10.1093/af/vfab057. [Google Scholar] [Crossref]

71. IEA – International Energy Agency (2021). Methane and climate change, Methane Tracker 2021. [Google Scholar] [Crossref]

72. Jablonka, E., and Lamb, M. (1995). Epigenetic inheritance and evolution. The Lamarckian dimension. New York: Oxford University Press. [Google Scholar] [Crossref]

73. Jablonka, E., and Raz, G. (2009). Transgenerational epigenetic inheritance: prevalence, mechanisms, and implications for the study of heredity and evolution. Q. Rev. Biol. 84, 131–175. [Google Scholar] [Crossref]

74. Jackson, R.B., Saunois, M., Bousquet, P., Canadell, JG., Poulter, B., et al. (2020). Increasing anthropogenic methane emissions arise equally from agricultural and fossil fuel sources. Environ. Research Lett. 15(7), 071002. 10.1088/1748-9326/ab9ed2. [Google Scholar] [Crossref]

75. Jiang, L., Jobst, P., Lai, L., Samuel, M., Ayares, D., Prather, R. S. (2007). Expression levels of growth-regulating imprinted genes in cloned piglets. Clon. Stem Cel. 9:97–106. [Google Scholar] [Crossref]

76. Johnson, P.L., Hickey, S., Knowler, K., Wing, J., Bryson, B., Hall, M., Jonker, A., Janssen, P.H., Dodds, K.G., McEwan, J.C., and Rowe, S.J. (2022), Genetic parameters for residual feed intake, methane emissions, and body composition in New Zealand maternal sheep. Front. Genet. 13:911639. doi: 10.3389/fgene.2022.911639. [Google Scholar] [Crossref]

77. Jones, H.E., Warkup, C.C., Williams, A., Audsley, E. (2008). The effect of genetic improvement on emissions from livestock systems. Conference of the 59th Annual Meeting of the European Association of Animal Production, 24–27 August 2008, Vilnius, Lithuania, Session 05, 6, 28. [Google Scholar] [Crossref]

78. Jonker, A., Hickey, S. M., Janssen, P. H., Shackell, G., Elmes, S., Bain, W. E., et al. (2018). Genetic parameters of methane emissions determined using portable accumulation chambers in lambs and ewes grazing pasture and genetic correlations with emissions determined in respiration chambers. J. Anim. Sci. 96, 3031–3042. doi:10.1093/jas/sky187. [Google Scholar] [Crossref]

79. Jonker, A., Hickey, S. M., Rowe, S., & Greer, G. J. (2022). Genetic progress in reducing methane emissions in New Zealand sheep. Proceedings of the New Zealand Society of Animal Production, 82, 109–112. [Google Scholar] [Crossref]

80. Jonker, A., Hickey, S., Pinares-Patino, C., Mc Ewan, J., Olinga, S., Díaz, A., Molano, G., MacLean, S., Sandoval, E., Harland, R. (2017) Sheep from low-methane-yield selection lines created on alfalfa pellets also have lower methane yield under pastoral farming conditions. Journal of Animal Science, 95: 3905-3913. [Google Scholar] [Crossref]

81. Joy A, Dunshea FR, Leury BJ, Clarke IJ, Digiacomo K, Chauhan SS (2020) The resilience of small ruminants to climate change and increased environmental temperature: A review. In Animals 10(5):867. https://doi.org/10.3390/ani10050867 [Google Scholar] [Crossref]

82. Kandel, PB, Vanrobays, ML, Vanlierde, A, Dehareng, F, Froidmont, E, Gengler, N, Soyeurt, H (2017) Genetic parameters for predicted methane emission traits and their relationship with milk production traits in Holstein cows. Journal of Dairy Science, in press. doi:10.3168/jds.2016-11954 [Google Scholar] [Crossref]

83. Karrow, N., Sharma, B., Fisher, R., Mallard, B. (2011). Epigenetics and animal health, in Comprehensive Biotechnology,381– 394. [Google Scholar] [Crossref]

84. Keady, R. N., McGee, M., & Moloney, A. P. (2023). Methane emissions and mitigation strategies in UK sheep systems. Irish Journal of Agricultural and Food Research, 62(1), 76–88. [Google Scholar] [Crossref]

85. Kittelmann, S., Pinares-Patiño, C.S., Seedorf, H., Kirk, M.R., Ganesh, S., McEwan, J.C., Janssen, P.H. (2014). Two Different Bacterial Community Types Are Linked with the Low-Methane Emission Trait in Sheep. PLoS ONE. 2014;9: e103171. doi: 10.1371/journal.pone.0103171. [Google Scholar] [Crossref]

86. Knap, P.W., Wang, L. (2012). Pig breeding for improved feed efficiency. In: Patience, J.F. (eds) Feed efficiency in swine. Wageningen Academic Publishers, Wageningen. https://doi.org/10.3920/978-90-8686-756-1_8. [Google Scholar] [Crossref]

87. Króliczewska, B, Pecka-Kiełb, E, Bujok, J. (2023). Strategies used to reduce methane emissions from ruminants: controversies and issues. Agriculture. 2023;13(3):602. doi: 10.3390/agriculture13030602. [Google Scholar] [Crossref]

88. Kugonza, D. R., et al. (2017). Mubende goat improvement through community breeding in Uganda. Tropical Animal Health and Production, 49(3), 651–659. [Google Scholar] [Crossref]

89. Lamuno, D., Sölkner, J., Mészáros, G., Nakimbugwe, H., Mulindwa, H., Nandolo, W., et al. (2018). Evaluation framework of community-based livestock breeding programs. Livest. Res. Rural Dev. 30 (3), 12. [Google Scholar] [Crossref]

90. Lassen, J., & Løvendahl, P. (2016). Genetic correlations between methane emissions, feed intake, and milk production traits in dairy cattle. Journal of Dairy Science, 99(3), 2106–2114. https://doi.org/10.3168/jds.2015-10110 [Google Scholar] [Crossref]

91. Lerner, I.M. 1950. Population genetics and animal improvement. Cambridge: Cambridge University Press. [Google Scholar] [Crossref]

92. Li, F., Li, C., & Guan, L. L. (2022). Host genetics and rumen microbiota in ruminant methane emissions: Opportunities and challenges. Frontiers in Microbiology, 13, 826088. https://doi.org/10.3389/fmicb.2022.826088 [Google Scholar] [Crossref]

93. Lin, C.S., Binns, M.R. and Lefkovitch, L.P. (1986) Stability analysis: where do we stand? Crop Science 26, 894–900. [Google Scholar] [Crossref]

94. López-Paredes, J., Goiri, I., Atxaerandio, R., García-Rodríguez, A., Ugarte, E., Jiménez-Montero, J. A., et al. (2020). Mitigation of Greenhouse Gases in Dairy Cattle via Genetic Selection: 1. Genetic Parameters of Direct Methane Using Noninvasive Methods and Proxies of Methane. J. Dairy Sci. 103, 7199–7209. doi:10.3168/jds.2019-17597. [Google Scholar] [Crossref]

95. Maciel, ICDF, Barbosa, FA, Tomich, TR, Ribeiro, LGP, Alvarenga, RC, Lopes, LS, et al. (2019) Could the breed composition improve performance and change the enteric methane emissions from beef cattle in a tropical intensive production system? PLoS ONE 14(7): e0220247. https://doi. org/10.1371/journal.pone.0220247. [Google Scholar] [Crossref]

96. Makuza, S. M., et al. (2021). Goat CBBPs in Zimbabwe: Experiences and prospects. Zimbabwe Veterinary Journal, 34(2), 18–29. [Google Scholar] [Crossref]

97. Manzanilla-Pech, CIV., P. L⊘vendahl, D. Mansan Gordo, G.F. Difford, J.E. Pryce, F. Schenkel, S. Wegmann, F. Miglior, T.C. Chud, P.J. Moate, S.R.O. Williams, C.M. Richardson, P. Stothard, J. Lassen, (2021) Breeding for reduced methane emission and feed-efficient Holstein cows: An international response, Journal of Dairy Science, 104(8):8983-9001.https://doi.org/10.3168/jds.2020-19889. [Google Scholar] [Crossref]

98. Marshall, K., Tebug, S. F., Mrode, R., & Ojango, J. M. K. (2021). Data systems for sustainable livestock genetic improvement in Africa: Principles and practice. Frontiers in Genetics, 12, 645917. https://doi.org/10.3389/fgene.2021.645917 [Google Scholar] [Crossref]

99. Maze, M., Taqi, M.O., Tolba, R. et al. Estimation of methane greenhouse gas emissions from livestock in Egypt during 1989 to 2021. Sci Rep 14, 14992 (2024). https://doi.org/10.1038/s41598-024-63011-0. [Google Scholar] [Crossref]

100. Mebrate, G., Tewodros, A., and Dawit, A. (2019). Methane Production in Ruminant Animals: Implication for Their Impact on Climate Change. Con. Dai. Vet. Sci. 2(4). CDVS. MS.ID.000142. DOI: 10.32474/CDVS.2019.02.000142. [Google Scholar] [Crossref]

101. Mijena., D., and Getiso A. (2021) Feeding and Nutritional Strategies to Reduce Methane Emission from Large Ruminants: Review. Journal of Aquaculture & Livestock Production. SRC/JALP-112. DOI: https://doi.org/10.47363/JALP/2021(2)109. [Google Scholar] [Crossref]

102. Mirkena, T, Duguma, G, Haile, A, Tibbo, M, Okeyo, AM, Wurzinger, M, Sölkner, J (2010). Review article Genetics of adaptation in domestic farm animals: A review. Livestock Science. 132 (2010) 1–12. doi: 10.1016/j.livsci.2010.05.003. [Google Scholar] [Crossref]

103. Mogeni, P., Njeru, R., Wamari, A., & Haskew, J. (2020). Leveraging digital health platforms for livestock and veterinary services: Adaptation of OpenSRP for livestock performance monitoring. mHealth, 6, 32. https://doi.org/10.21037/mhealth-20-45. [Google Scholar] [Crossref]

104. Mondal S., Singh RL (2021) (Eds) Emerging Issues in Climate Smart Livestock Production: Biological Tools and Techniques. Elsevier Inc. https://doi.org/10.1016/C2019-0-04196-9. [Google Scholar] [Crossref]

105. Mueller J, Haile A, Getachew T, Santos B, Rekik M, Belay B, Solomon D, Yeheyis L, Rischkowsky B. Going to scale-From community-based to population-wide genetic improvement and commercialized sheep meat supply in Ethiopia. Front Genet. 2023 Mar 17; 14:1114381. doi: 10.3389/fgene.2023.1114381. [Google Scholar] [Crossref]

106. Mueller, J. P., Rischkowsky, B., Haile, A., Philipsson, J., Mwai, O., Besbes, B., et al. (2015). Community-based livestock breeding programmes: Essentials and examples. J. Animal Breed. Genet. 132, 155–168. doi:10.1111/jbg.12136 [Google Scholar] [Crossref]

107. Nature. (2021). Editorial: Control methane to slow global warming fast. Nature 596, 461. doi: https://doi.org/10.1038/d41586-021-02287-y. [Google Scholar] [Crossref]

108. Ndiaye, M., et al. (2019). Community-based cattle selection in Senegal: Participatory pathways to genetic gains. Animal Genetic Resources, 65, 53–60. [Google Scholar] [Crossref]

109. Ndung’u, L., Wasike, C., & Mwai, O. (2024). Estimating methane emissions using mid-infrared spectroscopy in smallholder dairy cattle in Kenya. Frontiers in Sustainable Food Systems, 8, 113456. https://doi.org/10.3389/fsufs.2024.113456 [Google Scholar] [Crossref]

110. Niyas, PAA, Chaidanya, K, Shaji, S, Sejian, V, Bhatta, R, et al. (2015) Adaptation of Livestock to Environmental Challenges. J Vet Sci Med Diagn 4:3. [Google Scholar] [Crossref]

111. Noel, S. J., Difford, G. F., & Lassen, J. (2023). Microbial biomarkers linked to methane emission and feed efficiency in ruminants. Microbiome, 11(1), 18. https://doi.org/10.1186/s40168-022-01427-x [Google Scholar] [Crossref]

112. Nugent, C., and Shandra, J. M. (2009). State environmental protection efforts, women’s status, and world polity: a cross-national analysis. Organ. Environ. 22, 208–229. doi: 10.1177/1086026609338166 [Google Scholar] [Crossref]

113. Nyoni, M., et al. (2024). Indigenous goat breeding for climate resilience in Zimbabwe. Journal of Climate Smart Agriculture, 6(1), 32–44. [Google Scholar] [Crossref]

114. Ojango, J. M. K., et al. (2022). Scaling up CBBPs in East Africa: A systems approach. ILRI Research Report, 56. [Google Scholar] [Crossref]

115. Okitoi, L. O., et al. (2023). Community breeding of indigenous chickens in Western Kenya. Poultry Science Reports, 2(1), 1–12. [Google Scholar] [Crossref]

116. Olijhoek, DW, Løvendahl, P, Lassen, J, Hellwing, ALF, Höglund, JK, Weisbjerg, MR, Noel, SJ, McLean, F, Højberg, O, Lund, P. (2018) Methane production, rumen fermentation, and diet digestibility of Holstein and Jersey dairy cows being divergent in residual feed intake and fed at 2 forage-to-concentrate ratios. J Dairy Sci.,101(11):9926-9940. doi: 10.3168/jds.2017-14278. [Google Scholar] [Crossref]

117. Oliveira, M. P., Ferreira, M. B. D., & Silva, S. L. (2020). Residual feed intake and methane emission in Nelore cattle: Genetic and phenotypic relationships. Livestock Science, 233, 103954. https://doi.org/10.1016/j.livsci.2020.103954 [Google Scholar] [Crossref]

118. Orr, H.A. (2005). The genetic theory of adaptation: A brief history. Nature Reviews Genetics, 6(2), pp. 119–127. doi:10.1038/nrg1523. PMID 15716908. S2CID 17772950. [Google Scholar] [Crossref]

119. Pérez-Enciso, M, and Steibel, JP. (2021) Phenomes: the current frontier in animal breeding. Genet Sel Evol. 5;53(1):22. doi: 10.1186/s12711-021-00618-1. [Google Scholar] [Crossref]

120. Pickering, N. K., Oddy, V. H., McEwan, J. C., Basarab, J., Crews, D. H., & Snelling, W. M. (2015). Genetic parameters of methane emissions estimated using portable accumulation chambers in Angus beef cattle. Journal of Animal Science, 93(6), 2849–2858. https://doi.org/10.2527/jas.2014-8419 [Google Scholar] [Crossref]

121. Pinares-Patiño, CS, Hickey, SM, Young, EA, Dodds, KG, MacLean, S, Molano, G, Sandoval, E, Kjestrup, H, Harland, R, Hunt, C, Pickering, NK, McEwan, JC. (2013) Heritability estimates of methane emissions from sheep. Anim. 2(l 2), 316-21. doi: 10.1017/S1751731113000864. [Google Scholar] [Crossref]

122. Pszczola, M, Rzewuska, K, Mucha, S, Strabel, T. (2017). Heritability of methane emissions from dairy cows over a lactation measured on commercial farms. J Anim Sci. 95(11), 4813-4819. doi: 10.2527/jas2017.1842. [Google Scholar] [Crossref]

123. Quinton, C. D., F. S. Hely, P. R. Amer, T. J. Byrne, and A. R. Cromie. (2018). Prediction of effects of beef selection indexes on greenhouse gas emissions. Animal 12:889–897. https://doi.org/10.1017/ S1751731117002373. [Google Scholar] [Crossref]

124. RAB (2021). Rwanda’s strategic dairy breeding and community mobilization. Rwanda Agriculture Board Annual Report. [Google Scholar] [Crossref]

125. Richardson, C.M., Nguyen, T.T.T., Abdelsayed, M., Moate, P.J., Williams, S.R.O., Chud, T.C.S., Schenkel, F.S., Goddard, M.E., van den Berg, I., Cocks, B.G. (2021). Genetic parameters for methane emission traits in Australian dairy cows. J. Dairy Sci. 2021; 104:539–549. doi: 10.3168/jds.2020-18565. [Google Scholar] [Crossref]

126. Roehe, R., Dewhurst, R. J., Duthie, C. A., Rooke, J. A., McKain, N., Ross, D. W., & Wallace, R. J. (2016). Bovine host genetic variation influences rumen microbial methane production with best selection criterion for low methane emitting and efficiently feed converting hosts based on metagenomic gene abundance. Journal of Animal Science, 94(1), 143–151. https://doi.org/10.2527/jas.2015-9111 [Google Scholar] [Crossref]

127. Rowe, S. J., Hickey, S. M., & Pickering, N. K. (2019). Genetic parameters for methane emissions traits in sheep. Journal of Animal Science, 97(8), 3164–3171. https://doi.org/10.1093/jas/skz207 [Google Scholar] [Crossref]

128. Rowe, S., McEwan, J., Hickey, S., Anderson, R., Hyndman, D., Young, E., Baird, H., Dodds K., Pinares-Patiño C. and Pickering, N. (2014) Proc. 10th Wld Congr. Genet. Appl. Livest. Prod. Vancouver, Canada. [Google Scholar] [Crossref]

129. Schenkel, FS. (2021). Prospects for exploiting epigenetic effects in livestock production, Animal Frontiers, 11, 6,3–4, https://doi.org/10.1093/af/vfab071. [Google Scholar] [Crossref]

130. Singh, V., Singh, K. (2022). Additive Genetic Variance. In: Vonk, J., Shackelford, T.K. (eds) Encyclopedia of Animal Cognition and Behavior. Springer, Cham. https://doi.org/10.1007/978-3-319-55065-7_5. [Google Scholar] [Crossref]

131. Steves, F., Treisman, D., and Teytelboym, A. (2011). The Political Economy of Climate Change Policy in the Transition Region. The Low Carbon Transition. Brussels: European Bank for Reconstruction and Development (EBRD). [Google Scholar] [Crossref]

132. Storm, IMLD, Hellwing, ALF, Nielsen, NI and Madsen, J (2012). Methods for measuring and estimating methane emission from ruminants. Animals 2, 160–183. [Google Scholar] [Crossref]

133. Temple, B., and Edwards, R. (2002). Interpreters/translators and cross language research: reflexivity and border crossings. Int. J. Qual. Methods 1, 1–12. doi: 10.1177/160940690200100201. [Google Scholar] [Crossref]

134. Thakur, MS. (2022). CHAP: In book: Animal Husbandry. Ed - Sándor Kukovics, Basic Animal Breeding Methods10.5772/intechopen.104136. [Google Scholar] [Crossref]

135. Tomar, SS. (2010). Textbook of Animal Breeding.: Kalyani Publisher; 2010. New Delhi. India. [Google Scholar] [Crossref]

136. Traoré, A., et al. (2016). Zebu cattle selection through community-based approaches in Burkina Faso. Livestock Science, 186, 84–92. [Google Scholar] [Crossref]

137. van Engelen, M. A., van der Tol, P. P., Bastiaansen, J. W. M., & de Haas, Y. (2022). Genetic parameters of methane emission traits in dairy cattle estimated using milk mid-infrared spectra. Livestock Science, 261, 104970. https://doi.org/10.1016/j.livsci.2022.104970 [Google Scholar] [Crossref]

138. Van Marle-Köster, E., and Visser, C. (2018). Genetic improvement in South African livestock: Can genomics bridge the gap between the developed and developing sectors? Front. Genet. 9, 331. doi:10.3389/fgene.2018.00331. [Google Scholar] [Crossref]

139. van Middelaar, CE, Berentsen, PB, Dijkstra, J, van Arendonk, JA, de Boer, IJ. (2014) Methods to determine the relative value of genetic traits in dairy cows to reduce greenhouse gas emissions along the chain. J Dairy Sci.,97(8):5191-205. doi: 10.3168/jds.2013-7413. [Google Scholar] [Crossref]

140. VikingGenetic, (2021). Crossbreeding can reduce methane emissions by up to 6%. 12 Jul 2021. Ebeltoftvej 16, Assentoft, DK-8960Randers SØ. [Google Scholar] [Crossref]

141. Vojta, A., Dobrinic, P., Tadic, V., Bockor, L., Korac, P., and Julg, B. (2016). Repurposing the CRISPR-Cas9 system for targeted DNA methylation. Nucleic Acids Res.,44(12):5615–28. [Google Scholar] [Crossref]

142. Wurzinger, M., et al. (2021). Evolution of community-based breeding programs for small ruminants. Animal Frontiers, 11(1), 45–52. https://doi.org/10.1093/af/vfab003 [Google Scholar] [Crossref]

143. Wurzinger, M., Sölkner, J., and Iñiguez, L. (2011). Important aspects and limitations in considering community-based breeding programs for low-input smallholder livestock systems. Small Rumin. Res. 98 (1-3), 170–175. doi: 10.1016/j.smallrumres. 2011.03.035. [Google Scholar] [Crossref]

144. Yakubu, A., et al. (2020). Participatory genetic improvement of West African Dwarf goats in Nigeria. Nigerian Journal of Animal Production, 47(2), 145–153. [Google Scholar] [Crossref]

145. Yulistiani, D., Widiawati, Y., Puastuti, W., and Handiwirawani, E. (2021). Growth Rate, Feed Efficiency and Methane Production of Six Different Breeds of Sheep. Advances in Biological Sciences Research, 9th International Seminar on Tropical Animal Production (ISTAP 2021) volume 18, Atlantis Press. [Google Scholar] [Crossref]

146. Zaman, M., Kleineidam, K., Bakken, L., Berendt, J., Bracken, C., Butterbach-Bahl, K., Cai, Z., Chang, SX., Clough, T., Dawar K, Ding, WX, Dörsch, P, dos Reis Martins, M, Eckhardt C, Fiedler S, Frosch T, Goopy J, Görres C-M, Gupta A, Henjes S, Hofmann MEG, Horn MA, Jahangir MMR, Jansen-Willems A, Lenhart K, Heng L, Lewicka-Szczebak D, Lucic G, Merbold L, Mohn J, Molstad L, Moser G, Murphy P, Sanz-Cobena A, Šimek M, Urquiaga S, Well R, Wrage-Mönnig N, Zaman S, Zhang J, Müller C (2021) Measuring Emission of Agricultural Greenhouse Gases and Developing Mitigation Options Using Nuclear and Related Techniques Springer ISBN 978-3-030-55395-1, https://doi.org/10.1007/978-3-030- 55396-8. [Google Scholar] [Crossref]

147. Zetouni, L., Henryon, M., Kargo, M., and Lassen, J. (2017) Direct multitrait selection realizes the highest genetic response for ratio traits. J. Anim. Sci. 95, 1921–1925. [Google Scholar] [Crossref]

• Determinants of Adoption of Post-Harvest Losses Prevention Techniques among Banana/Plantain Marketers in Lagos State, Nigeria