Animal Bioscience

  • 제37권2호
  • /
  • Pages.173-183
  • /
  • 2024
  • /
  • 2765-0189(pISSN)
  • /
  • 2765-0235(eISSN)
아세아태평양축산학회 (Asian Australasian Association of Animal Production Societies)
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Exploring indicators of genetic selection using the sniffer method to reduce methane emissions from Holstein cows

  • Yoshinobu Uemoto (Graduate School of Agricultural Science, Tohoku University) ;
  • Tomohisa Tomaru (Gunma Prefectural Livestock Experiment Station) ;
  • Masahiro Masuda (Niikappu Station, National Livestock Breeding Center (NLBC)) ;
  • Kota Uchisawa (Niikappu Station, National Livestock Breeding Center (NLBC)) ;
  • Kenji Hashiba (Niikappu Station, National Livestock Breeding Center (NLBC)) ;
  • Yuki Nishikawa (Head office, National Livestock Breeding Center (NLBC)) ;
  • Kohei Suzuki (Head office, National Livestock Breeding Center (NLBC)) ;
  • Takatoshi Kojima (Head office, National Livestock Breeding Center (NLBC)) ;
  • Tomoyuki Suzuki (Institute of Livestock and Grassland Science, National Agriculture and Food Research Organization (NARO)) ;
  • Fuminori Terada (Institute of Livestock and Grassland Science, NARO)
  • 투고 : 2023.03.31
  • 심사 : 2023.08.24
  • 발행 : 2024.02.01
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초록

Objective: This study aimed to evaluate whether the methane (CH4) to carbon dioxide (CO2) ratio (CH4/CO2) and methane-related traits obtained by the sniffer method can be used as indicators for genetic selection of Holstein cows with lower CH4 emissions. Methods: The sniffer method was used to simultaneously measure the concentrations of CH4 and CO2 during milking in each milking box of the automatic milking system to obtain CH4/CO2. Methane-related traits, which included CH4 emissions, CH4 per energy-corrected milk, methane conversion factor (MCF), and residual CH4, were calculated. First, we investigated the impact of the model with and without body weight (BW) on the lactation stage and parity for predicting methane-related traits using a first on-farm dataset (Farm 1; 400 records for 74 Holstein cows). Second, we estimated the genetic parameters for CH4/CO2 and methane-related traits using a second on-farm dataset (Farm 2; 520 records for 182 Holstein cows). Third, we compared the repeatability and environmental effects on these traits in both farm datasets. Results: The data from Farm 1 revealed that MCF can be reliably evaluated during the lactation stage and parity, even when BW is excluded from the model. Farm 2 data revealed low heritability and moderate repeatability for CH4/CO2 (0.12 and 0.46, respectively) and MCF (0.13 and 0.38, respectively). In addition, the estimated genetic correlation of milk yield with CH4/CO2 was low (0.07) and that with MCF was moderate (-0.53). The on-farm data indicated that CH4/CO2 and MCF could be evaluated consistently during the lactation stage and parity with moderate repeatability on both farms. Conclusion: This study demonstrated the on-farm applicability of the sniffer method for selecting cows with low CH4 emissions.

키워드

과제정보

This work was supported by the MAFF Commissioned project study on "Development of Technologies to Reduce Greenhouse Gas Emissions in the Livestock Sector" (Grant Number JPJ011299).

참고문헌

  1. Myhre G, Shindell D, Breon FM, et al. Anthropogenic and natural radiative forcing. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge, UK and New York, NY, USA: Cambridge University Press; 2013.
  2. Shibata M, Terada F. Factors affecting methane production and mitigation in ruminants. Anim Sci J 2010;81:2-10. https://doi.org/10.1111/j.1740-0929.2009.00687.x
  3. Gerber PJ, Steinfeld H, Henderson B, et al. Tackling climate change through livestock-A global assessment of emissions and mitigation opportunities. Rome, Italy: Food and Agriculture Organization of the United Nations (FAO); 2013.
  4. Shibata M, Terada F, Iwasaki K, Kurihara M, Nishida T. Methane production in heifers, sheep and goats consuming diets of various hay-concentrate ratios. Anim Sci Technol 1992;63:1221-7. https://doi.org/10.2508/chikusan.63.1221
  5. Steinfeld H, Gerber P, Wassenaar T, Castel V, Rosales M, de Haan C. Livestock's long shadow: Environmental issues and options. Rome, Italy: Food and Agriculture Organization of the United Nations (FAO); 2006.
  6. Johnson KA, Johnson DE. Methane emissions from cattle. J Anim Sci 1995;73:2483-92. https://doi.org/10.2527/1995.7382483x
  7. Garnsworthy PC, Difford GF, Bell MJ, et al. Comparison of methods to measure methane for use in genetic evaluation of dairy cattle. Animals 2019;9:837. https://doi.org/10.3390/ani9100837
  8. Lassen J, Difford GF. Genetic and genomic selection as a methane mitigation strategy in dairy cattle. Animal 2020;14:s473-83. https://doi.org/10.1017/S1751731120001561
  9. Stranden I, Kantanen J, Lidauer MH, Mehtio T, Negussie E. Animal board invited review: Genomic-based improvement of cattle in response to climate change. Animal 2022;16:100673. https://doi.org/10.1016/j.animal.2022.100673
  10. Knapp JR, Laur GL, Vadas PA, Weiss WP, Tricarico JM. Invited review: Enteric methane in dairy cattle production: Quantifying the opportunities and impact of reducing emissions. J Dairy Sci 2014;97:3231-61. https://doi.org/10.3168/jds.2013-7234
  11. Madsen J, Bjerg BS, Hvelplund T, Weisbjerg MR, Lund P. Methane and carbon dioxide ratio in excreted air for quantification of the methane production from ruminants. Livest Sci 2010;129:223-7. https://doi.org/10.1016/j.livsci.2010.01.001
  12. Garnsworthy PC, Craigon J, Hernandez-Medrano JH, Saunders N. On-farm methane measurements during milking correlate with total methane production by individual dairy cows. J Dairy Sci 2012;95:3166-80. https://doi.org/10.3168/jds.2011-4605
  13. Lassen J, Lovendahl P, Madsen J. Accuracy of noninvasive breath methane measurements using Fourier transform infrared methods on individual cows. J Dairy Sci 2012;95:890-8. https://doi.org/10.3168/jds.2011-4544
  14. Oikawa K, Kamiya Y, Terada F, Suzuki T. The influence of breath concentration in the gas sample on the accuracy of methane to carbon dioxide ratio using the sniffer method in dairy cows. Anim Sci J 2022;93:e13745. https://doi.org/10.1111/asj.13745
  15. Tedeschi LO, Abdalla AL, Alvarez C, et al. Quantification of methane emitted by ruminants: a review of methods. J Anim Sci 2022;100:skac197. https://doi.org/10.1093/jas/skac197
  16. Suzuki T, Kamiya Y, Oikawa K, et al. Prediction of enteric methane emissions from lactating cows using methane to carbon dioxide ratio in the breath. Anim Sci J 2021;92:e13637. https://doi.org/10.1111/asj.13637
  17. 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;100:855-70. https://doi.org/10.3168/jds.2016-11246
  18. Tyrrell HF, Reid JT. Prediction of the energy value of cow's milk. J Dairy Sci 1965;48:1215-23. https://doi.org/10.3168/jds.S0022-0302(65)88430-2
  19. Richardson CM, Nguyen TTT, Abdelsayed M, et al. Genetic parameters for methane emission traits in Australian dairy cows. J Dairy Sci 2021;104:539-49. https://doi.org/10.3168/jds.2020-18565
  20. Gilmour AR, Gogel BJ, Cullis BR, Welham S, Thompson R. ASReml user guide release 4.1 structural specification. Hemel Hempstead, UK: VSN International, Ltd.; 2015.
  21. de Haas Y, Windig JJ, Calus MPL, et al. Genetic parameters for predicted methane production and potential for reducing enteric emissions through genomic selection. J Dairy Sci 2011;94:6122-34. https://doi.org/10.3168/jds.2011-4439
  22. Pickering NK, Chagunda MGG, Banos G, Mrode R, McEwan JC, Wall E. Genetic parameters for predicted methane production and laser methane detector measurements. J Anim Sci 2015;93:11-20. https://doi.org/10.2527/jas.2014-8302
  23. Yin T, Pinent T, Brugemann K, Simianer H, Konig S. Simulation, prediction, and genetic analyses of daily methane emissions in dairy cattle. J Dairy Sci 2015;98:5748-62. https://doi.org/10.3168/jds.2014-8618
  24. van Engelen S, Bovenhuis H, Dijkstra J, Van Arendonk JAM, Visker MHPW. Genetic study of methane production predicted from milk fat composition in dairy cows. J Dairy Sci 2015;98:8223-6. https://doi.org/10.3168/jds.2014-8989
  25. Kandel PB, Vanrobays ML, Vanlierde A, et al. Genetic parameters of mid-infrared methane predictions and their relationships with milk production traits in Holstein cattle. J Dairy Sci 2017;100:5578-91. https://doi.org/10.3168/jds.2016-11954
  26. Bell MJ, Potterton SL, Craigon J, et al. Variation in enteric methane emissions among cows on commercial dairy farms. Animal 2014;8:1540-6. https://doi.org/10.1017/S1751731114001530
  27. Olijhoek DW, Difford GF, Lund P, Lovendahl P. Phenotypic modeling of residual feed intake using physical activity and methane production as energy sinks. J Dairy Sci 2020;103:6967-81. https://doi.org/10.3168/jds.2019-17489
  28. Pszczola M, Rzewuska K, Mucha S, Strabel T. Heritability of methane emissions from dairy cows over a lactation measured on commercial farms. J Anim Sci 2017;95:4813-9. https://doi.org/10.2527/jas2017.1842
  29. van Breukelen AE, Aldridge MA, Veerkamp RF, de Haas Y. Genetic parameters for repeatedly recorded enteric methane concentrations of dairy cows. J Dairy Sci 2022;105:4256-71. https://doi.org/10.3168/jds.2021-21420
  30. Pedersen S, Blanes-Vidal V, Jorgensen H, et al. Carbon dioxide production in animal houses: a literature review. Agricultural Engineering International 2008;8:1-19.
  31. Huhtanen P, Cabezas-Garcia EH, Utsumi S, Zimmerman S. Comparison of methods to determine methane emissions from dairy cows in farm conditions. J Dairy Sci 2015;98:3394-409. https://doi.org/10.3168/jds.2014-9118
  32. van Engelen S, Bovenhuis H, Van der Tol PPJ, Visker MHPW. Genetic background of methane emission by Dutch Holstein Friesian cows measured with infrared sensors in automatic milking systems. J Dairy Sci 2018;101:2226-34. https://doi.org/10.3168/jds.2017-13441
  33. Manzanilla-Pech CIV, Lovendahl P, Gordo DM, et al. Breeding for reduced methane emission and feed-efficient Holstein cows: An international response. J Dairy Sci 2021;104:8983-9001. https://doi.org/10.3168/jds.2020-19889
  34. Falconer DS. Introduction to quantitative genetics. 4th ed. Harlow, Essex, UK: Longmans Green; 1996.