Animal Bioscience

Asian Australasian Association of Animal Production Societies (아세아태평양축산학회)
권호 표지
DOI QR코드

DOI QR Code

Eliminating prediction bias in CO2 emission models for lactating cows by incorporating feed intake: accurate quantification of methane-reducing effects using a CO2-based method, demonstrated by a case study on 3-nitrooxypropanol

  • Kohei Oikawa (Institute of Livestock and Grassland Science, National Agriculture and Food Research Organization (NARO)) ;
  • Fuminori Terada (Mito Research Center, Meiji Feed Co.) ;
  • Mitsunori Kurihara (Office of Smart Sustainable Agri-food Systems, National Agriculture and Food Research Organization (NARO)) ;
  • Tomoyuki Suzuki (Institute of Livestock and Grassland Science, National Agriculture and Food Research Organization (NARO)) ;
  • Itoko Nonaka (Institute of Livestock and Grassland Science, National Agriculture and Food Research Organization (NARO)) ;
  • Kenji Hosoda (Kyusyu-Okinawa Agricultural Research Center, National Agriculture and Food Research Organization (NARO)) ;
  • Yuko Kamiya (Institute of Livestock and Grassland Science, National Agriculture and Food Research Organization (NARO)) ;
  • Sanggun Roh (Graduate School of Agricultural Science, Tohoku University) ;
  • Satoshi Haga (Graduate School of Agricultural Science, Tohoku University)
  • Received : 2025.11.13
  • Accepted : 2026.02.11
  • Published : 2026.06.01
Download PDF
⟨ Previous Next ⟩

Abstract

Objective: The methane (CH4) emission prediction method, using predicted CO2 emissions and the CH4:CO2 concentration ratio, faces challenges in evaluating the efficacy of CH4-reducing feed additives due to CO2 prediction bias associated with energy utilization efficiency. We hypothesized that incorporating dry matter intake (DMI), along with metabolic body weight (MBW) and energy-corrected milk (ECM) as explanatory variables, would reduce this bias. The primary objective was to compare the performance of CO2 emission models with and without including DMI. The secondary objective was to assess the CO2-based method's applicability for quantifying CH4-reducing effects, through a case study of 3-nitrooxypropanol (3-NOP). Methods: Prediction models for CO2 emissions were developed including DMI, MBW, and ECM as explanatory variables, based on 219 records obtained from previous experiments with Holstein cows using respiration chambers or headboxes. The model performance was evaluated using cross-validation. Bias associated with energy utilization efficiency was assessed. The applicability of the CO2-based method to quantify the CH4-reducing effect of 3-NOP was assessed using data obtained from the literature, including 10 studies with 22 treatment and control mean comparisons. The agreement between the observed and predicted CH4 reductions was assessed. Results: Combining DMI along with MBW and ECM improved the predictive performance of CO2 emissions. While the models without DMI showed bias associated with energy utilization efficiency, it was eliminated when DMI was incorporated. Applicability assessment demonstrated that the models without DMI systematically underestimated the CH4-reducing effect of 3-NOP. In contrast, the models with DMI showed smaller discrepancies between observed and predicted CH4 reductions. Conclusion: This study highlights the importance of incorporating DMI as an explanatory variable to achieve accurate and unbiased predictions of CO2 emissions. These findings would contribute to the appropriate application of the CO2-based method for evaluating the CH4-reducing effects of feed additives.

Keywords

Acknowledgement

This study was supported by the MAFF-commissioned project, "Development of Technologies to Reduce Greenhouse Gas Emissions in the Livestock Sector [Grant Number JPJ011299]."

References

  1. 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
  2. Matra M, Suriyapha C, Dagaew G, et al. Advantageous effects of rumen-protected phytonutrients from tropical plant extracts on rumen fermentation efficiency and methane mitigation using in vitro fermentation technique. Anim Biosci 2025;38:489-500. https://doi.org/10.5713/ab.24.0576
  3. Hristov AN, Bannink A, Battelli M, et al. Feed additives for methane mitigation: recommendations for testing enteric methane-mitigating feed additives in ruminant studies. J Dairy Sci 2025;108:322-55. https://doi.org/10.3168/jds.2024-25050
  4. Lassen J, Løvendahl 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
  5. 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
  6. Oikawa K, Suzuki T, Kamiya Y, et al. Prediction of methane emissions from fattening cattle using the methane-to-carbon dioxide ratio. Anim Sci J 2023;94:e13828. https://doi.org/10.1111/asj.13828
  7. Huhtanen P, Bayat AR, Lund P, Hellwing ALF, Weisbjerg MR. Short communication: variation in feed efficiency hampers use of carbon dioxide as a tracer gas in measuring methane emissions in on-farm conditions. J Dairy Sci 2020; 103:9090-5. https://doi.org/10.3168/jds.2020-18559
  8. Kebreab E, Bannink A, Pressman EM, et al. A meta-analysis of effects of 3-nitrooxypropanol on methane production, yield, and intensity in dairy cattle. J Dairy Sci 2023;106:927-36. https://doi.org/10.3168/jds.2022-22211
  9. Martins LF, Maigaard M, Johansen M, et al. Lactational performance effects of 3-nitrooxypropanol supplementation to dairy cows: a meta-regression. J Dairy Sci 2025;108:1538-53. https://doi.org/10.3168/jds.2024-25653
  10. Kjeldsen MH, Johansen M, Weisbjerg MR, et al. Predicting CO2 production of lactating dairy cows from animal, dietary, and production traits using an international dataset. J Dairy Sci 2024;107:6771-84. https://doi.org/10.3168/jds.2023-24414
  11. Hosoda K, Nishida T, Park WY, Eruden B. Influence of Mentha×piperita L. (peppermint) supplementation on nutrient digestibility and energy metabolism in lactating dairy cows. Asian-Australas J Anim Sci 2005;18:1721-6. https://doi.org/10.5713/ajas.2005.1721
  12. Shibata M, Terada F, Kurihara M, Nishida T, Iwasaki K. Estimation of methane production in ruminants. Nihon Chikusan Gakkaiho 1993;64:790-6. https://doi.org/10.2508/chikusan.64.790
  13. Kurihara M, Muraoka M, Aii T. Effects of environmental temperature and feeding frequency on physiological responses, milk production and energy utilization in lactating dairy cows. In: The 8th AAAP Animal Science Congress Proceedings; 1996 Oct 13-18; Tokyo, Japan. Japanese Society of Zootechnical Science; 1996.
  14. Kurihara M, Nishida T, Purnomoadi A. Reduction of methane production from dairy cows by decreasing ruminal degradability of concentrated ingredients. In: Proceedings of the Greenhouse Gas Control Technologies: 6th International Conference; 2002 Oct 1-4; Kyoto, Japan. Elsevier; 2003. pp. 1791-4.
  15. Kurihara M, Kume S, Aii T, Takahashi S. Feeding value of fall panicum for dairy cows. Kyushu National Agricultural Experiment Station; 1989.
  16. Kurihara M, Takahashi S, Aii T, Kume S. The effects of environmental temperature on the energy metabolism of lactating cows given silage and hay. Nihon Chikusan Gakkaiho 1992;63:831-9. https://doi.org/10.2508/chikusan.63.831
  17. Kurihara M, Kume S, Aii T, Takahashi S, Shibata M, Nishida T. Feeding method for dairy cattle to cope with global warming. Technical assessment based on energy metabolism. Kyushu National Agricultural Experiment Station; 1995.
  18. Nakai F, Hirashima Y, Ueda K, et al. Effect of dietary protein degradability on nitrogen and energy utilization of lactating cows. Anim Sci J 1999;70:J390-6. https://doi.org/10.2508/chikusan.70.10_390
  19. Terada F, Muraoka M. Effect of heat stress on the efficiency of utilization of metabolizable energy for lactation. In: Energy metabolism of farm animals: Proceedings of the 13th Symposium; 1994 Sep 18-24; Mojácar, Spain. EAAP Publication; 1994. pp. 323-6.
  20. Shioya S, Terada F, Iwama Y. Physiological responses of lactating cows under the hot environment. Proc Jpn Soc Anim Nutr Metab 1997;41:61-68.
  21. Shioya S, Terada F, Iwama Y. Effect of environmental temperature and humidity on heat production and heat loss of dairy cattle. In: The 8th AAAP Animal Science Congress Proceedings; 1996 Oct 13-18; Tokyo, Japan. Japanese Society of Zootechnical Science; 1996. pp. 576-7.
  22. Nonaka I, Itoh F, Purnomoadi A, et al. Energy metabolism in lactating cows treated with recombinant bovine somatotropin under high environmental temperature. In: Proceedings of the 15th Symposium on Energy Metabolism in Animals; 2000 Sep 11-16; Snekkersten, Denmark. EAAP Publication; 2001. pp. 397-400.
  23. Oikawa K, Terada F, Kurihara M, et al. Methane emission prediction models for lactating cows based on feed intake, body weight, and milk yield and composition: variable methane conversion factor-based approach. J Dairy Sci 2025; 108:7248-61. https://doi.org/10.3168/jds.2025-26312
  24. Sjaunja LO, Baevre L, Junkkarinen L, Pedersen J, Setälä J. A nordic proposal for an energy corrected milk (ECM) formula. Performance recording of animals: state of the art 1990. In: Proceedings of the 27th Biennial Session of the International Committee for Animal Recording; 1990 Jul 2-6; Paris, France. International Committee for Animal Recording; 1991. pp. 156-57.
  25. Bates D, Mächler M, Bolker B, Walker S. Fitting linear mixedeffects models using lme4. J Stat Softw 2015;67:1-48. https://doi.org/10.18637/jss.v067.i01
  26. R Core Team. R: a language and environment for statistical computing. ver. 4.5.0. R Foundation for Statistical Computing; 2025.
  27. Lin LIK. A concordance correlation coefficient to evaluate reproducibility. Biometrics 1989;45:255-68. https://doi.org/10.2307/2532051
  28. de Ondarza MB, Hristov AN, Tricarico JM. A global dataset of enteric methane mitigation experiments with lactating and non-lactating dairy cows conducted from 1963 to 2022. Data Brief 2023;49:109459. https://doi.org/10.1016/j.dib.2023.109459
  29. Lopes JC, de Matos LF, Harper MT, et al. Effect of 3-nitrooxypropanol on methane and hydrogen emissions, methane isotopic signature, and ruminal fermentation in dairy cows. J Dairy Sci 2016;99:5335-44. https://doi.org/10.3168/jds.2015-10832
  30. Melgar A, Welter KC, Nedelkov K, et al. Dose-response effect of 3-nitrooxypropanol on enteric methane emissions in dairy cows. J Dairy Sci 2020;103:6145-56. https://doi.org/10.3168/jds.2019-17840
  31. Melgar A, Lage CFA, Nedelkov K, et al. Enteric methane emission, milk production, and composition of dairy cows fed 3-nitrooxypropanol. J Dairy Sci 2021;104:357-66. https://doi.org/10.3168/jds.2020-18908
  32. Schilde M, von Soosten D, Hüther L, Meyer U, Zeyner A, Dänicke S. Effects of 3-nitrooxypropanol and varying concentrate feed proportions in the ration on methane emission, rumen fermentation and performance of periparturient dairy cows. Arch Anim Nutr 2021;75:79-104. https://doi.org/10.1080/1745039X.2021.1877986
  33. van Gastelen S, Dijkstra J, Binnendijk G, et al. 3-Nitrooxypropanol decreases methane emissions and increases hydrogen emissions of early lactation dairy cows, with associated changes in nutrient digestibility and energy metabolism. J Dairy Sci 2020;103:8074-93. https://doi.org/10.3168/jds.2019-17936
  34. van Gastelen S, Burgers EEA, Dijkstra J, et al. Long-term effects of 3-nitrooxypropanol on methane emission and milk production characteristics in Holstein-Friesian dairy cows. J Dairy Sci 2024;107:5556-73. https://doi.org/10.3168/jds.2023-24198
  35. van Gastelen S, Dijkstra J, Heck JML, et al. Methane mitigation potential of 3-nitrooxypropanol in lactating cows is influenced by basal diet composition. J Dairy Sci 2022;105: 4064-82. https://doi.org/10.3168/jds.2021-20782
  36. Johansen M, Maigaard M, Lund P. Effect of Bovaer inclusion in diets with a high proportion of grass-clover silage of different nutritional quality on gas emissions and production performance in dairy cows. J Dairy Sci 2025;108:4975-87. https://doi.org/10.3168/jds.2024-25949
  37. Maigaard M, Weisbjerg MR, Ohlsson C, Walker N, Lund P. Effects of different doses of 3-nitrooxypropanol combined with varying forage composition on feed intake, methane emission, and milk production in dairy cows. J Dairy Sci 2025;108:2489-502. https://doi.org/10.3168/jds.2024-25343
  38. Maigaard M, Weisbjerg MR, Johansen M, Walker N, Ohlsson C, Lund P. Effects of dietary fat, nitrate, and 3-nitrooxypropanol and their combinations on methane emission, feed intake, and milk production in dairy cows. J Dairy Sci 2024; 107:220-41. https://doi.org/10.3168/jds.2023-23420
  39. National Academies of Sciences, Engineering, and Medicine. Nutrient requirements of dairy cattle. 8th ed. National Academies Press; 2021.
  40. Pedersen S, Blanes-Vidal V, Joergensen H, et al. Carbon dioxide production in animal houses: a literature review. Agric Eng Int CIGR J 2008;X:1-19.