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Accuracy of methane emissions predicted from milk mid-infrared spectra and measured by laser methane detectors in Brown Swiss dairy cows


Denninger, T. M.; Schwarm, A; Dohme-Meier, F; Münger, A; Bapst, B; Wegmann, S; Grandl, F; Vanlierde, A; Sorg, D; Ortmann, S; Clauss, Marcus; Kreuzer, Michael (2020). Accuracy of methane emissions predicted from milk mid-infrared spectra and measured by laser methane detectors in Brown Swiss dairy cows. Journal of Dairy Science, 103(2):2024-2039.

Abstract

Since heritability of CH4 emissions in ruminants was demonstrated, various attempts to generate large individual animal CH4 data sets have been initiated. Predicting individual CH4 emissions based on equations using milk mid-infrared (MIR) spectra is currently considered promising as a low-cost proxy. However, the CH4 emission predicted by MIR in individuals still has to be confirmed by measurements. In addition, it remains unclear how low CH4 emitting cows differ in intake, digestion, and efficiency from high CH4 emitters. In the current study, putatively low and putatively high CH4 emitting Brown Swiss cows were selected from the entire Swiss herdbook population (176,611 cows), using an MIR-based prediction equation. Eventually, 15 low and 15 high CH4 emitters from 29 different farms were chosen for a respiration chamber (RC) experiment in which all cows were fed the same forage-based diet. Several traits related to intake, digestion, and efficiency were quantified over 8 d, and CH4 emission was measured in 4 open circuit RC. Daily CH4 emissions were also estimated using data from 2 laser CH4 detectors (LMD). The MIR-predicted CH4 production (g/d) was quite constant in low and high emission categories, in individuals across sites (home farm, experimental station), and within equations (first available and refined versions). The variation of the MIR-predicted values was substantially lower using the refined equation. However, the predicted low and high emitting cows (n = 28) did not differ on average in daily CH4 emissions measured either with RC or estimated using LMD, and no correlation was found between CH4 predictions (MIR) and CH4 emissions measured in RC. When individuals were recategorized based on CH4 yield measured in RC, differences between categories of 10 low
and 10 high CH4 emitters were about 20%. Low CH4 emitting cows had a higher feed intake, milk yield, and residual feed intake, but they differed only weakly in eating pattern and digesta mean retention times. Low CH4 emitters were characterized by lower acetate and higher propionate proportions of total ruminal volatile fatty acids. We concluded that the current MIR-based CH4 predictions are not accurate enough to be implemented in breeding programs for cows fed forage-based diets. In addition, low CH4 emitting cows have to be characterized in more detail using mechanistic studies to clarify in more detail the properties that explain the functional differences found in comparison with other cows.

Abstract

Since heritability of CH4 emissions in ruminants was demonstrated, various attempts to generate large individual animal CH4 data sets have been initiated. Predicting individual CH4 emissions based on equations using milk mid-infrared (MIR) spectra is currently considered promising as a low-cost proxy. However, the CH4 emission predicted by MIR in individuals still has to be confirmed by measurements. In addition, it remains unclear how low CH4 emitting cows differ in intake, digestion, and efficiency from high CH4 emitters. In the current study, putatively low and putatively high CH4 emitting Brown Swiss cows were selected from the entire Swiss herdbook population (176,611 cows), using an MIR-based prediction equation. Eventually, 15 low and 15 high CH4 emitters from 29 different farms were chosen for a respiration chamber (RC) experiment in which all cows were fed the same forage-based diet. Several traits related to intake, digestion, and efficiency were quantified over 8 d, and CH4 emission was measured in 4 open circuit RC. Daily CH4 emissions were also estimated using data from 2 laser CH4 detectors (LMD). The MIR-predicted CH4 production (g/d) was quite constant in low and high emission categories, in individuals across sites (home farm, experimental station), and within equations (first available and refined versions). The variation of the MIR-predicted values was substantially lower using the refined equation. However, the predicted low and high emitting cows (n = 28) did not differ on average in daily CH4 emissions measured either with RC or estimated using LMD, and no correlation was found between CH4 predictions (MIR) and CH4 emissions measured in RC. When individuals were recategorized based on CH4 yield measured in RC, differences between categories of 10 low
and 10 high CH4 emitters were about 20%. Low CH4 emitting cows had a higher feed intake, milk yield, and residual feed intake, but they differed only weakly in eating pattern and digesta mean retention times. Low CH4 emitters were characterized by lower acetate and higher propionate proportions of total ruminal volatile fatty acids. We concluded that the current MIR-based CH4 predictions are not accurate enough to be implemented in breeding programs for cows fed forage-based diets. In addition, low CH4 emitting cows have to be characterized in more detail using mechanistic studies to clarify in more detail the properties that explain the functional differences found in comparison with other cows.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:05 Vetsuisse Faculty > Veterinary Clinic > Department of Small Animals
Dewey Decimal Classification:570 Life sciences; biology
630 Agriculture
Scopus Subject Areas:Life Sciences > Food Science
Life Sciences > Animal Science and Zoology
Life Sciences > Genetics
Uncontrolled Keywords:Food Science, Animal Science and Zoology, Genetics, digestion; feed efficiency; methane prediction; proxy
Language:English
Date:1 February 2020
Deposited On:28 Jan 2020 15:32
Last Modified:29 Jul 2020 13:23
Publisher:Elsevier
ISSN:0022-0302
OA Status:Closed
Free access at:Publisher DOI. An embargo period may apply.
Publisher DOI:https://doi.org/10.3168/jds.2019-17101
PubMed ID:31864736
Project Information:
  • : FunderEuropean Cooperation in Science and Technology
  • : Grant ID
  • : Project TitleCOST Action FA 1302
  • : FunderSwiss State Secretariat for Education, Research and Innovation
  • : Grant ID
  • : Project Title

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