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1000 Titel
  • Milk fatty acids estimated by mid-infrared spectroscopy and milk yield can predict methane emissions in dairy cows
1000 Autor/in
  1. Engelke, Stefanie W. |
  2. Daş, Gürbüz |
  3. Derno, Michael |
  4. Tuchscherer, Armin |
  5. Berg, Werner |
  6. Kuhla, Björn |
  7. Metges, Cornelia C. |
1000 Erscheinungsjahr 2018
1000 LeibnizOpen
1000 Publikationstyp
  1. Artikel |
1000 Online veröffentlicht
  • 2018-05-02
1000 Erschienen in
1000 Quellenangabe
  • 38:27
1000 FRL-Sammlung
1000 Copyrightjahr
  • 2018
1000 Lizenz
1000 Verlagsversion
  • https://doi.org/10.1007/s13593-018-0502-x |
1000 Publikationsstatus
1000 Begutachtungsstatus
1000 Sprache der Publikation
1000 Abstract/Summary
  • Ruminant enteric methane emission contributes to global warming. Although breeding low methane-emitting cows appears to be possible through genetic selection, doing so requires methane emission quantification by using elaborate instrumentation (respiration chambers, SF6 technique, GreenFeed) not feasible on a large scale. It has been suggested that milk fatty acids are promising markers of methane production. We hypothesized that methane emission can be predicted from the milk fatty acid concentrations determined by mid-infrared spectroscopy, and the integration of energy-corrected milk yield would improve the prediction. Therefore, we examined relationships between methane emission of cows measured in respiration chambers and milk fatty acids, predicted by mid-infrared spectroscopy, to derive diet-specific and general prediction equations based on milk fatty acid concentrations alone and with the additional consideration of energy-corrected milk yield. Cows were fed diets differing in forage type and linseed supplementation to generate a large variation in both CH4 emission and milk fatty acids. Depending on the diet, equations derived from regression analysis explained 61 to 96% of variation of methane emission, implying the potential of milk fatty acid data predicted by mid-infrared spectroscopy as novel proxy for direct methane emission measurements. When data from all diets were analyzed collectively, the equation with energy-corrected milk yield (CH4 (L/day) = − 1364 + 9.58 × energy-corrected milk yield + 18.5 × saturated fatty acids + 32.4 × C18:0) showed an improved coefficient of determination of cross-validation R2CV = 0.72 compared to an equation without energy-corrected milk yield (R2CV = 0.61). Equations developed for diets supplemented by linseed showed a lower R2CV as compared to diets without linseed (0.39 to 0.58 vs. 0.50 to 0.91). We demonstrate for the first time that milk fatty acid concentrations predicted by mid-infrared spectroscopy together with energy-corrected milk yield can be used to estimate enteric methane emission in dairy cows.
1000 Sacherschließung
lokal Milk fatty acids
lokal Dairy cows
lokal Enteric methane emission
lokal Methane proxy
lokal Mid-infrared spectroscopy
lokal Methane prediction equation
lokal Linseed supplementation
1000 Fächerklassifikation (DDC)
1000 Liste der Beteiligten
  1. https://frl.publisso.de/adhoc/creator/RW5nZWxrZSwgU3RlZmFuaWUgVy4=|http://orcid.org/0000-0001-6690-0050|https://frl.publisso.de/adhoc/creator/RGVybm8sIE1pY2hhZWw=|https://frl.publisso.de/adhoc/creator/VHVjaHNjaGVyZXIsIEFybWlu|https://frl.publisso.de/adhoc/creator/QmVyZywgV2VybmVy|https://frl.publisso.de/adhoc/creator/S3VobGEsIEJqw7Zybg==|https://frl.publisso.de/adhoc/creator/TWV0Z2VzLCBDb3JuZWxpYSBDLg==
1000 Label
1000 Förderer
  1. - |
  2. Federal Ministry of Food and Agriculture |
  3. Parliament of the Federal Republic of Germany |
  4. Federal Office for Agriculture and Food |
  5. Leibniz Institute for Farm Animal Biology (FBN), Dummerstorf, Germany |
1000 Fördernummer
  1. -
  2. -
  3. -
  4. 2817501011
  5. -
1000 Förderprogramm
  1. Innovation potential to reduce greenhouse gas emissions in the dairy supply chain (INNO MilCH4)
  2. -
  3. -
  4. Innovation support program
  5. Open Access Fund
1000 Dateien
1000 Förderung
  1. 1000 joinedFunding-child
    1000 Förderer - |
    1000 Förderprogramm Innovation potential to reduce greenhouse gas emissions in the dairy supply chain (INNO MilCH4)
    1000 Fördernummer -
  2. 1000 joinedFunding-child
    1000 Förderer Federal Ministry of Food and Agriculture |
    1000 Förderprogramm -
    1000 Fördernummer -
  3. 1000 joinedFunding-child
    1000 Förderer Parliament of the Federal Republic of Germany |
    1000 Förderprogramm -
    1000 Fördernummer -
  4. 1000 joinedFunding-child
    1000 Förderer Federal Office for Agriculture and Food |
    1000 Förderprogramm Innovation support program
    1000 Fördernummer 2817501011
  5. 1000 joinedFunding-child
    1000 Förderer Leibniz Institute for Farm Animal Biology (FBN), Dummerstorf, Germany |
    1000 Förderprogramm Open Access Fund
    1000 Fördernummer -
1000 Objektart article
1000 Beschrieben durch
1000 @id frl:6410214.rdf
1000 Erstellt am 2018-09-17T09:05:50.740+0200
1000 Erstellt von 25
1000 beschreibt frl:6410214
1000 Bearbeitet von 25
1000 Zuletzt bearbeitet Fri Jan 31 01:11:35 CET 2020
1000 Objekt bearb. Tue Jul 02 08:35:37 CEST 2019
1000 Vgl. frl:6410214
1000 Oai Id
  1. oai:frl.publisso.de:frl:6410214 |
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