Show simple item record

dc.contributor.authorVanlierde, Amélie
dc.contributor.authorDehareng, Frédéric
dc.contributor.authorGengler, Nicolas
dc.contributor.authorFroidmont, Eric
dc.contributor.authorMcParland, Sinead
dc.contributor.authorKreuzer, Michael
dc.contributor.authorBell, Matthew
dc.contributor.authorLund, Peter
dc.contributor.authorMartin, Cécile
dc.contributor.authorKuhla, Björn
dc.contributor.authorSoyeurt, Hélène
dc.date.accessioned2021-01-07T17:41:18Z
dc.date.available2021-01-07T17:41:18Z
dc.date.issued2020-11-22
dc.identifier.citationVanlierde, A., Dehareng, F., Gengler, N., Froidmont, E., McParland, S., Kreuzer, M., Bell, M., Lund, P., Martin, C., Kuhla, B. and Soyeurt, H. (2020), Improving robustness and accuracy of predicted daily methane emissions of dairy cows using milk mid‐infrared spectra. J Sci Food Agric. https://doi.org/10.1002/jsfa.10969en_US
dc.identifier.issn0022-5142
dc.identifier.urihttp://hdl.handle.net/11019/2360
dc.descriptionpeer-revieweden_US
dc.description.abstractBACKGROUND A robust proxy for estimating methane (CH4) emissions of individual dairy cows would be valuable especially for selective breeding. This study aimed to improve the robustness and accuracy of prediction models that estimate daily CH4 emissions from milk Fourier transform mid‐infrared (FT‐MIR) spectra by (i) increasing the reference dataset and (ii) adjusting for routinely recorded phenotypic information. Prediction equations for CH4 were developed using a combined dataset including daily CH4 measurements (n = 1089; g d−1) collected using the SF6 tracer technique (n = 513) and measurements using respiration chambers (RC, n = 576). Furthermore, in addition to the milk FT‐MIR spectra, the variables of milk yield (MY) on the test day, parity (P) and breed (B) of cows were included in the regression analysis as explanatory variables. RESULTS Models developed based on a combined RC and SF6 dataset predicted the expected pattern in CH4 values (in g d−1) during a lactation cycle, namely an increase during the first weeks after calving followed by a gradual decrease until the end of lactation. The model including MY, P and B information provided the best prediction results (cross‐validation statistics: R2 = 0.68 and standard error = 57 g CH4 d−1). CONCLUSIONS The models developed accounted for more of the observed variability in CH4 emissions than previously developed models and thus were considered more robust. This approach is suitable for large‐scale studies (e.g. animal genetic evaluation) where robustness is paramount for accurate predictions across a range of animal conditions. © 2020 Society of Chemical Industryen_US
dc.description.sponsorshipAgence Nationale de la Recherche
dc.language.isoenen_US
dc.publisherWileyen_US
dc.relation.ispartofseriesJournal of the Science of Food and Agriculture;
dc.rightsAttribution-NonCommercial-ShareAlike 4.0 International*
dc.rights.urihttp://onlinelibrary.wiley.com/termsAndConditions#vor
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/*
dc.subjectBiotechnologyen_US
dc.subjectMethaneen_US
dc.subjectMilken_US
dc.subjectMIR Spectraen_US
dc.subjectDairyen_US
dc.subjectPhenotypeen_US
dc.subjectReference methoden_US
dc.titleImproving robustness and accuracy of predicted daily methane emissions of dairy cows using milk mid‐infrared spectraen_US
dc.typeArticleen_US
dc.embargo.terms2021/11/22en_US
dc.identifier.doihttps://doi.org/10.1002/jsfa.10969
dc.identifier.pii10.1002/jsfa.10969
dc.contributor.sponsorEuropean Unionen_US
dc.contributor.sponsorGerman Federal Ministry of Food and Agriculture (BMBL)en_US
dc.contributor.sponsorFrench National Research Agency (ANR)en_US
dc.contributor.sponsorDanish Milk Levy Funden_US
dc.contributor.sponsorAarhus Universityen_US
dc.contributor.sponsorGrantNumber238562en_US
dc.contributor.sponsorGrantNumber613689en_US
dc.contributor.sponsorGrantNumberANR‐13‐JFAC‐0003‐01en_US
dc.source.journaltitleJournal of the Science of Food and Agriculture
dc.identifier.eissn1097-0010


Files in this item

Thumbnail
Name:
vanlierde2020.pdf
Embargo:
2021-11-22
Size:
1.507Mb
Format:
PDF
Description:
main article

This item appears in the following Collection(s)

  • Livestock Systems [207]
    Teagasc LIvestock Systems Department includes Dairy, Cattle and Sheep research.

Show simple item record