Methane prediction equations including genera of rumen bacteria as predictor variables improve prediction accuracy

Methane (CH 4 ) emissions from ruminants are of a significant environmental concern, necessitating accurate prediction for emission inventories. Existing models rely solely on dietary and host animal-related data, ignoring the predicting power of rumen microbiota, the source of CH 4 . To address thi...

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Published inScientific reports Vol. 13; no. 1; pp. 21305 - 11
Main Authors Zhang, Boyang, Lin, Shili, Moraes, Luis, Firkins, Jeffrey, Hristov, Alexander N., Kebreab, Ermias, Janssen, Peter H., Bannink, André, Bayat, Alireza R., Crompton, Les A., Dijkstra, Jan, Eugène, Maguy A., Kreuzer, Michael, McGee, Mark, Reynolds, Christopher K., Schwarm, Angela, Yáñez-Ruiz, David R., Yu, Zhongtang
Format Journal Article
LanguageEnglish
Published London Nature Publishing Group UK 02.12.2023
Nature Publishing Group
Nature Portfolio
Subjects
631/326
631/326/171
631/326/2565
Animal Feed - analysis
Animal models
Animals
Bacteria
Bacteria - genetics
Bayes Theorem
Bayesian analysis
Correlation coefficient
Diet - veterinary
Dry matter
Emission inventories
Emissions
Environmental perception
Female
Food and Nutrition
Humanities and Social Sciences
Lactation
Life Sciences
Mathematical models
Methane
Microbiota
multidisciplinary
Prediction models
Rumen
Ruminants
Science
Science (multidisciplinary)
Sheep
Online AccessGet full text
ISSN2045-2322
2045-2322
DOI10.1038/s41598-023-48449-y

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Abstract Methane (CH 4 ) emissions from ruminants are of a significant environmental concern, necessitating accurate prediction for emission inventories. Existing models rely solely on dietary and host animal-related data, ignoring the predicting power of rumen microbiota, the source of CH 4 . To address this limitation, we developed novel CH 4 prediction models incorporating rumen microbes as predictors, alongside animal- and feed-related predictors using four statistical/machine learning (ML) methods. These include random forest combined with boosting (RF-B), least absolute shrinkage and selection operator (LASSO), generalized linear mixed model with LASSO (glmmLasso), and smoothly clipped absolute deviation (SCAD) implemented on linear mixed models. With a sheep dataset (218 observations) of both animal data and rumen microbiota data (relative sequence abundance of 330 genera of rumen bacteria, archaea, protozoa, and fungi), we developed linear mixed models to predict CH 4 production (g CH 4 /animal·d, ANIM-B models) and CH 4 yield (g CH 4 /kg of dry matter intake, DMI-B models). We also developed models solely based on animal-related data. Prediction performance was evaluated 200 times with random data splits, while fitting performance was assessed without data splitting. The inclusion of microbial predictors improved the models, as indicated by decreased root mean square prediction error (RMSPE) and mean absolute error (MAE), and increased Lin’s concordance correlation coefficient (CCC). Both glmmLasso and SCAD reduced the Akaike information criterion (AIC) and Bayesian information criterion (BIC) for both the ANIM-B and the DMI-B models, while the other two ML methods had mixed outcomes. By balancing prediction performance and fitting performance, we obtained one ANIM-B model (containing 10 genera of bacteria and 3 animal data) fitted using glmmLasso and one DMI-B model (5 genera of bacteria and 1 animal datum) fitted using SCAD. This study highlights the importance of incorporating rumen microbiota data in CH 4 prediction models to enhance accuracy and robustness. Additionally, ML methods facilitate the selection of microbial predictors from high-dimensional metataxonomic data of the rumen microbiota without overfitting. Moreover, the identified microbial predictors can serve as biomarkers of CH 4 emissions from sheep, providing valuable insights for future research and mitigation strategies.
AbstractList Methane (CH4) emissions from ruminants are of a significant environmental concern, necessitating accurate prediction for emission inventories. Existing models rely solely on dietary and host animal-related data, ignoring the predicting power of rumen microbiota, the source of CH4. To address this limitation, we developed novel CH4 prediction models incorporating rumen microbes as predictors, alongside animal- and feed-related predictors using four statistical/machine learning (ML) methods. These include random forest combined with boosting (RF-B), least absolute shrinkage and selection operator (LASSO), generalized linear mixed model with LASSO (glmmLasso), and smoothly clipped absolute deviation (SCAD) implemented on linear mixed models. With a sheep dataset (218 observations) of both animal data and rumen microbiota data (relative sequence abundance of 330 genera of rumen bacteria, archaea, protozoa, and fungi), we developed linear mixed models to predict CH4 production (g CH4/animal·d, ANIM-B models) and CH4 yield (g CH4/kg of dry matter intake, DMI-B models). We also developed models solely based on animal-related data. Prediction performance was evaluated 200 times with random data splits, while fitting performance was assessed without data splitting. The inclusion of microbial predictors improved the models, as indicated by decreased root mean square prediction error (RMSPE) and mean absolute error (MAE), and increased Lin's concordance correlation coefficient (CCC). Both glmmLasso and SCAD reduced the Akaike information criterion (AIC) and Bayesian information criterion (BIC) for both the ANIM-B and the DMI-B models, while the other two ML methods had mixed outcomes. By balancing prediction performance and fitting performance, we obtained one ANIM-B model (containing 10 genera of bacteria and 3 animal data) fitted using glmmLasso and one DMI-B model (5 genera of bacteria and 1 animal datum) fitted using SCAD. This study highlights the importance of incorporating rumen microbiota data in CH4 prediction models to enhance accuracy and robustness. Additionally, ML methods facilitate the selection of microbial predictors from high-dimensional metataxonomic data of the rumen microbiota without overfitting. Moreover, the identified microbial predictors can serve as biomarkers of CH4 emissions from sheep, providing valuable insights for future research and mitigation strategies.Methane (CH4) emissions from ruminants are of a significant environmental concern, necessitating accurate prediction for emission inventories. Existing models rely solely on dietary and host animal-related data, ignoring the predicting power of rumen microbiota, the source of CH4. To address this limitation, we developed novel CH4 prediction models incorporating rumen microbes as predictors, alongside animal- and feed-related predictors using four statistical/machine learning (ML) methods. These include random forest combined with boosting (RF-B), least absolute shrinkage and selection operator (LASSO), generalized linear mixed model with LASSO (glmmLasso), and smoothly clipped absolute deviation (SCAD) implemented on linear mixed models. With a sheep dataset (218 observations) of both animal data and rumen microbiota data (relative sequence abundance of 330 genera of rumen bacteria, archaea, protozoa, and fungi), we developed linear mixed models to predict CH4 production (g CH4/animal·d, ANIM-B models) and CH4 yield (g CH4/kg of dry matter intake, DMI-B models). We also developed models solely based on animal-related data. Prediction performance was evaluated 200 times with random data splits, while fitting performance was assessed without data splitting. The inclusion of microbial predictors improved the models, as indicated by decreased root mean square prediction error (RMSPE) and mean absolute error (MAE), and increased Lin's concordance correlation coefficient (CCC). Both glmmLasso and SCAD reduced the Akaike information criterion (AIC) and Bayesian information criterion (BIC) for both the ANIM-B and the DMI-B models, while the other two ML methods had mixed outcomes. By balancing prediction performance and fitting performance, we obtained one ANIM-B model (containing 10 genera of bacteria and 3 animal data) fitted using glmmLasso and one DMI-B model (5 genera of bacteria and 1 animal datum) fitted using SCAD. This study highlights the importance of incorporating rumen microbiota data in CH4 prediction models to enhance accuracy and robustness. Additionally, ML methods facilitate the selection of microbial predictors from high-dimensional metataxonomic data of the rumen microbiota without overfitting. Moreover, the identified microbial predictors can serve as biomarkers of CH4 emissions from sheep, providing valuable insights for future research and mitigation strategies.
Methane (CH 4) emissions from ruminants are of a significant environmental concern, necessitating accurate prediction for emission inventories. Existing models rely solely on dietary and host animalrelated data, ignoring the predicting power of rumen microbiota, the source of CH 4. To address this limitation, we developed novel CH 4 prediction models incorporating rumen microbes as predictors, alongside animal-and feed-related predictors using four statistical/machine learning (ML) methods. These include random forest combined with boosting (RF-B), least absolute shrinkage and selection operator (LASSO), generalized linear mixed model with LASSO (glmmLasso), and smoothly clipped absolute deviation (SCAD) implemented on linear mixed models. With a sheep dataset (218 observations) of both animal data and rumen microbiota data (relative sequence abundance of 330 genera of rumen bacteria, archaea, protozoa, and fungi), we developed linear mixed models to predict CH 4 production (g CH 4 /animal•d, ANIM-B models) and CH 4 yield (g CH 4 /kg of dry matter intake, DMI-B models). We also developed models solely based on animal-related data. Prediction performance was evaluated 200 times with random data splits, while fitting performance was assessed without data splitting. The inclusion of microbial predictors improved the models, as indicated by decreased root mean square prediction error (RMSPE) and mean absolute error (MAE), and increased Lin's concordance correlation coefficient (CCC). Both glmmLasso and SCAD reduced the Akaike information criterion (AIC) and Bayesian information criterion (BIC) for both the ANIM-B and the DMI-B models, while the other two ML methods had mixed outcomes. By balancing prediction performance and fitting performance, we obtained one ANIM-B model (containing 10 genera of bacteria and 3 animal data) fitted using glmmLasso and one DMI-B model (5 genera of bacteria and 1 animal datum) fitted using SCAD. This study highlights the importance of incorporating rumen microbiota data in CH 4 prediction models to enhance accuracy and robustness. Additionally, ML methods facilitate the selection of microbial predictors from high-dimensional metataxonomic data of the rumen microbiota without overfitting. Moreover, the identified microbial predictors can serve as biomarkers of CH 4 emissions from sheep, providing valuable insights for future research and mitigation strategies.
Methane (CH 4 ) emissions from ruminants are of a significant environmental concern, necessitating accurate prediction for emission inventories. Existing models rely solely on dietary and host animal-related data, ignoring the predicting power of rumen microbiota, the source of CH 4 . To address this limitation, we developed novel CH 4 prediction models incorporating rumen microbes as predictors, alongside animal- and feed-related predictors using four statistical/machine learning (ML) methods. These include random forest combined with boosting (RF-B), least absolute shrinkage and selection operator (LASSO), generalized linear mixed model with LASSO (glmmLasso), and smoothly clipped absolute deviation (SCAD) implemented on linear mixed models. With a sheep dataset (218 observations) of both animal data and rumen microbiota data (relative sequence abundance of 330 genera of rumen bacteria, archaea, protozoa, and fungi), we developed linear mixed models to predict CH 4 production (g CH 4 /animal·d, ANIM-B models) and CH 4 yield (g CH 4 /kg of dry matter intake, DMI-B models). We also developed models solely based on animal-related data. Prediction performance was evaluated 200 times with random data splits, while fitting performance was assessed without data splitting. The inclusion of microbial predictors improved the models, as indicated by decreased root mean square prediction error (RMSPE) and mean absolute error (MAE), and increased Lin’s concordance correlation coefficient (CCC). Both glmmLasso and SCAD reduced the Akaike information criterion (AIC) and Bayesian information criterion (BIC) for both the ANIM-B and the DMI-B models, while the other two ML methods had mixed outcomes. By balancing prediction performance and fitting performance, we obtained one ANIM-B model (containing 10 genera of bacteria and 3 animal data) fitted using glmmLasso and one DMI-B model (5 genera of bacteria and 1 animal datum) fitted using SCAD. This study highlights the importance of incorporating rumen microbiota data in CH 4 prediction models to enhance accuracy and robustness. Additionally, ML methods facilitate the selection of microbial predictors from high-dimensional metataxonomic data of the rumen microbiota without overfitting. Moreover, the identified microbial predictors can serve as biomarkers of CH 4 emissions from sheep, providing valuable insights for future research and mitigation strategies.
Abstract Methane (CH4) emissions from ruminants are of a significant environmental concern, necessitating accurate prediction for emission inventories. Existing models rely solely on dietary and host animal-related data, ignoring the predicting power of rumen microbiota, the source of CH4. To address this limitation, we developed novel CH4 prediction models incorporating rumen microbes as predictors, alongside animal- and feed-related predictors using four statistical/machine learning (ML) methods. These include random forest combined with boosting (RF-B), least absolute shrinkage and selection operator (LASSO), generalized linear mixed model with LASSO (glmmLasso), and smoothly clipped absolute deviation (SCAD) implemented on linear mixed models. With a sheep dataset (218 observations) of both animal data and rumen microbiota data (relative sequence abundance of 330 genera of rumen bacteria, archaea, protozoa, and fungi), we developed linear mixed models to predict CH4 production (g CH4/animal·d, ANIM-B models) and CH4 yield (g CH4/kg of dry matter intake, DMI-B models). We also developed models solely based on animal-related data. Prediction performance was evaluated 200 times with random data splits, while fitting performance was assessed without data splitting. The inclusion of microbial predictors improved the models, as indicated by decreased root mean square prediction error (RMSPE) and mean absolute error (MAE), and increased Lin’s concordance correlation coefficient (CCC). Both glmmLasso and SCAD reduced the Akaike information criterion (AIC) and Bayesian information criterion (BIC) for both the ANIM-B and the DMI-B models, while the other two ML methods had mixed outcomes. By balancing prediction performance and fitting performance, we obtained one ANIM-B model (containing 10 genera of bacteria and 3 animal data) fitted using glmmLasso and one DMI-B model (5 genera of bacteria and 1 animal datum) fitted using SCAD. This study highlights the importance of incorporating rumen microbiota data in CH4 prediction models to enhance accuracy and robustness. Additionally, ML methods facilitate the selection of microbial predictors from high-dimensional metataxonomic data of the rumen microbiota without overfitting. Moreover, the identified microbial predictors can serve as biomarkers of CH4 emissions from sheep, providing valuable insights for future research and mitigation strategies.
Methane (CH ) emissions from ruminants are of a significant environmental concern, necessitating accurate prediction for emission inventories. Existing models rely solely on dietary and host animal-related data, ignoring the predicting power of rumen microbiota, the source of CH . To address this limitation, we developed novel CH prediction models incorporating rumen microbes as predictors, alongside animal- and feed-related predictors using four statistical/machine learning (ML) methods. These include random forest combined with boosting (RF-B), least absolute shrinkage and selection operator (LASSO), generalized linear mixed model with LASSO (glmmLasso), and smoothly clipped absolute deviation (SCAD) implemented on linear mixed models. With a sheep dataset (218 observations) of both animal data and rumen microbiota data (relative sequence abundance of 330 genera of rumen bacteria, archaea, protozoa, and fungi), we developed linear mixed models to predict CH production (g CH /animal·d, ANIM-B models) and CH yield (g CH /kg of dry matter intake, DMI-B models). We also developed models solely based on animal-related data. Prediction performance was evaluated 200 times with random data splits, while fitting performance was assessed without data splitting. The inclusion of microbial predictors improved the models, as indicated by decreased root mean square prediction error (RMSPE) and mean absolute error (MAE), and increased Lin's concordance correlation coefficient (CCC). Both glmmLasso and SCAD reduced the Akaike information criterion (AIC) and Bayesian information criterion (BIC) for both the ANIM-B and the DMI-B models, while the other two ML methods had mixed outcomes. By balancing prediction performance and fitting performance, we obtained one ANIM-B model (containing 10 genera of bacteria and 3 animal data) fitted using glmmLasso and one DMI-B model (5 genera of bacteria and 1 animal datum) fitted using SCAD. This study highlights the importance of incorporating rumen microbiota data in CH prediction models to enhance accuracy and robustness. Additionally, ML methods facilitate the selection of microbial predictors from high-dimensional metataxonomic data of the rumen microbiota without overfitting. Moreover, the identified microbial predictors can serve as biomarkers of CH emissions from sheep, providing valuable insights for future research and mitigation strategies.
Methane (CH4) emissions from ruminants are of a significant environmental concern, necessitating accurate prediction for emission inventories. Existing models rely solely on dietary and host animal-related data, ignoring the predicting power of rumen microbiota, the source of CH4. To address this limitation, we developed novel CH4 prediction models incorporating rumen microbes as predictors, alongside animal- and feed-related predictors using four statistical/machine learning (ML) methods. These include random forest combined with boosting (RF-B), least absolute shrinkage and selection operator (LASSO), generalized linear mixed model with LASSO (glmmLasso), and smoothly clipped absolute deviation (SCAD) implemented on linear mixed models. With a sheep dataset (218 observations) of both animal data and rumen microbiota data (relative sequence abundance of 330 genera of rumen bacteria, archaea, protozoa, and fungi), we developed linear mixed models to predict CH4 production (g CH4/animal·d, ANIM-B models) and CH4 yield (g CH4/kg of dry matter intake, DMI-B models). We also developed models solely based on animal-related data. Prediction performance was evaluated 200 times with random data splits, while fitting performance was assessed without data splitting. The inclusion of microbial predictors improved the models, as indicated by decreased root mean square prediction error (RMSPE) and mean absolute error (MAE), and increased Lin’s concordance correlation coefficient (CCC). Both glmmLasso and SCAD reduced the Akaike information criterion (AIC) and Bayesian information criterion (BIC) for both the ANIM-B and the DMI-B models, while the other two ML methods had mixed outcomes. By balancing prediction performance and fitting performance, we obtained one ANIM-B model (containing 10 genera of bacteria and 3 animal data) fitted using glmmLasso and one DMI-B model (5 genera of bacteria and 1 animal datum) fitted using SCAD. This study highlights the importance of incorporating rumen microbiota data in CH4 prediction models to enhance accuracy and robustness. Additionally, ML methods facilitate the selection of microbial predictors from high-dimensional metataxonomic data of the rumen microbiota without overfitting. Moreover, the identified microbial predictors can serve as biomarkers of CH4 emissions from sheep, providing valuable insights for future research and mitigation strategies.
ArticleNumber 21305
Author Firkins, Jeffrey
Eugène, Maguy A.
Zhang, Boyang
McGee, Mark
Kreuzer, Michael
Crompton, Les A.
Kebreab, Ermias
Reynolds, Christopher K.
Schwarm, Angela
Dijkstra, Jan
Hristov, Alexander N.
Bannink, André
Bayat, Alireza R.
Yáñez-Ruiz, David R.
Janssen, Peter H.
Lin, Shili
Moraes, Luis
Yu, Zhongtang
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  organization: Department of Statistics, The Ohio State University
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  fullname: Dijkstra, Jan
  organization: Animal Nutrition Group, Wageningen University & Research
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  organization: Institute of Agricultural Sciences, ETH Zurich
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  organization: School of Agriculture, Policy, and Development, University of Reading
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  surname: Schwarm
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  organization: Department of Animal and Aquacultural Sciences, Norwegian University of Life Sciences
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  organization: Estación Experimental del Zaidin (CSIC)
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  givenname: Zhongtang
  surname: Yu
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  organization: Department of Animal Sciences, The Ohio State University
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CitedBy_id crossref_primary_10_1016_j_apenergy_2024_124613
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CallahanBJMcMurdiePJRosenMJHanAWJohnsonAJHolmesSPDADA2: High-resolution sample inference from Illumina amplicon dataNat. Methods2016135815831:CAS:528:DC%2BC28XosVWitb4%3D10.1038/nmeth.3869272140474927377
Team, R. C. R: A Language and Environment for Statistical Computing (v. 4.0. 2) [Computer Software] (R Foundation for Statistical Computing, 2020).
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MarchandinHJuvonenRHaikaraATrujilloMEMegasphaeraBergey's Manual of Systematics of Archaea and Bacteria2015Wiley116
NiuMPrediction of enteric methane production, yield, and intensity in dairy cattle using an intercontinental databaseGlob. Chang. Biol.201824336833892018GCBio..24.3368N10.1111/gcb.14094294509806055644
RamosAFOTucuma oil shifted ruminal fermentation, reducing methane production and altering the microbiome but decreased substrate digestibility within a RUSITEC fed a mixed hay–concentrate dietFront. Microbiol.20189164710.3389/fmicb.2018.01647300938886071481
JamesGWittenDHastieTTibshiraniRTaylorJAn Introduction to Statistical Learning with Applications in R20212Springer44510.1007/978-1-0716-1418-11469.62002
Lopez-GarciaAFungal and ciliate protozoa are the main rumen microbes associated with methane emissions in dairy cattleGigascience202211881:CAS:528:DC%2BB3sXosF2nsrg%3D10.1093/gigascience/giab088
KittelmannSTwo different bacterial community types are linked with the low-methane emission trait in sheepPLoS ONE201492014PLoSO...9j3171K1:CAS:528:DC%2BC2cXhs1ertbfO10.1371/journal.pone.0103171250785644117531
DenmanSEMartinez FernandezGShinkaiTMitsumoriMMcSweeneyCSMetagenomic analysis of the rumen microbial community following inhibition of methane formation by a halogenated methane analogFront. Microbiol.20156108710.3389/fmicb.2015.01087265282534602129
HammondKJReview of current in vivo measurement techniques for quantifying enteric methane emission from ruminantsAnim. Feed Sci. Technol.201621913301:CAS:528:DC%2BC28XpvFakurc%3D10.1016/j.anifeedsci.2016.05.018
GreeningCDiverse hydrogen production and consumption pathways influence methane production in ruminantsISME J.201913261726321:CAS:528:DC%2BC1MXhtlGgs7rL10.1038/s41396-019-0464-2312433326776011
EllisonMJDiet and feed efficiency status affect rumen microbial profiles of sheepSmall Rumin. Res.2017156121910.1016/j.smallrumres.2017.08.009
HendersonGImproved taxonomic assignment of rumen bacterial 16S rRNA sequences using a revised SILVA taxonomic frameworkPeerJ201971:CAS:528:DC%2BC1MXitl2jsrbF10.7717/peerj.6496308636736407505
JohnsonKAJohnsonDEMethane emissions from cattleJ. Anim. Sci.199573248324921:CAS:528:DyaK2MXnsVCntb8%3D10.2527/1995.7382483x8567486
TapioISnellingTJStrozziFWallaceRJThe ruminal microbiome associated with methane emissions from ruminant livestockJ. Anim. Sci. Biotechnol.2017871:CAS:528:DC%2BC1cXlsVCru78%3D10.1186/s40104-017-0141-0281236985244708
Pinares-PatinoCSHeritability estimates of methane emissions from sheepAnimal20137Suppl 231632110.1017/S1751731113000864237394733691003
PatraAKYuZCombinations of nitrate, saponin, and sulfate additively reduce methane production by rumen cultures in vitro while not adversely affecting feed digestion, fermentation or microbial communitiesBioresour. Technol.20141551291351:CAS:528:DC%2BC2cXmslWqtbY%3D10.1016/j.biortech.2013.12.09924440491
NilssonRHThe UNITE database for molecular identification of fungi: Handling dark taxa and parallel taxonomic classificationsNucleic Acids Res.201947D259D2641:CAS:528:DC%2BC1MXhs1Cgt7nF10.1093/nar/gky102230371820
KamkeJRumen metagenome and metatranscriptome analyses of low methane yield sheep reveals a Sharpea-enriched microbiome characterised by lactic acid formation and utilisationMicrobiome201645610.1186/s40168-016-0201-2277605705069950
FriedmanJHastieTTibshiraniRRegularization paths for generalized linear models via coordinate descentJ. Stat. Softw.20103312210.18637/jss.v033.i01208087282929880
CunhaCSCompositional and structural dynamics of the ruminal microbiota in dairy heifers and its relationship to methane productionJ. Sci. Food Agric.2019992102181:CAS:528:DC%2BC1cXht12lsLfE10.1002/jsfa.916229851082
QuastCThe SILVA ribosomal RNA gene database project: Improved data processing and web-based toolsNucleic Acids Res.201341D5905961:CAS:528:DC%2BC38XhvV2ksb%2FN10.1093/nar/gks121923193283
AppuhamyJAFranceJKebreabEModels for predicting enteric methane emissions from dairy cows in North America, Europe, and Australia and New ZealandGlob. Chang. Biol.201622303930562016GCBio..22.3039A10.1111/gcb.1333927148862
van LingenHJPrediction of enteric methane production, yield and intensity of beef cattle using an intercontinental databaseAgric. Ecosyst. Environ.20192831:CAS:528:DC%2BC1MXhtlejurbF10.1016/j.agee.2019.106575
Stevenson, M. et al.EpiR: An R Package for the Analysis of Epidemiological Data v. 2.0.62 (2013).
Granja-SalcedoYTLong-term encapsulated nitrate supplementation modulates rumen microbial diversity and rumen fermentation to reduce methane emission in grazing steersFront. Microbiol.20191061410.3389/fmicb.2019.00614309841416449429
LiREffect of different forage-to-concentrate ratios on ruminal bacterial structure and real-time methane production in sheepPLoS ONE2019141:CAS:528:DC%2BC1MXhtV2jtb3O10.1371/journal.pone.0214777311167576530836
SatoYCalcium salts of long-chain fatty acids from linseed oil decrease methane production by altering the rumen microbiome in vitroPLoS ONE2020151:CAS:528:DC%2BB3cXitlGgur3P10.1371/journal.pone.0242158331708867654805
Greg, R. gbm: Generalized Boosted Regression Models v. 2.1.8.1 (2010).
BatesDMächlerMBolkerBWalkerSFitting linear mixed-effects models using lme4J. Stat. Softw.20156714810.18637/jss.v067.i01
van GastelenSLinseed oil and DGAT1 K232A polymorphism: Effects on methane emission, energy and nitrogen metabolism, lactation performance, ruminal fermentation, and rumen microbial composition of Holstein-Friesian cowsJ. Dairy Sci.2017100893989571:CAS:528:DC%2BC2sXhsFSmt7vF10.3168/jds.2016-1236728918153
ZhouMHernandez-SanabriaEGuanLLCharacterization of variation in rumen methanogenic communities under different dietary and host feed efficiency conditions, as determined by PCR-denaturing gradient gel electrophoresis analysisAppl. Environ. Microbiol.201076377637862010ApEnM..76.3776Z1:CAS:528:DC%2BC3cXptVenurY%3D10.1128/AEM.00010-10204184362893468
PereaKFeed efficiency phenotypes in lambs involve changes in ruminal, colonic, and small-intestine-located microbiotaJ. Anim. Sci.201795258525921:CAS:528:DC%2BC2sXht1Kns7vF10.2527/jas.2016.122228727071
HristovANSpecial topics–mitigation of methane and nitrous oxide emissions from animal operations: I. A review of enteric methane mitigation optionsJ. Anim. Sci.201391504550691:CAS:528:DC%2BC3sXhslKktrrL10.2527/jas.2013-658324045497
ZhaoYGO'ConnellNEYanTPrediction of enteric methane emissions from sheep offered fresh perennial ryegrass (Lolium perenne) using data measured in indirect open-circuit respiration chambersJ. Anim. Sci.201694242524351:CAS:528:DC%2BC28XhsFejurzJ10.2527/jas.2016-033427285918
HristovANSymposium review: Uncertainties in enteric methane inventories, measurement techniques, and prediction modelsJ. Dairy Sci.2018101665566741:CAS:528:DC%2BC1cXnvFemsbg%3D10.3168/jds.2017-1353629680642
PelchenAPetersKJMethane emissions from sheepSmall Rumin. Res.19982713715010.1016/S0921-4488(97)00031-X
Yang, L. Model-Based Clustering of Longitudinal Data in High Dimensions Thesis (Ph.D.) thesis (University of Rochester, 2021).
MurrayRMBryantAMLengRARates of production of methane in the rumen and large intestine of sheepBr. J. Nutr.1976361141:CAS:528:DyaE28XkvFSqt7o%3D10.1079/bjn19760053949464
BachAChanges in the rumen and colon microbiota and effects of live yeast dietary supplementation during the transition from the dry period to lactation of dairy cowsJ. Dairy Sci.2019102618061981:CAS:528:DC%2BC1MXovVOmsrs%3D10.3168/jds.2018-1610531056321
GrollATutzGVariable selection for generalized linear mixed models by L 1-penalized estimationStat. Comput.201224137154316554410.1007/s11222-012-9359-z1325.62139
McLoughlinSRumen microbiome composition is altered in sheep divergent in feed efficiencyFront. Microbiol.202011198110.3389/fmicb.2020.01981329830097477290
Masson-Delmotte, V. et al. Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (2021).
SaldanaDFFengYSIS: An R package for sure independence screening in ultrahigh-dimensional statistical modelsJ. Stat. Softw.201810.18637/jss.v083.i02
YG Zhao (48449_CR5) 2016; 94
YT Granja-Salcedo (48449_CR45) 2019; 10
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A Groll (48449_CR20) 2012; 24
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S Kittelmann (48449_CR13) 2014; 9
HJ van Lingen (48449_CR6) 2019; 283
RM Murray (48449_CR27) 1976; 36
KA Johnson (48449_CR2) 1995; 73
Y Sato (48449_CR42) 2020; 15
RH Nilsson (48449_CR17) 2019; 47
J Friedman (48449_CR19) 2010; 33
C Greening (48449_CR33) 2019; 13
A Lopez-Garcia (48449_CR34) 2022; 11
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AK Patra (48449_CR10) 2014; 155
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48449_CR26
References_xml – reference: PelchenAPetersKJMethane emissions from sheepSmall Rumin. Res.19982713715010.1016/S0921-4488(97)00031-X
– reference: MarchandinHJuvonenRHaikaraATrujilloMEMegasphaeraBergey's Manual of Systematics of Archaea and Bacteria2015Wiley116
– reference: NewboldCJRamos-MoralesEReview: Ruminal microbiome and microbial metabolome: Effects of diet and ruminant hostAnimal202014s78s861:STN:280:DC%2BB38%2FoslKlsg%3D%3D10.1017/S175173111900325232024572
– reference: Lopez-GarciaAFungal and ciliate protozoa are the main rumen microbes associated with methane emissions in dairy cattleGigascience202211881:CAS:528:DC%2BB3sXosF2nsrg%3D10.1093/gigascience/giab088
– reference: PereaKFeed efficiency phenotypes in lambs involve changes in ruminal, colonic, and small-intestine-located microbiotaJ. Anim. Sci.201795258525921:CAS:528:DC%2BC2sXht1Kns7vF10.2527/jas.2016.122228727071
– reference: AppuhamyJAFranceJKebreabEModels for predicting enteric methane emissions from dairy cows in North America, Europe, and Australia and New ZealandGlob. Chang. Biol.201622303930562016GCBio..22.3039A10.1111/gcb.1333927148862
– reference: HristovANSpecial topics–mitigation of methane and nitrous oxide emissions from animal operations: I. A review of enteric methane mitigation optionsJ. Anim. Sci.201391504550691:CAS:528:DC%2BC3sXhslKktrrL10.2527/jas.2013-658324045497
– reference: Stevenson, M. et al.EpiR: An R Package for the Analysis of Epidemiological Data v. 2.0.62 (2013).
– reference: JamesGWittenDHastieTTibshiraniRTaylorJAn Introduction to Statistical Learning with Applications in R20212Springer44510.1007/978-1-0716-1418-11469.62002
– reference: HristovANSymposium review: Uncertainties in enteric methane inventories, measurement techniques, and prediction modelsJ. Dairy Sci.2018101665566741:CAS:528:DC%2BC1cXnvFemsbg%3D10.3168/jds.2017-1353629680642
– reference: EllisonMJDiet and feed efficiency status affect rumen microbial profiles of sheepSmall Rumin. Res.2017156121910.1016/j.smallrumres.2017.08.009
– reference: McLoughlinSRumen microbiome composition is altered in sheep divergent in feed efficiencyFront. Microbiol.202011198110.3389/fmicb.2020.01981329830097477290
– reference: Pinares-PatinoCSHeritability estimates of methane emissions from sheepAnimal20137Suppl 231632110.1017/S1751731113000864237394733691003
– reference: MurrayRMBryantAMLengRARates of production of methane in the rumen and large intestine of sheepBr. J. Nutr.1976361141:CAS:528:DyaE28XkvFSqt7o%3D10.1079/bjn19760053949464
– reference: GreeningCDiverse hydrogen production and consumption pathways influence methane production in ruminantsISME J.201913261726321:CAS:528:DC%2BC1MXhtlGgs7rL10.1038/s41396-019-0464-2312433326776011
– reference: Greg, R. gbm: Generalized Boosted Regression Models v. 2.1.8.1 (2010).
– reference: SaldanaDFFengYSIS: An R package for sure independence screening in ultrahigh-dimensional statistical modelsJ. Stat. Softw.201810.18637/jss.v083.i02
– reference: HendersonGImproved taxonomic assignment of rumen bacterial 16S rRNA sequences using a revised SILVA taxonomic frameworkPeerJ201971:CAS:528:DC%2BC1MXitl2jsrbF10.7717/peerj.6496308636736407505
– reference: BachAChanges in the rumen and colon microbiota and effects of live yeast dietary supplementation during the transition from the dry period to lactation of dairy cowsJ. Dairy Sci.2019102618061981:CAS:528:DC%2BC1MXovVOmsrs%3D10.3168/jds.2018-1610531056321
– reference: OhSKoikeSKobayashiYEffect of ginkgo extract supplementation on in vitro rumen fermentation and bacterial profiles under different dietary conditionsAnim. Sci. J.201788173717431:CAS:528:DC%2BC2sXhslyms77I10.1111/asj.1287728707415
– reference: GrollATutzGVariable selection for generalized linear mixed models by L 1-penalized estimationStat. Comput.201224137154316554410.1007/s11222-012-9359-z1325.62139
– reference: BatesDMächlerMBolkerBWalkerSFitting linear mixed-effects models using lme4J. Stat. Softw.20156714810.18637/jss.v067.i01
– reference: FriedmanJHastieTTibshiraniRRegularization paths for generalized linear models via coordinate descentJ. Stat. Softw.20103312210.18637/jss.v033.i01208087282929880
– reference: Masson-Delmotte, V. et al. Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (2021).
– reference: DenmanSEMartinez FernandezGShinkaiTMitsumoriMMcSweeneyCSMetagenomic analysis of the rumen microbial community following inhibition of methane formation by a halogenated methane analogFront. Microbiol.20156108710.3389/fmicb.2015.01087265282534602129
– reference: RamosAFOTucuma oil shifted ruminal fermentation, reducing methane production and altering the microbiome but decreased substrate digestibility within a RUSITEC fed a mixed hay–concentrate dietFront. Microbiol.20189164710.3389/fmicb.2018.01647300938886071481
– reference: ChenLMegasphaeraelsdenii lactate degradation pattern shifts in rumen acidosis modelsFront. Microbiol.20191016210.3389/fmicb.2019.00162307927046374331
– reference: PatraAKYuZCombinations of nitrate, saponin, and sulfate additively reduce methane production by rumen cultures in vitro while not adversely affecting feed digestion, fermentation or microbial communitiesBioresour. Technol.20141551291351:CAS:528:DC%2BC2cXmslWqtbY%3D10.1016/j.biortech.2013.12.09924440491
– reference: KittelmannSTwo different bacterial community types are linked with the low-methane emission trait in sheepPLoS ONE201492014PLoSO...9j3171K1:CAS:528:DC%2BC2cXhs1ertbfO10.1371/journal.pone.0103171250785644117531
– reference: QuastCThe SILVA ribosomal RNA gene database project: Improved data processing and web-based toolsNucleic Acids Res.201341D5905961:CAS:528:DC%2BC38XhvV2ksb%2FN10.1093/nar/gks121923193283
– reference: CunhaCSCompositional and structural dynamics of the ruminal microbiota in dairy heifers and its relationship to methane productionJ. Sci. Food Agric.2019992102181:CAS:528:DC%2BC1cXht12lsLfE10.1002/jsfa.916229851082
– reference: CallahanBJMcMurdiePJRosenMJHanAWJohnsonAJHolmesSPDADA2: High-resolution sample inference from Illumina amplicon dataNat. Methods2016135815831:CAS:528:DC%2BC28XosVWitb4%3D10.1038/nmeth.3869272140474927377
– reference: SatoYCalcium salts of long-chain fatty acids from linseed oil decrease methane production by altering the rumen microbiome in vitroPLoS ONE2020151:CAS:528:DC%2BB3cXitlGgur3P10.1371/journal.pone.0242158331708867654805
– reference: LiREffect of different forage-to-concentrate ratios on ruminal bacterial structure and real-time methane production in sheepPLoS ONE2019141:CAS:528:DC%2BC1MXhtV2jtb3O10.1371/journal.pone.0214777311167576530836
– reference: Yang, L. Model-Based Clustering of Longitudinal Data in High Dimensions Thesis (Ph.D.) thesis (University of Rochester, 2021).
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Snippet Methane (CH 4 ) emissions from ruminants are of a significant environmental concern, necessitating accurate prediction for emission inventories. Existing...
Methane (CH ) emissions from ruminants are of a significant environmental concern, necessitating accurate prediction for emission inventories. Existing models...
Methane (CH4) emissions from ruminants are of a significant environmental concern, necessitating accurate prediction for emission inventories. Existing models...
Methane (CH 4) emissions from ruminants are of a significant environmental concern, necessitating accurate prediction for emission inventories. Existing models...
Abstract Methane (CH4) emissions from ruminants are of a significant environmental concern, necessitating accurate prediction for emission inventories....
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SubjectTerms 631/326
631/326/171
631/326/2565
Animal Feed - analysis
Animal models
Animals
Bacteria
Bacteria - genetics
Bayes Theorem
Bayesian analysis
Correlation coefficient
Diet - veterinary
Dry matter
Emission inventories
Emissions
Environmental perception
Female
Food and Nutrition
Humanities and Social Sciences
Lactation
Life Sciences
Mathematical models
Methane
Microbiota
multidisciplinary
Prediction models
Rumen
Ruminants
Science
Science (multidisciplinary)
Sheep
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Title Methane prediction equations including genera of rumen bacteria as predictor variables improve prediction accuracy
URI https://link.springer.com/article/10.1038/s41598-023-48449-y
https://www.ncbi.nlm.nih.gov/pubmed/38042941
https://www.proquest.com/docview/2896192229
https://www.proquest.com/docview/2896805970
https://hal.science/hal-04331242
https://pubmed.ncbi.nlm.nih.gov/PMC10693554
https://doaj.org/article/7e2d9a1b4d124d369879582869615c24
Volume 13
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