TY - JOUR
T1 - Prediction of enteric methane emissions by sheep using an intercontinental database
AU - Belanche, Alejandro
AU - Hristov, Alexander N.
AU - van Lingen, Henk J.
AU - Denman, Stuart E.
AU - Kebreab, Ermias
AU - Schwarm, Angela
AU - Kreuzer, Michael
AU - Niu, Mutian
AU - Eugène, Maguy
AU - Niderkorn, Vincent
AU - Martin, Cécile
AU - Archimède, Harry
AU - McGee, Mark
AU - Reynolds, Christopher K.
AU - Crompton, Les A.
AU - Bayat, Ali Reza
AU - Yu, Zhongtang
AU - Bannink, André
AU - Dijkstra, Jan
AU - Chaves, Alex V.
AU - Clark, Harry
AU - Muetzel, Stefan
AU - Lind, Vibeke
AU - Moorby, Jon M.
AU - Rooke, John A.
AU - Aubry, Aurélie
AU - Antezana, Walter
AU - Wang, Min
AU - Hegarty, Roger
AU - Hutton Oddy, V.
AU - Hill, Julian
AU - Vercoe, Philip E.
AU - Savian, Jean Víctor
AU - Abdalla, Adibe Luiz
AU - Soltan, Yosra A.
AU - Gomes Monteiro, Alda Lúcia
AU - Ku-Vera, Juan Carlos
AU - Jaurena, Gustavo
AU - Gómez-Bravo, Carlos A.
AU - Mayorga, Olga L.
AU - Congio, Guilhermo F.S.
AU - Yáñez-Ruiz, David R.
N1 - Funding Information:
Authors gratefully acknowledge the Joint Programming Initiative on Agriculture, Food Security and Climate Change (FACCE-JPI)'s ‘GLOBAL NETWORK’ project and the ‘Feeding and Nutrition Network’ of the Livestock Research Group within the Global Research Alliance for Agricultural Greenhouse Gases. National funding sources: AB has a Ramón y Cajal Grant funded by the Spanish Research Agency ( AEI: 10.13039/501100011033 , RYC 2019-027764-I ). DRYR was supported by INIA grant (ref. MIT01-GLOBALNET-EEZ ) and H2020 PATHWAYS project (grant agreement No 101000395 ). ANH was supported by the USDANational Institute of Food and Agriculture Federal Appropriations (Project PEN 04539, Ref.1000803). INRAE was funded by the French National Research Agency.
Publisher Copyright:
© 2022 The Authors
PY - 2023/1/15
Y1 - 2023/1/15
N2 - Enteric methane (CH4) emissions from sheep contribute to global greenhouse gas emissions from livestock. However, as already available for dairy and beef cattle, empirical models are needed to predict CH4 emissions from sheep for accounting purposes. The objectives of this study were to: 1) collate an intercontinental database of enteric CH4 emissions from individual sheep; 2) identify the key variables for predicting enteric sheep CH4 absolute production (g/d per animal) and yield [g/kg dry matter intake (DMI)] and their respective relationships; and 3) develop and cross-validate global equations as well as the potential need for age-, diet-, or climatic region-specific equations. The refined intercontinental database included 2,135 individual animal data from 13 countries. Linear CH4 prediction models were developed by incrementally adding variables. A universal CH4 production equation using only DMI led to a root mean square prediction error (RMSPE, % of observed mean) of 25.4% and an RMSPE-standard deviation ratio (RSR) of 0.69. Universal equations that, in addition to DMI, also included body weight (DMI + BW), and organic matter digestibility (DMI + OMD + BW) improved the prediction performance further (RSR, 0.62 and 0.60), whereas diet composition variables had negligible effects. These universal equations had lower prediction error than the extant IPCC 2019 equations. Developing age-specific models for adult sheep (>1-year-old) including DMI alone (RSR = 0.66) or in combination with rumen propionate molar proportion (for research of more refined purposes) substantially improved prediction performance (RSR = 0.57) on a smaller dataset. On the contrary, for young sheep (<1-year-old), the universal models could be applied, instead of age-specific models, if DMI and BW were included. Universal models showed similar prediction performances to the diet- and region-specific models. However, optimal prediction equations led to different regression coefficients (i.e. intercepts and slopes) for universal, age-specific, diet-specific, and region-specific models with predictive implications. Equations for CH4 yield led to low prediction performances, with DMI being negatively and BW and OMD positively correlated with CH4 yield. In conclusion, predicting sheep CH4 production requires information on DMI and prediction accuracy will improve national and global inventories if separate equations for young and adult sheep are used with the additional variables BW, OMD and rumen propionate proportion. Appropriate universal equations can be used to predict CH4 production from sheep across different diets and climatic conditions.
AB - Enteric methane (CH4) emissions from sheep contribute to global greenhouse gas emissions from livestock. However, as already available for dairy and beef cattle, empirical models are needed to predict CH4 emissions from sheep for accounting purposes. The objectives of this study were to: 1) collate an intercontinental database of enteric CH4 emissions from individual sheep; 2) identify the key variables for predicting enteric sheep CH4 absolute production (g/d per animal) and yield [g/kg dry matter intake (DMI)] and their respective relationships; and 3) develop and cross-validate global equations as well as the potential need for age-, diet-, or climatic region-specific equations. The refined intercontinental database included 2,135 individual animal data from 13 countries. Linear CH4 prediction models were developed by incrementally adding variables. A universal CH4 production equation using only DMI led to a root mean square prediction error (RMSPE, % of observed mean) of 25.4% and an RMSPE-standard deviation ratio (RSR) of 0.69. Universal equations that, in addition to DMI, also included body weight (DMI + BW), and organic matter digestibility (DMI + OMD + BW) improved the prediction performance further (RSR, 0.62 and 0.60), whereas diet composition variables had negligible effects. These universal equations had lower prediction error than the extant IPCC 2019 equations. Developing age-specific models for adult sheep (>1-year-old) including DMI alone (RSR = 0.66) or in combination with rumen propionate molar proportion (for research of more refined purposes) substantially improved prediction performance (RSR = 0.57) on a smaller dataset. On the contrary, for young sheep (<1-year-old), the universal models could be applied, instead of age-specific models, if DMI and BW were included. Universal models showed similar prediction performances to the diet- and region-specific models. However, optimal prediction equations led to different regression coefficients (i.e. intercepts and slopes) for universal, age-specific, diet-specific, and region-specific models with predictive implications. Equations for CH4 yield led to low prediction performances, with DMI being negatively and BW and OMD positively correlated with CH4 yield. In conclusion, predicting sheep CH4 production requires information on DMI and prediction accuracy will improve national and global inventories if separate equations for young and adult sheep are used with the additional variables BW, OMD and rumen propionate proportion. Appropriate universal equations can be used to predict CH4 production from sheep across different diets and climatic conditions.
KW - Age
KW - Climatic regions
KW - Diet composition
KW - Prediction models
KW - Rumen fermentation
UR - http://www.scopus.com/inward/record.url?scp=85144053697&partnerID=8YFLogxK
U2 - 10.1016/j.jclepro.2022.135523
DO - 10.1016/j.jclepro.2022.135523
M3 - Article
AN - SCOPUS:85144053697
SN - 0959-6526
VL - 384
JO - Journal of Cleaner Production
JF - Journal of Cleaner Production
M1 - 135523
ER -