TY - JOUR
T1 - Prediction of enteric methane production and yield in dairy cattle using a Latin America and Caribbean database
AU - Congio, Guilhermo F.S.
AU - Bannink, André
AU - Mayorga, Olga L.
AU - Rodrigues, João P.P.
AU - Bougouin, Adeline
AU - Kebreab, Ermias
AU - Silva, Ricardo R.
AU - Maurício, Rogério M.
AU - da Silva, Sila C.
AU - Oliveira, Patrícia P.A.
AU - Muñoz, Camila
AU - Pereira, Luiz G.R.
AU - Gómez, Carlos
AU - Ariza-Nieto, Claudia
AU - Ribeiro-Filho, Henrique M.N.
AU - Castelán-Ortega, Octavio A.
AU - Rosero-Noguera, Jaime R.
AU - Tieri, Maria P.
AU - Rodrigues, Paulo H.M.
AU - Marcondes, Marcos I.
AU - Astigarraga, Laura
AU - Abarca, Sergio
AU - Hristov, Alexander N.
N1 - Funding Information:
The LAC methane project was supported by AgResearch (S7-SOW21-Feed/Methane) which was funded by the New Zealand Government to support the objectives of the Livestock Research Group of the Global Research Alliance on Agricultural Greenhouse Gases. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The funding sources that allowed the collaborators to carry out their projects are listed in the Supplementary Material.
Publisher Copyright:
© 2022 Elsevier B.V.
PY - 2022/6/15
Y1 - 2022/6/15
N2 - Enteric methane (CH4) from ruminants is the major driver of global warming and climate change. Successful mitigation efforts entail accurate estimation of on-farm emission and prediction models can be an alternative to current laborious and costly in vivo CH4 measurement techniques. This study aimed to: (1) collate a database of individual dairy cattle CH4 emission data from studies conducted in the Latin America and Caribbean (LAC) region; (2) identify key variables for predicting CH4 production (g d−1) and yield [g kg−1 of dry matter intake (DMI)]; (3) develop and cross-validate these newly-developed models; and (4) compare models' predictive ability with equations currently used to support national greenhouse gas (GHG) inventories. A total of 42 studies including 1327 individual dairy cattle records were collated. After removing outliers, the final database retained 34 studies and 610 animal records. Production and yield of CH4 were predicted by fitting mixed-effects models with a random effect of study. Evaluation of developed models and fourteen extant equations was assessed on all-data, confined, and grazing cows subsets. Feed intake was the most important predictor of CH4 production. Our best-developed CH4 production models outperformed Tier 2 equations from the Intergovernmental Panel on Climate Change (IPCC) in the all-data and grazing subsets, whereas they had similar performance for confined animals. Developed CH4 production models that include milk yield can be accurate and useful when feed intake is missing. Some extant equations had similar predictive performance to our best-developed models and can be an option for predicting CH4 production from LAC dairy cows. Extant equations were not accurate in predicting CH4 yield. The use of the newly-developed models rather than extant equations based on energy conversion factors, as applied by the IPCC, can substantially improve the accuracy of GHG inventories in LAC countries.
AB - Enteric methane (CH4) from ruminants is the major driver of global warming and climate change. Successful mitigation efforts entail accurate estimation of on-farm emission and prediction models can be an alternative to current laborious and costly in vivo CH4 measurement techniques. This study aimed to: (1) collate a database of individual dairy cattle CH4 emission data from studies conducted in the Latin America and Caribbean (LAC) region; (2) identify key variables for predicting CH4 production (g d−1) and yield [g kg−1 of dry matter intake (DMI)]; (3) develop and cross-validate these newly-developed models; and (4) compare models' predictive ability with equations currently used to support national greenhouse gas (GHG) inventories. A total of 42 studies including 1327 individual dairy cattle records were collated. After removing outliers, the final database retained 34 studies and 610 animal records. Production and yield of CH4 were predicted by fitting mixed-effects models with a random effect of study. Evaluation of developed models and fourteen extant equations was assessed on all-data, confined, and grazing cows subsets. Feed intake was the most important predictor of CH4 production. Our best-developed CH4 production models outperformed Tier 2 equations from the Intergovernmental Panel on Climate Change (IPCC) in the all-data and grazing subsets, whereas they had similar performance for confined animals. Developed CH4 production models that include milk yield can be accurate and useful when feed intake is missing. Some extant equations had similar predictive performance to our best-developed models and can be an option for predicting CH4 production from LAC dairy cows. Extant equations were not accurate in predicting CH4 yield. The use of the newly-developed models rather than extant equations based on energy conversion factors, as applied by the IPCC, can substantially improve the accuracy of GHG inventories in LAC countries.
UR - http://www.scopus.com/inward/record.url?scp=85126760984&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85126760984&partnerID=8YFLogxK
U2 - 10.1016/j.scitotenv.2022.153982
DO - 10.1016/j.scitotenv.2022.153982
M3 - Article
C2 - 35202679
AN - SCOPUS:85126760984
SN - 0048-9697
VL - 825
JO - Science of the Total Environment
JF - Science of the Total Environment
M1 - 153982
ER -