Feed additives for methane mitigation: Modeling the impact of feed additives on enteric methane emission of ruminants—Approaches and recommendations

Over the past decade, there has been considerable attention on mitigating enteric methane (CH) emissions from ruminants through the utilization of antimethanogenic feed additives (AMFA). Administered in small quantities, these additives demonstrate potential for substantial reductions of methanogene...

Descripción completa

Detalles Bibliográficos
Autores: Dijkstra, J., Bannink, A., Congio, G.F.S., Ellis, J.L., Eugène, M., García, F., Niu, M., Vibart, R.E., Yáñez Ruiz, David R., Kebreab, E.
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2025
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/388548
Acceso en línea:http://hdl.handle.net/10261/388548
Access Level:acceso abierto
Palabra clave:Feed additive
Methane mitigation
Modeling
Mechanistic models
Empirical models
id ES_e4db30a996d28fe3315e40b7d04d72a2
oai_identifier_str oai:digital.csic.es:10261/388548
network_acronym_str ES
network_name_str España
repository_id_str
spelling Feed additives for methane mitigation: Modeling the impact of feed additives on enteric methane emission of ruminants—Approaches and recommendationsDijkstra, J.Bannink, A.Congio, G.F.S.Ellis, J.L.Eugène, M.García, F.Niu, M.Vibart, R.E.Yáñez Ruiz, David R.Kebreab, E.Feed additiveMethane mitigationModelingMechanistic modelsEmpirical modelsOver the past decade, there has been considerable attention on mitigating enteric methane (CH) emissions from ruminants through the utilization of antimethanogenic feed additives (AMFA). Administered in small quantities, these additives demonstrate potential for substantial reductions of methanogenesis. Mathematical models play a crucial role in comprehending and predicting the quantitative impact of AMFA on enteric CH emissions across diverse diets and production systems. This study provides a comprehensive overview of methodologies for modeling the impact of AMFA on enteric CH emissions in ruminants, culminating in a set of recommendations for modeling approaches to quantify the impact of AMFA on CH emissions. Key considerations encompass the type of models employed (i.e., empirical models including meta-analyses, machine learning models, and mechanistic models), the modeling objectives, data availability, modeling synergies and trade-offs associated with using AMFA, and model applications for enhanced understanding, prediction, and integration into higher levels of aggregation. Based on an evaluation of these critical aspects, a set of recommendations is presented concerning modeling approaches for quantifying the impact of AMFA on CH emissions and in support of farm-level, national, regional, and global inventories for accounting greenhouse gas emissions in ruminant production systems.The authors acknowledge the financial support of the Global Dairy Platform (Rosemont, IL) through its Pathways to Net Zero initiative. Funding by the Dutch ministry of Agriculture, Nature and Food Quality (Global Research Alliance GRA; BO-43.10-001-001) is gratefully acknowledged. F. Garcia was supported by the Global Dairy Platform. The Technical Guidelines to Develop Feed Additives to Reduce Enteric Methane is a Flagship Project of the Global Research Alliance on Agricultural Greenhouse Gases and contributes to the work of the GRA's Livestock Research Group and Feed and Nutrition NetworkAmerican Dairy Science AssociationElsevierMinistry of Agriculture, Nature and Food Quality (The Netherlands)Yáñez-Ruiz, David R. [0000-0003-4397-3905]Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]2025202520252025info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Publisher's versioninfo:eu-repo/semantics/publishedVersionhttp://hdl.handle.net/10261/388548reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Ingléshttp://dx.doi.org/10.3168/jds.2024-25049Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/3885482026-05-22T06:33:51Z
dc.title.none.fl_str_mv Feed additives for methane mitigation: Modeling the impact of feed additives on enteric methane emission of ruminants—Approaches and recommendations
title Feed additives for methane mitigation: Modeling the impact of feed additives on enteric methane emission of ruminants—Approaches and recommendations
spellingShingle Feed additives for methane mitigation: Modeling the impact of feed additives on enteric methane emission of ruminants—Approaches and recommendations
Dijkstra, J.
Feed additive
Methane mitigation
Modeling
Mechanistic models
Empirical models
title_short Feed additives for methane mitigation: Modeling the impact of feed additives on enteric methane emission of ruminants—Approaches and recommendations
title_full Feed additives for methane mitigation: Modeling the impact of feed additives on enteric methane emission of ruminants—Approaches and recommendations
title_fullStr Feed additives for methane mitigation: Modeling the impact of feed additives on enteric methane emission of ruminants—Approaches and recommendations
title_full_unstemmed Feed additives for methane mitigation: Modeling the impact of feed additives on enteric methane emission of ruminants—Approaches and recommendations
title_sort Feed additives for methane mitigation: Modeling the impact of feed additives on enteric methane emission of ruminants—Approaches and recommendations
dc.creator.none.fl_str_mv Dijkstra, J.
Bannink, A.
Congio, G.F.S.
Ellis, J.L.
Eugène, M.
García, F.
Niu, M.
Vibart, R.E.
Yáñez Ruiz, David R.
Kebreab, E.
author Dijkstra, J.
author_facet Dijkstra, J.
Bannink, A.
Congio, G.F.S.
Ellis, J.L.
Eugène, M.
García, F.
Niu, M.
Vibart, R.E.
Yáñez Ruiz, David R.
Kebreab, E.
author_role author
author2 Bannink, A.
Congio, G.F.S.
Ellis, J.L.
Eugène, M.
García, F.
Niu, M.
Vibart, R.E.
Yáñez Ruiz, David R.
Kebreab, E.
author2_role author
author
author
author
author
author
author
author
author
dc.contributor.none.fl_str_mv Ministry of Agriculture, Nature and Food Quality (The Netherlands)
Yáñez-Ruiz, David R. [0000-0003-4397-3905]
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Feed additive
Methane mitigation
Modeling
Mechanistic models
Empirical models
topic Feed additive
Methane mitigation
Modeling
Mechanistic models
Empirical models
description Over the past decade, there has been considerable attention on mitigating enteric methane (CH) emissions from ruminants through the utilization of antimethanogenic feed additives (AMFA). Administered in small quantities, these additives demonstrate potential for substantial reductions of methanogenesis. Mathematical models play a crucial role in comprehending and predicting the quantitative impact of AMFA on enteric CH emissions across diverse diets and production systems. This study provides a comprehensive overview of methodologies for modeling the impact of AMFA on enteric CH emissions in ruminants, culminating in a set of recommendations for modeling approaches to quantify the impact of AMFA on CH emissions. Key considerations encompass the type of models employed (i.e., empirical models including meta-analyses, machine learning models, and mechanistic models), the modeling objectives, data availability, modeling synergies and trade-offs associated with using AMFA, and model applications for enhanced understanding, prediction, and integration into higher levels of aggregation. Based on an evaluation of these critical aspects, a set of recommendations is presented concerning modeling approaches for quantifying the impact of AMFA on CH emissions and in support of farm-level, national, regional, and global inventories for accounting greenhouse gas emissions in ruminant production systems.
publishDate 2025
dc.date.none.fl_str_mv 2025
2025
2025
2025
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
Publisher's version
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/388548
url http://hdl.handle.net/10261/388548
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv http://dx.doi.org/10.3168/jds.2024-25049
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv American Dairy Science Association
Elsevier
publisher.none.fl_str_mv American Dairy Science Association
Elsevier
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
reponame_str DIGITAL.CSIC. Repositorio Institucional del CSIC
collection DIGITAL.CSIC. Repositorio Institucional del CSIC
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869422627903242240
score 15,812429

Similares en LA Referencia