Improved prediction by enteric methane emission models in ruminant production systems by integrating climate classification
- Autores
- Akinropo, Tobi; Ricci, Patricia; Faverin, Claudia; Ciganda, Veronica; Muñoz, Camila; Ungerfeld, Emilio; Urrutia, Natalie; Rodriguez, Romina; Morgavi, Diego; Eugène, Maguy
- Año de publicación
- 2025
- Idioma
- inglés
- Tipo de recurso
- artículo
- Estado
- versión publicada
- Descripción
- Several mathematical models predict enteric methane (CH4) production (g/d) from ruminants. These models were developed from data originated from different regions and production systems and are not always applicable elsewhere. This study evaluated the performance of selected existing models for predicting enteric CH4 emissions of ruminants raised in diverse production systems in Argentina, Brazil, Chile and Uruguay. Climate was classified using the Köppen climate classification, while diets were classified by composition, such as high- or low-NDF, ether extract (EE), starch content and NDF digestibility. These classifications were applied to the diets fed to cattle (dairy, beef) and sheep. Models were evaluated and ranked by the lowest root mean square prediction error (RMSPE) and the ratio of RMSPE to SD of observed values (RSR). Models from the Intergovernmental Panel on Climate Change and those developed with Latin American data were used as reference. In temperate, no dry season, hot summer climates, all models performed poorly (RSR > 1) for dairy cattle. By diet composition, four models performed well (RSR < 1) for high-NDF diets. For beef cattle, the model by Yan et al. (2009) developed for forage-fed beef cattle performed best (RSR = 0.85) as all diets had high NDF. For sheep, the model by Congio et al. (2022a), which incorporates DM intake (DMI) and feeding level, performed best (RSR = 0.63); however, for highNDF diets, the model from Belanche et al. (2023) performed best. In temperate, no dry season, warm summer (Cfb) climates, models by Mills et al. (2003), which included DMI, performed best for dairy cattle (RSR = 0.78), including when assessed by diet composition. For low-EE diets, CH4 production (g/d) was best predicted using models with NDF intake (NDFI) and EE. For beef cattle, van Lingen et al. (2019), which included DMI, had the lowest RSR (0.91). By diet composition, models integrating fatty acids (FA), DMI, and NDFI performed best for low-NDF diets, while FA- and DMI-based models were most accurate for high-NDF diets. For sheep in Cfb climate, all models performed poorly. In tropical savanna, dry winter climates, all models performed poorly for beef cattle; similarly, poor performance was obtained when assessed by diet compositions. In conclusion, to improve CH4 emission prediction, model development should consider the potential effect of climate zones, together with ruminant category, feed intake, dietary composition (including potential of mitigation strategies), and feed digestibility parameters.
EEA Balcarce
Fil: Akinropo, Tobi. Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE). Unité Mixte de Recherche sur les Herbivores; Francia
Fil: Akinropo, Tobi. Université Clermont; Francia
Fil: Akinropo, Tobi. VetAgroSup; Francia
Fil: Ricci, Patricia. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Balcarce; Argentina
Fil: Faverin, Claudia. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Balcarce; Argentina
Fil: Ciganda, Veronica. Instituto Nacional de Investigación Agropecuaria (INIA). Estación Experimental La Estanzuela; Uruguay
Fil: Muñoz, Camila. Instituto de Investigaciones Agropecuarias (INIA). Centro Regional de Investigación Remehue; Chile
Fil: Ungerfeld, Emilio. Instituto de Investigaciones Agropecuarias (INIA). Centro Regional de Investigación Carillanca; Chile
Fil: Urrutia, Natalie. Instituto de Investigaciones Agropecuarias (INIA). Centro Regional de Investigación Remehue; Chile
Fil: Rodriguez, Romina. Instituto de Investigaciones Agropecuarias (INIA). Centro Regional de Investigación Remehue; Chile
Fil: Morgavi, Diego. Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE). Unité Mixte de Recherche sur les Herbivores; Francia
Fil: Morgavi, Diego. Université Clermont; Francia
Fil: Morgavi, Diego. VetAgroSup; Francia
Fil: Eugène, Maguy. Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE). Unité Mixte de Recherche sur les Herbivores; Francia
Fil: Eugène, Maguy. Université Clermont; Francia
Fil: Eugène, Maguy. VetAgroSup; Francia
Fil: Eugène, Maguy. Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE). Centre Antilles-Guyane. Unité de recherches en Agroécologie, Génétique et Systèmes d’Élevage Tropicaux (ASSET); Francia - Fuente
- Animal 19 (11) : 101665. (November 2025)
- Materia
-
Metano Entérico
Emisión de Metano
Rumiante
Clima
Sistemas de Producción
Enteric Methane
Methane Emission
Ruminants
Climate
Production Systems - Nivel de accesibilidad
- acceso abierto
- Condiciones de uso
- http://creativecommons.org/licenses/by-nc-sa/4.0/
- Repositorio
.jpg)
- Institución
- Instituto Nacional de Tecnología Agropecuaria
- OAI Identificador
- oai:localhost:20.500.12123/25010
Ver los metadatos del registro completo
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Improved prediction by enteric methane emission models in ruminant production systems by integrating climate classificationAkinropo, TobiRicci, PatriciaFaverin, ClaudiaCiganda, VeronicaMuñoz, CamilaUngerfeld, EmilioUrrutia, NatalieRodriguez, RominaMorgavi, DiegoEugène, MaguyMetano EntéricoEmisión de MetanoRumianteClimaSistemas de ProducciónEnteric MethaneMethane EmissionRuminantsClimateProduction SystemsSeveral mathematical models predict enteric methane (CH4) production (g/d) from ruminants. These models were developed from data originated from different regions and production systems and are not always applicable elsewhere. This study evaluated the performance of selected existing models for predicting enteric CH4 emissions of ruminants raised in diverse production systems in Argentina, Brazil, Chile and Uruguay. Climate was classified using the Köppen climate classification, while diets were classified by composition, such as high- or low-NDF, ether extract (EE), starch content and NDF digestibility. These classifications were applied to the diets fed to cattle (dairy, beef) and sheep. Models were evaluated and ranked by the lowest root mean square prediction error (RMSPE) and the ratio of RMSPE to SD of observed values (RSR). Models from the Intergovernmental Panel on Climate Change and those developed with Latin American data were used as reference. In temperate, no dry season, hot summer climates, all models performed poorly (RSR > 1) for dairy cattle. By diet composition, four models performed well (RSR < 1) for high-NDF diets. For beef cattle, the model by Yan et al. (2009) developed for forage-fed beef cattle performed best (RSR = 0.85) as all diets had high NDF. For sheep, the model by Congio et al. (2022a), which incorporates DM intake (DMI) and feeding level, performed best (RSR = 0.63); however, for highNDF diets, the model from Belanche et al. (2023) performed best. In temperate, no dry season, warm summer (Cfb) climates, models by Mills et al. (2003), which included DMI, performed best for dairy cattle (RSR = 0.78), including when assessed by diet composition. For low-EE diets, CH4 production (g/d) was best predicted using models with NDF intake (NDFI) and EE. For beef cattle, van Lingen et al. (2019), which included DMI, had the lowest RSR (0.91). By diet composition, models integrating fatty acids (FA), DMI, and NDFI performed best for low-NDF diets, while FA- and DMI-based models were most accurate for high-NDF diets. For sheep in Cfb climate, all models performed poorly. In tropical savanna, dry winter climates, all models performed poorly for beef cattle; similarly, poor performance was obtained when assessed by diet compositions. In conclusion, to improve CH4 emission prediction, model development should consider the potential effect of climate zones, together with ruminant category, feed intake, dietary composition (including potential of mitigation strategies), and feed digestibility parameters.EEA BalcarceFil: Akinropo, Tobi. Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE). Unité Mixte de Recherche sur les Herbivores; FranciaFil: Akinropo, Tobi. Université Clermont; FranciaFil: Akinropo, Tobi. VetAgroSup; FranciaFil: Ricci, Patricia. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Balcarce; ArgentinaFil: Faverin, Claudia. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Balcarce; ArgentinaFil: Ciganda, Veronica. Instituto Nacional de Investigación Agropecuaria (INIA). Estación Experimental La Estanzuela; UruguayFil: Muñoz, Camila. Instituto de Investigaciones Agropecuarias (INIA). Centro Regional de Investigación Remehue; ChileFil: Ungerfeld, Emilio. Instituto de Investigaciones Agropecuarias (INIA). Centro Regional de Investigación Carillanca; ChileFil: Urrutia, Natalie. Instituto de Investigaciones Agropecuarias (INIA). Centro Regional de Investigación Remehue; ChileFil: Rodriguez, Romina. Instituto de Investigaciones Agropecuarias (INIA). Centro Regional de Investigación Remehue; ChileFil: Morgavi, Diego. Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE). Unité Mixte de Recherche sur les Herbivores; FranciaFil: Morgavi, Diego. Université Clermont; FranciaFil: Morgavi, Diego. VetAgroSup; FranciaFil: Eugène, Maguy. Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE). Unité Mixte de Recherche sur les Herbivores; FranciaFil: Eugène, Maguy. Université Clermont; FranciaFil: Eugène, Maguy. VetAgroSup; FranciaFil: Eugène, Maguy. Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE). Centre Antilles-Guyane. Unité de recherches en Agroécologie, Génétique et Systèmes d’Élevage Tropicaux (ASSET); FranciaElsevier2026-01-20T17:15:40Z2026-01-20T17:15:40Z2025-11info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfhttp://hdl.handle.net/20.500.12123/25010https://www.sciencedirect.com/science/article/pii/S17517311250024841751-7311https://doi.org/10.1016/j.animal.2025.101665Animal 19 (11) : 101665. (November 2025)reponame:INTA Digital (INTA)instname:Instituto Nacional de Tecnología Agropecuariaenginfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)2026-02-26T11:47:41Zoai:localhost:20.500.12123/25010instacron:INTAInstitucionalhttp://repositorio.inta.gob.ar/Organismo científico-tecnológicoNo correspondehttp://repositorio.inta.gob.ar/oai/requesttripaldi.nicolas@inta.gob.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:l2026-02-26 11:47:41.462INTA Digital (INTA) - Instituto Nacional de Tecnología Agropecuariafalse |
| dc.title.none.fl_str_mv |
Improved prediction by enteric methane emission models in ruminant production systems by integrating climate classification |
| title |
Improved prediction by enteric methane emission models in ruminant production systems by integrating climate classification |
| spellingShingle |
Improved prediction by enteric methane emission models in ruminant production systems by integrating climate classification Akinropo, Tobi Metano Entérico Emisión de Metano Rumiante Clima Sistemas de Producción Enteric Methane Methane Emission Ruminants Climate Production Systems |
| title_short |
Improved prediction by enteric methane emission models in ruminant production systems by integrating climate classification |
| title_full |
Improved prediction by enteric methane emission models in ruminant production systems by integrating climate classification |
| title_fullStr |
Improved prediction by enteric methane emission models in ruminant production systems by integrating climate classification |
| title_full_unstemmed |
Improved prediction by enteric methane emission models in ruminant production systems by integrating climate classification |
| title_sort |
Improved prediction by enteric methane emission models in ruminant production systems by integrating climate classification |
| dc.creator.none.fl_str_mv |
Akinropo, Tobi Ricci, Patricia Faverin, Claudia Ciganda, Veronica Muñoz, Camila Ungerfeld, Emilio Urrutia, Natalie Rodriguez, Romina Morgavi, Diego Eugène, Maguy |
| author |
Akinropo, Tobi |
| author_facet |
Akinropo, Tobi Ricci, Patricia Faverin, Claudia Ciganda, Veronica Muñoz, Camila Ungerfeld, Emilio Urrutia, Natalie Rodriguez, Romina Morgavi, Diego Eugène, Maguy |
| author_role |
author |
| author2 |
Ricci, Patricia Faverin, Claudia Ciganda, Veronica Muñoz, Camila Ungerfeld, Emilio Urrutia, Natalie Rodriguez, Romina Morgavi, Diego Eugène, Maguy |
| author2_role |
author author author author author author author author author |
| dc.subject.none.fl_str_mv |
Metano Entérico Emisión de Metano Rumiante Clima Sistemas de Producción Enteric Methane Methane Emission Ruminants Climate Production Systems |
| topic |
Metano Entérico Emisión de Metano Rumiante Clima Sistemas de Producción Enteric Methane Methane Emission Ruminants Climate Production Systems |
| dc.description.none.fl_txt_mv |
Several mathematical models predict enteric methane (CH4) production (g/d) from ruminants. These models were developed from data originated from different regions and production systems and are not always applicable elsewhere. This study evaluated the performance of selected existing models for predicting enteric CH4 emissions of ruminants raised in diverse production systems in Argentina, Brazil, Chile and Uruguay. Climate was classified using the Köppen climate classification, while diets were classified by composition, such as high- or low-NDF, ether extract (EE), starch content and NDF digestibility. These classifications were applied to the diets fed to cattle (dairy, beef) and sheep. Models were evaluated and ranked by the lowest root mean square prediction error (RMSPE) and the ratio of RMSPE to SD of observed values (RSR). Models from the Intergovernmental Panel on Climate Change and those developed with Latin American data were used as reference. In temperate, no dry season, hot summer climates, all models performed poorly (RSR > 1) for dairy cattle. By diet composition, four models performed well (RSR < 1) for high-NDF diets. For beef cattle, the model by Yan et al. (2009) developed for forage-fed beef cattle performed best (RSR = 0.85) as all diets had high NDF. For sheep, the model by Congio et al. (2022a), which incorporates DM intake (DMI) and feeding level, performed best (RSR = 0.63); however, for highNDF diets, the model from Belanche et al. (2023) performed best. In temperate, no dry season, warm summer (Cfb) climates, models by Mills et al. (2003), which included DMI, performed best for dairy cattle (RSR = 0.78), including when assessed by diet composition. For low-EE diets, CH4 production (g/d) was best predicted using models with NDF intake (NDFI) and EE. For beef cattle, van Lingen et al. (2019), which included DMI, had the lowest RSR (0.91). By diet composition, models integrating fatty acids (FA), DMI, and NDFI performed best for low-NDF diets, while FA- and DMI-based models were most accurate for high-NDF diets. For sheep in Cfb climate, all models performed poorly. In tropical savanna, dry winter climates, all models performed poorly for beef cattle; similarly, poor performance was obtained when assessed by diet compositions. In conclusion, to improve CH4 emission prediction, model development should consider the potential effect of climate zones, together with ruminant category, feed intake, dietary composition (including potential of mitigation strategies), and feed digestibility parameters. EEA Balcarce Fil: Akinropo, Tobi. Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE). Unité Mixte de Recherche sur les Herbivores; Francia Fil: Akinropo, Tobi. Université Clermont; Francia Fil: Akinropo, Tobi. VetAgroSup; Francia Fil: Ricci, Patricia. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Balcarce; Argentina Fil: Faverin, Claudia. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Balcarce; Argentina Fil: Ciganda, Veronica. Instituto Nacional de Investigación Agropecuaria (INIA). Estación Experimental La Estanzuela; Uruguay Fil: Muñoz, Camila. Instituto de Investigaciones Agropecuarias (INIA). Centro Regional de Investigación Remehue; Chile Fil: Ungerfeld, Emilio. Instituto de Investigaciones Agropecuarias (INIA). Centro Regional de Investigación Carillanca; Chile Fil: Urrutia, Natalie. Instituto de Investigaciones Agropecuarias (INIA). Centro Regional de Investigación Remehue; Chile Fil: Rodriguez, Romina. Instituto de Investigaciones Agropecuarias (INIA). Centro Regional de Investigación Remehue; Chile Fil: Morgavi, Diego. Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE). Unité Mixte de Recherche sur les Herbivores; Francia Fil: Morgavi, Diego. Université Clermont; Francia Fil: Morgavi, Diego. VetAgroSup; Francia Fil: Eugène, Maguy. Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE). Unité Mixte de Recherche sur les Herbivores; Francia Fil: Eugène, Maguy. Université Clermont; Francia Fil: Eugène, Maguy. VetAgroSup; Francia Fil: Eugène, Maguy. Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE). Centre Antilles-Guyane. Unité de recherches en Agroécologie, Génétique et Systèmes d’Élevage Tropicaux (ASSET); Francia |
| description |
Several mathematical models predict enteric methane (CH4) production (g/d) from ruminants. These models were developed from data originated from different regions and production systems and are not always applicable elsewhere. This study evaluated the performance of selected existing models for predicting enteric CH4 emissions of ruminants raised in diverse production systems in Argentina, Brazil, Chile and Uruguay. Climate was classified using the Köppen climate classification, while diets were classified by composition, such as high- or low-NDF, ether extract (EE), starch content and NDF digestibility. These classifications were applied to the diets fed to cattle (dairy, beef) and sheep. Models were evaluated and ranked by the lowest root mean square prediction error (RMSPE) and the ratio of RMSPE to SD of observed values (RSR). Models from the Intergovernmental Panel on Climate Change and those developed with Latin American data were used as reference. In temperate, no dry season, hot summer climates, all models performed poorly (RSR > 1) for dairy cattle. By diet composition, four models performed well (RSR < 1) for high-NDF diets. For beef cattle, the model by Yan et al. (2009) developed for forage-fed beef cattle performed best (RSR = 0.85) as all diets had high NDF. For sheep, the model by Congio et al. (2022a), which incorporates DM intake (DMI) and feeding level, performed best (RSR = 0.63); however, for highNDF diets, the model from Belanche et al. (2023) performed best. In temperate, no dry season, warm summer (Cfb) climates, models by Mills et al. (2003), which included DMI, performed best for dairy cattle (RSR = 0.78), including when assessed by diet composition. For low-EE diets, CH4 production (g/d) was best predicted using models with NDF intake (NDFI) and EE. For beef cattle, van Lingen et al. (2019), which included DMI, had the lowest RSR (0.91). By diet composition, models integrating fatty acids (FA), DMI, and NDFI performed best for low-NDF diets, while FA- and DMI-based models were most accurate for high-NDF diets. For sheep in Cfb climate, all models performed poorly. In tropical savanna, dry winter climates, all models performed poorly for beef cattle; similarly, poor performance was obtained when assessed by diet compositions. In conclusion, to improve CH4 emission prediction, model development should consider the potential effect of climate zones, together with ruminant category, feed intake, dietary composition (including potential of mitigation strategies), and feed digestibility parameters. |
| publishDate |
2025 |
| dc.date.none.fl_str_mv |
2025-11 2026-01-20T17:15:40Z 2026-01-20T17:15:40Z |
| dc.type.none.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion http://purl.org/coar/resource_type/c_6501 info:ar-repo/semantics/articulo |
| format |
article |
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publishedVersion |
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http://hdl.handle.net/20.500.12123/25010 https://www.sciencedirect.com/science/article/pii/S1751731125002484 1751-7311 https://doi.org/10.1016/j.animal.2025.101665 |
| url |
http://hdl.handle.net/20.500.12123/25010 https://www.sciencedirect.com/science/article/pii/S1751731125002484 https://doi.org/10.1016/j.animal.2025.101665 |
| identifier_str_mv |
1751-7311 |
| dc.language.none.fl_str_mv |
eng |
| language |
eng |
| dc.rights.none.fl_str_mv |
info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by-nc-sa/4.0/ Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) |
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openAccess |
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http://creativecommons.org/licenses/by-nc-sa/4.0/ Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) |
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application/pdf |
| dc.publisher.none.fl_str_mv |
Elsevier |
| publisher.none.fl_str_mv |
Elsevier |
| dc.source.none.fl_str_mv |
Animal 19 (11) : 101665. (November 2025) reponame:INTA Digital (INTA) instname:Instituto Nacional de Tecnología Agropecuaria |
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