Improving the accuracy of beef cattle methane inventories in Latin America and Caribbean countries
- Autores
- Congio, Guilhermo F.S.; Bannink, André; Mayorga, Olga L.; Rodrigues, João P. P.; Bougouin, Adeline; Kebreab, Ermias; Carvalho, Paulo C.F.; Berchielli, Telma T.; Mercadante, Maria E.Z.; Valadares-Filho, Sebastião C.; Borges, Ana L. C.C.; Berndt, Alexandre; Rodrigues, Paulo H.M.; Ku-Vera, Juan C.; Molina-Botero, Isabel C.; Arango, Jacobo; Reis, Ricardo A.; Posada-Ochoa, Sandra L.; Tomich, Thierry R.; Castelán-Ortega, Octavio A.; Marcondes, Marcos I.; Gómez, Carlos; Ribeiro-Filho, Henrique M.N.; Gere, José Ignacio; Ariza-Nieto, Claudia; Giraldo, Luis A.; Gonda, Horacio; Ceron Cucchi, Maria Esperanza; Hernandez, Olegario; Ricci, Patricia; Hristov, Alexander N.
- Año de publicación
- 2023
- Idioma
- inglés
- Tipo de recurso
- artículo
- Estado
- versión publicada
- Descripción
- On-farm methane (CH4) emissions need to be estimated accurately so that the mitigation effect of recommended practices can be accounted for. In the present study prediction equations for enteric CH4 have been developed in lieu of expensive animal measurement approaches. Our objectives were to: (1) compile a dataset from individual beef cattle data for the Latin America and Caribbean (LAC) region; (2) determine main predictors of CH4 emission variables; (3) develop and cross-validate prediction models according to dietary forage content (DFC); and (4) compare the predictive ability of these newly-developed models with extant equations reported in literature, including those currently used for CH4 inventories in LAC countries. After outlier's screening, 1100 beef cattle observations from 55 studies were kept in the final dataset (∼ 50 % of the original dataset). Mixed-effects models were fitted with a random effect of study. The whole dataset was split according to DFC into a subset for all-forage (DFC = 100 %), high-forage (94 % ≥ DFC ≥ 54 %), and low-forage (50 % ≥ DFC) diets. Feed intake and average daily gain (ADG) were the main predictors of CH4 emission (g d−1), whereas this was feeding level [dry matter intake (DMI) as % of body weight] for CH4 yield (g kg−1 DMI). The newly-developed models were more accurate than IPCC Tier 2 equations for all subsets. Simple and multiple regression models including ADG were accurate and a feasible option to predict CH4 emission when data on feed intake are not available. Methane yield was not well predicted by any extant equation in contrast to the newly-developed models. The present study delivered new models that may be alternatives for the IPCC Tier 2 equations to improve CH4 prediction for beef cattle in inventories of LAC countries based either on more or less readily available data.
Instituto de Patobiología
Fil: Congio, Guilhermo F. S. University of São Paulo. Luiz de Queiroz College of Agriculture. Department of Animal Science; Brasil
Fil: Bannink, André. Wageningen University & Research. Wageningen Livestock Research; Países Bajos
Fil: Mayorga, Olga L. Colombian Corporation for Agricultural Research; Colombia
Fil: Rodrigues, João P. P. Federal Rural University of Rio de Janeiro. Department of Animal Production. Animal Science Institute; Brasil
Fil: Bougouin, Adeline. University of California. Department of Animal Science; Estados Unidos
Fil: Kebreab, Ermias. University of California. Department of Animal Science; Estados Unidos
Fil: Carvalho, Paulo C. F. Federal University of Rio Grande do Sul. Department of Forage Plants and Agrometeorology; Brasil
Fil: Berchielli, Telma T. São Paulo State University. Department of Animal Science; Brasil
Fil: Mercadante, Maria E. Z. São Paulo Agribusiness Technology Agency. Institute of Animal Science; Brasil
Fil: Valadares-Filho, Sebastião C. Federal University of Viçosa. Department of Animal Science; Brasil
Fil: Borges, Ana L. C. C. Federal University of Minas Gerais. Department of Animal Science; Brasil
Fil: Berndt, Alexandre. Embrapa Southeast Livestock. Brazilian Agricultural Research Corporation; Brasil
Fil: Rodrigues, Paulo H. M. University of São Paulo. Faculty of Veterinary Medicine and Animal Science. Department of Animal Nutrition and Production; Brasil
Fil: Ku-Vera, Juan C. University of Yucatan. Faculty of Veterinary Medicine and Animal Science. Department of Animal Nutrition; México
Fil: Molina-Botero, Isabel C. National Agrarian University La Molina. Faculty of Animal Science. Department of Animal Husbandry; Perú
Fil: Arango, Jacobo. International Center for Tropical Agriculture; Colombia
Fil: Reis, Ricardo A. São Paulo State University. Department of Animal Science; Brasil
Fil: Posada-Ochoa, Sandra L. University of Antioquia. Faculty of Agricultural Sciences; Colombia
Fil: Tomich, Thierry R. Embrapa Dairy Cattle. Brazilian Agricultural Research Corporation; Brasil
Fil: Castelán-Ortega, Octavio A. Autonomous University of the State of Mexico. Faculty of Veterinary Medicine and Animal Science; México
Fil: Marcondes, Marcos I. Washington State University. Department of Animal Sciences; Estados Unidos
Fil: Gómez, Carlos. National Agrarian University La Molina. Faculty of Animal Science. Department of Animal Husbandry; Perú
Fil: Ribeiro-Filho, Henrique M. N. Santa Catarina State University. Department of Animal and Food Science; Brasil
Fil: Gere, Jose Ignacio. Universidad Tecnológica Nacional. División de Investigación y Desarrollo de Ingeniería; Argentina
Fil: Gere, Jose Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Ariza-Nieto, Claudia. Colombian Corporation for Agricultural Research; Colombia
Fil: Giraldo, Luis A. National University of Colombia. Faculty of Agricultural Sciences. Department of Animal Production; Colombia
Fil: Gonda, Horacio. Swedish University of Agricultural Sciences. Faculty of Veterinary Medicine and Animal Science. Department of Animal Nutrition and Management; Suecia
Fil: Ceron Cucchi, Maria Esperanza. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Patobiologia; Argentina
Fil: Ceron Cucchi, Maria Esperanza. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Hernandez, Olegario. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Santiago del Estero; Argentina
Fil: Ricci, Patricia. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Ricci, Patricia. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Balcarce; Argentina
Fil: Hristov, Alexander N. The Pennsylvania State University. Department of Animal Science; Estados Unidos - Fuente
- Science of the Total Environment 856 (2) : 159128 (Enero 2023)
- Materia
-
Nutrients
Gases de Efecto Invernadero
Análisis de la Regresión
Ganado de Carne
América Latina y el Caribe
Nutrientes
Greenhouse Gases
Regression Analysis
Beef Cattle
Methane Emission
Latin America and the Caribbean
Emisiones de Metano
Model Cross Validation
Validación Cruzada del Modelo - Nivel de accesibilidad
- acceso restringido
- Condiciones de uso
- http://creativecommons.org/licenses/by-nc-sa/4.0/
- Repositorio
.jpg)
- Institución
- Instituto Nacional de Tecnología Agropecuaria
- OAI Identificador
- oai:localhost:20.500.12123/16550
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Improving the accuracy of beef cattle methane inventories in Latin America and Caribbean countriesCongio, Guilhermo F.S.Bannink, AndréMayorga, Olga L.Rodrigues, João P. P.Bougouin, AdelineKebreab, ErmiasCarvalho, Paulo C.F.Berchielli, Telma T.Mercadante, Maria E.Z.Valadares-Filho, Sebastião C.Borges, Ana L. C.C.Berndt, AlexandreRodrigues, Paulo H.M.Ku-Vera, Juan C.Molina-Botero, Isabel C.Arango, JacoboReis, Ricardo A.Posada-Ochoa, Sandra L.Tomich, Thierry R.Castelán-Ortega, Octavio A.Marcondes, Marcos I.Gómez, CarlosRibeiro-Filho, Henrique M.N.Gere, José IgnacioAriza-Nieto, ClaudiaGiraldo, Luis A.Gonda, HoracioCeron Cucchi, Maria EsperanzaHernandez, OlegarioRicci, PatriciaHristov, Alexander N.NutrientsGases de Efecto InvernaderoAnálisis de la RegresiónGanado de CarneAmérica Latina y el CaribeNutrientesGreenhouse GasesRegression AnalysisBeef CattleMethane EmissionLatin America and the CaribbeanEmisiones de MetanoModel Cross ValidationValidación Cruzada del ModeloOn-farm methane (CH4) emissions need to be estimated accurately so that the mitigation effect of recommended practices can be accounted for. In the present study prediction equations for enteric CH4 have been developed in lieu of expensive animal measurement approaches. Our objectives were to: (1) compile a dataset from individual beef cattle data for the Latin America and Caribbean (LAC) region; (2) determine main predictors of CH4 emission variables; (3) develop and cross-validate prediction models according to dietary forage content (DFC); and (4) compare the predictive ability of these newly-developed models with extant equations reported in literature, including those currently used for CH4 inventories in LAC countries. After outlier's screening, 1100 beef cattle observations from 55 studies were kept in the final dataset (∼ 50 % of the original dataset). Mixed-effects models were fitted with a random effect of study. The whole dataset was split according to DFC into a subset for all-forage (DFC = 100 %), high-forage (94 % ≥ DFC ≥ 54 %), and low-forage (50 % ≥ DFC) diets. Feed intake and average daily gain (ADG) were the main predictors of CH4 emission (g d−1), whereas this was feeding level [dry matter intake (DMI) as % of body weight] for CH4 yield (g kg−1 DMI). The newly-developed models were more accurate than IPCC Tier 2 equations for all subsets. Simple and multiple regression models including ADG were accurate and a feasible option to predict CH4 emission when data on feed intake are not available. Methane yield was not well predicted by any extant equation in contrast to the newly-developed models. The present study delivered new models that may be alternatives for the IPCC Tier 2 equations to improve CH4 prediction for beef cattle in inventories of LAC countries based either on more or less readily available data.Instituto de PatobiologíaFil: Congio, Guilhermo F. S. University of São Paulo. Luiz de Queiroz College of Agriculture. Department of Animal Science; BrasilFil: Bannink, André. Wageningen University & Research. Wageningen Livestock Research; Países BajosFil: Mayorga, Olga L. Colombian Corporation for Agricultural Research; ColombiaFil: Rodrigues, João P. P. Federal Rural University of Rio de Janeiro. Department of Animal Production. Animal Science Institute; BrasilFil: Bougouin, Adeline. University of California. Department of Animal Science; Estados UnidosFil: Kebreab, Ermias. University of California. Department of Animal Science; Estados UnidosFil: Carvalho, Paulo C. F. Federal University of Rio Grande do Sul. Department of Forage Plants and Agrometeorology; BrasilFil: Berchielli, Telma T. São Paulo State University. Department of Animal Science; BrasilFil: Mercadante, Maria E. Z. São Paulo Agribusiness Technology Agency. Institute of Animal Science; BrasilFil: Valadares-Filho, Sebastião C. Federal University of Viçosa. Department of Animal Science; BrasilFil: Borges, Ana L. C. C. Federal University of Minas Gerais. Department of Animal Science; BrasilFil: Berndt, Alexandre. Embrapa Southeast Livestock. Brazilian Agricultural Research Corporation; BrasilFil: Rodrigues, Paulo H. M. University of São Paulo. Faculty of Veterinary Medicine and Animal Science. Department of Animal Nutrition and Production; BrasilFil: Ku-Vera, Juan C. University of Yucatan. Faculty of Veterinary Medicine and Animal Science. Department of Animal Nutrition; MéxicoFil: Molina-Botero, Isabel C. National Agrarian University La Molina. Faculty of Animal Science. Department of Animal Husbandry; PerúFil: Arango, Jacobo. International Center for Tropical Agriculture; ColombiaFil: Reis, Ricardo A. São Paulo State University. Department of Animal Science; BrasilFil: Posada-Ochoa, Sandra L. University of Antioquia. Faculty of Agricultural Sciences; ColombiaFil: Tomich, Thierry R. Embrapa Dairy Cattle. Brazilian Agricultural Research Corporation; BrasilFil: Castelán-Ortega, Octavio A. Autonomous University of the State of Mexico. Faculty of Veterinary Medicine and Animal Science; MéxicoFil: Marcondes, Marcos I. Washington State University. Department of Animal Sciences; Estados UnidosFil: Gómez, Carlos. National Agrarian University La Molina. Faculty of Animal Science. Department of Animal Husbandry; PerúFil: Ribeiro-Filho, Henrique M. N. Santa Catarina State University. Department of Animal and Food Science; BrasilFil: Gere, Jose Ignacio. Universidad Tecnológica Nacional. División de Investigación y Desarrollo de Ingeniería; ArgentinaFil: Gere, Jose Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Ariza-Nieto, Claudia. Colombian Corporation for Agricultural Research; ColombiaFil: Giraldo, Luis A. National University of Colombia. Faculty of Agricultural Sciences. Department of Animal Production; ColombiaFil: Gonda, Horacio. Swedish University of Agricultural Sciences. Faculty of Veterinary Medicine and Animal Science. Department of Animal Nutrition and Management; SueciaFil: Ceron Cucchi, Maria Esperanza. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Patobiologia; ArgentinaFil: Ceron Cucchi, Maria Esperanza. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Hernandez, Olegario. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Santiago del Estero; ArgentinaFil: Ricci, Patricia. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Ricci, Patricia. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Balcarce; ArgentinaFil: Hristov, Alexander N. The Pennsylvania State University. Department of Animal Science; Estados UnidosElsevier2024-01-12T16:19:52Z2024-01-12T16:19:52Z2023-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfhttp://hdl.handle.net/20.500.12123/16550https://www.sciencedirect.com/science/article/pii/S00489697220622711879-1026https://doi.org/10.1016/j.scitotenv.2022.159128Science of the Total Environment 856 (2) : 159128 (Enero 2023)reponame:INTA Digital (INTA)instname:Instituto Nacional de Tecnología Agropecuariaenginfo:eu-repo/semantics/restrictedAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)2025-09-29T13:46:19Zoai:localhost:20.500.12123/16550instacron:INTAInstitucionalhttp://repositorio.inta.gob.ar/Organismo científico-tecnológicoNo correspondehttp://repositorio.inta.gob.ar/oai/requesttripaldi.nicolas@inta.gob.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:l2025-09-29 13:46:19.593INTA Digital (INTA) - Instituto Nacional de Tecnología Agropecuariafalse |
| dc.title.none.fl_str_mv |
Improving the accuracy of beef cattle methane inventories in Latin America and Caribbean countries |
| title |
Improving the accuracy of beef cattle methane inventories in Latin America and Caribbean countries |
| spellingShingle |
Improving the accuracy of beef cattle methane inventories in Latin America and Caribbean countries Congio, Guilhermo F.S. Nutrients Gases de Efecto Invernadero Análisis de la Regresión Ganado de Carne América Latina y el Caribe Nutrientes Greenhouse Gases Regression Analysis Beef Cattle Methane Emission Latin America and the Caribbean Emisiones de Metano Model Cross Validation Validación Cruzada del Modelo |
| title_short |
Improving the accuracy of beef cattle methane inventories in Latin America and Caribbean countries |
| title_full |
Improving the accuracy of beef cattle methane inventories in Latin America and Caribbean countries |
| title_fullStr |
Improving the accuracy of beef cattle methane inventories in Latin America and Caribbean countries |
| title_full_unstemmed |
Improving the accuracy of beef cattle methane inventories in Latin America and Caribbean countries |
| title_sort |
Improving the accuracy of beef cattle methane inventories in Latin America and Caribbean countries |
| dc.creator.none.fl_str_mv |
Congio, Guilhermo F.S. Bannink, André Mayorga, Olga L. Rodrigues, João P. P. Bougouin, Adeline Kebreab, Ermias Carvalho, Paulo C.F. Berchielli, Telma T. Mercadante, Maria E.Z. Valadares-Filho, Sebastião C. Borges, Ana L. C.C. Berndt, Alexandre Rodrigues, Paulo H.M. Ku-Vera, Juan C. Molina-Botero, Isabel C. Arango, Jacobo Reis, Ricardo A. Posada-Ochoa, Sandra L. Tomich, Thierry R. Castelán-Ortega, Octavio A. Marcondes, Marcos I. Gómez, Carlos Ribeiro-Filho, Henrique M.N. Gere, José Ignacio Ariza-Nieto, Claudia Giraldo, Luis A. Gonda, Horacio Ceron Cucchi, Maria Esperanza Hernandez, Olegario Ricci, Patricia Hristov, Alexander N. |
| author |
Congio, Guilhermo F.S. |
| author_facet |
Congio, Guilhermo F.S. Bannink, André Mayorga, Olga L. Rodrigues, João P. P. Bougouin, Adeline Kebreab, Ermias Carvalho, Paulo C.F. Berchielli, Telma T. Mercadante, Maria E.Z. Valadares-Filho, Sebastião C. Borges, Ana L. C.C. Berndt, Alexandre Rodrigues, Paulo H.M. Ku-Vera, Juan C. Molina-Botero, Isabel C. Arango, Jacobo Reis, Ricardo A. Posada-Ochoa, Sandra L. Tomich, Thierry R. Castelán-Ortega, Octavio A. Marcondes, Marcos I. Gómez, Carlos Ribeiro-Filho, Henrique M.N. Gere, José Ignacio Ariza-Nieto, Claudia Giraldo, Luis A. Gonda, Horacio Ceron Cucchi, Maria Esperanza Hernandez, Olegario Ricci, Patricia Hristov, Alexander N. |
| author_role |
author |
| author2 |
Bannink, André Mayorga, Olga L. Rodrigues, João P. P. Bougouin, Adeline Kebreab, Ermias Carvalho, Paulo C.F. Berchielli, Telma T. Mercadante, Maria E.Z. Valadares-Filho, Sebastião C. Borges, Ana L. C.C. Berndt, Alexandre Rodrigues, Paulo H.M. Ku-Vera, Juan C. Molina-Botero, Isabel C. Arango, Jacobo Reis, Ricardo A. Posada-Ochoa, Sandra L. Tomich, Thierry R. Castelán-Ortega, Octavio A. Marcondes, Marcos I. Gómez, Carlos Ribeiro-Filho, Henrique M.N. Gere, José Ignacio Ariza-Nieto, Claudia Giraldo, Luis A. Gonda, Horacio Ceron Cucchi, Maria Esperanza Hernandez, Olegario Ricci, Patricia Hristov, Alexander N. |
| author2_role |
author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author |
| dc.subject.none.fl_str_mv |
Nutrients Gases de Efecto Invernadero Análisis de la Regresión Ganado de Carne América Latina y el Caribe Nutrientes Greenhouse Gases Regression Analysis Beef Cattle Methane Emission Latin America and the Caribbean Emisiones de Metano Model Cross Validation Validación Cruzada del Modelo |
| topic |
Nutrients Gases de Efecto Invernadero Análisis de la Regresión Ganado de Carne América Latina y el Caribe Nutrientes Greenhouse Gases Regression Analysis Beef Cattle Methane Emission Latin America and the Caribbean Emisiones de Metano Model Cross Validation Validación Cruzada del Modelo |
| dc.description.none.fl_txt_mv |
On-farm methane (CH4) emissions need to be estimated accurately so that the mitigation effect of recommended practices can be accounted for. In the present study prediction equations for enteric CH4 have been developed in lieu of expensive animal measurement approaches. Our objectives were to: (1) compile a dataset from individual beef cattle data for the Latin America and Caribbean (LAC) region; (2) determine main predictors of CH4 emission variables; (3) develop and cross-validate prediction models according to dietary forage content (DFC); and (4) compare the predictive ability of these newly-developed models with extant equations reported in literature, including those currently used for CH4 inventories in LAC countries. After outlier's screening, 1100 beef cattle observations from 55 studies were kept in the final dataset (∼ 50 % of the original dataset). Mixed-effects models were fitted with a random effect of study. The whole dataset was split according to DFC into a subset for all-forage (DFC = 100 %), high-forage (94 % ≥ DFC ≥ 54 %), and low-forage (50 % ≥ DFC) diets. Feed intake and average daily gain (ADG) were the main predictors of CH4 emission (g d−1), whereas this was feeding level [dry matter intake (DMI) as % of body weight] for CH4 yield (g kg−1 DMI). The newly-developed models were more accurate than IPCC Tier 2 equations for all subsets. Simple and multiple regression models including ADG were accurate and a feasible option to predict CH4 emission when data on feed intake are not available. Methane yield was not well predicted by any extant equation in contrast to the newly-developed models. The present study delivered new models that may be alternatives for the IPCC Tier 2 equations to improve CH4 prediction for beef cattle in inventories of LAC countries based either on more or less readily available data. Instituto de Patobiología Fil: Congio, Guilhermo F. S. University of São Paulo. Luiz de Queiroz College of Agriculture. Department of Animal Science; Brasil Fil: Bannink, André. Wageningen University & Research. Wageningen Livestock Research; Países Bajos Fil: Mayorga, Olga L. Colombian Corporation for Agricultural Research; Colombia Fil: Rodrigues, João P. P. Federal Rural University of Rio de Janeiro. Department of Animal Production. Animal Science Institute; Brasil Fil: Bougouin, Adeline. University of California. Department of Animal Science; Estados Unidos Fil: Kebreab, Ermias. University of California. Department of Animal Science; Estados Unidos Fil: Carvalho, Paulo C. F. Federal University of Rio Grande do Sul. Department of Forage Plants and Agrometeorology; Brasil Fil: Berchielli, Telma T. São Paulo State University. Department of Animal Science; Brasil Fil: Mercadante, Maria E. Z. São Paulo Agribusiness Technology Agency. Institute of Animal Science; Brasil Fil: Valadares-Filho, Sebastião C. Federal University of Viçosa. Department of Animal Science; Brasil Fil: Borges, Ana L. C. C. Federal University of Minas Gerais. Department of Animal Science; Brasil Fil: Berndt, Alexandre. Embrapa Southeast Livestock. Brazilian Agricultural Research Corporation; Brasil Fil: Rodrigues, Paulo H. M. University of São Paulo. Faculty of Veterinary Medicine and Animal Science. Department of Animal Nutrition and Production; Brasil Fil: Ku-Vera, Juan C. University of Yucatan. Faculty of Veterinary Medicine and Animal Science. Department of Animal Nutrition; México Fil: Molina-Botero, Isabel C. National Agrarian University La Molina. Faculty of Animal Science. Department of Animal Husbandry; Perú Fil: Arango, Jacobo. International Center for Tropical Agriculture; Colombia Fil: Reis, Ricardo A. São Paulo State University. Department of Animal Science; Brasil Fil: Posada-Ochoa, Sandra L. University of Antioquia. Faculty of Agricultural Sciences; Colombia Fil: Tomich, Thierry R. Embrapa Dairy Cattle. Brazilian Agricultural Research Corporation; Brasil Fil: Castelán-Ortega, Octavio A. Autonomous University of the State of Mexico. Faculty of Veterinary Medicine and Animal Science; México Fil: Marcondes, Marcos I. Washington State University. Department of Animal Sciences; Estados Unidos Fil: Gómez, Carlos. National Agrarian University La Molina. Faculty of Animal Science. Department of Animal Husbandry; Perú Fil: Ribeiro-Filho, Henrique M. N. Santa Catarina State University. Department of Animal and Food Science; Brasil Fil: Gere, Jose Ignacio. Universidad Tecnológica Nacional. División de Investigación y Desarrollo de Ingeniería; Argentina Fil: Gere, Jose Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Ariza-Nieto, Claudia. Colombian Corporation for Agricultural Research; Colombia Fil: Giraldo, Luis A. National University of Colombia. Faculty of Agricultural Sciences. Department of Animal Production; Colombia Fil: Gonda, Horacio. Swedish University of Agricultural Sciences. Faculty of Veterinary Medicine and Animal Science. Department of Animal Nutrition and Management; Suecia Fil: Ceron Cucchi, Maria Esperanza. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Patobiologia; Argentina Fil: Ceron Cucchi, Maria Esperanza. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Hernandez, Olegario. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Santiago del Estero; Argentina Fil: Ricci, Patricia. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Ricci, Patricia. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Balcarce; Argentina Fil: Hristov, Alexander N. The Pennsylvania State University. Department of Animal Science; Estados Unidos |
| description |
On-farm methane (CH4) emissions need to be estimated accurately so that the mitigation effect of recommended practices can be accounted for. In the present study prediction equations for enteric CH4 have been developed in lieu of expensive animal measurement approaches. Our objectives were to: (1) compile a dataset from individual beef cattle data for the Latin America and Caribbean (LAC) region; (2) determine main predictors of CH4 emission variables; (3) develop and cross-validate prediction models according to dietary forage content (DFC); and (4) compare the predictive ability of these newly-developed models with extant equations reported in literature, including those currently used for CH4 inventories in LAC countries. After outlier's screening, 1100 beef cattle observations from 55 studies were kept in the final dataset (∼ 50 % of the original dataset). Mixed-effects models were fitted with a random effect of study. The whole dataset was split according to DFC into a subset for all-forage (DFC = 100 %), high-forage (94 % ≥ DFC ≥ 54 %), and low-forage (50 % ≥ DFC) diets. Feed intake and average daily gain (ADG) were the main predictors of CH4 emission (g d−1), whereas this was feeding level [dry matter intake (DMI) as % of body weight] for CH4 yield (g kg−1 DMI). The newly-developed models were more accurate than IPCC Tier 2 equations for all subsets. Simple and multiple regression models including ADG were accurate and a feasible option to predict CH4 emission when data on feed intake are not available. Methane yield was not well predicted by any extant equation in contrast to the newly-developed models. The present study delivered new models that may be alternatives for the IPCC Tier 2 equations to improve CH4 prediction for beef cattle in inventories of LAC countries based either on more or less readily available data. |
| publishDate |
2023 |
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