Prediction of the Ym factor for livestock from on-farm accessible data
- Autores
- Jaurena, Gustavo; Cantet, Juan Manuel; Arroquy, Jose Ignacio; Palladino, Rafael Alejandro; Wawrzkiewicz, Marisa; Colombatto, Dario
- Año de publicación
- 2015
- Idioma
- inglés
- Tipo de recurso
- artículo
- Estado
- versión publicada
- Descripción
- Methane emission factor (Ym) is directly involved to calculate the worldwide livestock methane inventories, hence it is important to refine the estimation of this parameter for different livestock production systems. The purpose of this work was to generate refined mathematical models to predict CH4 emissions from an extensive compilated database at on-farm level and to compare them with different models already available in the literature. Methane emission predictive models (expressed as Ym, % gross energy intake; and methane production, CH4p, g an−1 d−1) where fitted taken into account the production system, the livestock type and the feed characteristics available at on-farm level within a reasonable uncertainty range. In order to develop the models, only easy available parameters were selected to fit new mathematical models. Hence, the full model included: ruminant types (beef cattle, dairy cattle, and sheep), fibre sources (fresh forage, conserved forage, and straw) and concentrate levels (DM basis) in the diet (Low, <35%; Intermediate, 35–65%; High, >65%). Full models were assessed by the Bayesian Information Criterion (BIC) and terms that did not reach significance level (P≤0.05) were dropped from the model. Furthermore, predicted results were assessed through correlation and regression analyses considering the model significance. Models developed in this study were compared by the degree of adjustment of a simple regression. Additive and technique terms were initially dropped from the full model used to predict Ym because they did not have effect in the prediction (P>0.10). Therefore, the final equation for Model 1 was: Ym(a)=Intercept−0.243(±0.051)×DMI (kg d−1)+5.9×10−3(±1.17×10−3)×NDF (g kg−1 DM−1)+5.7×10−3(±1.63×10−3)×DMD (g kg−1 MS−1) (BIC=559). All terms of this model, intercept factor (type of cattle×source of fibre×level of concentrate), DMI, NDF, and DMD were significant (P<0.0001). DMI was the term with the greatest weight in the model. The predicted Ym value decreased about 0.243 percentage units (P<0.0001) per each additional kg in DMI. When the equation was compared with previous publicated models, our model showed a satisfactory degree of fitting.
Fil: Jaurena, Gustavo. Universidad de Buenos Aires. Facultad de Agronomía; Argentina
Fil: Cantet, Juan Manuel. Universidad de Buenos Aires. Facultad de Agronomía; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Arroquy, Jose Ignacio. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Tucumán-Santiago del Estero. Estación Experimental Agropecuaria Santiago del Estero; Argentina. Universidad Nacional de Santiago del Estero. Facultad de Agronomía y Agroindustrias; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Palladino, Rafael Alejandro. Universidad de Buenos Aires. Facultad de Agronomía; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Wawrzkiewicz, Marisa. Universidad de Buenos Aires. Facultad de Agronomía; Argentina
Fil: Colombatto, Dario. Universidad de Buenos Aires. Facultad de Agronomía; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina - Materia
-
Methane
Green House Gases
Predictive Model - Nivel de accesibilidad
- acceso abierto
- Condiciones de uso
- https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
- Repositorio
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/41723
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Prediction of the Ym factor for livestock from on-farm accessible dataJaurena, GustavoCantet, Juan ManuelArroquy, Jose IgnacioPalladino, Rafael AlejandroWawrzkiewicz, MarisaColombatto, DarioMethaneGreen House GasesPredictive Modelhttps://purl.org/becyt/ford/4.2https://purl.org/becyt/ford/4Methane emission factor (Ym) is directly involved to calculate the worldwide livestock methane inventories, hence it is important to refine the estimation of this parameter for different livestock production systems. The purpose of this work was to generate refined mathematical models to predict CH4 emissions from an extensive compilated database at on-farm level and to compare them with different models already available in the literature. Methane emission predictive models (expressed as Ym, % gross energy intake; and methane production, CH4p, g an−1 d−1) where fitted taken into account the production system, the livestock type and the feed characteristics available at on-farm level within a reasonable uncertainty range. In order to develop the models, only easy available parameters were selected to fit new mathematical models. Hence, the full model included: ruminant types (beef cattle, dairy cattle, and sheep), fibre sources (fresh forage, conserved forage, and straw) and concentrate levels (DM basis) in the diet (Low, <35%; Intermediate, 35–65%; High, >65%). Full models were assessed by the Bayesian Information Criterion (BIC) and terms that did not reach significance level (P≤0.05) were dropped from the model. Furthermore, predicted results were assessed through correlation and regression analyses considering the model significance. Models developed in this study were compared by the degree of adjustment of a simple regression. Additive and technique terms were initially dropped from the full model used to predict Ym because they did not have effect in the prediction (P>0.10). Therefore, the final equation for Model 1 was: Ym(a)=Intercept−0.243(±0.051)×DMI (kg d−1)+5.9×10−3(±1.17×10−3)×NDF (g kg−1 DM−1)+5.7×10−3(±1.63×10−3)×DMD (g kg−1 MS−1) (BIC=559). All terms of this model, intercept factor (type of cattle×source of fibre×level of concentrate), DMI, NDF, and DMD were significant (P<0.0001). DMI was the term with the greatest weight in the model. The predicted Ym value decreased about 0.243 percentage units (P<0.0001) per each additional kg in DMI. When the equation was compared with previous publicated models, our model showed a satisfactory degree of fitting.Fil: Jaurena, Gustavo. Universidad de Buenos Aires. Facultad de Agronomía; ArgentinaFil: Cantet, Juan Manuel. Universidad de Buenos Aires. Facultad de Agronomía; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Arroquy, Jose Ignacio. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Tucumán-Santiago del Estero. Estación Experimental Agropecuaria Santiago del Estero; Argentina. Universidad Nacional de Santiago del Estero. Facultad de Agronomía y Agroindustrias; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Palladino, Rafael Alejandro. Universidad de Buenos Aires. Facultad de Agronomía; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Wawrzkiewicz, Marisa. Universidad de Buenos Aires. Facultad de Agronomía; ArgentinaFil: Colombatto, Dario. Universidad de Buenos Aires. Facultad de Agronomía; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaElsevier Science2015-07info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/41723Jaurena, Gustavo; Cantet, Juan Manuel; Arroquy, Jose Ignacio; Palladino, Rafael Alejandro; Wawrzkiewicz, Marisa; et al.; Prediction of the Ym factor for livestock from on-farm accessible data; Elsevier Science; Livestock Science; 177; 52; 7-2015; 52-621871-1413CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1016/j.livsci.2015.04.009info:eu-repo/semantics/altIdentifier/url/http://www.livestockscience.com/article/S1871-1413(15)00190-0/fulltextinfo:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S1871141315001900info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:20:01Zoai:ri.conicet.gov.ar:11336/41723instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 10:20:02.118CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse |
dc.title.none.fl_str_mv |
Prediction of the Ym factor for livestock from on-farm accessible data |
title |
Prediction of the Ym factor for livestock from on-farm accessible data |
spellingShingle |
Prediction of the Ym factor for livestock from on-farm accessible data Jaurena, Gustavo Methane Green House Gases Predictive Model |
title_short |
Prediction of the Ym factor for livestock from on-farm accessible data |
title_full |
Prediction of the Ym factor for livestock from on-farm accessible data |
title_fullStr |
Prediction of the Ym factor for livestock from on-farm accessible data |
title_full_unstemmed |
Prediction of the Ym factor for livestock from on-farm accessible data |
title_sort |
Prediction of the Ym factor for livestock from on-farm accessible data |
dc.creator.none.fl_str_mv |
Jaurena, Gustavo Cantet, Juan Manuel Arroquy, Jose Ignacio Palladino, Rafael Alejandro Wawrzkiewicz, Marisa Colombatto, Dario |
author |
Jaurena, Gustavo |
author_facet |
Jaurena, Gustavo Cantet, Juan Manuel Arroquy, Jose Ignacio Palladino, Rafael Alejandro Wawrzkiewicz, Marisa Colombatto, Dario |
author_role |
author |
author2 |
Cantet, Juan Manuel Arroquy, Jose Ignacio Palladino, Rafael Alejandro Wawrzkiewicz, Marisa Colombatto, Dario |
author2_role |
author author author author author |
dc.subject.none.fl_str_mv |
Methane Green House Gases Predictive Model |
topic |
Methane Green House Gases Predictive Model |
purl_subject.fl_str_mv |
https://purl.org/becyt/ford/4.2 https://purl.org/becyt/ford/4 |
dc.description.none.fl_txt_mv |
Methane emission factor (Ym) is directly involved to calculate the worldwide livestock methane inventories, hence it is important to refine the estimation of this parameter for different livestock production systems. The purpose of this work was to generate refined mathematical models to predict CH4 emissions from an extensive compilated database at on-farm level and to compare them with different models already available in the literature. Methane emission predictive models (expressed as Ym, % gross energy intake; and methane production, CH4p, g an−1 d−1) where fitted taken into account the production system, the livestock type and the feed characteristics available at on-farm level within a reasonable uncertainty range. In order to develop the models, only easy available parameters were selected to fit new mathematical models. Hence, the full model included: ruminant types (beef cattle, dairy cattle, and sheep), fibre sources (fresh forage, conserved forage, and straw) and concentrate levels (DM basis) in the diet (Low, <35%; Intermediate, 35–65%; High, >65%). Full models were assessed by the Bayesian Information Criterion (BIC) and terms that did not reach significance level (P≤0.05) were dropped from the model. Furthermore, predicted results were assessed through correlation and regression analyses considering the model significance. Models developed in this study were compared by the degree of adjustment of a simple regression. Additive and technique terms were initially dropped from the full model used to predict Ym because they did not have effect in the prediction (P>0.10). Therefore, the final equation for Model 1 was: Ym(a)=Intercept−0.243(±0.051)×DMI (kg d−1)+5.9×10−3(±1.17×10−3)×NDF (g kg−1 DM−1)+5.7×10−3(±1.63×10−3)×DMD (g kg−1 MS−1) (BIC=559). All terms of this model, intercept factor (type of cattle×source of fibre×level of concentrate), DMI, NDF, and DMD were significant (P<0.0001). DMI was the term with the greatest weight in the model. The predicted Ym value decreased about 0.243 percentage units (P<0.0001) per each additional kg in DMI. When the equation was compared with previous publicated models, our model showed a satisfactory degree of fitting. Fil: Jaurena, Gustavo. Universidad de Buenos Aires. Facultad de Agronomía; Argentina Fil: Cantet, Juan Manuel. Universidad de Buenos Aires. Facultad de Agronomía; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Arroquy, Jose Ignacio. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Tucumán-Santiago del Estero. Estación Experimental Agropecuaria Santiago del Estero; Argentina. Universidad Nacional de Santiago del Estero. Facultad de Agronomía y Agroindustrias; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Palladino, Rafael Alejandro. Universidad de Buenos Aires. Facultad de Agronomía; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Wawrzkiewicz, Marisa. Universidad de Buenos Aires. Facultad de Agronomía; Argentina Fil: Colombatto, Dario. Universidad de Buenos Aires. Facultad de Agronomía; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina |
description |
Methane emission factor (Ym) is directly involved to calculate the worldwide livestock methane inventories, hence it is important to refine the estimation of this parameter for different livestock production systems. The purpose of this work was to generate refined mathematical models to predict CH4 emissions from an extensive compilated database at on-farm level and to compare them with different models already available in the literature. Methane emission predictive models (expressed as Ym, % gross energy intake; and methane production, CH4p, g an−1 d−1) where fitted taken into account the production system, the livestock type and the feed characteristics available at on-farm level within a reasonable uncertainty range. In order to develop the models, only easy available parameters were selected to fit new mathematical models. Hence, the full model included: ruminant types (beef cattle, dairy cattle, and sheep), fibre sources (fresh forage, conserved forage, and straw) and concentrate levels (DM basis) in the diet (Low, <35%; Intermediate, 35–65%; High, >65%). Full models were assessed by the Bayesian Information Criterion (BIC) and terms that did not reach significance level (P≤0.05) were dropped from the model. Furthermore, predicted results were assessed through correlation and regression analyses considering the model significance. Models developed in this study were compared by the degree of adjustment of a simple regression. Additive and technique terms were initially dropped from the full model used to predict Ym because they did not have effect in the prediction (P>0.10). Therefore, the final equation for Model 1 was: Ym(a)=Intercept−0.243(±0.051)×DMI (kg d−1)+5.9×10−3(±1.17×10−3)×NDF (g kg−1 DM−1)+5.7×10−3(±1.63×10−3)×DMD (g kg−1 MS−1) (BIC=559). All terms of this model, intercept factor (type of cattle×source of fibre×level of concentrate), DMI, NDF, and DMD were significant (P<0.0001). DMI was the term with the greatest weight in the model. The predicted Ym value decreased about 0.243 percentage units (P<0.0001) per each additional kg in DMI. When the equation was compared with previous publicated models, our model showed a satisfactory degree of fitting. |
publishDate |
2015 |
dc.date.none.fl_str_mv |
2015-07 |
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 |
status_str |
publishedVersion |
dc.identifier.none.fl_str_mv |
http://hdl.handle.net/11336/41723 Jaurena, Gustavo; Cantet, Juan Manuel; Arroquy, Jose Ignacio; Palladino, Rafael Alejandro; Wawrzkiewicz, Marisa; et al.; Prediction of the Ym factor for livestock from on-farm accessible data; Elsevier Science; Livestock Science; 177; 52; 7-2015; 52-62 1871-1413 CONICET Digital CONICET |
url |
http://hdl.handle.net/11336/41723 |
identifier_str_mv |
Jaurena, Gustavo; Cantet, Juan Manuel; Arroquy, Jose Ignacio; Palladino, Rafael Alejandro; Wawrzkiewicz, Marisa; et al.; Prediction of the Ym factor for livestock from on-farm accessible data; Elsevier Science; Livestock Science; 177; 52; 7-2015; 52-62 1871-1413 CONICET Digital CONICET |
dc.language.none.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.livsci.2015.04.009 info:eu-repo/semantics/altIdentifier/url/http://www.livestockscience.com/article/S1871-1413(15)00190-0/fulltext info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S1871141315001900 |
dc.rights.none.fl_str_mv |
info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by-nc-nd/2.5/ar/ |
eu_rights_str_mv |
openAccess |
rights_invalid_str_mv |
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/ |
dc.format.none.fl_str_mv |
application/pdf application/pdf application/pdf application/pdf |
dc.publisher.none.fl_str_mv |
Elsevier Science |
publisher.none.fl_str_mv |
Elsevier Science |
dc.source.none.fl_str_mv |
reponame:CONICET Digital (CONICET) instname:Consejo Nacional de Investigaciones Científicas y Técnicas |
reponame_str |
CONICET Digital (CONICET) |
collection |
CONICET Digital (CONICET) |
instname_str |
Consejo Nacional de Investigaciones Científicas y Técnicas |
repository.name.fl_str_mv |
CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas |
repository.mail.fl_str_mv |
dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar |
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1844614177213120512 |
score |
13.070432 |