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
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/41723

id CONICETDig_d9d5fb738e15fb9c7bdc40ad7a7cc19c
oai_identifier_str oai:ri.conicet.gov.ar:11336/41723
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling 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
_version_ 1844614177213120512
score 13.070432