| |
|
|
|
Registro completo
|
|
Biblioteca (s) : |
INIA Las Brujas; INIA Treinta y Tres. |
|
Fecha : |
10/01/2023 |
|
Actualizado : |
23/01/2023 |
|
Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
|
Autor : |
BELANCHE, A.; HRISTOV, A.; VAN LINGEN, H.; DENMAN, S. E.; KEBREAB, E.; SCHWARM, A.; KREUZER, M.; NIU, M.; EUGÈNE, M.; NIDERKORN, V.; MARTIN, C.; ARCHIMÈDE, H.; MCGEE, M.; REYNOLDS, C. K.; CROMPTON, L. A.; BAYAT, A. R.; YU, Z.; BANNINK, A.; DIJKSTRA, J.; CHAVES, A. V.; CLARK, H.; MUETZEL, S.; LIND, V.; MOORBY, J. M.; ROOKE, J. A.; AUBRY, A.; ANTEZANA, W.; WANG, M.; HEGARTY, R.; HUTTON O. V.; HILL, J.; VERCOE, P. E.; SAVIAN, J.V.; ABDALLA, A. L.; SOLTAN, Y. A.; GOMES MONTEIRO, A. L.; KU-VERA, J. C.; JAURENA, G.; GÓMEZ-BRAVO, C. A.; MAYORGA, O. L.; CONGIO, G. F. S.; YÁÑEZ-RUIZ, D. R. |
|
Afiliación : |
ALEJANDRO BELANCHE, Estación Experimental del Zaidín (CSIC), Granada, Spain; Department of Animal Production and Food Sciences, IA2, University of Zaragoza, Zaragoza, Spain; ALEXANDER N. HRISTOV, Department of Animal Science, The Pennsylvania State University, University Park, United States; HENK J. VAN LINGEN, Laboratory of Systems and Synthetic Biology, Wageningen University & Research, Wageningen, Netherlands; STUART E. DENMAN, CSIRO Agriculture and Food, 306 Carmody Rd, St Lucia, QLD, Australia; ERMIAS KEBREAB, Department of Animal Science, University of California, Davis, CA, United States; ANGELA SCHWARM, Department of Animal and Aquacultural Sciences, Norwegian University of Life Sciences, PO Box 5003, 1432, Ås, Norway; MICHAEL KREUZER, ETH Zurich, Institute of Agricultural Sciences, Eschikon 27, Lindau, 8315, Switzerland; MUTIAN NIU, ETH Zurich, Institute of Agricultural Sciences, Eschikon 27, Lindau, 8315, Switzerland; MAGUY EUGÈNE, INRAE, Université Clermont Auvergne, VetAgro Sup, UMR 1213 Herbivores, Saint-Genès-Champanelle, 63122, France; VINCENT NIDERKORN, INRAE, Université Clermont Auvergne, VetAgro Sup, UMR 1213 Herbivores, Saint-Genès-Champanelle, 63122, France; CÉCILE MARTIN, INRAE, Université Clermont Auvergne, VetAgro Sup, UMR 1213 Herbivores, Saint-Genès-Champanelle, 63122, France; HARRY ARCHIMÈDE, INRAE, Unité de Recherches Zootechniques, Petit-Bourg, 97170, France; MARK MCGEE, Teagasc, Animal & Grassland Research and Innovation Centre, Grange, Dunsany, Co. Meath, Ireland; CHRISTOPHER K. REYNOLDS, School of Agriculture, Policy and Development, University of Reading, Reading, United Kingdom; LES A. CROMPTON, School of Agriculture, Policy and Development, University of Reading, Reading, United Kingdom; ALI REZA BAYAT, Animal Nutrition, Production Systems, Natural Resources Institute Finland (Luke), Jokioinen, 31600, Finland; ZHONGTANG YU, Department of Animal Sciences, The Ohio State University, Columbus OH, 43210, United States; ANDRÉ BANNINK, Wageningen Livestock Research, Wageningen University & Research, Wageningen, Netherlands; JAN DIJKSTRA, Animal Nutrition Group, Wageningen University and Research, PO Box 338, Wageningen, 6700 AH, Netherlands; ALEX V. CHAVES, School of Life and Environmental Sciences, Faculty of Science, The University of Sydney, 2006, NSW, Australia; HARRY CLARK, Grasslands Research Centre, New Zealand Agricultural Greenhouse Gas Research Centre, Palmerston North, New Zealand; STEFAN MUETZEL, Ag Research, Palmerston North, New Zealand; VIBEKE LIND, Norwegian Institute of Bioeconomy Research, NIBIO, Tjøtta, 8860, Norway; JON M. MOORBY, Institute of Biological, Environmental and Rural Sciences, Aberystwyth University, Aberystwyth, United Kingdom; JOHN A. ROOKE, SRUC, West Mains Road, Edinburgh, EH9 3JG, United Kingdom; AURÉLIE AUBRY, Agri-Food and Biosciences Institute, Co. Down, Hillsborough, BT26 6DR, United Kingdom; WALTER ANTEZANA, Facultad de Agronomía y Zootecnia, Universidad Nacional de San Antonio Abad del Cusco, Cusco, Peru; MIN WANG, Institute of Subtropical Agriculture, Chinese Academy of Sciences, Hunan, Changsha, China; ROGER HEGARTY, School of Environmental and Rural Science, University of New England, Armidale, 2351, NSW, Australia; ODDY V. HUTTON, Estación Experimental del Zaidín (CSIC), Granada, Spain; JULIAN HILL, Estación Experimental del Zaidín (CSIC), Granada, Spain; Department of Animal Production and Food Sciences, IA2, University of Zaragoza, Zaragoza, Spain; PHILIP E. VERCOE, Estación Experimental del Zaidín (CSIC), Granada, Spain; Department of Animal Science, The Pennsylvania State University, University Park, USA; Laboratory of Systems and Synthetic Biology, Wageningen University & Research, Wageningen, the Netherlands; JEAN VICTOR SAVIAN, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; CSIRO Agriculture and Food, 306 Carmody Rd, St Lucia, QLD, Australia; ADIBE L. ABDALLA, Estación Experimental del Zaidín (CSIC), Granada, Spain; Department of Animal Science, University of California, Davis, CA, USA; YOSRA A. SOLTAN, Estación Experimental del Zaidín (CSIC), Granada, Spain; Department of Animal and Aquacultural Sciences, Norwegian University of Life Sciences, PO Box 5003, 1432, Ås, Norway; ALDA LÚCIA GOMES MONTEIRO, Estación Experimental del Zaidín (CSIC), Granada, Spain; ETH Zurich, Institute of Agricultural Sciences, Eschikon 27, 8315, Lindau, Switzerland; JUAN CARLOS KU-VERA, Estación Experimental del Zaidín (CSIC), Granada, Spain; INRAE, Université Clermont Auvergne, VetAgro Sup, UMR 1213 Herbivores, 63122, Saint-Genés-Champanelle, France; GUSTAVO JAURENA, Estación Experimental del Zaidín (CSIC), Granada, Spain; INRAE, Unité de Recherches Zootechniques, Petit-Bourg, 97170, France; CARLOS A. GÓMEZ-BRAVO, Estación Experimental del Zaidín (CSIC), Granada, Spain; Teagasc, Animal & Grassland Research and Innovation Centre, Grange, Dunsany, Co. Meath, Ireland; OLGA L. MAYORGA, Estación Experimental del Zaidín (CSIC), Granada, Spain; School of Agriculture, Policy and Development, University of Reading, Reading, UK; GUILHERMO F. S. CONGIO, Estación Experimental del Zaidín (CSIC), Granada, Spain; Animal Nutrition, Production Systems, Natural Resources Institute Finland (Luke), 31600, Jokioinen, Finland; DAVID R. YÁÑEZ-RUIZ, Estación Experimental del Zaidín (CSIC), Granada, Spain. |
|
Título : |
Prediction of enteric methane emissions by sheep using an intercontinental database. |
|
Fecha de publicación : |
2023 |
|
Fuente / Imprenta : |
Journal of Cleaner Production, 15 January 2023, Volume 384, 135523. OPEN ACCESS. doi: https://doi.org/10.1016/j.jclepro.2022.135523 |
|
ISSN : |
0959-6526 |
|
DOI : |
10.1016/j.jclepro.2022.135523 |
|
Idioma : |
Inglés |
|
Notas : |
Article history: Received 24 May 2022; Received in revised form 11 November 2022; Accepted 3 December 2022; Available online 9 December 2022.
Corresponding author: Belanche, A.; Department of Animal Production and Food Sciences, IA2, University of Zaragoza, Zaragoza, Spain; email:belanche@unizar.es ;
Yáñez-Ruiz, D.R.; Estación Experimental del Zaidín (CSIC), Granada, Spain; email:david.yanez@eez.csic.es -- LICENSE: Hybrid Gold Open Access - Green Open Access -- FUNDING: Authors gratefully acknowledge the Joint Programming Initiative on Agriculture, Food Security and Climate Change (FACCE-JPI)'s 'GLOBAL NETWORK' project and the 'Feeding and Nutrition Network' of the Livestock Research Group within the Global Research Alliance for Agricultural Greenhouse Gases. National funding sources: AB has a Ramón y Cajal Grant funded by the Spanish Research Agency (AEI: 10.13039/501100011033, RYC 2019-027764-I). DRYR was supported by INIA grant (ref. MIT01-GLOBALNET-EEZ) and H2020 PATHWAYS project (grant agreement No 101000395). ANH was supported by the USDA National Institute of Food and Agriculture Federal Appropriations (Project PEN 04539, Ref.1000803). INRAE was funded by the French National Research Agency. |
|
Contenido : |
Enteric methane (CH4) emissions from sheep contribute to global greenhouse gas emissions from livestock. However, as already available for dairy and beef cattle, empirical models are needed to predict CH4 emissions from sheep for accounting purposes. The objectives of this study were to: 1) collate an intercontinental database of enteric CH4 emissions from individual sheep; 2) identify the key variables for predicting enteric sheep CH4 absolute production (g/d per animal) and yield [g/kg dry matter intake (DMI)] and their respective relationships; and 3) develop and cross-validate global equations as well as the potential need for age-, diet-, or climatic region-specific equations. The refined intercontinental database included 2,135 individual animal data from 13 countries. Linear CH4 prediction models were developed by incrementally adding variables. A universal CH4 production equation using only DMI led to a root mean square prediction error (RMSPE, % of observed mean) of 25.4% and an RMSPE-standard deviation ratio (RSR) of 0.69. Universal equations that, in addition to DMI, also included body weight (DMI + BW), and organic matter digestibility (DMI + OMD + BW) improved the prediction performance further (RSR, 0.62 and 0.60), whereas diet composition variables had negligible effects. These universal equations had lower prediction error than the extant IPCC 2019 equations. Developing age-specific models for adult sheep (>1-year-old) including DMI alone (RSR = 0.66) or in combination with rumen propionate molar proportion (for research of more refined purposes) substantially improved prediction performance (RSR = 0.57) on a smaller dataset. On the contrary, for young sheep (<1-year-old), the universal models could be applied, instead of age-specific models, if DMI and BW were included. Universal models showed similar prediction performances to the diet- and region-specific models. However, optimal prediction equations led to different regression coefficients (i.e. intercepts and slopes) for universal, age-specific, diet-specific, and region-specific models with predictive implications. Equations for CH4 yield led to low prediction performances, with DMI being negatively and BW and OMD positively correlated with CH4 yield. In conclusion, predicting sheep CH4 production requires information on DMI and prediction accuracy will improve national and global inventories if separate equations for young and adult sheep are used with the additional variables BW, OMD and rumen propionate proportion. Appropriate universal equations can be used to predict CH4 production from sheep across different diets and climatic conditions. © 2022 The Authors MenosEnteric methane (CH4) emissions from sheep contribute to global greenhouse gas emissions from livestock. However, as already available for dairy and beef cattle, empirical models are needed to predict CH4 emissions from sheep for accounting purposes. The objectives of this study were to: 1) collate an intercontinental database of enteric CH4 emissions from individual sheep; 2) identify the key variables for predicting enteric sheep CH4 absolute production (g/d per animal) and yield [g/kg dry matter intake (DMI)] and their respective relationships; and 3) develop and cross-validate global equations as well as the potential need for age-, diet-, or climatic region-specific equations. The refined intercontinental database included 2,135 individual animal data from 13 countries. Linear CH4 prediction models were developed by incrementally adding variables. A universal CH4 production equation using only DMI led to a root mean square prediction error (RMSPE, % of observed mean) of 25.4% and an RMSPE-standard deviation ratio (RSR) of 0.69. Universal equations that, in addition to DMI, also included body weight (DMI + BW), and organic matter digestibility (DMI + OMD + BW) improved the prediction performance further (RSR, 0.62 and 0.60), whereas diet composition variables had negligible effects. These universal equations had lower prediction error than the extant IPCC 2019 equations. Developing age-specific models for adult sheep (>1-year-old) including DMI alone (RSR = 0.66) or in c... Presentar Todo |
|
Palabras claves : |
Age; Climatic regions; Diet composition; Prediction models; Rumen fermentation. |
|
Asunto categoría : |
L02 Alimentación animal |
|
URL : |
https://www.sciencedirect.com/science/article/pii/S0959652622050971/pdf
|
|
Marc : |
LEADER 05812naa a2200709 a 4500
001 1063939
005 2023-01-23
008 2023 bl uuuu u00u1 u #d
022 $a0959-6526
024 7 $a10.1016/j.jclepro.2022.135523$2DOI
100 1 $aBELANCHE, A.
245 $aPrediction of enteric methane emissions by sheep using an intercontinental database.$h[electronic resource]
260 $c2023
500 $aArticle history: Received 24 May 2022; Received in revised form 11 November 2022; Accepted 3 December 2022; Available online 9 December 2022. Corresponding author: Belanche, A.; Department of Animal Production and Food Sciences, IA2, University of Zaragoza, Zaragoza, Spain; email:belanche@unizar.es ; Yáñez-Ruiz, D.R.; Estación Experimental del Zaidín (CSIC), Granada, Spain; email:david.yanez@eez.csic.es -- LICENSE: Hybrid Gold Open Access - Green Open Access -- FUNDING: Authors gratefully acknowledge the Joint Programming Initiative on Agriculture, Food Security and Climate Change (FACCE-JPI)'s 'GLOBAL NETWORK' project and the 'Feeding and Nutrition Network' of the Livestock Research Group within the Global Research Alliance for Agricultural Greenhouse Gases. National funding sources: AB has a Ramón y Cajal Grant funded by the Spanish Research Agency (AEI: 10.13039/501100011033, RYC 2019-027764-I). DRYR was supported by INIA grant (ref. MIT01-GLOBALNET-EEZ) and H2020 PATHWAYS project (grant agreement No 101000395). ANH was supported by the USDA National Institute of Food and Agriculture Federal Appropriations (Project PEN 04539, Ref.1000803). INRAE was funded by the French National Research Agency.
520 $aEnteric methane (CH4) emissions from sheep contribute to global greenhouse gas emissions from livestock. However, as already available for dairy and beef cattle, empirical models are needed to predict CH4 emissions from sheep for accounting purposes. The objectives of this study were to: 1) collate an intercontinental database of enteric CH4 emissions from individual sheep; 2) identify the key variables for predicting enteric sheep CH4 absolute production (g/d per animal) and yield [g/kg dry matter intake (DMI)] and their respective relationships; and 3) develop and cross-validate global equations as well as the potential need for age-, diet-, or climatic region-specific equations. The refined intercontinental database included 2,135 individual animal data from 13 countries. Linear CH4 prediction models were developed by incrementally adding variables. A universal CH4 production equation using only DMI led to a root mean square prediction error (RMSPE, % of observed mean) of 25.4% and an RMSPE-standard deviation ratio (RSR) of 0.69. Universal equations that, in addition to DMI, also included body weight (DMI + BW), and organic matter digestibility (DMI + OMD + BW) improved the prediction performance further (RSR, 0.62 and 0.60), whereas diet composition variables had negligible effects. These universal equations had lower prediction error than the extant IPCC 2019 equations. Developing age-specific models for adult sheep (>1-year-old) including DMI alone (RSR = 0.66) or in combination with rumen propionate molar proportion (for research of more refined purposes) substantially improved prediction performance (RSR = 0.57) on a smaller dataset. On the contrary, for young sheep (<1-year-old), the universal models could be applied, instead of age-specific models, if DMI and BW were included. Universal models showed similar prediction performances to the diet- and region-specific models. However, optimal prediction equations led to different regression coefficients (i.e. intercepts and slopes) for universal, age-specific, diet-specific, and region-specific models with predictive implications. Equations for CH4 yield led to low prediction performances, with DMI being negatively and BW and OMD positively correlated with CH4 yield. In conclusion, predicting sheep CH4 production requires information on DMI and prediction accuracy will improve national and global inventories if separate equations for young and adult sheep are used with the additional variables BW, OMD and rumen propionate proportion. Appropriate universal equations can be used to predict CH4 production from sheep across different diets and climatic conditions. © 2022 The Authors
653 $aAge
653 $aClimatic regions
653 $aDiet composition
653 $aPrediction models
653 $aRumen fermentation
700 1 $aHRISTOV, A.
700 1 $aVAN LINGEN, H.
700 1 $aDENMAN, S. E.
700 1 $aKEBREAB, E.
700 1 $aSCHWARM, A.
700 1 $aKREUZER, M.
700 1 $aNIU, M.
700 1 $aEUGÈNE, M.
700 1 $aNIDERKORN, V.
700 1 $aMARTIN, C.
700 1 $aARCHIMÈDE, H.
700 1 $aMCGEE, M.
700 1 $aREYNOLDS, C. K.
700 1 $aCROMPTON, L. A.
700 1 $aBAYAT, A. R.
700 1 $aYU, Z.
700 1 $aBANNINK, A.
700 1 $aDIJKSTRA, J.
700 1 $aCHAVES, A. V.
700 1 $aCLARK, H.
700 1 $aMUETZEL, S.
700 1 $aLIND, V.
700 1 $aMOORBY, J. M.
700 1 $aROOKE, J. A.
700 1 $aAUBRY, A.
700 1 $aANTEZANA, W.
700 1 $aWANG, M.
700 1 $aHEGARTY, R.
700 1 $aHUTTON O. V.
700 1 $aHILL, J.
700 1 $aVERCOE, P. E.
700 1 $aSAVIAN, J.V.
700 1 $aABDALLA, A. L.
700 1 $aSOLTAN, Y. A.
700 1 $aGOMES MONTEIRO, A. L.
700 1 $aKU-VERA, J. C.
700 1 $aJAURENA, G.
700 1 $aGÓMEZ-BRAVO, C. A.
700 1 $aMAYORGA, O. L.
700 1 $aCONGIO, G. F. S.
700 1 $aYÁÑEZ-RUIZ, D. R.
773 $tJournal of Cleaner Production, 15 January 2023, Volume 384, 135523. OPEN ACCESS. doi: https://doi.org/10.1016/j.jclepro.2022.135523
Descargar
Esconder MarcPresentar Marc Completo |
|
Registro original : |
INIA Las Brujas (LB) |
|
|
Biblioteca
|
Identificación
|
Origen
|
Tipo / Formato
|
Clasificación
|
Cutter
|
Registro
|
Volumen
|
Estado
|
Volver
|
|
|
|
Registro completo
|
|
Biblioteca (s) : |
INIA La Estanzuela. |
|
Fecha actual : |
21/02/2014 |
|
Actualizado : |
06/11/2018 |
|
Tipo de producción científica : |
Capítulo en Libro Técnico-Científico |
|
Autor : |
BÖCKING, B.; BANDEIRA, S. |
|
Afiliación : |
BERNARDO BÖCKING, Donistar (UY). Colonia Itapebí, Salto; SANTIAGO BANDEIRA, Donistar (UY). Colonia Itapebí, Salto. |
|
Título : |
Riego extensivo, experiencia comercial. |
|
Fecha de publicación : |
2010 |
|
Fuente / Imprenta : |
In: GRUPO DE DESARROLLO DE RIEGO; SEMINARIO INTERNACIONAL, 1ER. AGOSTO 2010, PAYSANDÚ, URUGUAY. Potencial del riego extensivo en cultivos y pasturas. Montevideo (Uruguay): INIA, 2010. |
|
Páginas : |
p. 183-192 |
|
ISBN : |
978-9974-38-296-1 |
|
Idioma : |
Español |
|
Thesagro : |
RIEGO EN PASTURAS. |
|
Asunto categoría : |
-- |
|
URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/11841/1/Bocking-B.-2010.pdf
|
|
Marc : |
LEADER 00553naa a2200157 a 4500
001 1048279
005 2018-11-06
008 2010 bl uuuu u00u1 u #d
020 $a978-9974-38-296-1
100 1 $aBÖCKING, B.
245 $aRiego extensivo, experiencia comercial.
260 $c2010
300 $ap. 183-192
650 $aRIEGO EN PASTURAS
700 1 $aBANDEIRA, S.
773 $tIn: GRUPO DE DESARROLLO DE RIEGO; SEMINARIO INTERNACIONAL, 1ER. AGOSTO 2010, PAYSANDÚ, URUGUAY. Potencial del riego extensivo en cultivos y pasturas. Montevideo (Uruguay): INIA, 2010.
Descargar
Esconder MarcPresentar Marc Completo |
|
Registro original : |
INIA La Estanzuela (LE) |
|
|
Biblioteca
|
Identificación
|
Origen
|
Tipo / Formato
|
Clasificación
|
Cutter
|
Registro
|
Volumen
|
Estado
|
Volver
|
| Expresión de búsqueda válido. Check! |
|
|
