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Registro completo
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Biblioteca (s) : |
INIA Las Brujas; INIA Treinta y Tres. |
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Fecha : |
10/01/2023 |
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Actualizado : |
23/01/2023 |
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Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
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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. |
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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. |
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Título : |
Prediction of enteric methane emissions by sheep using an intercontinental database. |
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Fecha de publicación : |
2023 |
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Fuente / Imprenta : |
Journal of Cleaner Production, 15 January 2023, Volume 384, 135523. OPEN ACCESS. doi: https://doi.org/10.1016/j.jclepro.2022.135523 |
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ISSN : |
0959-6526 |
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DOI : |
10.1016/j.jclepro.2022.135523 |
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Idioma : |
Inglés |
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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. |
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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 |
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Palabras claves : |
Age; Climatic regions; Diet composition; Prediction models; Rumen fermentation. |
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Asunto categoría : |
L02 Alimentación animal |
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URL : |
https://www.sciencedirect.com/science/article/pii/S0959652622050971/pdf
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Marc : |
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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
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Registro original : |
INIA Las Brujas (LB) |
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| Registros recuperados : 10 | |
| 1. |  | BRUNES, L.C.; BALDI, F.; COSTA, M.F.O.E.; QUINTANS, G.; BANCHERO, G.; LÔBO, R.B.; MAGNABOSCO, C.U. Early growth, backfat thickness and body condition has major effect on early heifer pregnancy in Nellore cattle. Anais da Academia Brasileira de Ciências, 2022, 94(1): e20191559. OPEN ACCESS. doi: https://doi.org/10.1590/0001-3765202120191559 Article history: Manuscript received on December 18, 2019; Accepted for publication on May 6, 2020.| Tipo: Artículos en Revistas Indexadas Internacionales | Circulación / Nivel : Internacional - -- |
| Biblioteca(s): INIA Las Brujas. |
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| 2. | | BRUNES, L.C.; FARIA, C.U.D.; MAGNABOSCO, C.U.; LOBO, R.B.; PERIPOLLI, E.; AGUILAR, I.; BALDI, F. Genomic prediction ability and genetic parameters for residual feed intake calculated using different approaches and their associations with growth, reproductive, and carcass traits in Nellore cattle. Journal of Applied Genetics, 2023, Volume 64, Issue 1, Pages 159 - 167. doi: https://doi.org/10.1007/s13353-022-00734-8 Article history: Received 25 February 2022; Revised 3 September 2022; Accepted 26 October 2022; Published online 15 November 2022; Published February 2023. -- Corresponding author: Brunes, L.C.; Animal Performance Center, Embrapa...| Tipo: Artículos en Revistas Indexadas Internacionales | Circulación / Nivel : Internacional - -- |
| Biblioteca(s): INIA Las Brujas. |
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| 3. | | RODRÍGUEZ, J.D.; PERIPOLLI, E.; LONDOÑO-GIL, M.; ESPIGOLAN, R.; LÔBO, R. B.; LÓPEZ-CORREA, R.; AGUILAR, I.; BALDI, F. Effect of minor allele frequency and density of single nucleotide polymorphism marker arrays on imputation performance and prediction ability using the single-step genomic Best Linear Unbiased Prediction in a simulated beef cattle population. Research paper. Animal Production Science. 2023, volume 63, issue 9, p. 844-852. https://doi.org/10.1071/AN21581 Article history: Submitted 1 December 2021, Accepted 1 March 2023, Published 4 April 2023. -- Correspondence to: Juan Diego Rodríguez,
Universidade Estadual Paulista (Unesp), Faculdade de Ciências Agrarias e Veterinárias, Departamento...| Tipo: Artículos en Revistas Indexadas Internacionales | Circulación / Nivel : Internacional - -- |
| Biblioteca(s): INIA Las Brujas. |
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| 4. | | LONDOÑO-GIL, M.; CARDONA-CIFUENTES, D.; ESPIGOLAN, R.; PERIPOLLI, E.; LÔBO, R. B.; PEREIRA, A. S. C.; AGUILAR, I.; BALDI, F. Genomic evaluation of commercial herds with different pedigree structures using the single-step genomic BLUP in Nelore cattle. Tropical Animal Health and Production, 2023, Volume 55, Issue 2, Article 95. doi: https://doi.org/10.1007/s11250-023-03508-4 Article history: Received 28 April 2022, Accepted 11 February 2023, To be Published April 2023. -- Correspondence author: Cardona-Cifuentes, D.; Faculdade de Ciências Agrárias e Veterinárias, Universidade Estadual Paulista Júlio de...| Tipo: Artículos en Revistas Indexadas Internacionales | Circulación / Nivel : Internacional - -- |
| Biblioteca(s): INIA Las Brujas. |
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| 5. | | RODRÍGUEZ NEIRA, J.D.; PERIPOLLI, E.; DE NEGREIROS M.P.M.; ESPIGOLAN, R.; LÓPEZ-CORREA R.; AGUILAR, I.; LOBO R.B.; BALDI, F. Prediction ability for growth and maternal traits using SNP arrays based on different marker densities in Nellore cattle using the ssGBLUP. Journal of Applied Genetics, 2022, Volume 63, Issue 2, pages 389-400. doi: https://doi.org/10.1007/s13353-022-00685-0 Article history: Received 26 September 2021; Revised 25 January 2022; Accepted 2 February 2022.
Corresponding author: Rodriguez Neira, J.D.; Departamento de Zootecnia, Faculdade de Ciências Agrarias e Veterinárias, Universidade...| Tipo: Artículos en Revistas Indexadas Internacionales | Circulación / Nivel : Internacional - -- |
| Biblioteca(s): INIA Las Brujas. |
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| 6. | | TONUSSI, R.L.; LONDOÑO-GIL, M.; DE OLIVEIRA SILVA, R.M.; MAGALHÃES, A.F.B.; AMORIM, S:T.; KLUSKA, S.; ESPIGOLAN, R.; PERIPOLLI, E.; PEREIRA, A.S.C.; LÔBO, R.B.; AGUILAR, I.; LOURENÇO, D.A.L.; BALDI, F. Accuracy of genomic breeding values and predictive ability for postweaning liveweight and age at first calving in a Nellore cattle population with missing sire information. Tropical Animal Health and Production, 2021, Volume 53, Issue 4, Article number 432. doi: https://doi.org/10.1007/s11250-021-02879-w Article history: Received 19 March 2021; Accepted 30 July 2021; Published online 10 August 2021.
Corresponding author: Londoño-Gil, M.; Grupo de Melhoramento Animal, Faculdade de Ciências Agrárias E Veterinárias, Universidade Estadual...| Tipo: Artículos en Revistas Indexadas Internacionales | Circulación / Nivel : Internacional - -- |
| Biblioteca(s): INIA Las Brujas. |
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| 7. | | SILVA, R.P.; ESPIGOLAN, R.; BERTON, M.P.; STAFUZZA, N.B.; SANTOS, F.S.; NEGREIROS, M.P.; SCHUCHMANN, R.K.; RODRIGUEZ, J.D.; LÔBO, R.B.; BANCHERO, G.; PEREIRA, A.S.C.; BERGMANN, J.A.G.; BALDI, F. Genetic parameters and genomic regions associated with calving ease in primiparous Nellore heifers. Livestock Science, October 2020, Volume 240, Article number 104183. Doi: https://doi.org/10.1016/j.livsci.2020.104183 Article history: Received 19 February 2020 /Received in revised form 25 June 2020 /Accepted 1 August 2020/Available online 02 August 2020. /Corresponding author: E-mail address: fernandobaldiuy@gmail.com (F. Baldi). This work was...| Tipo: Artículos en Revistas Indexadas Internacionales | Circulación / Nivel : Internacional - -- |
| Biblioteca(s): INIA La Estanzuela. |
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| 8. | | CARVALHO, F.E.; ESPIGOLAN, R.; BERTON, M.P.; NETO, J.B.S.; SILVA, R.P.; GRIGOLETTO, L.; SILVA, R.M.O.; FERRAZ, J.B.S.; ELER, J.P.; AGUILAR, I.; LÔBO, R.B.; BALDI, F. Genome-wide association study and predictive ability for growth traits in Nellore cattle. Livestock Science, January 2020, Volume 231, Article number 103861. OPEN ACCESS. Doi: https://doi.org/10.1016/j.livsci.2019.103861 Article history: Received 25 June 2019 / Revised 29 October 2019 / Accepted 4 November 2019 / Available online 6 November 2019.
Funding information: F.E. CARVALHO received a scholarship from Coordinating for the Improvement of Higher...| Tipo: Artículos en Revistas Indexadas Internacionales | Circulación / Nivel : Internacional - -- |
| Biblioteca(s): INIA Las Brujas. |
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| 9. | | TONUSSI, R.L.; SILVA, R.M.O.; MAGALHÃES, A.F.B.; PERIPOLLI , E.; OLIVIERI, B.F.; FEITOSA, F.L.B.; PEREIR, A.S.C.; LÔBO, R.B.; MAGNABOSCO, C.U.; AGUILAR, I.; BALDI, F. Impact of multiple sire mating system on the accuracy of genomic breeding value prediction in a beef cattle population under selection . [abstract 206]. Issue Section: Breeding and Genetics. Journal of Animal Science. 2017, Volume 95, Issue Supplement 4, Page 102. https://doi.org/10.2527/asasann.2017.206 Article history: Published 01 August 2017. --| Tipo: Abstracts/Resúmenes |
| Biblioteca(s): INIA Las Brujas. |
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| 10. | | TONUSSI, R. L.; SILVA, R. M. D. O.; MAGALHÃES, A.F.B.; ESPIGOLAN, R.; PERIPOLLI, E.; OLIVIERI, B. F.; FEITOSA, F. L. B.; LEMOS, M. V. A.; BERTON, M. P.; CHIAIA, H. L. J.; PEREIRA, A. S. C.; LÔBO, R. B.; BEZERRA, L. A. F.; MAGNABOSCO, C. D. U.; LOURENÇO, D.A.L.; AGUILAR, I.; BALDI, F. Application of single step genomic BLUP under different uncertain paternity scenarios using simulated data. (Research article). PLoS ONE, September 2017, Volume 12, Issue 9, Article number e0181752. OPEN ACCESS. Article history: Received September 22, 2016 // Accepted July 6, 2017 // Published September 28, 2017.
Data Availability Statement: All relevant data are within the paper, its Supporting Information files, and in Figshare.
Funding: This...| Tipo: Artículos en Revistas Indexadas Internacionales | Circulación / Nivel : Internacional - -- |
| Biblioteca(s): INIA Las Brujas. |
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| Registros recuperados : 10 | |
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