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Biblioteca (s) : |
INIA La Estanzuela. |
Fecha : |
27/03/2018 |
Actualizado : |
27/03/2018 |
Tipo de producción científica : |
Trabajos en Congresos/Conferencias |
Autor : |
PÉREZ-RUCHEL, A.; REPETTO, J.; ARROYO, J.M.; CAJARVILLE, C. |
Afiliación : |
UdelaR, Departamento de Nutrición, Facultad de Veterinaria, IPAV, San José, Uruguay.; JOSE LUIS REPETTO CAPELLO, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay./UdelaR, Departamento de Bovinos, Facultad de Veterinaria, IPAV, San José, Uruguay.; UdelaR, Departamento de Nutrición, Facultad de Veterinaria, IPAV, San José, Uruguay.; CECILIA CAJARVILLE, UdelaR, Departamento de Nutrición, Facultad de Veterinaria, IPAV, San José, Uruguay. |
Título : |
Engorde intensivo de corderos:estrategias de alimentación en sistemas mixtos que incluyen pasturas. (Intensive fattening of lambs: feeding strategies in mixed systems including pastures). |
Fecha de publicación : |
2018 |
Fuente / Imprenta : |
En: CONGRESO ASOCIACIÓN URUGUAYA DE PRODUCCIÓN ANIMAL (6º, Marzo, 2018, Tacuarembó, Uruguay). Tacuarembó: AUPA, 2018. |
Páginas : |
p.41-44. |
Idioma : |
Español |
Contenido : |
EN SÍNTESIS:
En los sistemas más intensivos de producción ovina que utilizan forrajes frescos de buena calidad, períodos de pastoreo alternados con el uso de RTM representaría una estrategia interesante. Permitiría mitigar los efectos negativos que se pueden generar en la microbiota ruminal ante el uso restringido de la pastura, con igual nivel de aprovechamiento digestivo y repercusiones favorables en los niveles de consumo. Además, las dietas mixtas permitirían lograr mejores eficiencias de conversión. Aunque, a los niveles de inclusión de pastura utilizados y en las condiciones en que se realizaron estos trabajos, no se han logrado evidenciar mejoras en la composición de la carne. |
Palabras claves : |
ALIMENTADOS SOLO CON PASTURAS; DIETAS MIXTAS; FERMENTACIÓN RUMINAL EN OVINO; MANEJO DEL TIEMPO DE ACCESO A LAS PASTURAS; RACIÓN TOTALMENTE MEZCLADA (RTM). |
Thesagro : |
PRODUCCIÓN OVINA. |
Asunto categoría : |
L02 Alimentación animal |
URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/9038/1/AUPA-2018-Perez-ruchel-et-al.p.41-44.pdf
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Marc : |
LEADER 01622nam a2200229 a 4500 001 1058364 005 2018-03-27 008 2018 bl uuuu u01u1 u #d 100 1 $aPÉREZ-RUCHEL, A. 245 $aEngorde intensivo de corderos$bestrategias de alimentación en sistemas mixtos que incluyen pasturas. (Intensive fattening of lambs: feeding strategies in mixed systems including pastures).$h[electronic resource] 260 $aEn: CONGRESO ASOCIACIÓN URUGUAYA DE PRODUCCIÓN ANIMAL (6º, Marzo, 2018, Tacuarembó, Uruguay). Tacuarembó: AUPA$c2018 300 $ap.41-44. 520 $aEN SÍNTESIS: En los sistemas más intensivos de producción ovina que utilizan forrajes frescos de buena calidad, períodos de pastoreo alternados con el uso de RTM representaría una estrategia interesante. Permitiría mitigar los efectos negativos que se pueden generar en la microbiota ruminal ante el uso restringido de la pastura, con igual nivel de aprovechamiento digestivo y repercusiones favorables en los niveles de consumo. Además, las dietas mixtas permitirían lograr mejores eficiencias de conversión. Aunque, a los niveles de inclusión de pastura utilizados y en las condiciones en que se realizaron estos trabajos, no se han logrado evidenciar mejoras en la composición de la carne. 650 $aPRODUCCIÓN OVINA 653 $aALIMENTADOS SOLO CON PASTURAS 653 $aDIETAS MIXTAS 653 $aFERMENTACIÓN RUMINAL EN OVINO 653 $aMANEJO DEL TIEMPO DE ACCESO A LAS PASTURAS 653 $aRACIÓN TOTALMENTE MEZCLADA (RTM) 700 1 $aREPETTO, J. 700 1 $aARROYO, J.M. 700 1 $aCAJARVILLE, C.
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Registro original : |
INIA La Estanzuela (LE) |
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Biblioteca (s) : |
INIA Las Brujas. |
Fecha actual : |
11/12/2018 |
Actualizado : |
18/06/2019 |
Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
Circulación / Nivel : |
Internacional - -- |
Autor : |
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. |
Afiliación : |
RAFAEL LARA TONUSSI, Department of Animal Science, School of Agricultural and Veterinarian Sciences, Jaboticabal, São Paulo, Brazil; RAFAEL MEDEIROS DE OLIVEIRA SILVA, Department of Animal Science, School of Agricultural and Veterinarian Sciences, Jaboticabal, São Paulo, Brazil; FABRÍCIA BRAGA MAGALHÃES, Department of Animal Science, School of Agricultural and Veterinarian Sciences, Jaboticabal, São Paulo, Brazil; RAFAEL ESPIGOLAN, Department of Animal Science, School of Agricultural and Veterinarian Sciences, Jaboticabal, São Paulo, Brazi; ELISA PERIPOLLI, Department of Animal Science, School of Agricultural and Veterinarian Sciences, Jaboticabal, São Paulo, Brazil; BIANCA FERREIRA OLIVIERI, Department of Animal Science, School of Agricultural and Veterinarian Sciences, Jaboticabal, São Paulo, Brazil; FABIELI LOISE BRAGA FEITOSA, Department of Animal Science, School of Agricultural and Veterinarian Sciences, Jaboticabal, São Paulo, Brazil; MARCOS VINICÍUS ANTUNES LEMOS, Department of Animal Science, School of Agricultural and Veterinarian Sciences, Jaboticabal, São Paulo, Brazil; MARIANA PIATTO BERTON, Department of Animal Science, School of Agricultural and Veterinarian Sciences, Jaboticabal, São Paulo, Brazil; HERMENEGILDO LUCAS JUSTINO CHIAIA, Department of Animal Science, School of Agricultural and Veterinarian Sciences, Jaboticabal, São Paulo, Brazil; ANGELICA SIMONE CRAVO PEREIRA, Department of Nutrition and Animal Production, Faculty of Animal Science and Food Engineering, Pirassununga, Brazil; RAYSILDO BARBOSA LÔBO, National Association of Breeders and Researchers (ANCP), Ribeirão Preto, Brazil; LUIZ ANTÔNIO FRAMARTINO BEZERRA, Department of Genetic, Medical School of Ribeirão Preto, Ribeirão Preto, Brazil; CLÁUDIO DE ULHOA MAGNABOSCO, Brazilian Agricultural Research Corporation (EMBRAPA), Distrito Federal, Brazil; DANIELA ANDRESSA LINO LOURENÇO, Department of Animal and Dairy Science, University of Georgia, Athens, Georgia, United States of America; IGNACIO AGUILAR GARCIA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; FERNANDO BALDI, Department of Animal Science, School of Agricultural and Veterinarian Sciences, Jaboticabal, São Paulo, Brazil. |
Título : |
Application of single step genomic BLUP under different uncertain paternity scenarios using simulated data. (Research article). |
Fecha de publicación : |
2017 |
Fuente / Imprenta : |
PLoS ONE, September 2017, Volume 12, Issue 9, Article number e0181752. OPEN ACCESS. |
ISSN : |
1932-6203 |
DOI : |
10.1371/journal.pone.0181752 |
Idioma : |
Inglés |
Notas : |
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 work was funded by the Sao Paulo Research Foundation (FAPESP), 2013/25910-0, Mr Rafael Lara Tonussi, and Sao Paulo Research Foundation (FAPESP), 2011/21241-0, PhD Fernando Bald. |
Contenido : |
ABSTRACT.
The objective of this study was to investigate the application of BLUP and single step genomic BLUP (ssGBLUP) models in different scenarios of paternity uncertainty with different strategies of scaling the G matrix to match the A22 matrix, using simulated data for beef cattle. Genotypes, pedigree, and phenotypes for age at first calving (AFC) and weight at 550 days (W550) were simulated using heritabilities based on real data (0.12 for AFC and 0.34 for W550). Paternity uncertainty scenarios using 0, 25, 50, 75, and 100% of multiple sires (MS) were studied. The simulated genome had a total length of 2,333 cM, containing 735,293 biallelic markers and 7,000 QTLs randomly distributed over the 29 BTA. It was assumed that QTLs explained 100% of the genetic variance. For QTL, the amount of alleles per loci randomly ranged from two to four. The BLUP model that considers phenotypic and pedigree data, and the ssGBLUP model that combines phenotypic, pedigree and genomic information were used for genetic evaluations. Four ways of scaling the mean of the genomic matrix (G) to match to the mean of the pedigree relationship matrix among genotyped animals (A22) were tested. Accuracy, bias, and inflation were investigated for five groups of animals: ALL = all animals; BULL = only bulls; GEN = genotyped animals; FEM = females; and YOUNG = young males. With the BLUP model, the accuracies of genetic evaluations decreased for both traits as the proportion of unknown sires in the population increased. The EBV accuracy reduction was higher for GEN and YOUNG groups. By analyzing the scenarios for YOUNG (from 0 to 100% of MS), the decrease was 87.8 and 86% for AFC and W550, respectively. When applying the ssGBLUP model, the accuracies of genetic evaluation also decreased as the MS in the pedigree for both traits increased. However, the accuracy reduction was less than those observed for BLUP model. Using the same comparison (scenario 0 to 100% of MS), the accuracies reductions were 38 and 44.6% for AFC and W550, respectively. There were no differences between the strategies for scaling the G matrix for ALL, BULL, and FEM groups under the different scenarios with missing pedigree. These results pointed out that the uninformative part of the A22 matrix and genotyped animals with paternity uncertainty did not influence the scaling of G matrix. On the basis of the results, it is important to have a G matrix in the same scale of the A22 matrix, especially for the evaluation of young animals in situations with missing pedigree information. In these situations, the ssGBLUP model is an appropriate alternative to obtain a more reliable and less biased estimate of breeding values, especially for young animals with few or no phenotypic records. For accurate and unbiased genomic predictions with ssGBLUP, it is necessary to assure that the G matrix is compatible with the A22 matrix, even in situations with paternity uncertainty.
© 2017 Tonussi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. MenosABSTRACT.
The objective of this study was to investigate the application of BLUP and single step genomic BLUP (ssGBLUP) models in different scenarios of paternity uncertainty with different strategies of scaling the G matrix to match the A22 matrix, using simulated data for beef cattle. Genotypes, pedigree, and phenotypes for age at first calving (AFC) and weight at 550 days (W550) were simulated using heritabilities based on real data (0.12 for AFC and 0.34 for W550). Paternity uncertainty scenarios using 0, 25, 50, 75, and 100% of multiple sires (MS) were studied. The simulated genome had a total length of 2,333 cM, containing 735,293 biallelic markers and 7,000 QTLs randomly distributed over the 29 BTA. It was assumed that QTLs explained 100% of the genetic variance. For QTL, the amount of alleles per loci randomly ranged from two to four. The BLUP model that considers phenotypic and pedigree data, and the ssGBLUP model that combines phenotypic, pedigree and genomic information were used for genetic evaluations. Four ways of scaling the mean of the genomic matrix (G) to match to the mean of the pedigree relationship matrix among genotyped animals (A22) were tested. Accuracy, bias, and inflation were investigated for five groups of animals: ALL = all animals; BULL = only bulls; GEN = genotyped animals; FEM = females; and YOUNG = young males. With the BLUP model, the accuracies of genetic evaluations decreased for both traits as the proportion of unknown sires in the popula... Presentar Todo |
Palabras claves : |
CATTLE; COMPUTER SIMULATION; GENETIC VARIABILITY; GENETICS; GENOMICS; INHERITANCE PATTERNS; PEDIGREE. |
Asunto categoría : |
L01 Ganadería |
URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/12157/1/journal.pone.0181752.pdf
https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0181752&type=printable
https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0181752#sec009
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Marc : |
LEADER 04899naa a2200433 a 4500 001 1059368 005 2019-06-18 008 2017 bl uuuu u00u1 u #d 022 $a1932-6203 024 7 $a10.1371/journal.pone.0181752$2DOI 100 1 $aTONUSSI, R. L. 245 $aApplication of single step genomic BLUP under different uncertain paternity scenarios using simulated data. (Research article).$h[electronic resource] 260 $c2017 500 $aArticle 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 work was funded by the Sao Paulo Research Foundation (FAPESP), 2013/25910-0, Mr Rafael Lara Tonussi, and Sao Paulo Research Foundation (FAPESP), 2011/21241-0, PhD Fernando Bald. 520 $aABSTRACT. The objective of this study was to investigate the application of BLUP and single step genomic BLUP (ssGBLUP) models in different scenarios of paternity uncertainty with different strategies of scaling the G matrix to match the A22 matrix, using simulated data for beef cattle. Genotypes, pedigree, and phenotypes for age at first calving (AFC) and weight at 550 days (W550) were simulated using heritabilities based on real data (0.12 for AFC and 0.34 for W550). Paternity uncertainty scenarios using 0, 25, 50, 75, and 100% of multiple sires (MS) were studied. The simulated genome had a total length of 2,333 cM, containing 735,293 biallelic markers and 7,000 QTLs randomly distributed over the 29 BTA. It was assumed that QTLs explained 100% of the genetic variance. For QTL, the amount of alleles per loci randomly ranged from two to four. The BLUP model that considers phenotypic and pedigree data, and the ssGBLUP model that combines phenotypic, pedigree and genomic information were used for genetic evaluations. Four ways of scaling the mean of the genomic matrix (G) to match to the mean of the pedigree relationship matrix among genotyped animals (A22) were tested. Accuracy, bias, and inflation were investigated for five groups of animals: ALL = all animals; BULL = only bulls; GEN = genotyped animals; FEM = females; and YOUNG = young males. With the BLUP model, the accuracies of genetic evaluations decreased for both traits as the proportion of unknown sires in the population increased. The EBV accuracy reduction was higher for GEN and YOUNG groups. By analyzing the scenarios for YOUNG (from 0 to 100% of MS), the decrease was 87.8 and 86% for AFC and W550, respectively. When applying the ssGBLUP model, the accuracies of genetic evaluation also decreased as the MS in the pedigree for both traits increased. However, the accuracy reduction was less than those observed for BLUP model. Using the same comparison (scenario 0 to 100% of MS), the accuracies reductions were 38 and 44.6% for AFC and W550, respectively. There were no differences between the strategies for scaling the G matrix for ALL, BULL, and FEM groups under the different scenarios with missing pedigree. These results pointed out that the uninformative part of the A22 matrix and genotyped animals with paternity uncertainty did not influence the scaling of G matrix. On the basis of the results, it is important to have a G matrix in the same scale of the A22 matrix, especially for the evaluation of young animals in situations with missing pedigree information. In these situations, the ssGBLUP model is an appropriate alternative to obtain a more reliable and less biased estimate of breeding values, especially for young animals with few or no phenotypic records. For accurate and unbiased genomic predictions with ssGBLUP, it is necessary to assure that the G matrix is compatible with the A22 matrix, even in situations with paternity uncertainty. © 2017 Tonussi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. 653 $aCATTLE 653 $aCOMPUTER SIMULATION 653 $aGENETIC VARIABILITY 653 $aGENETICS 653 $aGENOMICS 653 $aINHERITANCE PATTERNS 653 $aPEDIGREE 700 1 $aSILVA, R. M. D. O. 700 1 $aMAGALHÃES, A.F.B. 700 1 $aESPIGOLAN, R. 700 1 $aPERIPOLLI, E. 700 1 $aOLIVIERI, B. F. 700 1 $aFEITOSA, F. L. B. 700 1 $aLEMOS, M. V. A. 700 1 $aBERTON, M. P. 700 1 $aCHIAIA, H. L. J. 700 1 $aPEREIRA, A. S. C. 700 1 $aLÔBO, R. B. 700 1 $aBEZERRA, L. A. F. 700 1 $aMAGNABOSCO, C. D. U. 700 1 $aLOURENÇO, D.A.L. 700 1 $aAGUILAR, I. 700 1 $aBALDI, F. 773 $tPLoS ONE, September 2017, Volume 12, Issue 9, Article number e0181752. OPEN ACCESS.
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