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Registro completo
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Biblioteca (s) : |
INIA Las Brujas. |
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Fecha : |
24/03/2022 |
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Actualizado : |
24/03/2022 |
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Tipo de producción científica : |
Artículos en Revistas Agropecuarias |
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Autor : |
SANTANDER, D.; CLARIGET, J.M.; BANCHERO, G.; SIMÓN, C.; MARIOTTA, J.; CIGANDA, V. |
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Afiliación : |
DANIEL SANTANDER, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; JUAN MANUEL CLARIGET BRIZ, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; GEORGGET ELIZABETH BANCHERO HUNZIKER, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; CLAUDIA SIMÓN, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; JULIETA CAROLINA MARIOTTA MARIEYHARA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; VERONICA SOLANGE CIGANDA BRASCA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay. |
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Título : |
Emisiones de metano de novillos en fase de terminación alimentados con dietas contrastantes en los niveles de fibra. |
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Complemento del título : |
Sustentabilidad. |
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Fecha de publicación : |
2022 |
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Fuente / Imprenta : |
Revista INIA Uruguay, Marzo 2022, no.68, p.84-87. |
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Serie : |
(Revista INIA; 68). |
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ISSN : |
1510-9011 |
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Idioma : |
Español |
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Contenido : |
Contemplando los aspectos productivos y la sostenibilidad a ambiental de nuestros sistemas, INIA alcanza resultados promisorios en el estudio combinado de la productividad animal y el efecto mitigador de las emisiones de CH4 entérico de una dieta forrajera de mayor calidad respecto a una de menor calidad en bovinos en fase de terminación. |
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Palabras claves : |
EMISIÓN DE GASES DE EFECTO INVERNADERO (GEI); EMISIONES DE CARBONO. |
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Thesagro : |
BOVINOS PARA CARNE. |
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Asunto categoría : |
-- |
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URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/16348/1/Revista-INIA-68-Marzo-2022-17.pdf
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Marc : |
LEADER 01095naa a2200241 a 4500
001 1062903
005 2022-03-24
008 2022 bl uuuu u00u1 u #d
022 $a1510-9011
100 1 $aSANTANDER, D.
245 $aEmisiones de metano de novillos en fase de terminación alimentados con dietas contrastantes en los niveles de fibra.$h[electronic resource]
260 $c2022
490 $a(Revista INIA; 68).
520 $aContemplando los aspectos productivos y la sostenibilidad a ambiental de nuestros sistemas, INIA alcanza resultados promisorios en el estudio combinado de la productividad animal y el efecto mitigador de las emisiones de CH4 entérico de una dieta forrajera de mayor calidad respecto a una de menor calidad en bovinos en fase de terminación.
650 $aBOVINOS PARA CARNE
653 $aEMISIÓN DE GASES DE EFECTO INVERNADERO (GEI)
653 $aEMISIONES DE CARBONO
700 1 $aCLARIGET, J.M.
700 1 $aBANCHERO, G.
700 1 $aSIMÓN, C.
700 1 $aMARIOTTA, J.
700 1 $aCIGANDA, V.
773 $tRevista INIA Uruguay, Marzo 2022, no.68, p.84-87.
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INIA Las Brujas (LB) |
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 | Acceso al texto completo restringido a Biblioteca INIA Las Brujas. Por información adicional contacte bibliolb@inia.org.uy. |
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Registro completo
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Biblioteca (s) : |
INIA Las Brujas. |
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Fecha actual : |
09/11/2017 |
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Actualizado : |
25/11/2019 |
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Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
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Circulación / Nivel : |
Internacional - -- |
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Autor : |
MASUDA, Y; MISZTAL, I.; LEGARRA, A.; TSURUTA, S.; LOURENCO, D.A.L.; FRAGOMENI, B.O.; AGUILAR, I. |
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Afiliación : |
Y. MASUDA, Department of Animal and Dairy Science, University of Georgia; I. MISZTAL, Department of Animal and Dairy Science, University of Georgia; A. LEGARRA, INRA (Institut National de la Recherche Agronomique); S. TSURUTA, Department of Animal and Dairy Science, University of Georgia; D.A.L. LOURENCO, Department of Animal and Dairy Science, University of Georgia; B.O. FRAGOMENI, Department of Animal and Dairy Science, University of Georgia; IGNACIO AGUILAR GARCIA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay. |
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Título : |
Technical note: Avoiding the direct inversion of the numerator relationship matrix for genotyped animals in single-step genomic best linear unbiased prediction solved with the preconditioned conjugate gradient. |
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Fecha de publicación : |
2017 |
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Fuente / Imprenta : |
Journal of Animal Science, 2017, v. 95(1): 49-52. |
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DOI : |
10.2527/jas.2016.0699 |
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Idioma : |
Inglés |
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Notas : |
Article history: Received: July 05, 2016; Accepted: Aug 16, 2016; Published: February 2, 2017.
This research was partially funded by the United States Department of Agriculture?s National Institute of Food and Agriculture (Agriculture and Food Research Initiative competitive grant 2015-67015-22936). |
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Contenido : |
ABSTRACT.
This paper evaluates an efficient implementation to multiply the inverse of a numerator relationship matrix for genotyped animals () by a vector (q). The computation is required for solving mixed model equations in single-step genomic BLUP (ssGBLUP) with the preconditioned conjugate gradient (PCG). The inverse can be decomposed into sparse matrices that are blocks of the sparse inverse of a numerator relationship matrix (A−1) including genotyped animals and their ancestors. The elements of A−1 were rapidly calculated with the Henderson?s rule and stored as sparse matrices in memory. Implementation of was by a series of sparse matrix?vector multiplications. Diagonal elements of , which were required as preconditioners in PCG, were approximated with a Monte Carlo method using 1,000 samples. The efficient implementation of was compared with explicit inversion of A22 with 3 data sets including about 15,000, 81,000, and 570,000 genotyped animals selected from populations with 213,000, 8.2 million, and 10.7 million pedigree animals, respectively. The explicit inversion required 1.8 GB, 49 GB, and 2,415 GB (estimated) of memory, respectively, and 42 s, 56 min, and 13.5 d (estimated), respectively, for the computations. The efficient implementation required <1 MB, 2.9 GB, and 2.3 GB of memory, respectively, and <1 sec, 3 min, and 5 min, respectively, for setting up. Only <1 sec was required for the multiplication in each PCG iteration for any data sets. When the equations in ssGBLUP are solved with the PCG algorithm, is no longer a limiting factor in the computations.
Copyright © 2016. American Society of Animal Science. MenosABSTRACT.
This paper evaluates an efficient implementation to multiply the inverse of a numerator relationship matrix for genotyped animals () by a vector (q). The computation is required for solving mixed model equations in single-step genomic BLUP (ssGBLUP) with the preconditioned conjugate gradient (PCG). The inverse can be decomposed into sparse matrices that are blocks of the sparse inverse of a numerator relationship matrix (A−1) including genotyped animals and their ancestors. The elements of A−1 were rapidly calculated with the Henderson?s rule and stored as sparse matrices in memory. Implementation of was by a series of sparse matrix?vector multiplications. Diagonal elements of , which were required as preconditioners in PCG, were approximated with a Monte Carlo method using 1,000 samples. The efficient implementation of was compared with explicit inversion of A22 with 3 data sets including about 15,000, 81,000, and 570,000 genotyped animals selected from populations with 213,000, 8.2 million, and 10.7 million pedigree animals, respectively. The explicit inversion required 1.8 GB, 49 GB, and 2,415 GB (estimated) of memory, respectively, and 42 s, 56 min, and 13.5 d (estimated), respectively, for the computations. The efficient implementation required <1 MB, 2.9 GB, and 2.3 GB of memory, respectively, and <1 sec, 3 min, and 5 min, respectively, for setting up. Only <1 sec was required for the multiplication in each PCG iteration for any data sets. When t... Presentar Todo |
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Palabras claves : |
COMPUTATION; GENOMIC SELECTION; INVERSION; NUMERATOR RELATIONSHIP MATRIX; PRECONDITIONED CONJUGATE GRADIENT; SPARSE MATRIX. |
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Asunto categoría : |
-- |
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Marc : |
LEADER 02919naa a2200289 a 4500
001 1057743
005 2019-11-25
008 2017 bl uuuu u00u1 u #d
024 7 $a10.2527/jas.2016.0699$2DOI
100 1 $aMASUDA, Y
245 $aTechnical note$bAvoiding the direct inversion of the numerator relationship matrix for genotyped animals in single-step genomic best linear unbiased prediction solved with the preconditioned conjugate gradient.$h[electronic resource]
260 $c2017
500 $aArticle history: Received: July 05, 2016; Accepted: Aug 16, 2016; Published: February 2, 2017. This research was partially funded by the United States Department of Agriculture?s National Institute of Food and Agriculture (Agriculture and Food Research Initiative competitive grant 2015-67015-22936).
520 $aABSTRACT. This paper evaluates an efficient implementation to multiply the inverse of a numerator relationship matrix for genotyped animals () by a vector (q). The computation is required for solving mixed model equations in single-step genomic BLUP (ssGBLUP) with the preconditioned conjugate gradient (PCG). The inverse can be decomposed into sparse matrices that are blocks of the sparse inverse of a numerator relationship matrix (A−1) including genotyped animals and their ancestors. The elements of A−1 were rapidly calculated with the Henderson?s rule and stored as sparse matrices in memory. Implementation of was by a series of sparse matrix?vector multiplications. Diagonal elements of , which were required as preconditioners in PCG, were approximated with a Monte Carlo method using 1,000 samples. The efficient implementation of was compared with explicit inversion of A22 with 3 data sets including about 15,000, 81,000, and 570,000 genotyped animals selected from populations with 213,000, 8.2 million, and 10.7 million pedigree animals, respectively. The explicit inversion required 1.8 GB, 49 GB, and 2,415 GB (estimated) of memory, respectively, and 42 s, 56 min, and 13.5 d (estimated), respectively, for the computations. The efficient implementation required <1 MB, 2.9 GB, and 2.3 GB of memory, respectively, and <1 sec, 3 min, and 5 min, respectively, for setting up. Only <1 sec was required for the multiplication in each PCG iteration for any data sets. When the equations in ssGBLUP are solved with the PCG algorithm, is no longer a limiting factor in the computations. Copyright © 2016. American Society of Animal Science.
653 $aCOMPUTATION
653 $aGENOMIC SELECTION
653 $aINVERSION
653 $aNUMERATOR RELATIONSHIP MATRIX
653 $aPRECONDITIONED CONJUGATE GRADIENT
653 $aSPARSE MATRIX
700 1 $aMISZTAL, I.
700 1 $aLEGARRA, A.
700 1 $aTSURUTA, S.
700 1 $aLOURENCO, D.A.L.
700 1 $aFRAGOMENI, B.O.
700 1 $aAGUILAR, I.
773 $tJournal of Animal Science, 2017$gv. 95(1): 49-52.
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