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Registro completo
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Biblioteca (s) : |
INIA Las Brujas. |
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Fecha : |
13/04/2020 |
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Actualizado : |
13/04/2020 |
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Tipo de producción científica : |
Informes Agroclimáticos |
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Autor : |
INIA (INSTITUTO NACIONAL DE INVESTIGACIÓN AGROPECUARIA); GRAS |
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Afiliación : |
UNIDAD DE AGROCLIMA Y SISTEMAS DE INFORMACIÓN, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay. |
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Título : |
Informe agroclimático 2020- Situación a Febrero. |
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Fecha de publicación : |
2020 |
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Fuente / Imprenta : |
Montevideo (Uruguay): INIA, 2020. |
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Serie : |
(Informe Agroclimático; Año 15, No.2) |
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Idioma : |
Español |
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Notas : |
Equipo de trabajo INIA-GRAS (Unidad de Agtech y sistemas de Información): Adrián Cal, Guadalupe Tiscornia, Carlos Schiavi, Gabriel García. |
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Contenido : |
Contenido. Síntesis de la Situación Agroclimática de Febrero -- Perspectivas Climáticas Trimestrales elaboradas por el IRI de la Universidad de Columbia -- Índice de Vegetación (IVDN) -- Precipitaciones -- Porcentaje de Agua Disponible (PAD) -- Índice de Bienestar Hídrico (IBH) -- Agua No Retenida (ANR) -- Perspectivas Climáticas Mar-Abr-May elaboradas por el IRI de la Universidad de Columbia. Destacamos para este mes: Previsión de estrés calórico en bovinos. Se encuentra disponible en la web del GRAS dentro del ítem "Alertas y herramientas". Acceso directo es: http://www.inia.uy/gras/Alertas-y-herramientas/Prevision-ITH-Vacunos |
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Palabras claves : |
AGROCLIMA; AGROCLIMATOLOGÍA; AGTECH; BOLETIN AGROCLIMÁTICO; CARACTERIZACIÓN AGROCLIMÁTICA; DIRECCION VIENTO; ESTACIONES AGROMETEOROLOGICAS; ESTACIONES AUTOMATICAS; ESTACIONES INIA; ESTADO DEL TIEMPO; ESTRÉS HÍDRICO; GRAFICAS AGROCLIMATICAS; GRAS; HELIOFANOGRAFO; INFORMACION SATELITAL; INFORME AGROCLIMÁTICO 2020; INUNDACIONES; LLUVIAS DIARIAS; MAXIMA; MEDIA; MINIMA; PANEL SOLAR; PERSPECTIVAS CLIMATICAS; PLUVIOMETRO; PRECIPITACION NACIONAL; PREVENCION HELADAS; PRONOSTICO; SENSOR; SIMETRICO; TANQUE A; TERMOCUPLAS; TERMOHIDROGRAFO; VARIABLES AGROCLIMATICAS; VELETA. |
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Thesagro : |
AGROCLIMATOLOGIA; CAMBIO CLIMATICO; CLIMA; CLIMATOLOGIA; ESTACIONES METEOROLOGICAS; ESTRES HIDRICO; EVAPORACION; HUMEDAD; HUMEDAD RELATIVA; LLUVIA; METEOROLOGIA; PERSPECTIVAS; PLUVIOMETROS; PRONOSTICO DEL TIEMPO; SENSORES; SISTEMAS; SISTEMAS DE INFORMACION; SUELO; TEMPERATURA; TERMOMETROS. |
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Asunto categoría : |
P40 Meteorología y climatología |
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URL : |
http://www.inia.uy/Publicaciones/Documentos%20compartidos/Informe%20agroclimatico%20INIA-GRAS%20Febrero%20de%202020.pdf
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Marc : |
LEADER 02911nam a2200793 a 4500
001 1061019
005 2020-04-13
008 2020 bl uuuu u0uu1 u #d
100 1 $aINIA (INSTITUTO NACIONAL DE INVESTIGACIÓN AGROPECUARIA)
245 $aInforme agroclimático 2020- Situación a Febrero.$h[electronic resource]
260 $aMontevideo (Uruguay): INIA$c2020
490 $a(Informe Agroclimático; Año 15, No.2)
500 $aEquipo de trabajo INIA-GRAS (Unidad de Agtech y sistemas de Información): Adrián Cal, Guadalupe Tiscornia, Carlos Schiavi, Gabriel García.
520 $aContenido. Síntesis de la Situación Agroclimática de Febrero -- Perspectivas Climáticas Trimestrales elaboradas por el IRI de la Universidad de Columbia -- Índice de Vegetación (IVDN) -- Precipitaciones -- Porcentaje de Agua Disponible (PAD) -- Índice de Bienestar Hídrico (IBH) -- Agua No Retenida (ANR) -- Perspectivas Climáticas Mar-Abr-May elaboradas por el IRI de la Universidad de Columbia. Destacamos para este mes: Previsión de estrés calórico en bovinos. Se encuentra disponible en la web del GRAS dentro del ítem "Alertas y herramientas". Acceso directo es: http://www.inia.uy/gras/Alertas-y-herramientas/Prevision-ITH-Vacunos
650 $aAGROCLIMATOLOGIA
650 $aCAMBIO CLIMATICO
650 $aCLIMA
650 $aCLIMATOLOGIA
650 $aESTACIONES METEOROLOGICAS
650 $aESTRES HIDRICO
650 $aEVAPORACION
650 $aHUMEDAD
650 $aHUMEDAD RELATIVA
650 $aLLUVIA
650 $aMETEOROLOGIA
650 $aPERSPECTIVAS
650 $aPLUVIOMETROS
650 $aPRONOSTICO DEL TIEMPO
650 $aSENSORES
650 $aSISTEMAS
650 $aSISTEMAS DE INFORMACION
650 $aSUELO
650 $aTEMPERATURA
650 $aTERMOMETROS
653 $aAGROCLIMA
653 $aAGROCLIMATOLOGÍA
653 $aAGTECH
653 $aBOLETIN AGROCLIMÁTICO
653 $aCARACTERIZACIÓN AGROCLIMÁTICA
653 $aDIRECCION VIENTO
653 $aESTACIONES AGROMETEOROLOGICAS
653 $aESTACIONES AUTOMATICAS
653 $aESTACIONES INIA
653 $aESTADO DEL TIEMPO
653 $aESTRÉS HÍDRICO
653 $aGRAFICAS AGROCLIMATICAS
653 $aGRAS
653 $aHELIOFANOGRAFO
653 $aINFORMACION SATELITAL
653 $aINFORME AGROCLIMÁTICO 2020
653 $aINUNDACIONES
653 $aLLUVIAS DIARIAS
653 $aMAXIMA
653 $aMEDIA
653 $aMINIMA
653 $aPANEL SOLAR
653 $aPERSPECTIVAS CLIMATICAS
653 $aPLUVIOMETRO
653 $aPRECIPITACION NACIONAL
653 $aPREVENCION HELADAS
653 $aPRONOSTICO
653 $aSENSOR
653 $aSIMETRICO
653 $aTANQUE A
653 $aTERMOCUPLAS
653 $aTERMOHIDROGRAFO
653 $aVARIABLES AGROCLIMATICAS
653 $aVELETA
700 1 $aGRAS
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Registro original : |
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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