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Biblioteca (s) : |
INIA Las Brujas. |
Fecha : |
12/04/2023 |
Actualizado : |
12/04/2023 |
Tipo de producción científica : |
Artículos en Revistas Agropecuarias |
Autor : |
STEWART, S.; KASPARY, T. E.; GARCIA, A.; CABRERA, M.; MAZZILLI, S. |
Afiliación : |
SILVINA MARIA STEWART SONEIRA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; TIAGO EDU KASPARY, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; MILTON ALEJANDRO GARCIA LATASA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; ORLANDO MAURICIO CABRERA GARCIA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; SEBASTIÁN R. MAZZILLI, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay. |
Título : |
Aspectos de manejo a tener en cuenta para la siembra de colza en la zafra 2023. |
Complemento del título : |
Cultivos. |
Fecha de publicación : |
2023 |
Fuente / Imprenta : |
Revista INIA Uruguay, Marzo 2023, no.72, p.59-63. |
Serie : |
(Revista INIA; 72). |
ISSN : |
1510-9011 |
Idioma : |
Español |
Contenido : |
Para que el cultivo de colza pueda cumplir un rol central en la zafra 2023, es necesario considerar algunos aspectos de manejo que no eran demasiado relevantes cuando el área era menor. Desde el punto de vista de intensificación de la producción, mantener área de este cultivo en rotación con el resto de los cultivos de invierno permitirá un intenso uso de la tierra con cultivos de renta durante todo el año, algo inédito para el país y la región |
Thesagro : |
COLZA; SIEMBRA. |
Asunto categoría : |
F01 Cultivo |
URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/17043/1/Revista-INIA-72-marzo-2023-16.pdf
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Marc : |
LEADER 01054naa a2200217 a 4500 001 1064016 005 2023-04-12 008 2023 bl uuuu u00u1 u #d 022 $a1510-9011 100 1 $aSTEWART, S. 245 $aAspectos de manejo a tener en cuenta para la siembra de colza en la zafra 2023.$h[electronic resource] 260 $c2023 490 $a(Revista INIA; 72). 520 $aPara que el cultivo de colza pueda cumplir un rol central en la zafra 2023, es necesario considerar algunos aspectos de manejo que no eran demasiado relevantes cuando el área era menor. Desde el punto de vista de intensificación de la producción, mantener área de este cultivo en rotación con el resto de los cultivos de invierno permitirá un intenso uso de la tierra con cultivos de renta durante todo el año, algo inédito para el país y la región 650 $aCOLZA 650 $aSIEMBRA 700 1 $aKASPARY, T. E. 700 1 $aGARCIA, A. 700 1 $aCABRERA, M. 700 1 $aMAZZILLI, S. 773 $tRevista INIA Uruguay, Marzo 2023, no.72, p.59-63.
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INIA Las Brujas (LB) |
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Registro completo
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Biblioteca (s) : |
INIA Las Brujas. |
Fecha actual : |
21/02/2014 |
Actualizado : |
18/12/2018 |
Tipo de producción científica : |
Artículos Indexados |
Autor : |
MISZTAL, I.; FRAGOMENI, B.; LOURENÇO, D. A. L.; TSURUTA, S.; MASUDA, Y.; AGUILAR, I.; LEGARRA, A.; LAWLOR, T. J. |
Afiliación : |
IGNACIO AGUILAR GARCIA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay. |
Título : |
Efficient inversion of genomic relationship matrix by the Algorithm for Proven and Young (APY). |
Fecha de publicación : |
2015 |
Fuente / Imprenta : |
Interbull Bulletin, 2015, v. 49, p. 111-116. |
Idioma : |
Inglés |
Contenido : |
ABSTRACT.
The purpose of this study was to evaluate properties of the inverse of the genomic relationship matrix derived with the algorithm for proven and young (APY) and the accuracy of genomic selection in single-step genomic best linear unbiased prediction (ssGBLUP). The APY implements genomic recursions on a subset of genotyped animals. When that subset is small, the cost of APY is approximately linear in memory and computations, effectively removing restrictions on the number of genotypes. Tests involved 10 102 702 final scores from 6 930 618 Holstein cows. A total of 100 000 animals with genotypes were used in the analyses and included 23 174 sires, 27 215 cows and 49 611 young animals. Genomic estimated breeding values (GEBVs) were calculated using ssGBLUP with a regular inverse of the genomic relationship matrix (G) and with G inverse from APY. Many subsets were tested including only sires, only cows and random samples from 2 000 to 20 000 animals. When the number of animals in the subset was ≥15,000, the correlations between GEBV with APY and
GEBV with the regular inverse were ≥0.99. Best convergence rate was achieved with random samples. A theory on APY was derived and is based on the fact that additive effects of animals in the subset are linear functions of the effects of independent chromosome segments (ICSs); the number of segments is a function of the effective population size. Accuracy of GEBV with APY can be slightly superior to that of a regular inverse. The inverse with APY is computed from G, which in turn is derived from single nucleotide polymorphism (SNP) BLUP and indirectly from BayesB or other SNP-based prediction methods. Strategies like SNP selection, SNP weighting, and use of causative SNPs from sequence analysis can be incorporated in APY without additional cost. The APY removes size limitations from ssGBLUP and facilitates a model with a complex genetic architecture. MenosABSTRACT.
The purpose of this study was to evaluate properties of the inverse of the genomic relationship matrix derived with the algorithm for proven and young (APY) and the accuracy of genomic selection in single-step genomic best linear unbiased prediction (ssGBLUP). The APY implements genomic recursions on a subset of genotyped animals. When that subset is small, the cost of APY is approximately linear in memory and computations, effectively removing restrictions on the number of genotypes. Tests involved 10 102 702 final scores from 6 930 618 Holstein cows. A total of 100 000 animals with genotypes were used in the analyses and included 23 174 sires, 27 215 cows and 49 611 young animals. Genomic estimated breeding values (GEBVs) were calculated using ssGBLUP with a regular inverse of the genomic relationship matrix (G) and with G inverse from APY. Many subsets were tested including only sires, only cows and random samples from 2 000 to 20 000 animals. When the number of animals in the subset was ≥15,000, the correlations between GEBV with APY and
GEBV with the regular inverse were ≥0.99. Best convergence rate was achieved with random samples. A theory on APY was derived and is based on the fact that additive effects of animals in the subset are linear functions of the effects of independent chromosome segments (ICSs); the number of segments is a function of the effective population size. Accuracy of GEBV with APY can be slightly superior to that of a regular... Presentar Todo |
Palabras claves : |
BIG POPULATION; GENOMIC RECURSION; INVERSION; SINGLE-STEP METHOD. |
Asunto categoría : |
-- |
URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/12204/1/1387-2387-1-PB.pdf
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Marc : |
LEADER 02658naa a2200253 a 4500 001 1012458 005 2018-12-18 008 2015 bl uuuu u00u1 u #d 100 1 $aMISZTAL, I. 245 $aEfficient inversion of genomic relationship matrix by the Algorithm for Proven and Young (APY).$h[electronic resource] 260 $c2015 520 $aABSTRACT. The purpose of this study was to evaluate properties of the inverse of the genomic relationship matrix derived with the algorithm for proven and young (APY) and the accuracy of genomic selection in single-step genomic best linear unbiased prediction (ssGBLUP). The APY implements genomic recursions on a subset of genotyped animals. When that subset is small, the cost of APY is approximately linear in memory and computations, effectively removing restrictions on the number of genotypes. Tests involved 10 102 702 final scores from 6 930 618 Holstein cows. A total of 100 000 animals with genotypes were used in the analyses and included 23 174 sires, 27 215 cows and 49 611 young animals. Genomic estimated breeding values (GEBVs) were calculated using ssGBLUP with a regular inverse of the genomic relationship matrix (G) and with G inverse from APY. Many subsets were tested including only sires, only cows and random samples from 2 000 to 20 000 animals. When the number of animals in the subset was ≥15,000, the correlations between GEBV with APY and GEBV with the regular inverse were ≥0.99. Best convergence rate was achieved with random samples. A theory on APY was derived and is based on the fact that additive effects of animals in the subset are linear functions of the effects of independent chromosome segments (ICSs); the number of segments is a function of the effective population size. Accuracy of GEBV with APY can be slightly superior to that of a regular inverse. The inverse with APY is computed from G, which in turn is derived from single nucleotide polymorphism (SNP) BLUP and indirectly from BayesB or other SNP-based prediction methods. Strategies like SNP selection, SNP weighting, and use of causative SNPs from sequence analysis can be incorporated in APY without additional cost. The APY removes size limitations from ssGBLUP and facilitates a model with a complex genetic architecture. 653 $aBIG POPULATION 653 $aGENOMIC RECURSION 653 $aINVERSION 653 $aSINGLE-STEP METHOD 700 1 $aFRAGOMENI, B. 700 1 $aLOURENÇO, D. A. L. 700 1 $aTSURUTA, S. 700 1 $aMASUDA, Y. 700 1 $aAGUILAR, I. 700 1 $aLEGARRA, A. 700 1 $aLAWLOR, T. J. 773 $tInterbull Bulletin, 2015$gv. 49, p. 111-116.
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