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Registro completo
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Biblioteca (s) : |
INIA Las Brujas. |
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Fecha : |
11/12/2018 |
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Actualizado : |
18/06/2019 |
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Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
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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. |
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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. |
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Título : |
Application of single step genomic BLUP under different uncertain paternity scenarios using simulated data. (Research article). |
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Fecha de publicación : |
2017 |
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Fuente / Imprenta : |
PLoS ONE, September 2017, Volume 12, Issue 9, Article number e0181752. OPEN ACCESS. |
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ISSN : |
1932-6203 |
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DOI : |
10.1371/journal.pone.0181752 |
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Idioma : |
Inglés |
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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. |
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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 |
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Palabras claves : |
CATTLE; COMPUTER SIMULATION; GENETIC VARIABILITY; GENETICS; GENOMICS; INHERITANCE PATTERNS; PEDIGREE. |
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Asunto categoría : |
L01 Ganadería |
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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
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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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Registro original : |
INIA Las Brujas (LB) |
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Biblioteca (s) : |
INIA Tacuarembó; INIA Treinta y Tres. |
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Fecha actual : |
21/08/2018 |
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Actualizado : |
08/07/2019 |
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Tipo de producción científica : |
Serie Técnica |
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Autor : |
ZORRILLA DE SAN MARTÍN, G.; MARTÍNEZ, S.; TERRA, J.A.; SARAVIA, H. (Ed.). |
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Afiliación : |
GONZALO ROBERTO ZORRILLA DE SAN MARTÍN PEREYRA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; SEBASTIÁN MARTÍNEZ KOPP, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; JOSÉ ALFREDO TERRA FERNÁNDEZ, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; HORACIO SARAVIA DIAZ, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay. |
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Título : |
Arroz 2018. |
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Fecha de publicación : |
2018 |
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Fuente / Imprenta : |
Montevideo (Uruguay): INIA, 2018. |
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Páginas : |
92 p. |
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Serie : |
(INIA Serie Técnica; 246) |
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ISBN : |
978-9974-38-404-0 |
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ISSN : |
1688-9266 |
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DOI : |
http://doi.org/10.35676/INIA/ST.246 |
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Idioma : |
Español |
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Notas : |
Jornada Arroz 2018. INIA TREINTA Y TRES. "Alternativas tecnológicas para el sector arrocero en un escenario desafiante". |
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Contenido : |
AGROCLIMATOLOGÍA
Principales requerimientos climáticos: ¿cambió algo con 21 años más de datos? I. Macedo; M. Oxley; A. Roel.
Caracterización de la variabilidad espacial y temporal de la evapotranspiración de referencia (ET0) en Uruguay. S. Alvariño; A. Bocco; R. Terra; M. Bidegain; G. Cruz.
MEJORAMIENTO GENÉTICO
LI09197: cultivar de alta productividad y resistencia a Pyricularia. F. Pérez de Vida; G. Carracelas; J. Vargas.
Líneas experimentales promisorias de ciclo intermedio, alta productividad y resistencia a Pyricularia. F. Pérez de Vida.
Evaluacion de cultivares Clearfield® en ensayos en fajas. F. Molina; P. Blanco.
Evaluación final: Cultivares índica y japónica tropical. F. Pérez de Vida.
Evaluación de cultivares de calidad americana - E5. F. Molina; P. Blanco.
Tolerancia a retraso de cosecha en variedades comerciales de arroz. M. Villalba; J. Vargas; P. Blanco.
Híbridos de arroz. F. Molina; P. Blanco.
Ecofisiología del cultivo de arroz: incidencia de factores climáticos en el rendimiento y sus componentes. F. Pérez de Vida; S.Sheffel; I. Macedo.
Estado actual de las variedades de arroz en el Cono Sur y perspectivas de nuevos cultivares para la región. Y. Sanabria.
MANEJO DEL CULTIVO
MALEZAS
Intensidad de uso del sistema Clearfield® en arroz y ocurrencia de arroz maleza resistente a imidazolinonas. J. Rosas; B. Sprunk; L. Díaz; M. Ripoll; M. Pérez Ois; C. Nieto; B. Sosa; C. Marchesi; N. Saldain.
Resistencia en capines, una nueva realidad. C. Marchesi; N. Saldain.
Después de cinco zafras ¿el banco de semillas en el suelo de capín resistente al quinclorac continúa siendo relevante? A.L. Pereira; M. Oxley; N. Saldain; C. Marchesi; A. Pimienta.
La inducción limitada de la síntesis de etileno y cianuro estarían involucradas en la resistencia a quinclorac en capín. M.DiezVignola; C.Marchesi; N. Saldain; P. Diaz.
Metamifox y Aura aplicados en mezcla de tanque con otros herbicidas en el control del capín. N. Saldain; B. Sosa.
¿Cómo reducir los escapes de capín en sistemas intensivos en el uso de arroces Clearfield®? N. Saldain; B. Sosa.
Evaluación de mezclas de herbicidas para el control de capín. N. Saldain; B. Sosa.
Evaluación de desenvolvimento de arroz resistente a herbicidas inhibidores da enzima Accase. J.A. Noldin; A. de Andrade.
Use and management of Accase-resistant rice technology in the United States. E. P. Webster; B. M. McKnight; S. Y. Rustom, Jr.; M. J. Osterholt; L. Connor Webster; David C. Walker.
Deposición de clomazone en cultivo de arroz y la volatilización posterior a la aplicación. J. Villalba; N. Besil; S. Rezende; M. Colazzo; V. Cesio.
ENFERMEDADES
Las enfermedades de tallo y vaina en un ciclo completo del sistema de rotaciones arroceras. S. Martínez; F. Escalante.
ROTACIONES
Brechas de rendimiento del cultivo de arroz sobre distinto antecesor de verano para dos variedades de alto potencial. A. Hernández; G. Rovira; A. Bordagorri; F. Escalante; J. Castillo; S. Martínez; I. Macedo; J. Terra.
NUTRICIÓN
Curvas críticas de dilución de nitrógeno en Uruguay. G. Fabini; J. Castillo; C. Marchesi.
Optimización de la población y fertilización nitrogenada para nuevos cultivares INIA de alto rendimiento. C. Marchesi; J. Castillo; A. Ferreira. MenosAGROCLIMATOLOGÍA
Principales requerimientos climáticos: ¿cambió algo con 21 años más de datos? I. Macedo; M. Oxley; A. Roel.
Caracterización de la variabilidad espacial y temporal de la evapotranspiración de referencia (ET0) en Uruguay. S. Alvariño; A. Bocco; R. Terra; M. Bidegain; G. Cruz.
MEJORAMIENTO GENÉTICO
LI09197: cultivar de alta productividad y resistencia a Pyricularia. F. Pérez de Vida; G. Carracelas; J. Vargas.
Líneas experimentales promisorias de ciclo intermedio, alta productividad y resistencia a Pyricularia. F. Pérez de Vida.
Evaluacion de cultivares Clearfield® en ensayos en fajas. F. Molina; P. Blanco.
Evaluación final: Cultivares índica y japónica tropical. F. Pérez de Vida.
Evaluación de cultivares de calidad americana - E5. F. Molina; P. Blanco.
Tolerancia a retraso de cosecha en variedades comerciales de arroz. M. Villalba; J. Vargas; P. Blanco.
Híbridos de arroz. F. Molina; P. Blanco.
Ecofisiología del cultivo de arroz: incidencia de factores climáticos en el rendimiento y sus componentes. F. Pérez de Vida; S.Sheffel; I. Macedo.
Estado actual de las variedades de arroz en el Cono Sur y perspectivas de nuevos cultivares para la región. Y. Sanabria.
MANEJO DEL CULTIVO
MALEZAS
Intensidad de uso del sistema Clearfield® en arroz y ocurrencia de arroz maleza resistente a imidazolinonas. J. Rosas; B. Sprunk; L. Díaz; M. Ripoll; M. Pérez Ois; C. Nieto; B. Sosa; C. Marchesi; N. Saldain.
Resistencia en capines, una nueva realidad. C. Marchesi; N. Saldain.
Despué... Presentar Todo |
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Palabras claves : |
RICE (ORYZA SATIVA L.). |
|
Thesagro : |
AGROCLIMATOLOGIA; ARROZ; ENFERMEDADES DE LAS PLANTAS; FITOMEJORAMIENTO; MALEZAS; MANEJO DE CULTIVOS; NUTRICION VEGETAL; ROTACIONES; URUGUAY. |
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Asunto categoría : |
F01 Cultivo |
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URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/11223/1/ST-246-Agosto2018.pdf
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Marc : |
LEADER 04272nam a2200337 a 4500
001 1058974
005 2019-07-08
008 2018 bl uuuu u00u1 u #d
020 $a978-9974-38-404-0
022 $a1688-9266
024 7 $ahttp://doi.org/10.35676/INIA/ST.246$2DOI
100 1 $aZORRILLA DE SAN MARTÍN, G.
245 $aArroz 2018.$h[electronic resource]
260 $aMontevideo (Uruguay): INIA$c2018
300 $a92 p.
490 $a(INIA Serie Técnica; 246)
500 $aJornada Arroz 2018. INIA TREINTA Y TRES. "Alternativas tecnológicas para el sector arrocero en un escenario desafiante".
520 $aAGROCLIMATOLOGÍA Principales requerimientos climáticos: ¿cambió algo con 21 años más de datos? I. Macedo; M. Oxley; A. Roel. Caracterización de la variabilidad espacial y temporal de la evapotranspiración de referencia (ET0) en Uruguay. S. Alvariño; A. Bocco; R. Terra; M. Bidegain; G. Cruz. MEJORAMIENTO GENÉTICO LI09197: cultivar de alta productividad y resistencia a Pyricularia. F. Pérez de Vida; G. Carracelas; J. Vargas. Líneas experimentales promisorias de ciclo intermedio, alta productividad y resistencia a Pyricularia. F. Pérez de Vida. Evaluacion de cultivares Clearfield® en ensayos en fajas. F. Molina; P. Blanco. Evaluación final: Cultivares índica y japónica tropical. F. Pérez de Vida. Evaluación de cultivares de calidad americana - E5. F. Molina; P. Blanco. Tolerancia a retraso de cosecha en variedades comerciales de arroz. M. Villalba; J. Vargas; P. Blanco. Híbridos de arroz. F. Molina; P. Blanco. Ecofisiología del cultivo de arroz: incidencia de factores climáticos en el rendimiento y sus componentes. F. Pérez de Vida; S.Sheffel; I. Macedo. Estado actual de las variedades de arroz en el Cono Sur y perspectivas de nuevos cultivares para la región. Y. Sanabria. MANEJO DEL CULTIVO MALEZAS Intensidad de uso del sistema Clearfield® en arroz y ocurrencia de arroz maleza resistente a imidazolinonas. J. Rosas; B. Sprunk; L. Díaz; M. Ripoll; M. Pérez Ois; C. Nieto; B. Sosa; C. Marchesi; N. Saldain. Resistencia en capines, una nueva realidad. C. Marchesi; N. Saldain. Después de cinco zafras ¿el banco de semillas en el suelo de capín resistente al quinclorac continúa siendo relevante? A.L. Pereira; M. Oxley; N. Saldain; C. Marchesi; A. Pimienta. La inducción limitada de la síntesis de etileno y cianuro estarían involucradas en la resistencia a quinclorac en capín. M.DiezVignola; C.Marchesi; N. Saldain; P. Diaz. Metamifox y Aura aplicados en mezcla de tanque con otros herbicidas en el control del capín. N. Saldain; B. Sosa. ¿Cómo reducir los escapes de capín en sistemas intensivos en el uso de arroces Clearfield®? N. Saldain; B. Sosa. Evaluación de mezclas de herbicidas para el control de capín. N. Saldain; B. Sosa. Evaluación de desenvolvimento de arroz resistente a herbicidas inhibidores da enzima Accase. J.A. Noldin; A. de Andrade. Use and management of Accase-resistant rice technology in the United States. E. P. Webster; B. M. McKnight; S. Y. Rustom, Jr.; M. J. Osterholt; L. Connor Webster; David C. Walker. Deposición de clomazone en cultivo de arroz y la volatilización posterior a la aplicación. J. Villalba; N. Besil; S. Rezende; M. Colazzo; V. Cesio. ENFERMEDADES Las enfermedades de tallo y vaina en un ciclo completo del sistema de rotaciones arroceras. S. Martínez; F. Escalante. ROTACIONES Brechas de rendimiento del cultivo de arroz sobre distinto antecesor de verano para dos variedades de alto potencial. A. Hernández; G. Rovira; A. Bordagorri; F. Escalante; J. Castillo; S. Martínez; I. Macedo; J. Terra. NUTRICIÓN Curvas críticas de dilución de nitrógeno en Uruguay. G. Fabini; J. Castillo; C. Marchesi. Optimización de la población y fertilización nitrogenada para nuevos cultivares INIA de alto rendimiento. C. Marchesi; J. Castillo; A. Ferreira.
650 $aAGROCLIMATOLOGIA
650 $aARROZ
650 $aENFERMEDADES DE LAS PLANTAS
650 $aFITOMEJORAMIENTO
650 $aMALEZAS
650 $aMANEJO DE CULTIVOS
650 $aNUTRICION VEGETAL
650 $aROTACIONES
650 $aURUGUAY
653 $aRICE (ORYZA SATIVA L.)
700 1 $aMARTÍNEZ, S.
700 1 $aTERRA, J.A.
700 1 $aSARAVIA, H.
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