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| 42. |  | DIEZ VIGNOLA, M.; SAINZ, M.; SALDAIN, N.E.; MARCHESI, C.; BONNECARRERE, V.; DÍAZ GADEA, P. Limited induction of ethylene and cyanide synthesis are observed in quinclorac-resistant barnyardgrass (Echinochloa crus-galli) in Uruguay. Weed Science, 1 July 2020, Volume 68, Issue 4, Pages 348-357. Doi: https://doi.org/10.1017/wsc.2020.32 Article history: Article accepted and Published online by Cambridge University Press: 28 April 2020| Biblioteca(s): INIA Treinta y Tres. |
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| 43. | | QUERO, G.; SIMONDI, S.; CERETTA, S.; OTERO, A.; GARAYCOCHEA, S.; FERNANDEZ, S.; BORSANI, O.; BONNECARRERE, V. An integrative analysis of yield stability for a GWAS in a small soybean breeding population. Crop Science, May 2021, volume 61, issue 3, pages 19003-1914. Doi: https://doi.org/10.1002/csc2.20490 Article history: Received, 3 November 2020; Accepted, 11 February 2021; Published online, 14 April 2021.
Associate Editor: Junping Chen.
The authors thank Edgardo Rey and Wanda Iriarte for technical assistance in field experiment and...| Biblioteca(s): INIA Las Brujas. |
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| 44. | | SIMONDI, S.; CASARETTO, E.; QUERO, S.; CERETTA, S.; BONNECARRERE, V.; BORSANI, O. A simple and accurate method based on a water-consumption model for phenotyping soybean genotypes under hydric deficit conditions. Agronomy, 2022, Volume 12, Issue 3, Article number 575. GOLD OPEN ACCESS. doi: https://doi.org/10.3390/agronomy12030575 Article history: Received 14 December 2021; Revised 17 February 2022; Accepted 24 February 2022; Published 25 February 2022.
Corresponding author: Borsani, O.; Departamento de Biología Vegetal, Facultad de Agronomía, Universidad de la...| Biblioteca(s): INIA Las Brujas. |
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| 45. | | GALLINO, J. P.; CASTILLO, A.; CERETTA, S.; ESTEVES, P.; BONNECARRERE, V. A simple and inexpensive procedure to more quickly obtain new varieties in soybean. Crop Breeding and Applied Biotechnology, 2022, volume 22, Issue 1, e38212216. OPEN ACCESS. doi: https://doi.org/10.1590/1984-70332022v22n1a06 Article history: Received 24 May 2021; Accepted 12 Aug 2021; Published 30 Mar 2022; Publication in this collection 04 May 2022; Date of issue 2022.
Corresponding author: Gallino, J.P.; INIA, Estación Experimental "Wilson Ferreira...| Biblioteca(s): INIA Las Brujas. |
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| 49. | | FEINGOLD, S.; BONNECARRERE, V.; NEPOMUCENO, A.; HINRICHSEN, P.; CARDOZO, L.; MOLINARI, H.; BARBA, P.; EYHERABIDE, G.; CERETTA, S.; DUJACK, CH. Edición génica: una oportunidad para la región. [Debate]. Revista de Investigaciones Agropecuarias (RIA), 2018, Volume 44, Issue 3, Pages 424-427. OPEN ACCESS 2-s2.0-85068609552 Article history: Publicado online 5 de diciembre de 2018.
Este documento fue originado durante la ?Primera Reunión del Núcleo de Estudio de Nuevas Técnicas de Mejoramiento Genético? del PROCISUR, realizada en
Montevideo entre el 25 y 26...| Biblioteca(s): INIA Las Brujas. |
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| 50. | | ROSAS, J.E.; ESCOBAR, M.; MARTÍNEZ, S.; BLANCO, P.H.; PÉREZ DE VIDA, F.; QUERO, G.; GUTIÉRREZ, L.; BONNECARRERE, V. Epistasis and quantitative resistance to Pyricularia oryzae revealed by GWAS in advanced rice breeding populations. Agriculture 2020, 10(12), 622. Open Access. DOI: https://doi.org/10.3390/agriculture10120622 Article history: Received: 30 October 2020 / Revised: 23 November 2020 / Accepted: 24 November 2020 / Published: 11 December 2020.| Biblioteca(s): INIA Treinta y Tres. |
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| 51. | | PORTA, B.; CONDON, F.; FRANCO, J.; IRIARTE, W.; BONNECARRERE, V.; GUIMARAENS-MOREIRA, M; VIDAL, R.; GALVAN, G.A. Genetic structure, core collection and regeneration quality in white dent corn landraces. Crop Science, v.58: 1-15, July-August 2018. Article history: Received: Dec 31, 2017 / Accepted: Mar 05, 2018 / Published: April 26, 2018.| Biblioteca(s): INIA La Estanzuela. |
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| 52. | | REBOLLO, I.; PÉREZ DE VIDA, F.; BLANCO, P.H.; MOLINA, F.; CRUZ, M.; BONNECARRERE, V.; GARAYCOCHEA, S.; ROSAS, J.E. Mapeo asociado de tolerancia a bajas temperaturas en germoplasma avanzado de arroz. [Poster]. En: Jornadas de Investigación, Facultad de Agronomía (UdelaR), 8-9, nov. 2018, Montevideo, UY.| Biblioteca(s): INIA Treinta y Tres. |
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| 53. | | REBOLLO, I.; CRUZ, M.; PÉREZ DE VIDA, F.; BLANCO, P.H.; MOLINA, F.; BONNECARRERE, V.; GARAYCOCHEA, S.; ROSAS, J.E. Mapeo asociativo de tolerancia a bajas temperaturas en germoplasma avanzado de arroz. In: UNIVERSIDAD DE LA REPÚBLICA (UDELAR). FACULTAD DE AGRONOMÍA. Resúmenes. Jornadas de Investigación, 8-9 nov., 2018, Montevideo, Uruguay. Montevideo; FAGRO, 2019. p. 18| Biblioteca(s): INIA Treinta y Tres. |
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| 55. | | BERBERIAN, N.; BONNECARRERE, V.; BLANCO, P.H.; PÉREZ DE VIDA, F.; ROSAS, J.E.; MARTÍNEZ, S.; GUTIÉRREZ, L. Model comparison and experimental design simulation including natural field variability in rice crop (Oryza sativa L.). In: UNIVERSIDAD DE LA REPÚBLICA (UDELAR). FACULTAD DE AGRONOMÍA. Resúmenes. Jornadas de Investigación, 8-9 nov., 2018, Montevideo, Uruguay. Montevideo; FAGRO, 2019. p. 14 Trabajo originalmente presentado en: Berberian, N.; Bonecarrere, V.; Blaco, P.; Pérez de Vida, F.; Rosas, J.; Martínez, S.; Gutíerrez, L. 2018. International Biometric Conference, 29. Model comparison and experimental design simulation...| Biblioteca(s): INIA Treinta y Tres. |
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| 56. | | MONTEVERDE, E.; ROSAS, J.E.; BLANCO, P.H.; PÉREZ DE VIDA, F.; BONNECARRERE, V.; QUERO, G.; GUTIERREZ, L.; MCCOUCH, S. Multienvironment models increase prediction accuracy of complex traits in advanced breeding lines of rice (O. sativa). Crop Science, 2018, 58:1519-1530. Article history: Accepted on May 09, 2018. Published online June 21, 2018.| Biblioteca(s): INIA Treinta y Tres. |
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| 57. | | MONTEVERDE, E.; GUTIERREZ, L.; BLANCO, P.H.; PÉREZ DE VIDA, F.; ROSAS, J.E.; BONNECARRERE, V.; QUERO, G.; MCCOUCH, SUSAN Integrating molecular markers and environmental covariates to interpret genotype by environment interaction in rice (Oryza sativa L.) grown in subtropical areas. G3: GENES, GENOMES, GENETICS May 1, 2019, v.9 (5), p. 1519-1531. OPEN ACCESS. Article history: Manuscript received February 6, 2019 // Accepted for publication March 5, 2019// Published Early Online March 15, 2019.
Supplemental material available at Figshare: https://doi.org/10.25387/g3.7685636| Biblioteca(s): INIA Treinta y Tres. |
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| 59. | | ROSAS, J.E.; SPRUNCK, B.; IRIARTE, W.; REBOLLO, I.; BONNECARRERE, V.; MOLINA, F.; BLANCO, P.H.; PÉREZ DE VIDA, F. Validación de SNP asociados a variables de interés en germoplasma Japónica tropical de INIA. In: Terra, J. A.; Martínez, S.; Saravia, H. (Eds.) Arroz 2019. Montevideo (UY): INIA, 2019. p. 89-92. (INIA Serie Técnica; 250)| Biblioteca(s): INIA Treinta y Tres. |
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| 60. | | BONNECARRERE, V.; QUERO, G.; MONTEVERDE, E.; ROSAS, J.E.; PÉREZ DE VIDA, F.; CRUZ, M.; CORREDOR, E.; GARAYCOCHEA, S.; MONZA, J.; BORSANI, O. Candidate gene markers associated with cold tolerance in vegetative stage of rice (Oryza sativa L.). Euphytica, 2015, v. 203 no. 2, p. 385-398. p. 385-398. Received: 17 June 2014 / Accepted: 23 October 2014 / Published online: 2 November 2014| Biblioteca(s): INIA Las Brujas; INIA Treinta y Tres. |
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| Registros recuperados : 85 | |
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 | Acceso al texto completo restringido a Biblioteca INIA Treinta y Tres. Por información adicional contacte bibliott@inia.org.uy. |
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Registro completo
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Biblioteca (s) : |
INIA Treinta y Tres. |
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Fecha actual : |
11/05/2018 |
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Actualizado : |
28/05/2019 |
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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 : |
MONTEVERDE, E.; ROSAS, J.E.; BLANCO, P.H.; PÉREZ DE VIDA, F.; BONNECARRERE, V.; QUERO, G.; GUTIERREZ, L.; MCCOUCH, S. |
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Afiliación : |
ELIANA MONTEVERDE, Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, USA.; JUAN EDUARDO ROSAS CAISSIOLS, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; PEDRO HORACIO BLANCO BARRAL, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; FERNANDO BLAS PEREZ DE VIDA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; MARIA VICTORIA BONNECARRERE MARTINEZ, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; GASTÓN QUERO CORRALLO, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; LUCÍA GUTIERREZ, Department of Agronomy, University of Wisconsin, WI, USA.; SUSAN MCCOUCH, Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, USA. |
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Título : |
Multienvironment models increase prediction accuracy of complex traits in advanced breeding lines of rice (O. sativa). |
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Fecha de publicación : |
2018 |
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Fuente / Imprenta : |
Crop Science, 2018, 58:1519-1530. |
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DOI : |
10.2135/cropsci2017.09.0564 |
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Idioma : |
Inglés |
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Notas : |
Article history: Accepted on May 09, 2018. Published online June 21, 2018. |
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Contenido : |
ABSTRACT: Genotype x environment interaction (G x E) is the differential response of genotypes in different environments and represents a major challenge for breeders. Genotype x year-interaction (G x Y) is a relevant component of G x E, and accounting for it is an important strategy for identifying lines with stable and superior performance across years. In this study, we compared the prediction accuracy of modeling G x Y using covariance structures that differ in their ability to
accommodate correlation among environments.
We present the use of these approaches in two different rice (Oryza sativa L.) breeding populations (indica and tropical japonica) for predicting grain yield, plant height, and three milling quality traits—milling yield, head rice percentage, and grain chalkiness—under different cross-validation (CV) scenarios. We also compared model performance in the context of global predictions (i.e., predictions across years). Most of the benefits of multienvironment models come from modeling genetic correlations between environments when predicting performance of lines that have been tested in some environments but not others (CV2). For predicting the performance of newly developed lines (CV1), modeling between environment correlations has no effect compared with considering environments independently. Response to selection of multienvironment models when modeling covariance structures that accommodate covariances between environments was always beneficial when predicting the performance of lines across years. We also show that, for some traits, high prediction accuracies can be obtained in untested years, which is important for resource allocation in small breeding programs. MenosABSTRACT: Genotype x environment interaction (G x E) is the differential response of genotypes in different environments and represents a major challenge for breeders. Genotype x year-interaction (G x Y) is a relevant component of G x E, and accounting for it is an important strategy for identifying lines with stable and superior performance across years. In this study, we compared the prediction accuracy of modeling G x Y using covariance structures that differ in their ability to
accommodate correlation among environments.
We present the use of these approaches in two different rice (Oryza sativa L.) breeding populations (indica and tropical japonica) for predicting grain yield, plant height, and three milling quality traits—milling yield, head rice percentage, and grain chalkiness—under different cross-validation (CV) scenarios. We also compared model performance in the context of global predictions (i.e., predictions across years). Most of the benefits of multienvironment models come from modeling genetic correlations between environments when predicting performance of lines that have been tested in some environments but not others (CV2). For predicting the performance of newly developed lines (CV1), modeling between environment correlations has no effect compared with considering environments independently. Response to selection of multienvironment models when modeling covariance structures that accommodate covariances between environments was always beneficial when pr... Presentar Todo |
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Palabras claves : |
GENOTYPE X ENVIRONMENT INTERACTION; INTERACCIONES GENOTIPO-AMBIENTE. |
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Thesagro : |
ARROZ; GENOTIPOS; RICE. |
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Asunto categoría : |
F30 Genética vegetal y fitomejoramiento |
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Marc : |
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024 7 $a10.2135/cropsci2017.09.0564$2DOI
100 1 $aMONTEVERDE, E.
245 $aMultienvironment models increase prediction accuracy of complex traits in advanced breeding lines of rice (O. sativa).$h[electronic resource]
260 $c2018
500 $aArticle history: Accepted on May 09, 2018. Published online June 21, 2018.
520 $aABSTRACT: Genotype x environment interaction (G x E) is the differential response of genotypes in different environments and represents a major challenge for breeders. Genotype x year-interaction (G x Y) is a relevant component of G x E, and accounting for it is an important strategy for identifying lines with stable and superior performance across years. In this study, we compared the prediction accuracy of modeling G x Y using covariance structures that differ in their ability to accommodate correlation among environments. We present the use of these approaches in two different rice (Oryza sativa L.) breeding populations (indica and tropical japonica) for predicting grain yield, plant height, and three milling quality traits—milling yield, head rice percentage, and grain chalkiness—under different cross-validation (CV) scenarios. We also compared model performance in the context of global predictions (i.e., predictions across years). Most of the benefits of multienvironment models come from modeling genetic correlations between environments when predicting performance of lines that have been tested in some environments but not others (CV2). For predicting the performance of newly developed lines (CV1), modeling between environment correlations has no effect compared with considering environments independently. Response to selection of multienvironment models when modeling covariance structures that accommodate covariances between environments was always beneficial when predicting the performance of lines across years. We also show that, for some traits, high prediction accuracies can be obtained in untested years, which is important for resource allocation in small breeding programs.
650 $aARROZ
650 $aGENOTIPOS
650 $aRICE
653 $aGENOTYPE X ENVIRONMENT INTERACTION
653 $aINTERACCIONES GENOTIPO-AMBIENTE
700 1 $aROSAS, J.E.
700 1 $aBLANCO, P.H.
700 1 $aPÉREZ DE VIDA, F.
700 1 $aBONNECARRERE, V.
700 1 $aQUERO, G.
700 1 $aGUTIERREZ, L.
700 1 $aMCCOUCH, S.
773 $tCrop Science, 2018, 58:1519-1530.
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