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| Registros recuperados : 15 | |
| 1. |  | BENTANCOR, L.; HERNÁNDEZ, J.; DEL PINO, A.; CALIFRA, A.; RESQUÍN, F.; GONZÁLEZ-BARRIOS, P. Evaluation of the biomass production, energy yield and nutrient removal of Eucalyptus dunnii Maiden grown in short rotation coppice under two initial planting densities and harvest systems. Biomass and Bioenergy, 2019, v. 122, p. 165-174. Article history: Received 31 August 2018 // Received in revised form 18 January 2019 // Accepted 21 January 2019.| Biblioteca(s): INIA Tacuarembó. |
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| 2. | | LADO, B.; GONZÁLEZ BARRIOS, P.; QUINCKE, M.; SILVA, P.; GUTIÉREZ, L. Genomic selection in wheat breeding program modelling genotype by environment interaction using mixed models. In: International Wheat Conference, 9., 2015, Sydney, AU. Program and abstracts: posters. Sidney: ICMS Australasia, 2015. p. 168.| Biblioteca(s): INIA La Estanzuela. |
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| 5. | | GONZÁLEZ BARRIOS, P.; PÉREZ, O.; CASTRO, M.; CERETTA, S.; VILARO, D.; GUTIÉRREZ, L. Identificación de limitantes a la expresión del potencial de rendimiento en girasol en Uruguay mediante GGE biplots y PLS regression. In: IV Encuentro Iberoamericano de Biometría; 4o. y XVIII Reunión Científica del GAB, 17o., Setiembre 2013, Mar del Plata ,ROMERO, M.C.; MARINELLI, C.; CEPEDA, R. Eds., La Plata, Bs As, Argentina: Grupo Argentino de Biometría. p. 236-239| Biblioteca(s): INIA La Estanzuela. |
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| 6. | | ANDINO, M.; GAIERO, P.; GONZÁLEZ-BARRIOS, P.; GALVÁN, G.; VILARÓ, F.; SPERANZA, P. Potato introgressive hybridisation breeding for bacterial wilt resistance using Solanum commersonii Dun. as Donor: genetic and agronomic characterisation of a backcross 3 progeny. Potato Research, 2021. Doi: https://doi.org/10.1007/s11540-021-09512-1 Article history: Received 30 October 2020; Accepted 26 April 2021; Published Online 17 June 2021.
Corresponding author: Gaiero, P., Facultad de Agronomía, Universidad de la República, Garzón 780, Montevideo, Uruguay;...| Biblioteca(s): INIA Las Brujas. |
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| 7. | | MOREIRA, V.; FERRONATO, B.; DE BENEDETTI, F.; GONZÁLEZ-BARRIOS, P.; MONDINO, P.; ALANIZ, S. Incidence of Colletotrichum latent infections during olive fruit development under Uruguayan environmental conditions. International Journal of Pest Management, 2022, vol. 68, issue 4: "Uruguayan Society of Phytopathology (SUFIT): Plant protection for a sustainable agriculture", p.286-294. doi: https://doi.org/10.1080/09670874.2022.2119490 Article history: Received 30 April 2022, Accepted 25 August 2022, Published online 08 September 2022. Corresponding author: Moreira, V.; Departamento de Protección Vegetal, Facultad de Agronomía, Universidad de la República, Montevideo,...| Biblioteca(s): INIA Las Brujas. |
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| 9. | | HERNÁNDEZ-LISTA, N.; MOREIRA, V.; GONZÁLEZ-BARRIOS, P.; RODRÍGUEZ, G.; GONZÁLEZ-ARCOS, M.; VILARÓ, F.; GAIERO, P. Evaluación de la resistencia a tizón tardío (Phytophthora infestans) en genotipos de una colección núcleo de parientes silvestres de papa en condiciones controladas. 273. (resúmen Áreas temáticas: Genética. In: Physiological Mini Reviews, 2022, volume 15, Special Issue: III (3er) Congreso Nacional de Biociencias Octubre 2022, Montevideo, Uruguay. p.157. Resumen publicado en las jornadas de BIOCIENCIAS: II Jornadas Binacionales Argentina-Uruguay; III Congreso Nacional 2022 "Ciencia para el desarrollo sustentable".| Biblioteca(s): INIA Las Brujas. |
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| 10. | | DE ARMAS, S.; GALVÁN, G. A.; LAPAZ, M. I.; GONZÁLEZ-BARRIOS, P.; VICENTE, E.; PIANZZOLA, M. J.; SIRI, M. I. Phylogeny and identification of Pantoea species associated with bulb rot and bacterial leaf blight of onion crops in Uruguay. Plant Disease, 2022, volume 106, issue 4, pp. 1216-1225. doi: https://doi.org/10.1094/PDIS-06-21-1140-RE Published Online:27 Mar 2022.
Corresponding author: María I. Siri; Email: msiri@fq.edu.uy.| Biblioteca(s): INIA Treinta y Tres. |
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| 11. | | VIERA, L.; ALCOBA, F.; GONZÁLEZ-BARRIOS, P.; RODRÍGUEZ, G.; GONZÁLEZ-ARCOS, M.; FERREIRA, V.; GALVÁN, G.; SIRI, M.I.; GAIERO, P.; VILARÓ, F. Evaluación de la resistencia a marchitez bacteriana en germoplasma avanzado de Papa (Solanum tuberosum). 153. (resúmen). Áreas temáticas: Genética. In: Physiological Mini Reviews, 2022, volume 15, Special Issue: III (3er) Congreso Nacional de Biociencias Octubre 2022, Montevideo, Uruguay. p.151-152. Resumen publicado en las jornadas de BIOCIENCIAS: II Jornadas Binacionales Argentina-Uruguay; III Congreso Nacional 2022 "Ciencia para el desarrollo sustentable".| Biblioteca(s): INIA Las Brujas. |
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| 12. | | RAEGAN HOEFLER; GONZALEZ-BARRIOS , P.; MADHAV BHATTA; NUNES, J.A.R.; BERRO, I.; NALIN, R.S.; BORGES, A.; COVARRUBIAS, E.; DIAZ-GARCIA, L.; QUINCKE, M.; GUTIERREZ, L. Do Spatial Designs Outperform Classic Experimental Designs?. Journal of Agricultural, Biological, and Environmental Statistics, 1 December 2020, volume 25, number 4, pag.523-552, 1 December 2020. OPEN ACCESS. Doi: https://doi.org/10.1007/s13253-020-00406-2 Article history: Received 15 October 2019/Accepted 01 July 2020/Published 29 August 2020. This project was partially funded through a USDA_AFRI_NIFA_2018-67013-27620 award and by the Hatch Act Formula Fund WISO1984 and WIS03002....| Biblioteca(s): INIA La Estanzuela. |
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| 13. | | GLISON, N.; GONZÁLEZ BARRIOS, P.; CORREA, E.; VIEGA, L.; SALDANHA, S.; REYNO, R.; MUSACCHIO, E.; RUSH, P.; SCHRAUF, G.; SPERANZA, P. Responses of seedling emergence of three Paspalum species to environmental factors. In: International Symposium of Forage Breeding (5., Buenos Aires, AR), 19-21 Octubre, 2015. Abstract.| Biblioteca(s): INIA Tacuarembó. |
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| 14. | | GLISON, N.; GONZÁLEZ BARRIOS, P.; CORREA, E.; VIEGA, L.; SALDANHA, S.; REYNO, R.; MUSACCHIO, E.; RUSH, P.; SCHRAUF, G.; SPERANZA, P. Responses of seedling emergence of three Paspalum species to environmental factors. [Poster]. In: International Symposium of Forage Breeding (5., Buenos Aires, AR), 19-21 Octubre, 2015.| Biblioteca(s): INIA Tacuarembó. |
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| 15. | | SCARLATO, M.; RIEPPI, M.; ALLIAUME, F.; ILLARZE, G.; BAJSA, N.; BERTONI, P.; BIANCHI, F.J.J.A.; ECHEVERRIBORDA, G.; GALVÁN, G.; GARCÍA DE SOUZA, M.; GILSANZ, J.C.; GONZÁLEZ BARRIOS, P.; DIESTE, J.P.; TRASANTE, T.; ROSSING, W.A.H.; DOGLIOTTI, S. Towards the development of cover crop - reduced tillage systems without herbicides and synthetic fertilizers in onion cultivation: Promising but challenges remain. Soil and Tillage Research. 2024, Volume 240, Article 106061. https://doi.org/10.1016/j.still.2024.106061 --- OPEN ACCESS. Article history: Received 8 May 2023, Revised 15 February 2024, Accepted 20 February 2024, Available online 6 March 2024, Version of Record 6 March 2024. -- Correspondence: Scarlato, M.; Facultad de Agronomía, Universidad de la...| Biblioteca(s): INIA Las Brujas. |
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| Registros recuperados : 15 | |
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Registro completo
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Biblioteca (s) : |
INIA Las Brujas. |
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Fecha actual : |
12/08/2016 |
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Actualizado : |
02/01/2017 |
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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 : |
LADO, B.; GONZÁLEZ BARRIOS, P.; QUINCKE, M.; SILVA, P.; GUTIÉRREZ, L. |
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Afiliación : |
BETTINA LADO, Universidad de la República (UdelaR)/ Facultad de Agronomía; PABLO GONZÁLEZ BARRIOS, Universidad de la República (UdelaR)/ Facultad de Agronomía; MARTIN CONRADO QUINCKE WALDEN, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; MARIA PAULA SILVA VILLELLA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; LUCIA GUTIÉRREZ, Universidad de la República (UdelaR)/ Facultad de Agronomía. |
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Título : |
Modeling genotype x environment interaction for genomic selection with unbalanced data from a wheat breeding program. |
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Fecha de publicación : |
2016 |
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Fuente / Imprenta : |
Crop Science, 2016, v. 56, p. 1-15. OPEN ACCESS. |
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DOI : |
http://dx.doi.org/10.2135/cropsci2015.04.0207 |
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Idioma : |
Inglés |
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Contenido : |
ABSTRACT.
Genomic selection (GS) has successfully been used in plant breeding to improve selection efficiency and reduce breeding time and cost. However, there is not a clear strategy on how to incorporate genotype ? environment interaction (GEI) to GS models. Increased prediction accuracy could be achieved using mixed models to exploit GEI by borrowing information from other environments. The objective of this work was to compare strategies to exploit GEI in GS using mixed models. Specifically, we compared strategies to
predict new genotypes by borrowing information from other environments modeling the correlation matrix across environments and to design sets of environments aiming for low GEI to predict genomic performance in new environments. We evaluated 1477 advanced wheat (Triticum aestivum L.) lines for yield in 35 location?year combinations genotyped with genotyping-bysequencing (GBS). Mixed models were used to obtain either overall or by-environment predictions for different sets of environments. Overall accuracy was high (0.5). Borrowing information from relatives evaluated in multiple environments and modeling the correlation matrix across environments was the best strategy to predict new
genotypes. On the other hand, the best strategy for predicting the performance of genotypes in new environments was either to predict across locations for single years or to predict within defined mega-environments (MEs) for any year or location. In summary, higher predictive ability was obtained by characterizing and by modeling GEI in the GS context.
© 2016. Crop Science Society of America, Inc. MenosABSTRACT.
Genomic selection (GS) has successfully been used in plant breeding to improve selection efficiency and reduce breeding time and cost. However, there is not a clear strategy on how to incorporate genotype ? environment interaction (GEI) to GS models. Increased prediction accuracy could be achieved using mixed models to exploit GEI by borrowing information from other environments. The objective of this work was to compare strategies to exploit GEI in GS using mixed models. Specifically, we compared strategies to
predict new genotypes by borrowing information from other environments modeling the correlation matrix across environments and to design sets of environments aiming for low GEI to predict genomic performance in new environments. We evaluated 1477 advanced wheat (Triticum aestivum L.) lines for yield in 35 location?year combinations genotyped with genotyping-bysequencing (GBS). Mixed models were used to obtain either overall or by-environment predictions for different sets of environments. Overall accuracy was high (0.5). Borrowing information from relatives evaluated in multiple environments and modeling the correlation matrix across environments was the best strategy to predict new
genotypes. On the other hand, the best strategy for predicting the performance of genotypes in new environments was either to predict across locations for single years or to predict within defined mega-environments (MEs) for any year or location. In summary, higher predictive ab... Presentar Todo |
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Palabras claves : |
GENOMIC SELECTION; WHEAT. |
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Thesagro : |
TRIGO. |
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Asunto categoría : |
-- |
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URL : |
http://dx.doi.org/10.2135/cropsci2015.04.0207
http://www.ainfo.inia.uy/digital/bitstream/item/5875/1/Lado-B.-2016.-Crop-Science.pdf
http://www.ainfo.inia.uy/digital/bitstream/item/5876/1/Lado-B.-2016.-Crop-Science-supplement.pdf
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Marc : |
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024 7 $ahttp://dx.doi.org/10.2135/cropsci2015.04.0207$2DOI
100 1 $aLADO, B.
245 $aModeling genotype x environment interaction for genomic selection with unbalanced data from a wheat breeding program.$h[electronic resource]
260 $c2016
520 $aABSTRACT. Genomic selection (GS) has successfully been used in plant breeding to improve selection efficiency and reduce breeding time and cost. However, there is not a clear strategy on how to incorporate genotype ? environment interaction (GEI) to GS models. Increased prediction accuracy could be achieved using mixed models to exploit GEI by borrowing information from other environments. The objective of this work was to compare strategies to exploit GEI in GS using mixed models. Specifically, we compared strategies to predict new genotypes by borrowing information from other environments modeling the correlation matrix across environments and to design sets of environments aiming for low GEI to predict genomic performance in new environments. We evaluated 1477 advanced wheat (Triticum aestivum L.) lines for yield in 35 location?year combinations genotyped with genotyping-bysequencing (GBS). Mixed models were used to obtain either overall or by-environment predictions for different sets of environments. Overall accuracy was high (0.5). Borrowing information from relatives evaluated in multiple environments and modeling the correlation matrix across environments was the best strategy to predict new genotypes. On the other hand, the best strategy for predicting the performance of genotypes in new environments was either to predict across locations for single years or to predict within defined mega-environments (MEs) for any year or location. In summary, higher predictive ability was obtained by characterizing and by modeling GEI in the GS context. © 2016. Crop Science Society of America, Inc.
650 $aTRIGO
653 $aGENOMIC SELECTION
653 $aWHEAT
700 1 $aGONZÁLEZ BARRIOS, P.
700 1 $aQUINCKE, M.
700 1 $aSILVA, P.
700 1 $aGUTIÉRREZ, L.
773 $tCrop Science, 2016$gv. 56, p. 1-15. OPEN ACCESS.
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