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Registros recuperados : 62 | |
3. | | GUTIÉRREZ, L.; LADO, B.; GONZÁLEZ, P.; SILVA, P.; QUINCKE, M. Handling Genotype-By-Environment Interaction in Genomic Selection to Predict New Genotypes and New Environments. [P0814] In: International Plant & Animal Genome, Conference PAG XXIV, "The largest Ag-genomics Meeting in the World San Diego, CA, USA; January 9-13, 2016. [Abstract] .Biblioteca(s): INIA Las Brujas. |
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5. | | PÉREZ, O.; VIEGA, L.; GUTIÉRREZ, L.; CASTRO, M. Post-anthesis water deficit in spring wheat: effects on yield components and relative water content. In: SEMINARIO INTERNACIONAL DE TRIGO, 2014, La Estanzuela, Colonia, UY. GERMÁN, S., et al. (Org.). 1914-2014, un siglo de mejoramiento de trigo en La Estanzuela: un valioso legado para el futuro: resúmenes; posters. La Estanzuela, Colonia, UY: INIA, 2014. p. 41.Biblioteca(s): INIA La Estanzuela. |
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6. | | PÉREZ, O.; VIEGA, L.; GUTIERREZ, L.; CASTRO, M. Post-anthesis water deficit in spring wheat: effects on yield components and relative water content. [Poster]. In: German, S.; Quincke, M.; Vázquez, D.; Castro, M.; Pereyra, S.; Silva, P.; García, A. (Eds.). Seminario Internacional "1914-2014: Un siglo de mejoramiento de trigo en La Estanzuela". Montevideo (UY): INIA, 2018. P.130. (INIA Serie Técnica; 241).Biblioteca(s): INIA La Estanzuela. |
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7. | | PEREYRA, S.; GERMAN, S.; GONZÁLEZ, S.N.; CASTRO, A.; GAMBA, F.; GUTIERREZ, L. Advances in the integrated management of leaf blotches in Uruguay. In: International Workshop on Barley Leaf Diseases , 2o. Rabat, Morocco: The International Center for Agricultural Research in the Dry Areas (ICARDA), April 5-7, 2017. p. 46.Biblioteca(s): INIA La Estanzuela. |
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8. | | BORGES, A.; GONZÁLEZ-REYMUNDEZ, A.; ERNST, O.; CADENAZZI, M.; TERRA, J.A.; GUTIÉRREZ, L. Can spatial modeling substitute experimental design in agricultural experiments? Crop Science, 2018, v. 59, no. 1, p. 1-10. Article history: Accepted paper, posted 10/05/18. Published online December, 13. 2018.Biblioteca(s): INIA Treinta y Tres. |
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9. | | LADO,B.; BATTENFIELD, S.; POLAND, J.; QUINCKE, M.; SILVA, P.; GUTIÉRREZ, L. Comparación de metodologías de predicción de cruzamientos para rendimiento en trigo. MV 14 - COMUNICACIONES LIBRES - MV. MEJORAMIENTO VEGETAL In: JOURNAL OF BASIC & APPLIED GENETICS, 2016, Vol.27, Iss. 1 (Supp.). XVI LATIN AMERICAN CONGRESS OF GENETICS, IV CONGRESS OF THE URUGUAYAN SOCIETY OF GENETICS, XLIX ANNUAL MEETING OF THE GENETICS SOCIETY OF CHILE, XLV ARGENTINE CONGRESS OF GENETICS, 9-12 October 2016. PROCEEDINGS. Montevideo (Uruguay): SAG, 2016 p. 287.Biblioteca(s): INIA La Estanzuela. |
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10. | | GUTIERREZ, L.; BORGES, A.; QUERO, G.; GONZALEZ-REYMUNDEZ, A.; BERRO, I.; LADO, B.; CASTRO, A. Biostatistical tools for plant breeding in the genomics era. In: German, S.; Quincke, M.; Vázquez, D.; Castro, M.; Pereyra, S.; Silva, P.; García, A. (Eds.). Seminario Internacional "1914-2014: Un siglo de mejoramiento de trigo en La Estanzuela". Montevideo (UY): INIA, 2018. p.46-57. (INIA Serie Técnica; 241).Biblioteca(s): INIA La Estanzuela. |
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11. | | GUTIÉRREZ, L.; BERBERIAN, N.; CAPETTINI, F.; GERMAN, S.; PEREYRA, S.; PÉREZ, C.; CASTRO, A. Disease resistance QTLs in barley germplasm from Latin America In: INTERNATIONAL ANIMAL AND PLANT GENOME CONFERENCE, 20., 2012, San Diego, CA, US. Posters: wheat, barley, oat, and related. P0350. [s.l.: INTL-PAG], 2012.Biblioteca(s): INIA La Estanzuela. |
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12. | | CAJARVILLE, C.; BRITOS, A.; ERRANDONEA, N.; GUTIÉRREZ, L.; COZZOLINO, D.; REPETTO, J.L. Diurnal changes in water-soluble carbohydrate concentration in lucerne and tall fescue in autumn and the effects on in vitro fermentation. Research Article. New Zealand Journal of Agricultural Research, 2015, v. 58, no.3, p. 281-291. Article history: Received 23 January 2014 // Accepted 5 February 2015.Biblioteca(s): INIA Las Brujas. |
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13. | | PORTA, B.; CONDON, F.; BONNECARRERE, V.; GUTIÉRREZ, L.; FRANCO, J.; GALVÁN, G. Diversidad y estructura genética del germoplasma de maíz blanco dentad de Uruguay mediante microsatélites. [Resumen]. In: SIMPÓSIO DE RECURSOS GENÉTICOS PARA A AMÉRICA LATINA E CARIBE, 10., 2015, Bento Gonçalves. Recursos genéticos no século 21: de Vavilov a Svalbard. Anais... [s.l.]: Sociedade Brasileira de Recursos Genéticos, 2015. p.65. Agradecimientos: Comisión Sectorial de Investigación Científica, CSIC - UdelaR, Uruguay.Biblioteca(s): INIA Las Brujas. |
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14. | | QUERO, C.; FERNANDEZ, S.; BRANDARIZ, S.B.; SIMONDI, S.; GUTIÉRREZ, L. Herramientas de análisis y visualización genómica. MV 10 - COMUNICACIONES LIBRES - MV. MEJORAMIENTO VEGETAL In: JOURNAL OF BASIC & APPLIED GENETICS, 2016, Vol.27, Iss. 1 (Supp.). XVI LATIN AMERICAN CONGRESS OF GENETICS, IV CONGRESS OF THE URUGUAYAN SOCIETY OF GENETICS, XLIX ANNUAL MEETING OF THE GENETICS SOCIETY OF CHILE, XLV ARGENTINE CONGRESS OF GENETICS, 9-12 October 2016. PROCEEDINGS. Montevideo (Uruguay): SAG, 2016. p. 285Biblioteca(s): INIA Las Brujas. |
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16. | | REBOLLO, I.; AGUILAR, I.; PÉREZ DE VIDA, F.; MOLINA, F.; GUTIÉRREZ, L.; ROSAS, J.E. Genotype by environment interaction characterization and its modeling with random regression to climatic variables in two rice breeding populations. Original article. Crop Science. 2023, Volume 63, Issue 4, Pages 2220-2240. https://doi.org/10.1002/csc2.21029 -- OPEN ACCESS. Article history: Received 21 November 2022, Accepted 10 May 2023, Published online 16 June 2023. -- Correspondence: Rosas, J.E.; INIA, Estación Experimental Treinta y Tres, Road 8 km 281, Treinta y Tres, Uruguay; email:jrosas@inia.org.uy...Biblioteca(s): INIA Las Brujas. |
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18. | | BERBERIAN, N.; CASTRO, A.; CAPETTINI, F.; FROS, D.; GERMAN, S.; PEREYRA, S.; PEREZ, C.; GUTIÉRREZ, L. Modelos mixtos para la identificación de QTL para enfermedades en cebada a traves de mapeo asociativo. In: REUNIÓN CIENTIFICA DEL GRUPO ARGENTINO DE BIOMETRÍA, 16., 2011, Salta, AR. Libro de resúmenes: modelos lineales y generalizados mixtos. La Plata: GAB, 2011. p. 108.Biblioteca(s): INIA La Estanzuela. |
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19. | | 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. 236-239Biblioteca(s): INIA La Estanzuela. |
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Registros recuperados : 62 | |
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| Acceso al texto completo restringido a Biblioteca INIA Treinta y Tres. Por información adicional contacte bibliott@inia.org.uy. |
Registro completo
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Biblioteca (s) : |
INIA Treinta y Tres. |
Fecha actual : |
16/10/2018 |
Actualizado : |
11/02/2019 |
Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
Circulación / Nivel : |
Internacional - -- |
Autor : |
BORGES, A.; GONZÁLEZ-REYMUNDEZ, A.; ERNST, O.; CADENAZZI, M.; TERRA, J.A.; GUTIÉRREZ, L. |
Afiliación : |
ALEJANDRA BORGES, Departamento de Estadística. Facultad de Agronomía, UdelaR.; AGUSTÍN GONZÁLEZ-REYMUNDEZ, Departamento de Estadística. Facultad de Agronomía, UdelaR.; OSVALDO, ERNST, Departamento de Producción de Cultivos. EEMAC, Facultad de Agronomía, UdelaR.; MÓNICA CADENAZZI, Departamento de Estadística. Facultad de Agronomía, UdelaR.; JOSÉ ALFREDO TERRA FERNÁNDEZ, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; LUCÍA GUTIÉRREZ, Department of Agronomy, University of Wisconsin. |
Título : |
Can spatial modeling substitute experimental design in agricultural experiments? |
Fecha de publicación : |
2018 |
Fuente / Imprenta : |
Crop Science, 2018, v. 59, no. 1, p. 1-10. |
DOI : |
10.2135/cropsci2018.03.0177 |
Idioma : |
Inglés |
Notas : |
Article history: Accepted paper, posted 10/05/18. Published online December, 13. 2018. |
Contenido : |
Abstract:
One of the most critical aspects of agricultural experimentation is the proper choice of experimental design to control field heterogeneity, especially for large experiments. However, even with complex experimental designs, spatial variability may not be properly controlled if it occurs at scales smaller than blocks. Therefore, modeling spatial variability can be beneficial and some studies even propose spatial modeling instead of experimental design. Our goal was to evaluate the effect of experimental design, spatial modeling, and a combination of both under real field conditions using GIS and simulating experiments. Yield data from cultivars was simulated using real spatial variability from a large uniformity trial of one hundred independent locations and different sizes of experiments for four experimental designs: completely randomized design (CRD), randomized complete block design (RCBD), alpha-lattice incomplete block design (ALPHA), and partially replicated design (PREP). Each realization was analyzed using different levels of spatial correction. Models were compared by precision, accuracy, and the recovery of superior genotypes. For moderate and large experiment sizes, ALPHA was the best experimental design in terms of precision and accuracy. In most situations, models that included spatial correlation were better than models with no spatial correlation but they did not outperformed better experimental designs. Therefore, spatial modeling is not a substitute for good experimental design. MenosAbstract:
One of the most critical aspects of agricultural experimentation is the proper choice of experimental design to control field heterogeneity, especially for large experiments. However, even with complex experimental designs, spatial variability may not be properly controlled if it occurs at scales smaller than blocks. Therefore, modeling spatial variability can be beneficial and some studies even propose spatial modeling instead of experimental design. Our goal was to evaluate the effect of experimental design, spatial modeling, and a combination of both under real field conditions using GIS and simulating experiments. Yield data from cultivars was simulated using real spatial variability from a large uniformity trial of one hundred independent locations and different sizes of experiments for four experimental designs: completely randomized design (CRD), randomized complete block design (RCBD), alpha-lattice incomplete block design (ALPHA), and partially replicated design (PREP). Each realization was analyzed using different levels of spatial correction. Models were compared by precision, accuracy, and the recovery of superior genotypes. For moderate and large experiment sizes, ALPHA was the best experimental design in terms of precision and accuracy. In most situations, models that included spatial correlation were better than models with no spatial correlation but they did not outperformed better experimental designs. Therefore, spatial modeling is not a substitut... Presentar Todo |
Palabras claves : |
EFFICIENCY STATISTICS; EXPERIMENTAL DESIGN; FIELD VARIABILITY; SPATIAL MODELS; UNIFORMITY TRIAL. |
Thesagro : |
DISENO ESTADISTICO; DISENO EXPERIMENTAL; MODELOS ESTADISTICOS; VARIABILIDAD. |
Asunto categoría : |
U30 Métodos de investigación |
Marc : |
LEADER 02512naa a2200313 a 4500 001 1059193 005 2019-02-11 008 2018 bl uuuu u00u1 u #d 024 7 $a10.2135/cropsci2018.03.0177$2DOI 100 1 $aBORGES, A. 245 $aCan spatial modeling substitute experimental design in agricultural experiments?$h[electronic resource] 260 $c2018 500 $aArticle history: Accepted paper, posted 10/05/18. Published online December, 13. 2018. 520 $aAbstract: One of the most critical aspects of agricultural experimentation is the proper choice of experimental design to control field heterogeneity, especially for large experiments. However, even with complex experimental designs, spatial variability may not be properly controlled if it occurs at scales smaller than blocks. Therefore, modeling spatial variability can be beneficial and some studies even propose spatial modeling instead of experimental design. Our goal was to evaluate the effect of experimental design, spatial modeling, and a combination of both under real field conditions using GIS and simulating experiments. Yield data from cultivars was simulated using real spatial variability from a large uniformity trial of one hundred independent locations and different sizes of experiments for four experimental designs: completely randomized design (CRD), randomized complete block design (RCBD), alpha-lattice incomplete block design (ALPHA), and partially replicated design (PREP). Each realization was analyzed using different levels of spatial correction. Models were compared by precision, accuracy, and the recovery of superior genotypes. For moderate and large experiment sizes, ALPHA was the best experimental design in terms of precision and accuracy. In most situations, models that included spatial correlation were better than models with no spatial correlation but they did not outperformed better experimental designs. Therefore, spatial modeling is not a substitute for good experimental design. 650 $aDISENO ESTADISTICO 650 $aDISENO EXPERIMENTAL 650 $aMODELOS ESTADISTICOS 650 $aVARIABILIDAD 653 $aEFFICIENCY STATISTICS 653 $aEXPERIMENTAL DESIGN 653 $aFIELD VARIABILITY 653 $aSPATIAL MODELS 653 $aUNIFORMITY TRIAL 700 1 $aGONZÁLEZ-REYMUNDEZ, A. 700 1 $aERNST, O. 700 1 $aCADENAZZI, M. 700 1 $aTERRA, J.A. 700 1 $aGUTIÉRREZ, L. 773 $tCrop Science, 2018$gv. 59, no. 1, p. 1-10.
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