Identifi cation of representative and discriminatory environments to select genotypes using GGE Biplot.
Theobjective of this work was to identify representative and discriminatoryenvironments to select rice genotypes using theBiplot GGE technique. The rice data base of the Rice Project2001-2009 was used. Garin yield and proportion of wholegrains, individually and by the use of a selection index (yield+ whole grains) were analyzed using Biplot GGE. Every Biplotgenerated was analyzed for distance in mm. between reallocalities and the ideal; distances were later standardized. Inaddition, the discriminatory capacity and representativity ofeach locality were determined. With the exception of Alanje,the localities more appropriate for higher yields (Soná,Barú), were not the same for the obtention of more wholegrains (Tonosí, Barú, Divisa). The selection index identifi edappropriate locations for select (Tonosí, Alanje, Calabacito,Soná, Barú). All localities were effective in their discriminatorycapacity for yield. Differences in representativitywere observed, with Calabacito and Changuinola occupyingthe highest and lowest positions, respectively. All localitiesshowed similar discriminatory capacity and representativityfor whole grains. Integrating yield and more whole grains itbecame posible to separate more discriminatory (Remedios,Tanara, Alanje) and more representative (Calabacito, Tonosí,Barú) locations. The practical implication of this work is thatit allows us to prioritize research in localities more appropriatefor the identifi cation of superior genotypes.
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Universidad de Costa Rica
2011
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Camargo-Buitrago, Ismael Quirós-McIntire, Evelyn Itzel Gordón-Mendoza, Román |
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Camargo-Buitrago, Ismael Quirós-McIntire, Evelyn Itzel Gordón-Mendoza, Román Identifi cation of representative and discriminatory environments to select genotypes using GGE Biplot. |
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Camargo-Buitrago, Ismael Quirós-McIntire, Evelyn Itzel Gordón-Mendoza, Román |
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Camargo-Buitrago, Ismael |
title |
Identifi cation of representative and discriminatory environments to select genotypes using GGE Biplot. |
title_short |
Identifi cation of representative and discriminatory environments to select genotypes using GGE Biplot. |
title_full |
Identifi cation of representative and discriminatory environments to select genotypes using GGE Biplot. |
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Identifi cation of representative and discriminatory environments to select genotypes using GGE Biplot. |
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Identifi cation of representative and discriminatory environments to select genotypes using GGE Biplot. |
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identifi cation of representative and discriminatory environments to select genotypes using gge biplot. |
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Theobjective of this work was to identify representative and discriminatoryenvironments to select rice genotypes using theBiplot GGE technique. The rice data base of the Rice Project2001-2009 was used. Garin yield and proportion of wholegrains, individually and by the use of a selection index (yield+ whole grains) were analyzed using Biplot GGE. Every Biplotgenerated was analyzed for distance in mm. between reallocalities and the ideal; distances were later standardized. Inaddition, the discriminatory capacity and representativity ofeach locality were determined. With the exception of Alanje,the localities more appropriate for higher yields (Soná,Barú), were not the same for the obtention of more wholegrains (Tonosí, Barú, Divisa). The selection index identifi edappropriate locations for select (Tonosí, Alanje, Calabacito,Soná, Barú). All localities were effective in their discriminatorycapacity for yield. Differences in representativitywere observed, with Calabacito and Changuinola occupyingthe highest and lowest positions, respectively. All localitiesshowed similar discriminatory capacity and representativityfor whole grains. Integrating yield and more whole grains itbecame posible to separate more discriminatory (Remedios,Tanara, Alanje) and more representative (Calabacito, Tonosí,Barú) locations. The practical implication of this work is thatit allows us to prioritize research in localities more appropriatefor the identifi cation of superior genotypes. |
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Universidad de Costa Rica |
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2011 |
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https://revistas.ucr.ac.cr/index.php/agromeso/article/view/8683 |
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AT camargobuitragoismael identificationofrepresentativeanddiscriminatoryenvironmentstoselectgenotypesusingggebiplot AT quirosmcintireevelynitzel identificationofrepresentativeanddiscriminatoryenvironmentstoselectgenotypesusingggebiplot AT gordonmendozaroman identificationofrepresentativeanddiscriminatoryenvironmentstoselectgenotypesusingggebiplot AT camargobuitragoismael identificaciondeambientesrepresentativosydiscriminatoriosparaseleccionargenotiposdearrozmedianteelbiplotgge AT quirosmcintireevelynitzel identificaciondeambientesrepresentativosydiscriminatoriosparaseleccionargenotiposdearrozmedianteelbiplotgge AT gordonmendozaroman identificaciondeambientesrepresentativosydiscriminatoriosparaseleccionargenotiposdearrozmedianteelbiplotgge |
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oai:portal.ucr.ac.cr:article86832023-06-16T13:51:20Z Identifi cation of representative and discriminatory environments to select genotypes using GGE Biplot. Identificación de ambientes representativos y discriminatorios para seleccionar genotipos de arroz mediante el Biplot GGE Camargo-Buitrago, Ismael Quirós-McIntire, Evelyn Itzel Gordón-Mendoza, Román Rice selection genotype x environment interaction discriminatory and representative environments ideal environment. Selección arroz interacción genotipoambiente ambientes discriminatorios y representativos ambiente ideal. Theobjective of this work was to identify representative and discriminatoryenvironments to select rice genotypes using theBiplot GGE technique. The rice data base of the Rice Project2001-2009 was used. Garin yield and proportion of wholegrains, individually and by the use of a selection index (yield+ whole grains) were analyzed using Biplot GGE. Every Biplotgenerated was analyzed for distance in mm. between reallocalities and the ideal; distances were later standardized. Inaddition, the discriminatory capacity and representativity ofeach locality were determined. With the exception of Alanje,the localities more appropriate for higher yields (Soná,Barú), were not the same for the obtention of more wholegrains (Tonosí, Barú, Divisa). The selection index identifi edappropriate locations for select (Tonosí, Alanje, Calabacito,Soná, Barú). All localities were effective in their discriminatorycapacity for yield. Differences in representativitywere observed, with Calabacito and Changuinola occupyingthe highest and lowest positions, respectively. All localitiesshowed similar discriminatory capacity and representativityfor whole grains. Integrating yield and more whole grains itbecame posible to separate more discriminatory (Remedios,Tanara, Alanje) and more representative (Calabacito, Tonosí,Barú) locations. The practical implication of this work is thatit allows us to prioritize research in localities more appropriatefor the identifi cation of superior genotypes. El objetivo del presente estudio fue identifi carambientes representativos y discriminatorios para seleccionargenotipos de arroz mediante el Biplot GGE. Se recurrió a labase de datos del proyecto de arroz periodo 2001-2009. Seanalizaron: rendimiento de grano (toneladas/hectárea) y laproporción de granos enteros, de manera individual, y medianteun índice de selección (rendimiento + granos enteros).La información fue analizada mediante el programa BiplotGGE. A cada Biplot generado se le determinó la distancia enmilímetros entre localidades verdaderas y la ideal; posteriormentelas distancias fueron estandarizadas. Además se estimóla capacidad discriminatoria y representatividad de las localidades.A excepción de Alanje, las localidades más apropiadaspara rendimiento (Soná, Barú), no fueron las mismas paragranos enteros (Tonosí, Barú, Divisa). El índice de selecciónidentifi có las localidades apropiadas para seleccionar (Tonosí,Alanje, Calabacito, Soná, Barú). Todas las localidades fueronefectivas en su capacidad discriminatoria para rendimiento. Seencontraron diferencias en representatividad, siendo Calabacitoy Changuinola, las de mayor y menor representatividad,respectivamente. Las localidades presentaron similar capacidaddiscriminatoria y representatividad para granos enteros.Al integrar rendimiento más granos enteros, se hizo posibleseparar las localidades más discriminatorias (Remedios, Tanara,Alanje) y las más representativas (Calabacito, Tonosí,Barú). Las implicaciones prácticas de este trabajo es que nospermitirá priorizar la investigación en aquellas localidadesmás apropiadas para identifi car genotipos superiores. Universidad de Costa Rica 2011-12-01 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Article application/pdf https://revistas.ucr.ac.cr/index.php/agromeso/article/view/8683 10.15517/am.v22i2.8683 Agronomía Mesoamericana; 2011: Agronomía Mesoamericana: Vol. 22, Issue 2 (July-December); 245-255 Agronomía Mesoamericana; 2011: Agronomía Mesoamericana: Vol 22, No 2 (Julio-diciembre); 245-255 Agronomía Mesoamericana; 2011: Agronomía Mesoamericana: Vol. 22, Issue 2 (July-December); 245-255 2215-3608 1021-7444 spa https://revistas.ucr.ac.cr/index.php/agromeso/article/view/8683/8195 |