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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Main Authors: Camargo-Buitrago, Ismael, Quirós-McIntire, Evelyn Itzel, Gordón-Mendoza, Román
Format: Digital revista
Language:spa
Published: Universidad de Costa Rica 2011
Online Access:https://revistas.ucr.ac.cr/index.php/agromeso/article/view/8683
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id oai:portal.ucr.ac.cr:article8683
record_format ojs
institution UCR
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country Costa Rica
countrycode CR
component Revista
access En linea
databasecode rev-agromeso
tag revista
region America Central
libraryname Bibioteca de la Facultad de Agronomía
language spa
format Digital
author Camargo-Buitrago, Ismael
Quirós-McIntire, Evelyn Itzel
Gordón-Mendoza, Román
spellingShingle Camargo-Buitrago, Ismael
Quirós-McIntire, Evelyn Itzel
Gordón-Mendoza, Román
Identifi cation of representative and discriminatory environments to select genotypes using GGE Biplot.
author_facet Camargo-Buitrago, Ismael
Quirós-McIntire, Evelyn Itzel
Gordón-Mendoza, Román
author_sort 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.
title_fullStr Identifi cation of representative and discriminatory environments to select genotypes using GGE Biplot.
title_full_unstemmed Identifi cation of representative and discriminatory environments to select genotypes using GGE Biplot.
title_sort identifi cation of representative and discriminatory environments to select genotypes using gge biplot.
description 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.
publisher Universidad de Costa Rica
publishDate 2011
url https://revistas.ucr.ac.cr/index.php/agromeso/article/view/8683
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spelling 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