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.
Main Authors: | , , |
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Format: | Digital revista |
Language: | spa |
Published: |
Universidad de Costa Rica
2011
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Online Access: | https://revistas.ucr.ac.cr/index.php/agromeso/article/view/8683 |
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Summary: | 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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