Adaptability and grain yield stability of rice hybrids and varieties in Venezuela

Author:

Acevedo Barona Marco1ORCID,Silva Díaz Rubén1ORCID,Rea Suárez Ramón2ORCID

Affiliation:

1. Instituto Nacional de Investigaciones Agrícolas INIA CENIAP

2. Fundación Instituto de Estudios Avanzados (IDEA)

Abstract

The development of new high-performance and stable cultivars requires test multi-environmental validation to deal with the effect of genotype by environment interaction (GEI). With the objective to determine adaptability and stability for grain yield in hybrids and rice varieties through the models AMMI, SREG and REML/BLUP. Six experiments were evaluated during the 2015-2016 dry season in the main producing regions of Venezuela. The ANOVA detected differences for genotype (G), environment (E) and their interaction (GEI), representing 19, 65 and 16 % of the total variation, respectively, with prevalence of hybrid by localities interaction. The first major components of the AMMI and GGE biplot models explained 77 and 83 % of GEI, respectively. The three models coincided and identified the hybrid RHA-180 (H6) with improved average performance, adapted and stable. The hybrid HIAAL (H3) was the most prominent. Among the checks, 'Pionero FL' (V3) was the most stable with moderate yield; the opposite occurred with ‘Soberana FL’ (V4) and ‘SD-20A’ (V1), that the AMMI and GGE biplot models identified with high and unstable performances and specific adaptation to locality INIA Guárico (L1), not coinciding with the mixed model. Two mega-environments were identified with the winning genotypes H6 and V4. There was divergence between AMMI and GGE biplot to identify discriminatory and representative locations. The Plot 199 (L3) was the most representative, while the location L1 discriminated better the genotypes. The GGE biplot analysis was more informative and complete for the GEI analysis.

Publisher

Bioagro, Universidad Centroccidental Lisandro Alvarado

Subject

General Agricultural and Biological Sciences

Reference27 articles.

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2. Acevedo-Barona, M., R. Álvarez, R. Silva, O. Torres and E. Reyes. 2019. Interacción genotipo ambiente en arroz para identificar mega-ambientes y ambientes ideales mediante el modelo de Regresión por sitios (SREG) and biplot GGE. Bioagro 31(1): 35-44.

3. Acevedo-Barona, M., R. Silva-Díaz, R. Álvarez-Parra, O. Torres-Angarita and E. Reyes-Ramone. 2020. Environmental stratification of rice by genotype x environment interaction analysis using five methods. Agronomía Mesoamericana 31(1): 43-57.

4. Camargo-Buitrago, I., E. Mc Intire and R. Gordón-Mendoza. 2011. Identificación de mega-ambientes para potenciar el uso de genotipos superiores de arroz en Panamá. Pesquisa Agropecuaria Brasileira 46(9): 1601-1069.

5. Colombari-Filho, J., M. de Resende, O. de Morais, A. de Castro, E. Guimarães, J. Pereira et al. 2013. Upland rice breeding in Brazil: a simultaneous genotypic evaluation of stability, adaptability and grain yield. Euphytica 192: 117-129.

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