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

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.

Exploring Genetics by Environment Interactions in Some Rice Genotypes across Varied Environmental Conditions

“…Consequently, all the interaction information could be speculated by plotting these two PCs. Our findings provide proofing for the results of several earlier investigations obtained by others that support the idea that the first two IPCs of the AMMI model are the most important during quantifying GEI [ 14 , 34 , 35 , 36 , 37 ].…”

“…These variability among the environmental conditions may have activated some yield-enhancing genes in different genotypes, which developed a significant GEI and resulted in high yield variability ( Figure 1 A,B). While investigating the genotypic yield stability across environments, the research reported by Barona et al 2021, Dewi et al 2014, Tariku et al 2013, and Zewdu et al 2020 [ 34 , 35 , 36 , 37 ] also concluded that the environmental effect recorded the highest impact. Meanwhile, other investigations reported genotypes’ effects to be the most effective impacts.…”

Evaluación De Líneas Elites De Arroz De Empresas Obtentoras De Venezuela, Inia Y Aproscello, Portuguesa, Ciclo 2016 – 2017

“…Los testigos SD20A y PIONERO también fueron utilizados por Acevedo et al (2021), para determinar la adaptabilidad y estabilidad del rendimiento de granos de seis híbridos y cuatro variedades de arroz a través del uso de tres modelos multivariados, donde la variedad PIONERO resultó con alta estabilidad fenotípica, pero con rendimientos moderados, mientras que SD20A obtuvo altos rendimientos, pero con adaptación específica a uno de los ambientes utilizados en el estudio. También Delgado et al (2021), evaluaron 50 líneas avanzadas de arroz más un testigo en condiciones de sabanas de Colombia, logrando identificar dos líneas que superaron en rendimiento de manera significativa al testigo.…”

Genotype x environment interaction for yield of rice hybrids and inbred varieties in Venezuela

“…Recent studies describe the predicting efficiency of AMMI and GGE biplots by using the genotype average yield, the relationship between genotypes and the evaluation of the environments (Acevedo et al, 2021;Acevedo et al, 2019;Rebolledo et al, 2018;Haider et al, 2017;Akter et al, 2015). Nevertheless, few publications are available in Venezuela and Latin America on rice hybrid GEI research.…”