Adaptability and yield stability of soybean genotypes by REML/BLUP and GGE Biplot

Gonçalves, Gabriel de Moraes Cunha; Ferreira-Gomes, Regina Lucia; Lopes, Ângela Célis de Almeida; Vieira, Paulo Fernando de Melo Jorge

doi:10.1590/1984-70332020v20n2a33

Cited by 23 publications

(14 citation statements)

References 20 publications

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“…There are several biometric models proposed to analyze the GEI and explore adaptability and stability. However, multiplicative models that look at the response of genotypes to specific environments or to different environments have more accurate criteria to analyze this phenomenon in different crops (Goncalves et al, 2020). Various statistical models such as AMMI (Gauch, 2006) and GGE biplot models (Yan et al, 2000) are widely used across the seasons to assess their stability and to quantify the effect of genotype x environment (GxE) interaction on the yield of genotypes (Balakrishnan et al, 2016;Rasul et al, 2017).…”

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confidence: 99%

GGE Biplot Analysis for Stability in Diverse Maturity Groups of Rice (Oryza sativa L.) Advanced Lines

Sreedhar¹,

Anıl²,

Lingaiah³

2022

IJBSM

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The experiment was carried out under three seasons with 15 genotypes at Agricultural Research Station, Kunaram, Telangana state, India during rabi season (December to April) 2014–15 (E1), kharif season (July to November) 2015 (E2) and rabi season (December to April) 2015–16 (E3). The objective of the study was to assess the stability and adaptability of 15 rice genotypes of the various maturity groups over three seasons. The GGE biplot tool of these 15 rice genotypes of various maturity durations expressed a significant genotype, environment and G×E interaction for yield and days to 50% flowering. Genotype and environment interaction effect was responsible for the greatest part of the variation, followed by genotypes and environment effects for grain yield. Days to 50% flowering of genotypes was highly affected by environments followed by genotypes, and genotype and environment interaction. It also detected that rabi season 2014–15 (E1) was identified as the best suited season for the potential expression of the grain yield, while kharif season 2015 (E2) was the right season for the expression of reduced days to 50% flowering. Further, the what–won–where model indicated that short duration rice genotype G14 (KNM 1690) and medium duration genotype G9 (KNM 1632) in the environments rabi season 2014–15 (E1) and kharif season 2015 (E2), respectively and the early line G11 (KNM 1684) in the environment rabi season 2015–16 (E3) were the winning genotypes and suitable for their respective environments for grain yield. G7 (KNM 1616) was the vertex early genotype and closer to the ideal genotype expressed high yield and stability for all the environments. G13 (KNM 1689) and G14 (KNM 1690) were found to be stable for earliness across all the seasons and could be utilized for the development of early duration varieties. The rice genotype, G15 (BPT 5204) was found to be stable for lateness for all the seasons.

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confidence: 99%

GGE Biplot Analysis for Stability in Diverse Maturity Groups of Rice (Oryza sativa L.) Advanced Lines

Sreedhar¹,

Anıl²,

Lingaiah³

2022

IJBSM

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“…According to the criteria of Resende and Alves (2020), the selective accuracies estimated in this study were considered very high (r ĝg > 0.90), which is a favorable scenario for genotype recommendation. In recent years, increases in soybean yields have been reported (Gonçalves et al 2020), and the main factor responsible for this result is the increased accuracy of evaluation methods and a better understanding of G×E interactions (Van Eeuwijk et al 2016).…”

Section: Discussionmentioning

confidence: 99%

Environmental stratification and genotype recommendation toward the soybean ideotype: a Bayesian approach

Evangelista

Peixoto

Coelho

et al. 2021

Crop Breed. Appl. Biotechnol.

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The genotype × environment (G×E) interaction plays an essential role in phenotypic expression and can lead to difficulties in genotypes recommendation. Thus, the objectives of this study were: i) propose the Multi-Environment Index Based on Factor Analysis and Ideotype-Design/Markov Chain Monte Carlo (FAI/ MCMC index), and ii) apply it for soybean genotypes recommendation. To this end, a data set with 30 soybean genotypes evaluated in 10 environments for grain yield trait was used. Variance components, genetic parameters and genetic values were estimated through MCMC algorithm. Environmental stratification was conducted by factor analyses and the selection of soybean genotypes was performed using the FAI/MCMC index. The results indicated the existence of genotypic variability and G×E interaction. The environments were grouped into three factors. The predicted genetic gains from indirect selection was 4.81%. Thus, our results suggest that the FAI/MCMC index can be successfully used in soybean breeding.

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“…is the fourth most widely grown crop globally and is one of the most outstanding species in the world scenario of agricultural commodities (Ludke et al., 2019; Ravelombola et al., 2020; Wiggins et al., 2019). Soybean presents great socioeconomic importance for Brazil, because it is the basis for various food products and animal feed (Gonçalves et al., 2020). Brazil consolidates as the leading world soybean producer and exporter, which, combined with the United States, accounts for ∼85% of the global soybean trade (USDA, 2020).…”

Section: Introductionmentioning

confidence: 99%

Genetic diversity and interaction between the maintainers of commercial soybean cultivars using self‐organizing maps

et al. 2022

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Information on the genetic diversity of commercial cultivars is of fundamental importance for crop improvement. In addition, information about possible interactions between the maintainers developing these cultivars can help design a breeding program. The objective of this work was to study the genetic diversity of soybean [Glycine max (L.) Merr.] cultivars released in Brazil from 1998 to 2017 and compare the similarity between maintainers of these cultivars based on the phenotypic information disclosed. Data was collected on 1,587 soybean cultivars registered in the National Register of Cultivars of the Ministry of Agriculture, Livestock, and Supply belonging to 59 different maintainers. Among these cultivars, 12 agromorphological traits were evaluated. To perform the grouping and select the main discriminating traits of the cultivars, the Random Forest method was used. Multiple correspondence analysis was performed to obtain the variance explanation percentage of the traits evaluated in each dimension and coordinates among the maintainers. The maintainers were organized through Kohonen self‐organizing map (SOM) through the respective coordinates. There is a wide variety of maintainers of soybean cultivars in Brazil with different objectives for launching new cultivars. Among the maintainers, Soymax presented the most restricted genetic base. The other private companies showed more remarkable similarities and a broader genetic base. The public institution EMBRAPA presented the greatest genetic diversity in its population base. Federal University of Uberlândia and Federal University of Viçosa can be partners to launch soybean cultivars given the proximity between neurons in Kohonen analysis. The high diversity of traits shows that the genetic base of soybean in Brazil is large.

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Adaptability and yield stability of soybean genotypes by REML/BLUP and GGE Biplot

Cited by 23 publications

References 20 publications

GGE Biplot Analysis for Stability in Diverse Maturity Groups of Rice (Oryza sativa L.) Advanced Lines

GGE Biplot Analysis for Stability in Diverse Maturity Groups of Rice (Oryza sativa L.) Advanced Lines

Environmental stratification and genotype recommendation toward the soybean ideotype: a Bayesian approach

Genetic diversity and interaction between the maintainers of commercial soybean cultivars using self‐organizing maps

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