Biplot pattern interaction analysis and statistical test for crossover and non-crossover genotype-by-environment interaction in peach

Angelini, Julia; Faviere, Gabriela; Bortolotto, Eugenia; Arroyo, Luis; Valentini, Gabriel Hugo; Cervigni, Gerardo Domingo Lucio

doi:10.1016/j.scienta.2019.03.024

Cited by 13 publications

(15 citation statements)

References 55 publications

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“…The discriminativeness and representativeness abilities of the GGE biplot make it more sophisticated than AMMI (Sharma et al, 2020). Apart from the representativeness and discriminativeness capabilities of the GGE model, its "which-wonwhere" pattern and the stability vs. mean performance of the genotypes in the multi-environment trials (Yan et al, 2000;Angelini et al, 2019) were also revealed. Thus, AMMI has the ability to identify GEI in many environments with different characteristics by using a biplot model.…”

Section: Gge Biplotmentioning

confidence: 99%

AMMI and GGE biplot analyses of Bambara groundnut [Vigna subterranea (L.) Verdc.] for agronomic performances under three environmental conditions

et al. 2023

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IntroductionThe two most common styles to analyze genotype-by-environment interaction (GEI) and estimate genotypes are additive main effects and multiplicative interaction (AMMI) and genotype + genotype × environment (GGE) biplot. Therefore, the aim of this study was to find the winning genotype(s) under three locations, as well as to investigate the nature and extent of GEI effects on Bambara groundnut production.MethodsThe experiment was carried out in the fields of three environments with 15 Bambara groundnut accessions using the randomized complete block design (RCBD) with three replications each in Ibadan, Osun, and Odeda. Yield per plant, fresh seed weight, total number of pods per plant, hundred seed weight, length of seeds, and width of seeds were estimatedResultsAccording to the combined analysis of variance over environments, genotypes and GEI both had a significant (p < 0.001) impact on Bambara groundnut (BGN) yield. This result revealed that BGN accessions performed differently in the three locations. A two-dimensional GGE biplot was generated using the first two principal component analyses for the pattern of the interaction components with the genotype and GEI. The first two principal component analyses (PCAs) for yield per plant accounted for 59.9% in PCA1 and 40.1% in PCA2. The genotypes that performed best in each environment based on the “which-won-where” polygon were G8, G3, G2, G11, G6, and G4. They were also the vertex genotypes for each environment. Based on the ranking of genotypes, the ideal genotypes were G2 and G6 for YPP, G1 and G5 for FPW, G15 and G13 for TNPP, G3 and GG7 for HSW, G7 and G12 for LOS, and G10 and G7 for WOS. G8 was recorded as the top most-yielding genotype. G8, G4, G7, and G13 were high yielding and the most stable across the environments; G11, G14, and G9 were unstable, but they yielded above-average performance; G14, G12, G15, and G1 were unstable and yielded poorly, as their performances were below average. Bowen was the most discriminating and representative environment and is classified as the superior environment.DiscussionBased on the performance of accessions in each region, we recommend TVSU 455 (G8) and TVSU 458 (G3) in Bowen, TVSU 455 (G8) and TVSU 939 (G6) and TVSU 454 (G1) in Ibadan, and TVSU 158 (G2) and TVSU 2096 (G10) in Odeda. The variety that performed best in the three environments was TVSU 455 (G8). They could also be used as parental lines in breeding programs.

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Section: Gge Biplotmentioning

confidence: 99%

AMMI and GGE biplot analyses of Bambara groundnut [Vigna subterranea (L.) Verdc.] for agronomic performances under three environmental conditions

et al. 2023

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“…The concept of GGE biplot involves the use of biplot to show the two important factors, which are also sources of variation (viz., G and GE). GGE biplot fits best for genotype evaluation (mean vs. stability), test environments which provide discriminating power vs. representativeness, and multi-environment analysis (such as "which-won-where" pattern) [20,21]. GGE biplot is a versatile method with the ability to analyze a range of data types using a two-way structure [22].…”

Section: Introductionmentioning

confidence: 99%

GGE Biplot Analysis of Genotype × Environment Interaction and Yield Stability in Bambara Groundnut

et al. 2021

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In plant breeding and agricultural research, biplot analysis has become an important statistical technique. The goal of this study was to find the winning genotype(s) for the test settings in a part of the Southwest region of Nigeria, as well as to investigate the nature and extent of genotype X environment interaction (GEI) effects on Bambara groundnut (BGN) production. The experiment was carried out in four environments (two separate sites, Ibadan and Ikenne, for two consecutive years, 2018 and 2019) with ninety-five BGN accessions. According to the combined analysis of variance over environments, genotypes and GEI both had a substantial (p < 0.001) impact on BGN yield. The results revealed that BGN accessions performed differently in different test conditions, indicating that the interaction was crossover in nature. The results revealed that BGN accessions performed differently in different test conditions, indicating that the interaction was crossover in nature. To examine and show the pattern of the interaction components, biplots with the genotype main effect and genotype X environment interaction (GEI) were used. The first two PCs explained 80% of the total variation of the GGE model (i.e., G + GE) (PC1 = 48.59%, PC2 = 31.41%). The accessions that performed best in each environment based on the “which-won-where” polygon were TVSu-2031, TVSu-1724, TVSu-1742, TVSu-2022, TVSu-1943, TVSu-1892, TVSu-1557, TVSu-2060, and TVSu-2017. Among these accessions, TVSu-2017, TVSu-1557, TVSu-2060, TVSu-1892, and TVSu-1943 were among the highest-yielding accessions on the field. The adaptable accessions were TVSu-1763, TVSu-1899, TVSu-2019, TVSu-1898, TVSu-1957, TVSu-2021, and TVSu-1850, and the stable accessions were TVSu-1589, TVSu-1905, and TVSu-2048. In terms of discriminating and representativeness for the environments, Ibadan 2019 is deemed to be a superior environment. The selected accessions are recommended as parental lines in breeding programs for grain yield improvement in Ibadan or Ikenne or similar agro-ecological zones.

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“…Genotypes whose requirements do not match the climatic conditions of the region cannot be recommended for sustainable production. Angelini et al (2019) showed that erratic behavior can be reduced by the release of cultivars adapted to a set of environments. Therefore, understanding of the nature of GE and, consequently, the matching of a genotype with appropriate locations to ensure a high and stable production is a major aim of any genetic program.…”

Section: Resultsmentioning

confidence: 99%

“…Peach yield is a character whose main characteristic is its COI (Maulión et al 2014;Angelini et al 2019), so that its statistical modeling is a real challenge. Measured and genetic parameters have been studied in reproductive characters (Souza et al 1998a) and associated with peach fruits (Souza et al 1998b) using LMM under the frequentist paradigm.…”

Section: Introductionmentioning

confidence: 99%

Parameter estimation and selection efficiency under Bayesian and frequentist approaches in peach trials

Angelini

Bortolotto²,

Faviere³

et al. 2022

Euphytica

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Identification of stable and high-yielding genotypes is a real challenge in peach breeding, since genotype-byenvironment interaction (GE) masks the performance of the materials. The aim of this work was to evaluate the effectiveness of parameter estimation and genotype selection solving the LMM under frequentist and Bayesian approaches. Fruit yield of 308 peach genotypes were assessed under different seasons and replication numbers arranged in a completely randomized design. Under the frequentist framework the restricted maximum likelihood method to estimate variance component and genotypic prediction was used. Different models considering environment, genotype and GE effects according to the likelihood ratio test and Akaike information criteria were compared. In the Bayesian approach, the mean and the variance components were assumed to be random variables having a priori non-informative distributions with known parameters.According the deviance information criteria the most suitable Bayesian model was selected. The full model was the most appropriate to calculate parameters and genotypic predictions, which were very similar in both approaches. Due to imbalance data, Cullis's method was the most appropriate to estimate heritability. It was calculated at 0.80, and selecting above 5% of the genotypes, the realized gain of 14.81 kg.tree 1 was attained.Genotypic frequentist and Bayesian predictions showed a positive correlation (r = 0.9991; P = 0.0001). Since the Bayesian method incorporates the credible interval for genetic parameters, genotypic Bayesian prediction would be a more useful tool than the frequentist approach and allowed the selection of 17 high-yielding and stable genotypes.

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Biplot pattern interaction analysis and statistical test for crossover and non-crossover genotype-by-environment interaction in peach

Cited by 13 publications

References 55 publications

AMMI and GGE biplot analyses of Bambara groundnut [Vigna subterranea (L.) Verdc.] for agronomic performances under three environmental conditions

AMMI and GGE biplot analyses of Bambara groundnut [Vigna subterranea (L.) Verdc.] for agronomic performances under three environmental conditions

GGE Biplot Analysis of Genotype × Environment Interaction and Yield Stability in Bambara Groundnut

Parameter estimation and selection efficiency under Bayesian and frequentist approaches in peach trials

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