Genotype-environment interaction and stability analyses: an update.

Kang, M. S.

doi:10.1079/9781789240214.0140

Cited by 24 publications

(15 citation statements)

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“…Changes in certain morphological traits and yield stability have led to an increase in the genetic potential of the genotypes, most probably through the increased tolerance to biotic and abiotic stress factors [ 48 ]. Contribution of each individual yield component could vary in the different environmental conditions; testing in one environment could provide only limited information, while testing in different environments provides additional useful information (e.g., a GEI component can be estimated) [ 49 ]. Since that interaction represents the heterogeneity of agro-ecological conditions, it is important to identify genotypes that are very efficient, which means that they are productive and stable genotypes, but also genotypes with favorable response to soil amelioration.…”

Section: Resultsmentioning

confidence: 99%

Multivariate Interaction Analysis of Winter Wheat Grown in Environment of Limited Soil Conditions

Ljubičić

Popović

Ćirić

et al. 2021

Plants

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The less productive soils present one of the major problems in wheat production. Because of unfavorable conditions, halomorphic soils could be intensively utilized using ameliorative measures and by selecting suitable stress tolerant wheat genotypes. This study examined the responses of ten winter wheat cultivars on stressful conditions of halomorphic soil, solonetz type in Banat, Serbia. The wheat genotypes were grown in field trails of control and treatments with two soil amelioration levels using phosphor gypsum, in amounts of 25 and 50 tha−1. Across two vegetation seasons, phenotypic variability and genotype by environment interaction (GEI) for yield traits of wheat were studied. The additive main effects and multiplicative interaction (AMMI) models were used to study the GEI. AMMI analyses revealed significant genotype and environmental effects, as well as GEI effect. Analysis of GEI using the IPCA (Interaction Principal Components) analysis showed a statistical significance of the first two main components, IPCA1 and IPCA2 for yield, which jointly explained 70% of GEI variation. First source of variation IPCA1 explained 41.15% of the GEI for the grain weight per plant and 78.54% for the harvest index. The results revealed that wheat genotypes responded differently to stressful conditions and ameliorative measures.

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Section: Resultsmentioning

confidence: 99%

Multivariate Interaction Analysis of Winter Wheat Grown in Environment of Limited Soil Conditions

Ljubičić

Popović

Ćirić

et al. 2021

Plants

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show abstract

“…In contrast, breeding for wide adaptation aims to develop a cultivar that performs well in the whole target region by selecting for GEI minimization across sites that contrast for GEI pattern (Cooper, Woodruff, Eisemann, Brennan, & DeLacy, 1995). Those GEI effects relative to interactions of genotypes with the time factor can only be coped with by selection for greater yield stability (Kang, 2020).…”

Section: Introductionmentioning

confidence: 99%

Breeding gain from exploitation of regional adaptation: An alfalfa case study

Annicchiarico¹

2021

Crop Science

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• Genotype × environment interaction patterns were repeatable across sets of trials • Selection for specific adaptation was more efficient than wide-adaptation selection • Specific-adaptation selection was essential to out-yield best commercial varieties • Genotypic selection produced higher yield gains than phenotypic selection ABSTRACT Exploiting genotype × environment interaction (GEI) by specific selection for well-defined subregions could contribute to more productive and sustainable cropping systems. This case This article is protected by copyright. All rights reserved. 2 study aimed to verify this hypothesis by assessing actual genetic gains from phenotypic and genotypic selection for alfalfa biomass yield in two subregions of Northern Italy with contrasting GEI pattern (subregion A, featuring sandy-loam soil and no summer drought due to irrigation; subregion C, featuring silty-clay soil and summer drought due to rainfed cropping). Selection and test environments were mainly represented by four managed environments created by the factorial combination of the relevant soil types and drought stress levels. Genotypic selection depended on yield responses of 90 half-sib families in the environment representing each subregion (specific adaptation) or across these environments (wide adaptation); phenotypic selection for wide or specific adaptation was stratified for single plants in different environments. Half-sib family data indicated modest genetic correlation between subregions (r g = 0.56). Biomass yield data of six selections and six cultivars providing their genetic base revealed at least twofold greater yield gains for specific adaptation over wide adaptation for phenotypic or genotypic selection, with a minimum within-subregion advantage of 43%. On average, genotypic selections displayed over 10-fold greater yield progress than phenotypic selections. Adaptation strategies had negligible impact on seed yield and leaf-to-stem ratio of the selections. Experiment data from seven agricultural environments highlighted the importance of breeding for specific adaptation to achieve yield progress over locally top-performing commercial varieties. Circumstances that support a specific-adaptation strategy were discussed.

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“…Simultaneous selection is a key phrase in plant breeding programs for releasing highyielding and stable cultivars , because growers would prefer to use a high-yielding cultivar that performs consistently from year to year 23 . Simultaneous selection for yield and stability reduces the probability of committing type II errors (accepting the null hypothesis when it is false) 24 . Researchers proposed several methods of simultaneous selection for yield and stability 6,25,26 .…”

Section: Stability Of Rice Mutant Lines By Linear Mixed Model and Suggestion A New Index Based For Yield Performance And Stabilitymentioning

confidence: 99%

Stability of Rice Mutant Lines By Linear Mixed Model And Suggestion A New Index Based For Yield Performance And Stability

Sharifi¹,

Ebadi²,

Hallajian³

2021

Preprint

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Fourteen rice mutant lines with four cultivars were evaluated in a randomized complete block design with three replications in three locations in Iran (Rasht, ChaparSar and Fars province) during two growing seasons (2014-2016). In addition, a new index namely as equivalent index of stability and performance (EISP) is suggested for simultaneous evaluation of yield performance and stability. The heat map of yield performance and WAASB (weighted average of absolute scores based on BLUP (best linear unbiased prediction)) identified G3, G9, G6, G12 and G5 as highly productive and stable genotypes. Based on the analysis by multi-trait stability index (MTSI) G7, G5 and G1 were selected as superior genotypes. The top five superior genotypes based on harmonic mean and of the relative performance of genotypic values (HMRPGV) were G5, G12, G7, G2 and G1. For verification of EISP, its value was calculated for some of multi and univariate stability indices and identified genotypes G5 and G12 as the best ones. Principal component analysis indicated yield positively correlated with HMGV, RPGV, HMRPGV, EIS2P EIbP and EIPiP. In conclusion, G12, G5 and G9 had a significant advantage over all genotypes and could undergo selection or cultivar introduction processes.

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Genotype-environment interaction and stability analyses: an update.

Cited by 24 publications

References 0 publications

Multivariate Interaction Analysis of Winter Wheat Grown in Environment of Limited Soil Conditions

Multivariate Interaction Analysis of Winter Wheat Grown in Environment of Limited Soil Conditions

Breeding gain from exploitation of regional adaptation: An alfalfa case study

Stability of Rice Mutant Lines By Linear Mixed Model And Suggestion A New Index Based For Yield Performance And Stability

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