A Systematic Narration of Some Key Concepts and Procedures in Plant Breeding

Yan, Weikai

doi:10.3389/fpls.2021.724517

Cited by 14 publications

(23 citation statements)

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“…The interaction of genotype x environment is a prerequisite for multilayer evaluation of varieties in different growing conditions (Kang, 2020;Pour-Aboughadareh et al, 2022). The collection of such information requires the organization of many factorial field trials at different locations, which have significant differences in growing conditions with each other (Yan et al, 2021). These conditions are expected to be not only different, but also to have a significant impact on the size and variation of yield, or any quantitative feature (Vulchinkov et al, 2020).…”

Section: Discussionmentioning

confidence: 99%

Analysis of genotype by environmental interaction of common wheat (Triticum Aestivum L.) by non-parametric stability methods

Tsenov¹,

Gubatov²,

Yanchev³

2022

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Data from the Multi Environmental Field Trail (MET), which examined 24 varieties of common wheat, were divided into three "datasets" related to three of the five study locations. Using meta-analysis, these three data sets were compared with those from the whole experiment. The aim of the study is to determine whether a 4-year growing period in three country-specific locations, it is possible to establish a significant impact of the environment on the stability of a group of varieties. The analysis of the genotype x environment interaction (GEI) was performed in parallel in the three groups of data, which were compared with the entire MET database. A direct comparison was made on them regarding the possibilities of non-parametric methods to assess the stability of the variety. The analysis of the results of the four "datasets" is done through a number of statistical approaches, allowing them to be correctly compared at different levels. Genotype x environment interaction was found in each of the studied locations. The variation in yield in them is a result of the direct effect of the "year" and the combined effect of the genotype x year. At all three locations, the GEI is broken down into four main components, which is evidence of the strong linear and non-linear nature of the dispersion of grain yield. These results are a prerequisite for an objective assessment of genotype stability. All applied parameters give completely similar stability information for each of them, regardless of the test location. Data from one location are sufficient to assess the stability of each variety in a group. This may be the case if significant differences between the seasons of the trial, are found. The applied non-parametric methods for stability assessment give correct information about the varieties, in the presence of GEI, regardless of the conditions from which the data for analysis are selected.

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Section: Discussionmentioning

confidence: 99%

Analysis of genotype by environmental interaction of common wheat (Triticum Aestivum L.) by non-parametric stability methods

Tsenov¹,

Gubatov²,

Yanchev³

2022

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“…Plant breeding has entered the genomic selection era (Crossa et al., 2017; Heffner et al., 2009; Heslot et al., 2015; Jannink et al., 2010) and the strategy to deal with GE in the context of genomic selection is to develop ME‐specific genomic prediction models (Yan, 2021a; Yan et al., 2019). Once reliable ME‐specific genomic models are developed, they can be used to design ME‐specific crosses (Step 1 above) and to select ME‐specific breeding lines (Step 2).…”

Section: Developing Mega‐environment Specific Cultivarsmentioning

confidence: 99%

“…The purpose of mega‐environment analysis is to seek opportunities to utilize repeatable GE, to improve breeding and selection efficiency, and to maximize global production through maximizing regional adaptation. Modified from Eberhart's (1970) breeder's equation, breeding success for a target trait, B , may be measured by the following equation (Yan, 2021a):

\begin{equation}B\ = \ \mu + \frac{{ih{\sigma }_{\rm{G}}}}{{{\rm{yc}}}},\end{equation}

where μ is the mean of the breeding population, i is the selection intensity, h is the square root of heritability, σ G is the square root of genotypic variance, y is the number of years in a breeding cycle, and c is the operation cost per year. The population mean μ was included in the equation to emphasize its vital importance to developing superior cultivars.…”

Section: Theory Of Mega‐environment Analysismentioning

confidence: 99%

“…The potential of a breeding population to produce superior cultivars may be measured by its

\sqrt {\mu {\sigma }_{\rm{G}}}

(Yan, 2021a; Yan et al., 2019). Selection intensity i has a quantitative relation with the population size (Yan, 2021a, 2022). For a given breeding population, μ, i , and σ G are all fixed.…”

Section: Theory Of Mega‐environment Analysismentioning

confidence: 99%

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Mega‐environment analysis and breeding for specific adaptation

2023

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Mega-environment (ME) analysis is analysis of multi-year, multi-location crop variety trial data conducted in a target region of a crop to understand the magnitude and nature of genotype-by-environment interaction (GE) of the crop in the region.If repeatable GE patterns are identified, then the target region must be divided into subregions or MEs. Breeding and utilizing ME-specific cultivars will convert the repeatable GE into genotypic main effect (G) within ME, thereby improving heritability (selection reliability) and selection gain and maximize regional and overall productivity. If no repeatable GE is found, then the target region must be treated as a single ME and the GE must be accommodated by testing adequately, that is, at a sufficient number of locations in a sufficient number of years. This paper presents a theoretical framework of ME analysis, describes graphical tools to reveal the whichwon-where patterns in a genotype-by-environment dataset, and demonstrates LG (location-grouping) biplot analysis for revealing repeatable GE patterns and delineating MEs. The concept of G + GE or GGE, that is, GE relative to G, is emphasized. It is the relative GE that is the basis for ME analysis and breeding for specific adaptation; absolute magnitude of GE has little relevance for these purposes. Breeding ME-specific oat cultivars in Canada is demonstrated with a real-world example. INTRODUCTIONA mega-environment (ME) is a subregion within a crop's growing areas with similar genotypic responses and best performing cultivars. Mega-environment analysis is analysis of Abbreviations: AEZ, agro-ecological zone; BRDC, Brandon Research and

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“…Once optimal culling levels are achieved, independent culling and index selection lead to comparable genetic gains (Batista et al, 2021). Selection on multiple traits includes independent culling and index selection was recommended (Yan, 2021). This article aimed to compare the single trait selection with independent culling levels in improving yield and its attributes under normal irrigation and water deficit.…”

Section: Introductionmentioning

confidence: 99%

Single Trait Selection and Independent Culling Levels in Improving Egyptian Cotton Yield (G. barbadense L.) under Normal Irrigation and Water Deficit

et al. 2022

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Egyptian Journal of Agronomy http://agro.journals.ekb.eg/ 1324 T RADITIONAL breeding is an important way to develop drought-tolerant varieties. There is a dire need to develop cotton cultivars that can produce acceptable yields in water limited. This work aimed to compare single trait selection and independent culling levels for six traits in improving the seed cotton yield (SCY/p) under water deficit and normal irrigation. The genetic material was the F 2 -population of the cross Giza 90 ˟ Giza 95 (long staple). In the F 2 , the phenotypic (PCV%) and genotypic (GCV%) coefficients of variability were mostly higher under normal irrigation than under drought stress. The correlations among traits indicated that SCY/p depended mainly upon number of bolls, boll weight and seed index, and moderately on lint index, and the early plants had high yields. The GCV and PCV were greatly depleted by selection and were higher in most cases under drought stress than under normal irrigation. Independent culling levels preserved genetic variability more than single trait selection. Narrow sense heritability was mostly high under normal irrigation. The selections of both methods were evaluated in the F 4 under both environments. Selection for SCY/p under normal irrigation increased SCY/p by 16.9% under normal irrigation and 8.90% under stress, while selection under stress increased SCY/p by 12.05 and 10.69% of the mid-parent under the respective environments. Single trait selection proved that selection under optimum environment performed well under optimum, and selection under drought stress was better under stress. Otherwise, ICL method of selection did well under drought stress.

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A Systematic Narration of Some Key Concepts and Procedures in Plant Breeding

Cited by 14 publications

References 100 publications

Analysis of genotype by environmental interaction of common wheat (Triticum Aestivum L.) by non-parametric stability methods

Analysis of genotype by environmental interaction of common wheat (Triticum Aestivum L.) by non-parametric stability methods

Mega‐environment analysis and breeding for specific adaptation

Single Trait Selection and Independent Culling Levels in Improving Egyptian Cotton Yield (G. barbadense L.) under Normal Irrigation and Water Deficit

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