The application of AMMI model for barley cultivars evaluation in multi-year trials

Mirosavljević, Milan; Pržulj, Novo; Boćanski, Jan; Stanisavljević, Dušan; Mitrović, Bojan

doi:10.2298/gensr1402445m

Cited by 18 publications

(11 citation statements)

References 23 publications

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“…It should be mentioned that several other researchers have also reported high year to year, or site to site grain yield variation in spring barley genotypes in Eastern European conditions (Pržulj & Momčilović, 2012;Mirosavljević et al, 2014;Pržulj et al, 2015) and in particular in Ukraine (Solonechnyi et al, 2015;Marukhnyak, 2018). Thus, testing genotypes in various environmental conditions (multi-environment trial) is effective for assessment of genotype by environment interactions and selecting desirable ones.…”

Section: Discussionmentioning

confidence: 93%

Assessment of ecological stability in yield for breeding of spring barley cultivars with increased adaptive potential

Hudzenko

Demydov

Kavunets

et al. 2020

Regul. Mech. Biosyst.

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Increasing crop adaptability in terms of ensuring a stable level of productivity in the genotype – environment interaction is still the central problem of plant breeding theory and practice. The aim of the present study is to theoretically substantiate and practically test a scheme of multi-environment trials, as well as interpret experimental data using modern statistical tools for evaluation of the genotype by environment interaction, and highlight the best genotypes with combining yield performance and ecological stability at the final stage of the spring barley breeding process. For this purpose in the first year of competitive testing (2016) at the V. M. Remeslo Myronivka Institute of Wheat of the National Academy of Agrarian Sciences of Ukraine we selected nine promising spring barley breeding lines. In 2017 and 2018 these breeding lines were additionally tested in two other scientific institutions located in different agroclimatic zones of Ukraine. For a more reliable assessment, the breeding lines were compared not only with standard cultivar, but also with ten spring barley cultivars widespread in agricultural production of Ukraine. Thus, for three years of competitive testing, we received experimental genotype-environmental data from seven environments, which represent a combination of contrasting agroclimatic zones (Central part of the Forest-Steppe, Polissia and Northern Steppe of Ukraine) and different years (2016–2018). Our results revealed significant variability of mean yield of genotypes, as well as cross-over genotype by environment interaction. The first two principal components of both AMMI and GGE biplot explained more than 80% of the genotype by environment interaction. In general, the peculiarities we revealed indicate the effectiveness of the proposed combination of spatial (agroclimatic zones) and temporal (years) gradients to identify the best spring barley genotypes with the optimal combination of yield performance and ecological stability. Using AMMI and GGE biplot models was effective for the comprehensive differentiation of genotypes in terms of wide and specific adaptability, as well as for qualitative characterization of test environments and providing mega-environment analysis. As a practical result of the multi-environment trial, four spring barley breeding lines have been submitted to the State Variety Testing of Ukraine as new cultivars MIP Sharm, MIP Tytul, MIP Deviz and MIP Zakhysnyk, respectively.

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Section: Discussionmentioning

confidence: 93%

Assessment of ecological stability in yield for breeding of spring barley cultivars with increased adaptive potential

Hudzenko

Demydov

Kavunets

et al. 2020

Regul. Mech. Biosyst.

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show abstract

“…Among them, cross-over interaction is the most significant for breeders as it implies that the choice of the best genotype is determined by the environment (Malosetti et al, 2013). Several other researchers also reported about significant year to year, or site to site grain yield variation and cross-over genotype by environment interaction for spring barley genotypes (Mirosavljević et al, 2014;Pržulj et al, 2015;Solonechnyi et al, 2015).…”

Section: Grain Yield Performance Of Spring Barley Genotypesmentioning

confidence: 99%

Identification of Spring Barley Breeding Lines With Superior Yield Performance and Stability

Hudzenko¹,

Поліщук²,

Demydov³

et al. 2020

Acta Univ. Agric. Silvic. Mendelianae Brun.

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The aim of the present study was to substantiate theoretically and to test in practice scheme of multi-environment trials at the final stage of spring barley breeding process and to distinguish the genotypes which combine superior yield performance and stability. In the first year of competitive testing (2015) nine promising spring barley breeding lines have been selected under condition of the Central part of Forest-Steppe of Ukraine (latitude 49°64′, longitude 31°08′, altitude 153 m). In 2016 and 2017, the genotypes were additionally tested in two other different agro-climatic zones of Ukraine: Polissia (latitude 50°93′, longitude 31°69′, altitude 126 m) and Northern Steppe (latitude 48°56′, longitude 32°32′, altitude 171 m). In addition to the standard variety Vzirets, the breeding lines were compared with ten widespread spring barley varieties in agricultural production. Significant total yield variability of the genotypes and cross-over genotype by environment interaction has been revealed. It confirmed the validity of proposed combination of spatial (zones) and temporal (years) gradients for more efficient evaluation of the genotype by environment interaction and differentiation of genotypes in terms of yield performance and stability. As a practical result, using additive main effects and multiplicative interaction (AMMI) and genotype main effects plus genotype by environment interaction (GGE) models, four spring barley breeding lines with combination of superior yield performance and high stability have been identified.

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“…Adebola et al (2013) state that the AMMI Model is an important technique to observe the interaction in the identification and selection of excellent genotypes in terms of potential outcomes against various or specific environments. AMMI's analysis is a successful model used to study GEI, and it is a combination of ANOVA and major component analysis (PCA) (Mirosavljevic et al, 2014). According to Sa'diyah and Hadi 2016, AMMI analysis is a two-way experimental data analysis technique the main effect of which is additive and multiplicative.…”

Section: Selection For Stable Genotype Of Sweet Potato Based On Ammi mentioning

confidence: 99%

Selection of superior genotype of sweet-potato in Indonesia based on stability and adaptability

Mustamu

Tjintokohadi²,

Grüneberg

et al. 2018

Chil. j. agric. res.

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Sweet-potato (Ipomoea batatas [L.] Lam.) is the 3 rd most important root and tuber plant in the world, after potatoes and cassava. The knowledge of the structure of Genotype × Environment interaction (GEI) could assist in the development of cultivars of sweet-potato. The objective of this research was to select superior sweet-potato genotypes in Indonesia based on Finlay-Wilkinson regression, additive main effect and multiplicative interaction (AMMI) analysis, AMMI stability value (ASV), and Genotype plus Genotype × Environment (GGE) biplot methods. Genetic materials evaluated in this research were 10 Indonesian superior genotypes developed by the

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The application of AMMI model for barley cultivars evaluation in multi-year trials

Cited by 18 publications

References 23 publications

Assessment of ecological stability in yield for breeding of spring barley cultivars with increased adaptive potential

Assessment of ecological stability in yield for breeding of spring barley cultivars with increased adaptive potential

Identification of Spring Barley Breeding Lines With Superior Yield Performance and Stability

Selection of superior genotype of sweet-potato in Indonesia based on stability and adaptability

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