Genotype × environment interaction and yield stability in barley (Hordeum vulgare L.) genotypes in the central highland of Ethiopia

Fekadu, Wondimu; Mekbib, Firew; Lakew, Berhane; Haussmann, Bettina I. G.

doi:10.1007/s12892-022-00166-0

Cited by 10 publications

(6 citation statements)

References 25 publications

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“…The vertex genotypes (G16, G1, G20, G24, G17, G8, and G12) located far from the biplot origin created a polygon, and genotypes located at the vertices of a polygon had the highest grain yield in one or all environments that fell in the sector. This result is in agreement with the findings of Yan (2002), Yan et al (2010), Zerihun (2011), andWondimu et al (2022). The genotypes contained within the sector of a polygon were less responsive than the vertex genotypes in that sector, and vertex genotypes located on the opposite side of the mega-environments had the poorest performance in all environments.…”

Section: Which-won-where and Mega-environment Identificationsupporting

confidence: 91%

“…The GGE biplot analysis supports the classification of the studied genotypes and environments based on the performance of genotypes and the response of the growing environments (Gauch & Zobel, 1997; Wondimu et al., 2022; Yan et al., 2000). According to Yan (2001), Kaya et al.…”

Section: Discussionmentioning

confidence: 54%

“…(2010), Zerihun (2011), and Wondimu et al. (2022). The genotypes contained within the sector of a polygon were less responsive than the vertex genotypes in that sector, and vertex genotypes located on the opposite side of the mega‐environments had the poorest performance in all environments.…”

Section: Discussionmentioning

confidence: 99%

“…Like the AMMI analysis model, the GGE biplot is a hybrid model that requires a two‐way data matrix, and it combines the additive main effects from the ANOVA and the multiplicative effects from the principal component (PC) analysis of GEI (Gauch, 2006; Gauch & Zobel, 1997). The GGE biplot analysis model is often used for GEI studies of METs in different crops (Fentaw et al., 2015; Irfan, 2018; Mare et al., 2017; Massaine et al., 2018; Tamene & Tadese, 2014; Tazebachew & Cherinet, 2018; Wondimu et al., 2022). However, the study of GEI using the GGE biplot analysis model on oat genotypes is limited and not properly documented in Ethiopia.…”

Section: Introductionmentioning

confidence: 99%

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GGE biplot analysis of genotype by environment interaction and grain yield stability of oat (Avena sativa L.) in Ethiopia

Kebede

Worku

Jifar

et al. 2023

Agrosystems Geosci & Env

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A total of 24 oat genotypes were evaluated across 9 environments using a randomized complete block design with 3 replications. The combined analysis of variance indicated that grain yield was significantly affected by genotype, environment, and genotype by environment interaction (GEI). The environment's main effect explained 44.62% of the total variation, whereas the genotype and GEI captured 28.84% and 26.54%, respectively. The genotype plus genotype by environment (GGE) biplot of the first two principal components also explained (PC1 = 68.46%) and (PC2 = 11.84%) of the GEI sum of squares. G8 (SRCPX80Ab2267), G17 (SRCPX80Ab2310), G5 (Sorataf), G14 (Was), G19 (Bonsa), G22 (SRCPX80Ab2691), G12 (SRCPX80Ab2291), G11 (SRCPX80Ab2806), G4 (CI‐8251), and G16 (Jassari), which were located at the left side of the PC1, showed better grain yield performance. The smaller PC1 values were observed for G19, G14, G8, G11, G12, G17, and G22 showing better grain yield stability. The ideal genotype located at the center of the concentric circles was G19, whereas the desirable genotypes were G14, G8, G11, G12, G17, and G22. The polygon view of the GGE biplot showed that the first mega‐environment contains environment E3 (Ginchi) with G16, whereas the second mega‐environment contains environments E7 (Bekoji) and E8 (Kuyu) with G12 being the winning genotypes. The vertices of the fourth mega‐environments (E6—Kulumsa) were G8 and G17, whereas the remaining environments grouped in the third mega‐environment without vertex genotype. Furthermore, E2 (Ada‐Berga) was an ideal test environment, whereas E5 (D/Zeit), E1 (Holetta), and E4 (Jeldu) were identified as favorable environments. Regarding the mean grain yield and stability, G19, G14, G8, G11, G12, G17, and G22 were the best genotypes that can be considered adapted genotypes to wider environments.

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Section: Which-won-where and Mega-environment Identificationsupporting

confidence: 91%

Section: Discussionmentioning

confidence: 54%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

GGE biplot analysis of genotype by environment interaction and grain yield stability of oat (Avena sativa L.) in Ethiopia

Kebede

Worku

Jifar

et al. 2023

Agrosystems Geosci & Env

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“…Malting barley productivity is directly linked to the sustainable supply of grain to malthouses, and grain quality can impact malting and brewing efficiencies, beer quality, and flavor (AMBA, 2021; Bamforth, 2006). Previous studies have found the interaction of genotype and environment to play an important role in barley yield and quality in multiple production regions worldwide (Bantayehu, 2013; Fekadu et al., 2023; Laidig et al., 2017; Nielsen & Munck, 2003; Przulj et al., 2014) and in the United States (Choi et al., 2020; Zhou et al., 2020). However, those studies measured either agronomic traits or grain quality traits stipulated by industry standards.…”

Section: Introductionmentioning

confidence: 99%

Effects of genotype and environment on productivity and quality in Californian malting barley

Ramanan¹,

Nelsen²,

Lundy³

et al. 2023

Agronomy Journal

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Malting barley (Hordeum vulgare L.) productivity and grain quality are of critical importance to the malting and brewing industry. In this study, we analyzed 12 malting barley genotypes across eight locations in California and three years (2017‐18, 2018‐19 and 2020‐21). The effects of genotype (G), location (L), year (Y) and their interactions were assessed on grain yield (kg ha −1), grain protein content (GPC; %), individual‐grain weight (mg), thousand kernel weight (TKW; g), grain size (plump and thin; %), onset gelatinization temperature (GT; temperature at which starch starts to gelatinize), peak GT, offset GT, difference between onset and peak GT and difference between peak and offset GT. L, Y and their interaction explained the largest variance for all traits except TKW, peak GT, and difference between onset and peak GT, for which G explained the largest variance. Yield and plump (%) were weakly negatively correlated with onset and peak GT (Pearson's r of ‐0.15 to ‐0.21) but showed a positive correlation with the difference between peak and offset GT (Pearson's r of 0.37 and 0.36). The 2020‐21 samples formed partially distinct clusters in principal component analysis, mainly discriminated by high percentage of thins and high onset GT. These findings illustrate the key roles of G, L and Y in determining malting barley productivity and quality.This article is protected by copyright. All rights reserved

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Fertilizer regime and cultivar affect barley growth and rhizobiome composition

Kindtler,

Sheikh,

Richardy

et al. 2024

Applied Soil Ecology

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Genotype × environment interaction and yield stability in barley (Hordeum vulgare L.) genotypes in the central highland of Ethiopia

Cited by 10 publications

References 25 publications

GGE biplot analysis of genotype by environment interaction and grain yield stability of oat (Avena sativa L.) in Ethiopia

GGE biplot analysis of genotype by environment interaction and grain yield stability of oat (Avena sativa L.) in Ethiopia

Effects of genotype and environment on productivity and quality in Californian malting barley

Fertilizer regime and cultivar affect barley growth and rhizobiome composition

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