Assessing the agronomic potential of linseed genotypes by multivariate analyses and association mapping of agronomic traits

Soto‐Cerda, Braulio J.; Westermeyer, Fernando; Iñiguez-Luy, Federico L.; Muñoz, Gastón; Montenegro, A.; Cloutier, Sylvie

doi:10.1007/s10681-013-1012-1

Cited by 18 publications

(13 citation statements)

References 64 publications

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“…The ANOVA and high H observed suggest an important genetic control for the agronomic and seed-quality traits assessed, albeit E and GE interactions also played an important role in the expression of these traits at the two locations (Fofana et al, 2006;Soto-Cerda et al, 2014a). The ANOVA and high H observed suggest an important genetic control for the agronomic and seed-quality traits assessed, albeit E and GE interactions also played an important role in the expression of these traits at the two locations (Fofana et al, 2006;Soto-Cerda et al, 2014a).…”

Section: Discussionmentioning

confidence: 91%

“…Fertilization and weed control followed standard recommendations for linseed as described by Soto-Cerda et al (2014a). Fertilization and weed control followed standard recommendations for linseed as described by Soto-Cerda et al (2014a).…”

Section: Determination Of Cyanogenic Glycosides By High-performance mentioning

confidence: 99%

“…In the last 7 yr, the cultivated area for linseed has ranged from 487 ha in 2007 to 2500 ha in 2014 (Berti et al, 2009;FAOSTAT, 2014). These issues are explained partially by the fact that in Chile, only introduced cultivars are available, which may perform quite differently from the environment(s) where they were originally bred because of genotype ´ environment interactions (Soto-Cerda et al, 2014a). These issues are explained partially by the fact that in Chile, only introduced cultivars are available, which may perform quite differently from the environment(s) where they were originally bred because of genotype ´ environment interactions (Soto-Cerda et al, 2014a).…”

mentioning

confidence: 99%

“…Despite its high-quality bioactive compounds and the potential for use in the development of value-added food products, Chilean farmers have been reluctant to expand the area sown with linseed because of unstable yield, taller plants than expected, and slow drying of stems that impede the harvest, increasing yield losses. These issues are explained partially by the fact that in Chile, only introduced cultivars are available, which may perform quite differently from the environment(s) where they were originally bred because of genotype ´ environment interactions (Soto-Cerda et al, 2014a). To attain suitable agronomic performance along with farmers and industry acceptance, new linseed cultivars need to be shorter in height, early flowering, and resistant to local rust races, with stable oil and seed yield, balanced and stable fatty acid profiles, and low content of antinutritional compounds.…”

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confidence: 99%

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Agronomic and Quality Assessment of Linseed Advanced Breeding Lines Varying in Seed Mucilage Content and Their Use for Food and Feed

2017

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Linseed (Linum usitatissimum L.) is well known for containing functional compounds with health‐related benefits. Assessed were the agronomic and seed‐quality traits of 13 linseed advanced breeding lines (ABLs) varying in seed mucilage content (SMC) that could be better suited to Chilean environments and food and feed market needs. Analysis of variance revealed highly significant genotype and environment effects for most of the traits assessed. Seed mucilage content ranged from 0.89 to 5.45%, and various ABLs exhibited similar yield and yield‐related traits to the controls, but some outperformed them for harvest index, plant height, and days to 5% flowering. The yellow‐seeded ABLs showed the highest and lowest oil and hull content, respectively, as compared with the brown‐seeded lines. The majority of the ABLs exhibited high linolenic acid content, ∼60%. The four most promising ABLs were further characterized for the content of the cyanogenic glycosides (CGs) linustatin and neolinustatin, where LuCGNA11 and LuCGNA67 had the lowest values for both CGs. We evaluated the effects of two ABLs contrasting in SMC on laying hen weight (control = 1794 g), egg production (control = 117 eggs), and egg weight (control = 62.9 g) over a 4‐wk period. Hens fed the low SMC diet had an increase of 78.6 g, 13 eggs, and 3.86 g, while the high SMC diet had a reduction of 36.8 g, 25 eggs, and 2.8 g for body weight, egg production, and egg weight, respectively. These new high and low SMC ABLs offer opportunities to the food and feed industry for the generation of new value‐added products.

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Section: Discussionmentioning

confidence: 91%

Section: Determination Of Cyanogenic Glycosides By High-performance mentioning

confidence: 99%

mentioning

confidence: 99%

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confidence: 99%

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Agronomic and Quality Assessment of Linseed Advanced Breeding Lines Varying in Seed Mucilage Content and Their Use for Food and Feed

2017

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“…The relative versatility of the GGE biplot, especially in mega-environment analysis and genotype selection, is worth exploiting for selection of genotypes for specific environments. More importantly, breeders will be able to identify stable genotypes with relatively consistent performance across a range of environments (Mohammadi, Amri, 2013;Mohammadi et al, 2014;Soto-Cerda et al, 2014). Thus, the GGE biplot technique has become a powerful tool to estimate and visualize genotype by environment interaction, which is widely used by breeders and agronomists all over the world (Casanoves et al, 2005;Koutis et al, 2012;Hagos, Abay, 2013;Sagar et al, 2014;Agyeman et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

GGE biplot analysis of genotype by environment interaction of spring barley varieties

Solonechnyi

Васько

Naumov

et al. 2015

Zemdirbyste-Agriculture

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The article presents results of genotype (G) main effect and genotype by environment (GE) interaction (G × GE) biplot analysis of a multi environmental trial (MET) data in 17 varieties of spring barley conducted in 2013-2014. The objective of this study was to determine the effects of genotype, environment and their interaction on grain yield and to identify stable spring barley genotypes. The experimental layout was a randomized complete block design with three replications in each environment. Combined ANOVA indicated that the main effects due to environments, genotypes and genotype by environment interaction were highly significant. The contribution of environment, genotype and genotype by environment interaction to the total variation in grain yield was about 88.05, 3.61 and 7.54 %, respectively. The G × GE effects were further partitioned using a GGE biplot model. The first two principal components obtained by singular value decomposition of the centred data of yield accounted for 88.50% of the total variability caused by G × GE. Out of these variations, PC1 and PC2 accounted for 51.38% and 37.12% of variability, respectively. The first mega-environment contains environments E1, E3 and E4 with variety 'Donetskyy 15' producing the highest yield; the second mega-environment only contains environment E2 with variety 'Vzirets' being the most yielding. According to the biplot analysis of the "ideal" environment, it was concluded that E1 was the closest to the "ideal" environment and, therefore, the most desirable of all the test environments. GGE biplot view of this study identified the most productive and stable varieties -'Donetskyy 14', 'Kosar' and 'Alegro'. Variety 'Stepovyk' is noticeable for its maximum fidelity to the "ideal" genotype in terms of performance and stability.

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Genetic characterization and quantitative trait relationship using multivariate techniques reveal diversity among tomato germplasms

Ene

Abtew

Oselebe

et al. 2022

Food Science & Nutrition

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Tomato accessions collected from different sources were evaluated to study their diversity, genotype–traits association, as well as pinpoint most selective trait(s) in a controlled environment in Jimma, Ethiopia. The two terms pot experiments were carried out in randomized complete block design with three replications. The genotype–trait (GT) biplot revealed high percentage variability above 70% in related growth traits for the first and second principal components (PC) summed up, in the two trials, whereas related floral and fruit traits association indicated medium to high (55%–65%) total explained variations in both seasons. It further showed that ‘wild parent’, ‘CLN2498D’, ‘CLN2498F’, ‘UC Dan India’, ‘Ruma’, ‘PT4722A’, ‘CLN2679F’, ‘CLN2585C’ and ‘CLN2585D’ were the best performers in most of the related growth, floral, and fruit traits in those seasons. Principal component analysis showed that traits, such as plant height, number of branches, leaves, nodes, internodes, stem girth, style length, stigma length and diameter, flower length and width, number of flowers per truss, number of fruits per truss, and fruit weight per plant, in the first dimension were positively related to yield and consistent with high loading factors in both seasons and could be underpinned highly important in breeding for increased fruit yield. Clustering and its comparison of means showed that ‘CLN2498D’, ‘PT4722A’, ‘Ruma’, ‘Tropimech’, and ‘UC Dan India’ of cluster I in both trials expressed the best traits including related growth, floral, and fruit traits. Therefore, selection for any trait would favor accessions in this cluster.

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Assessing the agronomic potential of linseed genotypes by multivariate analyses and association mapping of agronomic traits

Cited by 18 publications

References 64 publications

Agronomic and Quality Assessment of Linseed Advanced Breeding Lines Varying in Seed Mucilage Content and Their Use for Food and Feed

Agronomic and Quality Assessment of Linseed Advanced Breeding Lines Varying in Seed Mucilage Content and Their Use for Food and Feed

GGE biplot analysis of genotype by environment interaction of spring barley varieties

Genetic characterization and quantitative trait relationship using multivariate techniques reveal diversity among tomato germplasms

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