Determination of Correlation and Path Analysis among Yield Components and Seed Yield in Oil Flax Varieties (Linum usitatissimum L.)

Çopur, Osman; Gür, Mehmet Atilla; Karakuş, Mehmet; Demirel, Ufuk

doi:10.3923/jbs.2006.738.743

Cited by 19 publications

(3 citation statements)

References 3 publications

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“…Então, o número de cápsulas pode ser utilizado para seleção indireta. Adicionalmente, é importante frisar que, em pesquisas de Copur et al (2006), Tadesse et al (2009), Ottai et al (2011), Hassanein et al (2012, Reddy et al (2013) e Tariq et al (2014), tem sido demonstrada também associação positiva do número de cápsulas com a produtividade de grãos de linho. Ainda, Tadesse et al (2009) verificaram que a biomassa de linho tem associação linear positiva com a produtividade de grãos.…”

Section: Resultsunclassified

Relações lineares entre caracteres de linho

et al. 2016

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INTRODUÇÃOO linho (Linum usitatissimum L.) pode ser utilizado como fibra têxtil, como óleo para a indústria, na alimentação de bovinos e suínos (Floss 1983) e para o consumo humano (Novello e Pollonio 2011). É uma planta herbácea, de ciclo anual, com 30 a 120 cm de altura, conhecida popularmente como linhaça (Floss 1983).Em culturas agrícolas, como o linho, é importante obter plantas com elevada produção de massas verde e seca de cápsulas e de parte aérea. No melhoramento de plantas, para a seleção direta de plantas com maiores massas verde e seca, é necessário destruir as mesmas para a pesagem dessas massas. Caracteres como a altura de planta, o número de ramos e o número de cápsulas podem ser mensurados de forma não destrutiva. Esses caracteres têm sido avaliados na cultura de linho por Tomassoni et al. (2013) e Rossi et al. (2014. Porém, as relações lineares não têm sido investigadas. Existindo relações lineares entre esses caracteres e as massas verde e seca, é possível a seleção indireta, sem a destruição das plantas.O coeficiente de correlação linear de Pearson (r) varia entre −1 (correlação linear negativa perfeita) e 1 (correlação linear positiva perfeita) e é adequado para medir o grau de relacionamento entre dois caracteres. Já a análise de trilha é apropriada para os casos em que há mais de dois caracteres. Na análise de trilha, os coeficientes de correlação são desdobrados em efeitos diretos e indiretos, o que permite medir a influência de uma variável sobre a outra, independentemente das demais (Cruz e Carneiro 2003). Assim, é importante aplicar esses DOI: http://dx.doi.org/10.1590DOI: http://dx.doi.org/10. /1678 ABSTRACT: The objectives of this study were to evaluate the

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Section: Resultsunclassified

Relações lineares entre caracteres de linho

et al. 2016

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“…Previous studies 115,116 also con rmed the most direct contribution of boll or fruit number to ax seed yield.…”

Section: Phenotypic Variabilitymentioning

confidence: 68%

Genomic prediction for agronomic traits in a diverse Flax (Linum usitatissimum L.) germplasm collection

Hoque

Anderson

Rahman

2023

Preprint

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Breeding programs require exhaustive phenotyping of germplasms, which is time-demanding and expensive. Genomic prediction based on next-generation sequencing techniques helps breeders harness the diversity of any collection to bypass phenotyping. Here, we examined the genomic prediction’s potential for seed yield and nine agronomic traits using 26171 single nucleotide polymorphism (SNP) markers in a set of 337 flax (Linum usitatissimum L.) germplasm, phenotyped in five environments. We evaluated 14 prediction models and several factors affecting predictive ability based on cross-validation schemes. Most models gave close predictive ability values across traits for the whole marker set. Models covering non-additive effects yielded better predictive ability for low heritable traits, though no single model worked best across all traits. Marker subsets based on linkage disequilibrium decay distance gave similar predictive abilities to the whole marker set, but for randomly selected markers, it reached a plateau above 3000 markers. Markers having significant association with traits improved predictive abilities compared to the whole marker set, when marker selection was made on the whole population instead of the training set indicating a clear overfitting. The correction for population structure did not increase predictive abilities compared to the whole collection. However, stratified sampling by picking representative genotypes from each cluster improved predictive abilities. The indirect predictive ability for a trait was proportionate to its correlation with other traits. These results will help breeders to select the best models, optimum marker set, and suitable genotype set to perform an indirect selection for quantitative traits in this diverse flax germplasm collection.

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“…Разнообразие сведений о генетических корреляциях не всегда позволяет выявить полноценную картину взаимосвязей, особенно при значимом воздействии отдельного фактора. Например, известно о достоверном влиянии на продуктивность некоторых морфологических признаков льна (El-Hariri et al, 2004;Copur, 2006;Brutch, Porokhovinova, 2011).…”

Section: группаunclassified

Phenotypic variability of <i>Linum usitatissimum</i> l. accessions under the conditions of the Northern Trans-urals

Korolev

Bome

Колоколова

2023

Trudy po prikladnoy botanike, genetike i selektsii

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Background. One of the ways to increase flax production is to expand cropping areas, including nontraditional ones. To solve this problem, it is necessary to carefully select cultivars that can produce stable high yields and have competitive raw material quality. The aim of this study was to identify the adaptability potential of flax accessions and select sources of valuable properties for their use under the conditions of the Northern Trans-Urals.Materials and methods. Sixty flax accessions were studied in the fields of the south of Tyumen Province in 2017–2020. Phenotypic screening was performed according to the published lists of descriptors, taking into account outdoor seed germination, plant height, seed yield, flax straw, retted stalks, and fiber. Statistical data processing included the multivariate analysis of variance and correlation analysis. Environmental screening was performed according to S. A. Eberhart, W. A. Russell.Results and conclusion. Based on the long-term data, sources of traits were identified: 4 for outdoor seed germination, 4 for plant height, 11 for the duration of the growing season, 21 for straw yield, 19 for the yield of retted stalks, 9 for seed yield, 6 for total fiber yield, and 5 for 1000 seed weight. The manifestation of traits was shown to be largely induced by the impact of the environment factor and the complex genotype × environment interaction. On the basis of screening results, 12 accessions were selected. The correlation analysis helped to find out that on average over the years of research there were high and stable direct relationships of the yield of straw (r = 0.73–1.00) and retted stalks (r = 0.68–1.00) with plant height. Under relatively favorable conditions for plant growth and development during the growing seasons of 2017 and 2020, the influence of outdoor seed germination on plant height (r = 0.62–1.00), straw yield (r = 0.78–1.00), and seed yield (r = 0.52–0.91) increased.

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Determination of Correlation and Path Analysis among Yield Components and Seed Yield in Oil Flax Varieties (Linum usitatissimum L.)

Cited by 19 publications

References 3 publications

Relações lineares entre caracteres de linho

Relações lineares entre caracteres de linho

Genomic prediction for agronomic traits in a diverse Flax (Linum usitatissimum L.) germplasm collection

Phenotypic variability of <i>Linum usitatissimum</i> l. accessions under the conditions of the Northern Trans-urals

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