Adaptability and phenotypic stability of soybean cultivars for grain yield and oil content

Kb, Silva; At, Bruzi; Zuffo, Alan Mário; Ev, Zambiazzi; Io, Soares; Pm, de Rezende; Fronza,; Gd, Vilela; Fb, Botelho; Teixeira, Cátia M.; Coelho, de O

doi:10.4238/gmr.15026756

Cited by 16 publications

(27 citation statements)

References 16 publications

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“…This consistency will help us to use it as an asset genetic fixed under various environments conditions in soybean breeding programs to increase yielding, quality traits, resistance for diseases, adverse environmental conditions through crossing it with sensitive and moderate entries for the environmental stresses such as, salinity and water stress, then the simple selection method will be the second stage among continuing for several generations of agriculture evaluation for accessing to genetic stability and this trend in the genetic improvement will not be unless the parents were highly genetic stability as we explained previously, So we can say that the previous genotypes characterized with highly genetic stability under all conditions of the twelve environments specially the cultivars; (Crawford, Giza 21, Giza 22, Giza 35, Giza 83, Giza 111) for 1000-grain weight, number of grains/plant, grain weight/plant and grain yield/plant traits in figure (1) and (A, B, C, D histograms), respectively. Similar results were in agreement with those reported by Gill and Kumar (1989), Hossain et al, (2003), Popovic et al, (2013), Lakew et al, (2014), Hamawaki et al, (2015) and Silva et al, (2016).…”

Section: Mean Performancesupporting

confidence: 83%

“…These results confirmed that adapting and suitability of these soybean entries under all conditions and a strong signal for their genetic stability for different environments studied or even all other environments, Gill and Kumar (1989), Hossain et al, (2003), Popovic et al, (2013), Lakew et al, (2014), Hamawaki et al, (2015) and Silva et al, (2016).…”

Section: Mean Performancesupporting

confidence: 72%

“…The differences of mean square due to (environments + (Genotypes X Environments) were highly significant for all traits studied which exhibited that considerable interaction of all entries under different environmental conditions of years, locations and treatments in different years. The variances due to entries were showed to be highly significant for all the traits studied against pooled deviation which means the presence of sufficient genetic variability between the genotypes, Gill and Kumar (1989), Popovic et al, (2013), Selvi et al, (2015), Hamawaki et al, (2015), Akter et al, (2015)and Silva et al, (2016).…”

Section: Variation and Interactionmentioning

confidence: 99%

“…On the other hand the rest of environments were revealed low genetic stability in their interaction using the previous soybean entries. Accordingly, we note that the genotypes; (Crawford, Giza 21, Giza 22, Giza 35, Giza 83, Giza 111) were the most genetic stability entries and recommended for using it under different environments, while the cultivar (Giza 82) coming in the second rank, Gill and Kumar (1989), Mohan and Har Ram (2006), Vera et al, (2013), Popovic et al, (2013), Dewi et al, (2014), Lemes et al, (2015), Lodhi et al, (2015), Hamawaki et al, (2015), Gunjan et al, (2016), Silva et al, (2016) and Tiwari et al, (2016).…”

Section: Stability Parametersmentioning

confidence: 99%

“…They revealed that the genotypes UFU-008 and UFU-0013 were assorted in special group ( E ) with a concave pattern of adaptability and stability in addition, the entries UFU-001, UFU-002, UFU-006, UFU-0010 and UFU-0011 were bespoken for using in the location (Brazilian Cerrado growing) as well as these cultivars were highly yielding potential. Silva et al, (2016) detected the stability and adaptability in 37 soybean entries during four regions and two seasons for yield components and oil content traits. They revealed that the highest mean values of grain yield and its components traits under different locations for the two seasons were exhibited in the entries (BRSGO204 (Goiânia) and BRSMG (Garantia), while the highest genotype for oil content under the same experiments was (BRSMG 760 SRR), which indicated that these cultivars were recorded high genetic stability and adaptability under many conditions.…”

Section: Introductionmentioning

confidence: 99%

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Molecular Characterization and Stability Analysis for Yield and its Components Traits in Soybean (Glycine max L.)

Mouhamady¹,

Rahman²,

Elewa³

et al. 2017

Int.J.Curr.Microbiol.App.Sci

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Seven soybean entries namely;(Crawford, Giza 21, Giza 22, Giza 35, Giza 82, Giza 83 and Giza 111) with different reaction for highly yielding, tolerance for salinity, water deficit conditions and resistance for diseases were evaluated under twelve environments through two years (2015, 2016 seasons), three locations (the farm of agricultural research in Mansoura city, Dakahlia Governorate, the farm of agriculture research center, Sakha station Kafr El-Sheikh governorate and the farm of national research Centre in Nubaria, Beheira Governorate, Egypt) in addition two planting dates (the first planting date was 15 may, while the second date was 1 June) to study the range of genetic stability, the behavior responsible for the persistence cultivar, the interactions between these cultivars and all different environments and their effect on some yield components, respectively. Stability analysis was done using data calculated obtained from plant height, first pod height, number of pods/plant, 1000-grain weight, number of grains/plant, grain weight/plant and grain yield/plant traits beside (Random amplified polymorphic DNA (RAPD) through using four primers for the previous seven cultivars. The final results revealed that mean square variances of (Genotypes x Environments), (Environments+(Genotypes x Environments), the linear components of (Environments) and (Genotypes x Environments) and pooled deviation were highly significant and detected that the genotypes; (Crawford, Giza 21, Giza 22, Giza 35, Giza 83, Giza 111) were the most genetic stability entries according to the results of stability analysis specially (bi, S 2 di, R 2 ) and recommended for using it under different environments, while the cultivar (Giza 82) coming in the second rank. Heritability in broad sense was high in plant height, number of pods/plant and grain yield/plant traits which indicated that the environment effect on these traits through using the previous entries was very low with intension the genetic stability unlike the rest of traits studied. DNA Fingerprinting analysis was conducted to compare between the seven soybean entries using four primers namely; (OPC10, OPF-4, OPA-17, and OPG-5). The four primers recorded 12 amplicons, where 8 of them were polymorphic with 66.67% polymorphism, while 4 fragments were monomorphic. Cluster analysis divided the seven genotypes into two main clusters, where the first one contained the genotypes (2, 4), while the second cluster involved the rest of genotypes, respectively.

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Section: Mean Performancesupporting

confidence: 83%

Section: Mean Performancesupporting

confidence: 72%

Section: Variation and Interactionmentioning

confidence: 99%

Section: Stability Parametersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Molecular Characterization and Stability Analysis for Yield and its Components Traits in Soybean (Glycine max L.)

Mouhamady¹,

Rahman²,

Elewa³

et al. 2017

Int.J.Curr.Microbiol.App.Sci

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Heritability and the genotype × environment interaction in soybean

Bianchi

Bruzi

Soares³

et al. 2020

Agrosystems Geosci & Env

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The objectives of this study were to examine the implication of the genotype × environment interaction (G × E) in the identification of genetically superior soybean [Glycine max (L.) Merr.] progenies; to obtain estimates of genetic and phenotypic parameters for agronomic traits in soybean progenies; and to select genetically superior progenies to obtain lineages. Progenies F 3:4 were evaluated in the municipalities of Lavras-MG and Itutinga-MG, Brazil, in the agricultural year 2016-2017. Progenies F 3:5 were evaluated in the summer of 2017-2018 in the municipalities of Lavras-MG, Itutinga-MG, and Ijaci-MG. The traits of days to flowering, days to full maturity, plant height, bottom pod height, lodging, and grain yield were evaluated. The genetic and phenotypic parameters, expected gain from selection, genetic correlation, correlated response, achieved heritability, and frequency distribution of the adjusted means were estimated. The estimates of the components of variance showed the existence of variability among the progenies, enabling the selection of superior genotypes. There was an effect of the G × E interaction for all traits, and most of the interaction was due to complex interaction. From the analysis of genotypic correlation, significant estimates were observed between the traits. The G × E interaction affected the estimates of genetic and phenotypic parameters in the soybean, and the achieved heritability is a tool to study this interaction.

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Implications of the population effect in the selection of soybean progeny

Pereira

Bruzi

Matos³

et al. 2017

Plant Breeding

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The goal of this study was to verify the influence of the population effect in the estimates of genetic and phenotypic components and to identify the best soybean progeny or lines in a commercial soybean breeding programme. We evaluated 292 populations for grain yield and absolute maturity during three agricultural years. To quantify the efficiency of the inclusion of the population's effect in the model, we estimated genetic gain with the selection, the Spearman's correlation, the coincidence index, the realized gain and correlated response of selected genotypes with and without the effect of the population. It was found that the variance components, heritability and coefficient of experimental variation were better estimated when the effect of the population was included, providing greater gain with selection for grain yield and absolute maturity. Coincidence and ranking among the selected progeny with and without the effect of the population are of greater magnitude in more advanced inbreeding generations and at higher percentage of selected progeny. The use of the population effect has greater importance in earlier generations of inbreeding.

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Adaptability and phenotypic stability of soybean cultivars for grain yield and oil content

Cited by 16 publications

References 16 publications

Molecular Characterization and Stability Analysis for Yield and its Components Traits in Soybean (Glycine max L.)

Molecular Characterization and Stability Analysis for Yield and its Components Traits in Soybean (Glycine max L.)

Heritability and the genotype × environment interaction in soybean

Implications of the population effect in the selection of soybean progeny

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