scholarly journals Multivariate and Association Analysis for Yield and Yield Attributing Traits in Quality Protein Maize (QPM) Inbred Lines

2021 ◽  
pp. 11-20
Author(s):  
Vaskar Subba ◽  
Anirban Nath ◽  
Aditi Ghosh ◽  
Amitava Ghosh ◽  
Sabyasachi Kundagrami
Keyword(s):  
Principal Component Analysis ◽  
Grain Yield ◽  
Morphological Traits ◽  
Principal Component ◽  
Component Analysis ◽  
Inbred Lines ◽  
Quality Protein Maize ◽  
Path Coefficient ◽  
Development Programs ◽  
Hybrid Maize

The present investigation reveals the diversity existing among thirty inbred lines of Quality Protein Maize (QPM) in terms of yield and yield attributing traits. The study further elucidates the mutual association among the various morphological traits recorded among the inbred lines. The inbred lines were evaluated during the Rabi seasons of 2016-17, 2017-18 and 2018-19. The analysis of variance calculated over the mean performances of the inbred lines across three rabi seasons revealed significant differences among the inbred lines in terms of yield and yield attributing traits. The diversity among the inbred lines were further determined using cluster analysis which classified the inbred lines into 3 phylogenetically distinct groups. Additionally, a principal component analysis was performed which revealed three principal components (i.e., PC I, II and III) elucidating eighty six percent of the total observable variance among the inbred lines, with traits like grain yield, cob length, cob diameter, number of grain rows per cob, number of grains per row and number of grains per cob contributing to nearly half of the total variance explained by the Principal Component Analysis (PCA). The correlation as well as path coefficient analysis performed for the various traits further indicated significant influence of morphological traits like cob length, cob diameter, number of grain rows per cob and number of grains per cob over the observable grain yield per plant. Overall, the observations from the current investigation can be helpful in identifying superior parental lines to be used in future hybrid maize development programs.

Evaluation of the morphological diversity of tea roses (Rosa x odorata) based on phenotypic traits

2020 ◽  
Vol 18 (3) ◽  
pp. 149-158
Author(s):  
Bixuan Cheng ◽  
Chao Yu ◽  
Heling Fu ◽  
Lijun Zhou ◽  
Le Luo ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Correlation Analysis ◽  
Morphological Traits ◽  
Significant Positive Correlation ◽  
Flower Colour ◽  
Principal Component ◽  
Morphological Diversity ◽  
Component Analysis ◽  
Germplasm Resources ◽  
Trait Correlation

AbstractRosa x odorata (sect. Chinenses, Rosaceae) is an important species distributed only in Yunnan Province, China. There is an abundance of wild variation within the species. Using 22 germplasm resources collected from the wild, as well as R. chinensis var. spontanea, R. chinensis ‘Old Blush’ and R. lucidissima, this study involved morphological variation analysis, inter-trait correlation analysis, principal component analysis and clustering analysis based on 16 morphological traits. This study identified a high degree of morphological diversity in R. x odorata germplasm resources and the variation coefficients had a distribution range from 18.00 to 184.04%. The flower colour had the highest degree of variation, while leaflet length/width had the lowest degree of variation. Inter-trait correlation analysis revealed that there was an extremely significant positive correlation between leaflet length and leaflet width. There was also a significant positive correlation between the number of petals and duration of blooming, and the L* and a* values of flower colour were significantly negatively correlated. Principal component analysis screened five principal components with the highest cumulative contribution rate (81.679%) to population variance. Among the 16 morphological traits, style length, sepal width, flower diameter, flower colour, leaflet length and leaflet width were important indices that influenced the morphology of R. x odorata. This study offers guidance for the further development and utilization of R. x odorata germplasm resources.

Characterization of medicinal Senna genetic resources

2009 ◽  
Vol 7 (03) ◽  
pp. 257-259 ◽  
Author(s):  
J. B. Morris
Keyword(s):  
Pancreatic Cancer ◽  
Principal Component Analysis ◽  
Medicinal Plants ◽  
Genetic Resources ◽  
Morphological Traits ◽  
Principal Component ◽  
Component Analysis ◽  
Pharmaceutical Products ◽  
Seed Reproduction

At 50% maturity, regeneratingSennaspecies were characterized for morphological traits, seed reproduction, and evaluated for regeneration. Quality plants regenerated from all accessions produced 1018 to more than 21,215 total seeds. Principal component analysis revealed which traits contributed the greatest to variability among coffee senna accessions.Sennaspecies have potential to produce pharmaceutical products and can be grown as medicinal plants. The flavonoids quercetin and kaempferol found inSennaspecies have been clinically shown to have anti-pancreatic cancer properties.

Study on Prediction Model of Grain Yield Based on Principal Component Analysis and BP Neural Network

2015 ◽  
Vol 713-715 ◽  
pp. 1939-1942
Author(s):  
Xing Mei Xu ◽  
Li Ying Cao ◽  
Jing Zhou
Keyword(s):  
Neural Network ◽  
Principal Component Analysis ◽  
Prediction Model ◽  
Grain Yield ◽  
Bp Neural Network ◽  
Principal Component ◽  
Component Analysis ◽  
Jilin Province ◽  
Average Error ◽  
Main Factors

Taking the grain yield data from 1980 to 2012 of Jilin Province for example, this paper analyzes the main factors that influences the grain yield based on the principle component analysis method. According to these main factors, the input samples of BP neutral network are definite. Thereby, the BP neutral networks could be trained to predict. The results show that the fertilizer consumption, large cattle head number, end grain sowing area, effective irrigation area and rural per capita living space are the main effect factor on grain yield. The BP neural network was built by using it as the input samples. The number of input nodes of the network is determined. Then build the prediction model of grain production in Jilin province. The simulation results show that, the average error of prediction results of BP neural network model based on principal component analysis is 4.48%.

scholarly journals GENETIC DIVERGENCE ON QUANTITATIVE CHARACTERS OF EXOTIC MAIZE INBREDS (Zea mays L.)

2015 ◽  
Vol 26 (2) ◽  
pp. 09-14 ◽  
Author(s):  
M. G. Azam Azam ◽  
U. K. Sarker Sarker ◽  
M. A. K. Mian Mian ◽  
B. R. Banik Banik ◽  
M. Z. A. Talukder
Keyword(s):  
Genetic Diversity ◽  
Grain Yield ◽  
Morphological Traits ◽  
Zea Mays L ◽  
Principal Component ◽  
Inbred Lines ◽  
Quantitative Characters ◽  
Cluster Distance ◽  
Ear Height ◽  
Maize Inbreds

Forty nine CIMMYT, India Maize inbred lines were characterized based on some morphological traits and grain yield. Genetic divergences of inbred lines of maize were estimated using D2 and principal component analysis. The genotypes under study fell into five clusters. The inter cluster distance were higher than intra cluster distance suggesting wider genetic diversity among the genotypes of different groups. The maximum intra cluster value was observed in cluster IV and minimum in cluster V. The inter cluster D2 values revealed that the maximum distance among the cluster. The highest inter cluster distance was observed between cluster II & I and the lowest inter cluster distance was illustrated in cluster III & I. The cluster means were higher for days to 50% tasseling, days to 50% sillking, plant height, ear height, cob length, number of rows per cob, number of grains per row in cluster IV; cob diameter and grain yield per plant was found higher in cluster II. It is expected that crossing of inbred lines belonging high to medium D2 values tend to produce high heterosis for yield.

scholarly journals Multivariate Analysis and its Application for Screening Mungbean [Vigna radiata (L.) Wilczek] Landraces

2022 ◽  
Author(s):  
Berk Benlioglu ◽  
Ugur Ozkan
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Multivariate Analysis ◽  
Vigna Radiata ◽  
Morphological Traits ◽  
Principal Component ◽  
Component Analysis ◽  
Multivariate Statistical ◽  
Breeding Programs ◽  
High Level

Background: Mungbean [Vigna radiata (L.) Wilczek] is known as one of the important crop of the Vigna group. In order to determine morphological traits of mungbean, multivariate analysis will provide important advantages in the selection phase of future breeding programs. Multivariate statistical analysis was used to determine and classify these traits. Multivariate analysis, that includes principal component analysis (PCA) and cluster analysis (CA), is considered the best tool for selecting promising genotypes in the future breeding programs. Methods: Eighteen landraces and two species were used to classify morphological traits in this study. Nine different morphological traits were observed during the research period. These are; days to 50% flowering (DFT), plant height (PH), branches per plant (BPP), clusters per plant (CPP), number of pods per cluster (PPC), seed yield per plot (SYPP), biomass yield per plot (BYPP), harvest index (HI), 1000 seed weight (SW). Result: Principal component analysis (PCA) revealed a high level of variation among the genotypes. Therefore, high variability was observed in DFT (36-59 day), PH (39-76 cm), BPP (3-7), CPP (4-21), SYPP (231-824 g), BYPP (3300-10300 g), HI (6.77-11.25%) and 1000 SW (19.95-50.50 g). According to cluster analysis, landraces with the least genetic diversity distance between them in terms of morphological traits examined were determined as 2 and 3.

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