scholarly journals Character association and principal component analysis for seed yield and its contributing characters in greengram (Vigna radiata (L.) Wilczek)

2020 ◽  
Vol 11 (01) ◽  
Keyword(s):  
Principal Component Analysis ◽  
Seed Yield ◽  
Vigna Radiata ◽  
Principal Component ◽  
Component Analysis ◽  
Character Association

↵Genetic Diversity and Principal Component Analysis in Mungbean [Vigna radiata (L.) Wilczek] under Rainfed Condition

2021 ◽  
Author(s):  
Deepak Gupta ◽  
Suresh Muralia ◽  
N.K. Gupta ◽  
Sunita Gupta ◽  
M.L. Jakhar ◽  
...  
Keyword(s):  
Genetic Diversity ◽  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Seed Yield ◽  
Agricultural Research ◽  
Principal Component ◽  
Component Analysis ◽  
Grain Legume ◽  
Research Station ◽  
Rainfed Condition

Background: Mungbean is a short duration grain legume widely grown in south and Southeast Asia. The extent of variability through Principal Component Analysis (PCA) and cluster analysis in promising mungbean genotypes should be known for possible yield improvement. A study was undertaken to work out the extent of variability among twenty four mungbean genotypes through cluster analysis and Principal Component Analysis (PCA). Methods: The experiment was laid out in a randomized block design with three replications during kharif 2018 and 2019 at the experimental field of Agricultural Research Station, Navgaon (Alwar) under rainfed condition. Result: Principal component analysis revealed that the first three main PCAs amounted 78.80% of the total variation among genotypes for different traits. Out of total principal components, PC1 accounts for maximum variability in the data with respect to succeeding components. Number of branches per plant (28.62%), number of clusters per plant (23.55%) and seed yield (15.58%) showed maximum per cent contribution towards total genetic divergence on pooled basis. Cluster analysis showed that genotypes fall into seven different clusters and their inter and intra cluster distance showed genetic diversity between different genotypes. The maximum number of genotypes i.e., 8 was found in cluster II followed by cluster III comprising of 6 genotypes. Genotypes RMG-1138 and IPM-02-03 representing the mono genotypic cluster signifies that it can be the most diverse variety and it would be the appropriate genotype for hybridization with ones present in other clusters to tailor the agriculturally important traits and ultimately to boost the seed yield in mungbean under rainfed conditions.

Application of Principal Component Analysis (PCA) for Blackgram [Vigna mungo (L.) Hepper] Germplasm Evaluation under Normal and Water Stressed Conditions

2020 ◽  
Author(s):  
V.A. Mohanlal ◽  
K. Saravanan ◽  
T. Sabesan
Keyword(s):  
Principal Component Analysis ◽  
Water Stress ◽  
Seed Yield ◽  
Cropping Systems ◽  
Principal Component ◽  
Component Analysis ◽  
Vigna Mungo ◽  
Yield Potential ◽  
Positive Interaction ◽  
Drought Tolerant

Background: Blackgram [Vigna mungo (L.) Hepper] is a popularly known pulse crop in India for its nutritional quality and adaptability to many cropping systems. The crop is mostly cultivated in areas experiencing water stress which reduces the yield potential. Thus, it is imperative to assess the genetic variability present in the existing blackgram germplasm under drought condition. For this, principal component analysis was carried to visualize the complex dataset. This study was aimed to identify key traits and drought tolerant genotypes. Methods: Twenty-one blackgram genotypes were screened in the field condition for water stress where the experiment was laid out in RBD with two replications. Principal component analysis was carried out with thirteen traits in twenty-one genotypes of blackgram under normal and water stressed conditions.Result: In T0 and T1, more than 75% of total variability among thirteen traits was explained by five and four principal component axes respectively. Under water stress, pod length was highly correlated with seed yield per plant. Based on the interaction vectors and PC scores of genotypes, VBG-12062 had a positive interaction with seed yield. Thus, VBG-12062 can be a reliable candidate for breeding high yielding drought tolerant variety.

scholarly journals Evaluation and diversity analysis in Indian mustard [Brassica juncea (L.) Czern & Coss.] germplasm accessions on the basis of principal component analysis

2017 ◽  
Vol 9 (4) ◽  
pp. 2485-2490
Author(s):  
Ram Avtar ◽  
Manmohan Manmohan ◽  
Minakshi Jattan ◽  
Babita Rani ◽  
Nisha Kumari ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Total Variation ◽  
Brassica Juncea ◽  
Seed Yield ◽  
Oil Content ◽  
Indian Mustard ◽  
Principal Component ◽  
Component Analysis ◽  
Principal Factor ◽  
Germplasm Accessions

Principal component analysis was carried out with 20 morphological traits (including quantitative as well as qualitative) among 96 germplasm lines of Indian mustard [Brassica juncea (L.) Czern & Coss.]. Principal factor analysis led to the identification of eight principal components (PCs) which explained about 70.41% variability. The first principal component (PC1) explained 16.21% of the total variation. The remaining PC’s explained progressively lesser and lesser of the total variation. Varimax Rotation enabled loading of similar type of variables on a common principal factor (PF) permitting to designate them as yield factor, maturity factor and oil factor etc. Based on PF scores and cluster mean values the germplasm accessions viz., RC2, RC32 and RC51 (cluster I), RC95 and RC96 (cluster X) were found superior for seed yield/plant and yield related factors like primary and secondary branches/plant; while the accessions RC34, RC185 and RC195 (cluster III) and RC53 (cluster VIII) were found superior for oil content. These accessions may further be utilized in breeding programmes for evolving mustard varieties having high seed yield and oil content. Hierarchical cluster analysis resulted into ten clusters containing two to 26 accessions. The results of cluster and principal factor analyses were in confirmation of each other.

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.

scholarly journals Principal Component Analysis for Yield in Blackgram(Vignamungo L. Hepper) under Organic and Inorganic Fertilizer Managements

2021 ◽  
pp. 26-34
Author(s):  
A. Kavitha Reddy ◽  
M. Shanthi Priya ◽  
D. Mohan Reddy ◽  
B.Ravindra Reddy
Keyword(s):  
Principal Component Analysis ◽  
Seed Yield ◽  
Selection Criteria ◽  
Principal Component ◽  
Component Analysis ◽  
Inorganic Fertilizer ◽  
Number Of Clusters ◽  
Breeding Programmes ◽  
Total Variability ◽  
High Yielding Varieties

The present investigation on thirty black gram diverse genotypes for 12 yield and yield attributing traits under organic and inorganic fertilizer managements was carried out to study genetic variation among traits and genotypes in the respective environments that would equip the selection criteria using principal Component Analysis. First four vectors with threshold Eigen value greater than one (>1) contributed to 77.56% and 70.74% variation under organic and inorganic fertilizer managements respectively. Characters viz., number of clusters per plant (0.395), number of seeds per pod (0.354), days to maturity (0.336), number of pods per plant (0.300), harvest index (0.244), plant height (0.073) and seed yield per plant (0.015), whereas under inorganic fertilizer management number of primary branches per plant (0.43), followed by number of pods per plant (0.43), seed yield per plant (0.31), number of pods per cluster (0.29), number of clusters per plant (0.29) explained the maximum variance in first principal component (PC1) under organic conditions. Based on comparison of trait contribution to total variability under PC1 under both the managements it can be concluded that the traits viz., number of clusters per plant, number of pods per plant and seed yield per plant were the potential traits that accounted for maximum share towards variability. These traits may be taken into consideration as selection criteria in breeding programmes aimed at developing high yielding varieties.

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