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.

↵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.

scholarly journals Morpho-physiological characterization of soybean genotypes under subtropical environment

Genetika ◽  
2017 ◽  
Vol 49 (1) ◽  
pp. 297-311
Author(s):  
Gaffar Al-Hadi ◽  
Rafiqul Islam ◽  
Abdul Karim ◽  
Tofazzal Islam
Keyword(s):  
Seed Yield ◽  
Plant Traits ◽  
Cropping Systems ◽  
Principal Component ◽  
Yield Potential ◽  
High Yield ◽  
Oilseed Crop ◽  
Wide Range ◽  
Soybean Genotypes ◽  
Cluster 2

Soybean is a promising oilseed crop in rice-based cropping systems in South and Southeast Asia. In spite of immense scope of its expansion, the crop is not being popular to the farmers because of poor yield of the existing cultivars. Therefore, this study evaluated eighty-soybean genotypes of diverse growth habits with a view to searching genotype(s) of desirable morpho-physiological characters and high yield potential. Sixteen quantitative plant traits were evaluated to classify the genotypes into different groups using various multivariate methods. A wide range of variation was found in almost all qualitative plant traits. The study reveals that plants tend to become taller as the phenological cycle is longer. Seed yield was the product of the number of pods per plant, pod weight and seeds per pod. The first three components of principal component analysis explained 75% of the total variations of the soybean genotypes. Using Dendrogram from cluster analysis, the genotypes were grouped into six clusters. The maximum number of genotypes was concentrated in cluster 5 followed by clusters 4. The phenology, plant height, the number of pods and seed yield were the important discriminating variables in grouping the genotypes. The number of pods per plant displayed the principal role in explaining the maximum variance in the genotypes. The clustering pattern of the genotypes revealed that the genotypes under cluster 2 and cluster 6 were long statures, late maturing and produced higher seed yield. The genotype G00003 under cluster 2 is the best entry giving the highest seed yield. From cluster 6, the genotype G00209 could be the better choice for much better seed yield. The cluster 3 genotypes were comparatively early maturing and gave reasonable yield. It is concluded that the genotypes under clusters 2 and 6 and 3 can be important resources for developing a high yielding variety and sustainability of growing soybean in the subtropical conditions.

scholarly journals Genetic Diversity Studies in Drought Tolerant Rice (Oryza sativa L.) Genotypes

2021 ◽  
Author(s):  
A. Sheeba ◽  
S. Mohan
Keyword(s):  
Genetic Diversity ◽  
Principal Component Analysis ◽  
Association Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Large Set ◽  
Panicle Length ◽  
Rice Varieties ◽  
Drought Tolerant ◽  
Rice Genotypes

Background: Assessing the genetic diversity and relationship among breeding materials isan invaluable aid for any crop improvement programme. Principal component analysis (PCA) is a multivariate statistical technique attempt to simplify and analyze the inter relationship among a large set of variables in term of a relatively a small set of variables or components without losing any essential information of original data set. Methods: The present investigation was carried out to study the genetic diversity and relationship among the sixty five rice genotypes including popular rice varieties of Tamil Nadu, drought tolerant rice varieties, aerobic rice genotypes and land races. These genotypes were raised at Rice Research Station, Tiruvallur, during kharif, 2015 in randomized block design with three replications under aerobic condition. Data on eight yield and yield attributing traits were recorded and subjected to principal component analysis and association analysis. Result: In principal component analysis, PC1accounted for 22.91% and PC2 accounted for 19.53% of the total variation. The traits panicle length, no. of grains per panicle, plant height, days to 50% flowering, no of productive tillers per plant from the first two principal components accounted for major contribution to the total variability. Cluster analysis grouped the genotypes into six discrete clusters. The association analysis revealed that the traits viz., no. of productive tillers/plant, panicle length and hundred seed weight had positive association with higher direct effect on plot yield which could be used as selection criteria for developing high yielding rice varieties. The results of the present study have revealed the high level of genetic variation existing in the genotypes studied and explains the traits contributing for this diversity.

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 Assessing organic potato cropping systems at the end of the first cycle of four-year rotations using principal component analysis

2008 ◽  
Vol 88 (4) ◽  
pp. 543-552 ◽  
Author(s):  
K. Liu ◽  
A M Hammermeister ◽  
D G Patriquin ◽  
R C Martin
Keyword(s):  
Principal Component Analysis ◽  
Management Practice ◽  
Cropping Systems ◽  
Soil Amendments ◽  
Cropping System ◽  
Principal Component ◽  
Component Analysis ◽  
High Background ◽  
Soil Variables ◽  
Organic Potato

A single or a few variables may not be sufficient to evaluate management practice effects in a complicated cropping system, so six plant and 13 soil variables were integrated using principal component analysis (PCA) to examine nine 4-yr organic potato rotations. The rotations were combinations of three forage levels (0, 1, and 2 yr of forages) with three soil amendments (monogastric compost, ruminant compost, and alfalfa meal). Quantities of amendments were estimated by soil test recommendations and amendment nutrient availabilities. In the 4th potato year, one half of each original plot was not amended ("the 4th year unamended plots"), while the other half received soil amendments ("4th year amended plots"). The first three principal components explained 67 and 63% of the overall variation for the 4th-yr amended and unamended plots, respectively. PCA ordination plots indicated that, overall, the type of soil amendments had larger effects on soil and plant variables, but forage frequencies were influential for the amendments showing weaker effects. PCA loading plots indicated that plant nutrient uptake and potato total tuber weight would be the best single variables for characterizing the current cropping systems. Plant variables, except for potato petiole nitrate, were closely displayed, but they were not strongly correlated with soil variables, which may reflect the high background fertility of this site. Applications of soil amendments in the 4th yr affected the relationships among variables, most notably the strength of relationships between soil pH and soil N variables. The results suggest that PCA provides an effective way to compare complex cropping systems, especially in situations with high site heterogeneity. Key words: Principal component analysis, soil amendment, livestock system, forage, potato, organic crop rotation

Genetic Diversity and Association Studies in Greengram [Vigna Radiata (L.) Wilczek]

2019 ◽  
Author(s):  
S Mohan ◽  
A Sheeba ◽  
T Kalaimagal
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Path Analysis ◽  
Seed Yield ◽  
Morphological Traits ◽  
Principal Component ◽  
Component Analysis ◽  
Positive Direct ◽  
Number Of Seeds ◽  
Number Of Branches

The present study was conducted to evaluate 44 greengram genotypes using correlation, path analysis, principal component analysis and cluster analysis based on ten morphological traits. Basic descriptive statistics showed considerable variance for all the traits. Association analysis indicated that, number of pods per plant, number of pod clusters per plant, number of seeds per pod and number of branches per plant showed significant positive association with seed yield per plant. Path analysis specified that the highest positive direct effect on single plant yield was exerted by days to 50 % flowering, number of pods per plant and number of seeds per pod. Principal component analysis (PCA) revealed 79.12 per cent of the variability by the first five components. PC1 was associated mainly with seed yield per plant, number of pod clusters per plant, number of pods per plant and number of branches per plant. The Wards method of hierarchical cluster analysis grouped the accessions into six major clusters. The clustering of greengram genotypes based on different morphological traits would be useful to identify the promising genotypes for effective utilization in future breeding programmes..

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.

Sign in / Sign up

Export Citation Format

Share Document