scholarly journals Genetic evaluation of farmer's rice varieties for physiological and yield attributing responses exploiting principal component analysis

2021 ◽  
Vol 58 (3) ◽  
pp. 384-393
Author(s):  
Nivesh Khoth ◽  
Sanjay Singh ◽  
R Shiv Ramakrishnan ◽  
GK Koutu ◽  
Radheshyam Sharma ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Leaf Area ◽  
Principal Component ◽  
Component Analysis ◽  
High Yield ◽  
Biochemical Traits ◽  
Rice Varieties ◽  
Area Index ◽  
Yield Attributes ◽  
Genetic Components

An experiment was conducted on 30 farmer's rice varieties collected from different districts of Madhya Pradesh to identify the genetic components contributing to phenophasic development, physiological, yield attributes and biochemical traits. Principal component analysis was performed to rank the farmer's varieties based on PC scores acquired as per the trait studied. Out of twenty-six traits, only five principal components (PCs) exhibited more than 1.00 Eigen value and showed 85.80% of total cumulative variability. The PC1 showed 58.55%, while PC 2, PC 3, PC 4 and PC 5, exhibited 10.29%, 7.03%, 5.23% and 4.69% variability, respectively. The PC 1 reported the highest variability, which was associated with physiological and yield related traits. The PC 2 was dominated by biochemical traits, while PC3 was mostly dominated for yield traits. The PC 4 was dominated by physiological traits, and PC5 for phenological and yield-related traits. Farmer's variety Pandu was superior for Chlorophyll content index (38.27), total dry matter production (38.15 g plant-1), Leaf area index (4.09), Leaf area duration (17982 cm2 days) and crop growth rate (0.00282 g m-2 day-1). PCA revealed that genotype Pandu (7.224) acquired highest PC score followed by Raibua (5.364), Bahurupi (5.103) and Chinnor 1 (4.750) respectively. Farmers varieties Pandu, Chhindikapoor, Bahurupi, Sitha Chandan, Chinnor 2, Chinnor 1 and ChhotaSathiya were contributed their presence in maximum PCs of this investigation. The identified lines will be utilized in the rice breeding programme to develop improved rice varieties for high yield and maximum physiological efficiency.

scholarly journals Grouping sunflower genotypes for yield, oil content, and reaction to Alternaria leaf spot using GGE biplot

2015 ◽  
Vol 50 (8) ◽  
pp. 649-657 ◽  
Author(s):  
Regina Maria Villas Bôas de Campos Leite ◽  
Maria Cristina Neves de Oliveira
Keyword(s):  
Principal Component Analysis ◽  
Disease Severity ◽  
Principal Components ◽  
Leaf Spot ◽  
Oil Content ◽  
Principal Component ◽  
Component Analysis ◽  
High Yield ◽  
Gge Biplot ◽  
Alternaria Leaf Spot

Abstract:The objective of this work was to evaluate the suitability of the multivariate method of principal component analysis (PCA) using the GGE biplot software for grouping sunflower genotypes for their reaction to Alternaria leaf spot disease (Alternariaster helianthi), and for their yield and oil content. Sixty-nine genotypes were evaluated for disease severity in the field, at the R3 growth stage, in seven growing seasons, in Londrina, in the state of Paraná, Brazil, using a diagrammatic scale developed for this disease. Yield and oil content were also evaluated. Data were standardized using the software Statistica, and GGE biplot was used for PCA and graphical display of data. The first two principal components explained 77.9% of the total variation. According to the polygonal biplot using the first two principal components and three response variables, the genotypes were divided into seven sectors. Genotypes located on sectors 1 and 2 showed high yield and high oil content, respectively, and those located on sector 7 showed tolerance to the disease and high yield, despite the high disease severity. The principal component analysis using GGE biplot is an efficient method for grouping sunflower genotypes based on the studied variables.

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 Assessment of the genetic components and trait associations in diverse set of fieldpea (Pisum sativum L.) genotypes

2015 ◽  
Vol 43 (3) ◽  
pp. 323-330 ◽  
Author(s):  
AK Parihar ◽  
GP Dixit ◽  
V Pathak ◽  
D Singh
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Principal Components ◽  
Agronomic Traits ◽  
Principal Component ◽  
Component Analysis ◽  
Genetic Components ◽  
Wide Range ◽  
Cluster Distance ◽  
Biological Yield

One hundred and 40 genotypes of fieldpea were used to assess the genetic divergence for various agronomic traits. The study revealed that all the accessions were significantly different for the traits and a wide range of variability exists for most of the traits. Correlation studies exhibited that seed yield had positive significant correlation with most of the traits. Cluster analysis classified 140 genotypes into 12 distinct groups. A large number of genotypes (30) were placed in cluster IV followed by cluster III with 24 genotypes. The maximum inter-cluster distance was observed between clusters III and IV indicating the possibility of high heterotic effect if the individuals from these clusters are cross-bred. Principal component analysis yielded 12 Eigen vectors and PCA analysis revealed significant variations among traits with seven major principal components explaining about 90% of variations. The estimates of Eigen value associated with the principal components and their respective relative and accumulated variances explained 50.16% of total variation in the first two components. The characters with highest weight in component first were biological yield, pods/plant, yield/plant and branches/plant which explained 34.22% of the total variance. The results of principal component analysis were closely in line with those of the cluster analysis. The grouping of genotypes and hybridization among genetically diverse genotypes of different cluster would be helpful in broadening the genetic base of fieldpea and producing desirable recombinants for developing new fieldpea varieties. DOI: http://dx.doi.org/10.3329/bjb.v43i3.21605 Bangladesh J. Bot. 43(3): 323-330, 2014 (December)

scholarly journals Comprehensive Evaluation of 17 Qualities of 84 Types of Rice Based on Principal Component Analysis

Foods ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 2883
Author(s):  
Shijie Shi ◽  
Enting Wang ◽  
Chengxuan Li ◽  
Hui Zhou ◽  
Mingli Cai ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Protein Content ◽  
Comprehensive Evaluation ◽  
Principal Component ◽  
Component Analysis ◽  
Analysis Method ◽  
Rice Varieties ◽  
Rice Quality ◽  
Coefficients Of Variation ◽  
Cluster Analysis Method

Rice quality is a complex indicator, and people are paying more and more attention to the quality of rice. Therefore, we used seven rice varieties for twelve nitrogen fertilizer treatments and obtained eighty-four rice types with seventeen qualities. It was found that 17 quality traits had different coefficients of variation. Among them, the coefficient of variation of chalkiness and protein content was the largest, 44.60% and 17.89% respectively. The cluster analysis method was used to define four categories of different rice qualities. The principal component analysis method was used to comprehensively evaluate 17 qualities of 84 rice. It was found that rice quality was better under low nitrogen conditions, Huanghuazhan and Lvyinzhan were easier to obtain better comprehensive rice quality during cultivation. Future rice research should focus on reducing protein content and increasing peak viscosity.

scholarly journals Assessment of genetic variability for micronutrient content and agro-morphological traits in rice (Oryza sativa L.)

2020 ◽  
Vol 80 (02) ◽  
Author(s):  
P. Madhubabu ◽  
R. Surendra ◽  
K. Suman ◽  
M. Chiranjeevi ◽  
R. Abdul Fiyaz ◽  
...  
Keyword(s):  
Genetic Diversity ◽  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Oryza Sativa L ◽  
Development Program ◽  
Principal Component ◽  
Component Analysis ◽  
High Yield ◽  
Polished Rice ◽  
Iron And Zinc

Assessment of rice genetic diversity is critical step for trait specific varietal development program. In the present study, a collection of 281 Indian germplasm accessions were evaluated for genetic diversity study using 30 agro-morphological characters and grain iron and zinc contents in brown and polished rice. To identify the pattern of relatedness and associations, cluster analysis and principal component analysis coupled with correlation were used. Cluster analysis grouped 281 accessions into six main groups. Cluster 4 is the largest and had accessions with higher yield, zinc and iron content. Six components of principal component analysis indicated 76.4% of the total variation. The Principal Component (PC)1 showed 19.05%, while, PC2, PC3, PC4, PC5 and PC6 exhibited 14.23%, 13.61%, 11.58%, 7.59%, and 6.71% variability, respectively. Among the germplasm, three accessions IC145407, IC145357 and IC248034 have shown significant iron and zinc content in polished rice along with desirable grain yield. The information presented here will be useful in the development of rice varieties with high yield and micronutrient content.

scholarly journals Phenological patterns of defoliation and refoliation processes of rubber tree clones in the Colombian northwest

2020 ◽  
Vol 73 (3) ◽  
pp. 9293-9303
Author(s):  
Juan José Guerra-Hincapié ◽  
Óscar De Jesús Córdoba-Gaona ◽  
Juan Pablo Gil-Restrepo ◽  
Danilo Augusto Monsalve-García ◽  
Juan David Hernández-Arredondo ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Management Strategies ◽  
Rubber Tree ◽  
Principal Component ◽  
Canopy Openness ◽  
Area Index ◽  
The Relationship ◽  
Climatic Characteristic

The knowledge of the defoliation-refoliation process in rubber cultivation allows the development of management strategies in the production system to improve rubber yield. The objective of this study was to determine the intensity and duration of defoliation-refoliation of rubber clones FX 3864, IAN 710 and IAN 873 in the municipality of Tarazá and the FX 3864 and IAN 873 clones in the municipality of Nechí (northwestern Colombia). From October 2015 to June 2016, the measurements of the necromass were carried out in each location for each clone. The light environment was quantified, employing the hemispheric photographs technique to estimate canopy openness percentage (CO) and leaf area index. The assessed weeks were grouped by Principal Component Analysis (PCA) based on the original phenology and climatic variables. The defoliation-refoliation process was analyzed descriptively using graphical representations of the trend for the phenological variables that best described this process. The relationship between climatic and phenological variables in the period evaluated was evidenced; the rainfall was the most critical climatic characteristic in the induction of the defoliation process. The leaf area index was reduced to a minimum value in February, with values of 0.52 for IAN 710 clone in Tarazá, and 0.64 for the IAN 873 clone in Nechí, which corresponded to the highest defoliation stage in both locations. The refoliation period was short (4 to 6 weeks) and occurred during the dry season for all the clones in both places.

scholarly journals Agronomic and molecular evaluation of rice lines in a breeding program

2020 ◽  
Author(s):  
Luis Marqués ◽  
Susana Barceló ◽  
José María Osca
Keyword(s):  
Principal Component Analysis ◽  
Field Tests ◽  
Principal Component ◽  
Component Analysis ◽  
Indirect Selection ◽  
Rice Varieties ◽  
Complex Process ◽  
Total Variability ◽  
Yield Values ◽  
Molecular Marker Analysis

Abstract Obtaining new and improved varieties of rice requires long and complex plant breeding programs. The early detection of desirable characteristics is a complex process, especially when seeking to improve yield, as the interaction between the environment and plants may hinder selection in early generations considerably. Techniques that facilitate the selection of plants with desirable characteristics in early generations are highly valuable to plant breeders. An indirect selection method in early generations of rice was examined by principal component analysis of performance supported by field tests with a honeycomb design. This study used double haploid lines of rice obtained by crossing two rice varieties, namely ‘Benisants’ and ‘Gigante Vercelli’. This method was compared to indirect selection using genomic tools such as high-throughput molecular marker analysis. The main factors that can be used in indirect selection have been selected by principal component analysis. The model resulting from the phenological evaluation and principal component analysis with six selected variables explained 98.73% of the total variability of yield. The variable that contributes the most to the model is the Harvest Index. The best selected lines provided 32% and 43% higher yield values than the parentals and match the results from indirect selection with molecular markers.

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