Studies on ripening of rice varieties grown under low temperature. IV. Application of principal component analysis on some ripening characters.

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.

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

Foods ◽

10.3390/foods10112883 ◽

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.

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Agronomic and molecular evaluation of rice lines in a breeding program

10.21203/rs.3.rs-131889/v1 ◽

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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Clustering and Principal Component Analysis of Nerica Mutant Rice Lines Growing Under Rainfed Condition

International Journal of Applied Sciences and Biotechnology ◽

10.3126/ijasbt.v7i3.25703 ◽

2019 ◽

Vol 7 (3) ◽

pp. 327-334

Author(s):

Md. Nuruzzaman ◽

Md. Shohel Rana ◽

Aleya Ferdausi ◽

Md. Monjurul Huda ◽

Lutful Hassan ◽

...

Keyword(s):

Principal Component Analysis ◽

Cluster Analysis ◽

Morphological Traits ◽

Principal Component ◽

Component Analysis ◽

Rice Varieties ◽

Cluster Distance ◽

Rice Improvement ◽

Clear Idea ◽

Scree Test

A field experiment was conducted at subtropical region in Bangladesh to assess the contribution of morphological traits to variability in some NERICA mutant rice lines. The experiment was conducted following RCBD with three replications. Thirty-one NERICA rice genotypes (twenty-eight mutant lines along with three parents) of advanced generations were used. Data were collected on twelve morphological traits. The results of the principal component analysis showed that the first four components account for 80% of total variation giving a clear idea of the structure underlying the variables analyzed. This result was also supported by scree test. Cluster analysis using Ward's method classified the thirty-one genotypes into five distinct groups. The maximum inter-cluster distance was observed between clusters indicating the possibility of high heterosis if individuals from these clusters are cross-bred. The results of PCA were closely in line with those of the cluster analysis. These results can now be used by breeders to develop drought tolerant high yielding rice varieties and new breeding protocols for rice improvement. Int. J. Appl. Sci. Biotechnol. Vol 7(3): 327-334

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

ORYZA- An International Journal on Rice ◽

10.35709/ory.2021.58.3.5 ◽

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.

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Morphological Similarity Analysis of Elite Rice Varieties Released in 1980–2011

Buletin Plasma Nutfah ◽

10.21082/blpn.v24n1.2018.p31-42 ◽

2018 ◽

Vol 24 (1) ◽

pp. 31

Author(s):

Trias Sitaresmi ◽

Nani Yunani ◽

Nafisah Nafisah ◽

Satoto Satoto ◽

Aan A. Daradjat

Keyword(s):

Principal Component Analysis ◽

Cluster Analysis ◽

Genetic Relationship ◽

Principal Component ◽

Component Analysis ◽

Yield Potential ◽

Rice Varieties ◽

Irrigated Rice ◽

And Cluster Analysis ◽

The Impact

<p>High acceptance of farmer to variety with have similar to IR64 type has led to almost all new varieties always be assessed based on their degree of similarity with IR64. Closely relations between elite upland varieties may contribute to the stagnation of yield potential and also give the impact un-durable of the resistance to pest and diseases. The aim of this study was to elucidate the morphology similarity kinship characters of elite rice varieties which were released from 1980 to 2011. The study was conducted in September–January 2012 in Indonesian Center for Rice Research field experiment. The material consisted of 46 rice varieties representing the released varieties from 1980–2011. The material was grown in 2 m × 5 m of plot size with 3 replications. Observations were conducted on qualitative and quantitative characters based on UPOV descriptors. Data were analyzed by Principal Component Analysis and Cluster Analysis. Principal component analysis revealed 40 components with 79,86% of cumulative variation that was used to determine the genetic relationship by cluster analysis. Based on the principal component analysis and cluster analysis, irrigated rice varieties released before and in 2000 and after 2008 (Inpari group) tend to be one big group and have a high phenotypic similarity. While the upland rice varieties tend to spread or were grouped in small groups. This high similarity suggested that the irrigated rice varieties have a close genetic relationship, which is derived from Ciherang or IR64.</p>

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A German version of the Intermittent Claudication Questionnaire (ICQ): cultural adaptation and validation

VASA ◽

10.1024/0301-1526/a000218 ◽

2012 ◽

Vol 41 (5) ◽

pp. 333-342 ◽

Cited By ~ 3

Author(s):

Kirchberger ◽

Finger ◽

Müller-Bühl

Keyword(s):

Principal Component Analysis ◽

Intermittent Claudication ◽

Completion Time ◽

Short Form ◽

Principal Component ◽

Component Analysis ◽

German Version ◽

Average Completion Time ◽

Sf 36 ◽

Related Quality

Background: The Intermittent Claudication Questionnaire (ICQ) is a short questionnaire for the assessment of health-related quality of life (HRQOL) in patients with intermittent claudication (IC). The objective of this study was to translate the ICQ into German and to investigate the psychometric properties of the German ICQ version in patients with IC. Patients and methods: The original English version was translated using a forward-backward method. The resulting German version was reviewed by the author of the original version and an experienced clinician. Finally, it was tested for clarity with 5 German patients with IC. A sample of 81 patients were administered the German ICQ. The sample consisted of 58.0 % male patients with a median age of 71 years and a median IC duration of 36 months. Test of feasibility included completeness of questionnaires, completion time, and ratings of clarity, length and relevance. Reliability was assessed through a retest in 13 patients at 14 days, and analysis of Cronbachs alpha for internal consistency. Construct validity was investigated using principal component analysis. Concurrent validity was assessed by correlating the ICQ scores with the Short Form 36 Health Survey (SF-36) as well as clinical measures. Results: The ICQ was completely filled in by 73 subjects (90.1 %) with an average completion time of 6.3 minutes. Cronbachs alpha coefficient reached 0.75. Intra-class correlation for test-retest reliability was r = 0.88. Principal component analysis resulted in a 3 factor solution. The first factor explained 51.5 of the total variation and all items had loadings of at least 0.65 on it. The ICQ was significantly associated with the SF-36 and treadmill-walking distances whereas no association was found for resting ABPI. Conclusions: The German version of the ICQ demonstrated good feasibility, satisfactory reliability and good validity. Responsiveness should be investigated in further validation studies.

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