Analysis of Trait Association and Genetic Diversity in Garden Pea (Pisum sativum L.) Genotypes under Middle Gangetic Plain Region of India

2021 ◽  
Author(s):  
S.K. Sanwal ◽  
Hari Kesh ◽  
Jyoti Devi ◽  
B. Singh
Keyword(s):  
Genetic Diversity ◽  
Regression Analysis ◽  
Principle Component Analysis ◽  
Crop Improvement ◽  
Component Analysis ◽  
Stepwise Multiple Regression ◽  
Gangetic Plain ◽  
Garden Pea ◽  
Principle Component ◽  
Pod Yield

Background: Garden pea is a cool season vegetable crop cultivated extensively throughout the world. Besides nutritional quality it also boosts soils through the fixation of atmospheric nitrogen. The most important task of pea breeding is to develop varieties with high and stable production, different maturity types and resistance against biotic and abiotic stresses. To fulfil these objectives, analysis of genetic diversity is the prerequisite to choose genetically diverse parents for a successful hybridization program and to know the source of genes for a particular trait within the available germplasm. Methods: A study was conducted at ICAR-Indian Institute of Vegetable Research (IIVR), Varanasi during 2015, using principle component analysis, correlation analysis and stepwise regression analysis approaches to assess the genetic diversity present in 160 pea genotypes for the identification of diverse parents for use in crop improvement. Result: Based on the phenotypic data, three superior genotypes VRPD-2, VRPR-15 and VRP-292 were identified on the basis of pod yield, number of pods per plant, ten pod weight, pod length and number of seeds per pod whereas three other genotypes VRPE-45-1, VRPE-55 and VRPE-36 were found early flowering. Principle Component Analysis revealed that first four principle components contributed to 85% of the total variation so these four were given due importance for further explanation. Stepwise multiple regression analysis revealed that number of pods per plant, ten pod weight and number of internode for first pod were the best predictors of pod yield per plant.

scholarly journals Assessment of Genetic Diversity in Nigella (Nigella sativa L.) Collections Using Principle Component Analysis

2019 ◽  
pp. 1-11
Author(s):  
S. P. Singh ◽  
Avinash Kumar ◽  
Banshidhar . ◽  
Sandeep Kumar Suman ◽  
Ashutosh Kumar ◽  
...  
Keyword(s):  
Genetic Diversity ◽  
Principle Component Analysis ◽  
Nigella Sativa ◽  
Block Design ◽  
Component Analysis ◽  
Rapid Improvement ◽  
Principle Component ◽  
Randomized Block Design ◽  
Eigen Value ◽  
Cluster Distance

Seventeen land races of Nigella along with one released variety (Rajendra Shyama) as a check; collected at farmer’s field from different parts of Bihar were evaluated in Randomized Block Design with three replications at Seed production Farm, TCA, Dholi, Bihar during Rabi 2015-16 to identify  diverse Nigella genotypes. Principle component analysis (PCA) showed that first three PCs had >1.00 Eigen value and accounted to 84.71% of total variation. Rotated component matrix for various traits revealed that PC1 was strongly associated with secondary branches/plant followed by yield/plant, length of fruit, fruit per plant, primary branches/plant, height of the plant, days to 50% flowering and grains/plant. The traits that mostly contributed to PC2 were grains/plant followed by height of the plant and width of fruit whereas, days to maturity followed by width of fruit, height of the plant, days to 50% flowering and length of fruit contributed mostly to the PC3.  The characters that contributed most to the PC4 were height of the plant, fruit/plant and length of fruit. Therefore, intensive selection procedures can be adopted to bring about rapid improvement of above mentioned traits. The k-mean of different clusters indicated that genotype falling in cluster III possess high values for all the traits under study indicating their potentiality as a parent in hybridization programmes for further improvement of Nigella. Highest inter-cluster distance was noted between cluster III and V indicating the genetic diversity among genotypes of these two clusters. Therefore, genotypes from these two clusters are recommended to use in hybridization programmes for further improvement.

ANALYSIS OF VOICE CUES IN RECOGNITION OF SARCASM

2019 ◽  
Vol 12 ◽  
Author(s):  
Basavaraj N Hiremath ◽  
Malini M Patilb
Keyword(s):  
Feature Extraction ◽  
Computer Program ◽  
Principle Component Analysis ◽  
Voice Recognition ◽  
Component Analysis ◽  
Recognition System ◽  
Principle Component ◽  
Whole Process ◽  
The Voice

The voice recognition system is about cognizing the signals, by feature extraction and identification of related parameters. The whole process is referred to as voice analytics. The paper aims at analysing and synthesizing the phonetics of voice using a computer program called “PRAAT”. The work carried out in the paper also supports the analysis of voice segmentation labelling, analyse the unique features of voice cues, understanding physics of voice, further the process is carried out to recognize sarcasm. Different unique features identified in the work are, intensity, pitch, formants related to read, speak, interactive and declarative sentences by using principle component analysis.

Four challenges for cognitive neuroscience and the cortico-hippocampal division of memory

2003 ◽  
Vol 26 (6) ◽  
pp. 681-682
Author(s):  
Harry Howard
Keyword(s):  
Independent Component Analysis ◽  
Recent Work ◽  
Cognitive Neuroscience ◽  
Principle Component Analysis ◽  
Pattern Analysis ◽  
Component Analysis ◽  
Independent Component ◽  
Principle Component ◽  
Complementary Methods ◽  
Current State

Jackendoff's criticisms of the current state of theorization in cognitive neuroscience are defused by recent work on the computational complementarity of the hippocampus and neocortex. Such considerations lead to a grounding of Jackendoff's processing model in the complementary methods of pattern analysis effected by independent component analysis (ICA) and principle component analysis (PCA).

Sign in / Sign up

Export Citation Format

Share Document