<div><strong>Background:</strong> Dolichos bean occupies a unique position among the legume vegetables of Indian origin for its high nutritive value and wider climatic adaptability. Despite its wide genetic diversity, no much effort has been undertaken towards genetic improvement of this vegetable crop. Knowledge on genetic variability is an essential pre-requisite as hybrid between two diverse parental lines generates broad spectrum of variability in segregating population. The current study aims to assess the genetic diversity in dolichos genotypes to make an effective selection for yield improvement.<br />
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<strong>Methods:</strong> Twenty genotypes collected from different regions were evaluated during year 2016-17 and 2017-18. Data on twelve quantitative traits was analysed using principal component analysis and single linkage cluster analysis for estimation of genetic diversity.<br />
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<strong>Result: </strong>Principal component analysis revealed that first five principal components possessed Eigen value &gt; 1, cumulatively contributed &gt; 82.53% of total variability. The characters positively contributing towards PC-I to PC-V may be considered for dolichos improvement programme as they are major traits involved in genetic variation of pod yield. All genotypes were grouped into three clusters showing non parallelism between geographic and genetic diversity. Cluster-I was best for earliness and number of cluster/plant. Cluster-II for vine length, per cent fruit set, pod length, pod width, pod weight and number of seed /pod, cluster III for number of pods/cluster and pod yield /plant. Selection of parent genotypes from divergent cluster and component having more than one positive trait of interest for hybridization is likely to give better progenies for development of high yielding varieties in Dolichos bean.</div>


<div>Genetic Diversity Assessment in Dolichos Bean (<em>Lablab purpureus</em> L.) Based on Principal Component Analysis and Single Linkage Cluster Analysis</div>


<div><strong>Background: </strong>The seed yield of chickpea can be improved by selection of superior genotypes on the basis of different yield and yield component traits. These genotypes exclusively utilize in breeding programs. Yield is a complex trait which is affected by several factors, hence, a well-known technique known as principal component analysis was used to identify and minimize the number of traits for effective selection. To obtain efficient recombinants, the identified component traits need to be combined from diverse parents through recombination breeding followed by selection of transgressive segregants. Hence, the present study is envisaged to measure the genetic diversity among genotypes of chickpea.<br />
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<strong>Methods: </strong>The experimental material comprised of 40 chickpea genotypes evaluated in randomized block design with three replications. The experimental unit was four rows per plot with 4 m length and spacing between row to row and plant to plant maintained as 30 x 10 cm. NPK (20:40:00) fertilizers was applied as basal doses. The data were recorded for each genotype on nine quantitative traits as per standard methods. Descriptive statistics and PCA analysis was performed by using the statistical package SPSS 16.0 version and cluster analysis was done using the Wards method of hierarchical clustering technique.<br />
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<strong>Result:</strong> Out of nine PCs only three PCs exhibited more than 1.0 Eigen value and showed about 73.4% variability. PC1 contributed 28.6% of the total variation and correlated with days to flowering, days to maturity, plant height, first pod height, seeds per pod and number of pods per plant while PC2 explained 21.00% of the total variation and dominated by plant height, first pod height and seed yield. PC3 explained an additional 13.00% of the total variation and dominated by primary branches per plant. Genotype commonly found in more PC, were BG 4016, IPCB 2015-165, IPC 2011-247, GNG2459 and RKG 19-4. Hierarchical clustering technique grouped 40 genotypes into two main clusters (A and B) and nine sub clusters. The present investigation depicted that the chickpea germplasm displayed considerable genetic diversity for most of the traits under consideration.&nbsp;</div>


<div>Principal Component and Genetic Diversity Analysis for Seed Yield and Its Related Components in the Genotypes of Chickpea (<em>Cicer arietinum</em> L.)</div>


<div><strong>Background:</strong> A complex quantitative characteristic yield is heavily impacted by the environment. The productivity of groundnut can be increased less effectively through direct selection for grain yield. The current study aimed to study the variation among diverse groundnut genotypes.<br />
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<strong>Methods:</strong> Phenotypic data was collected on seven quantitative and six qualitative characters for 24 genotypes under study carried out in randomised block design (RBD). GRAPES software has been used for analysis.<br />
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<strong>Result:</strong> Analysis of variance revealed significant differences among the genotypes for all the characters indicating the prevalence of ample genetic variability within the genotypes. Significant positive associations were observed for primary branches, secondary branches, 100-pod weight, shelling per cent, protein and zinc content. Path analysis revealed that plant height, primary branches per plant, hundred pod weight, shelling percent, protein content and zinc content are the most important characters which could be used as selection criteria for effective improvement of pod yield. Using GRAPES software, Fourteen Principal components are extracted based on mean values of which the first five PCs showed 73.24% variation with eigen values more than 1. Biplot constructed by Principal component analysis revealed Hundred pod weight and hundred kernel weight as important traits for study.</div>


<p>Principal Component Analysis and Path Coefficient Analysis for Groundnut Yield and Seed Quality Attributes (<em>Arachis hypogaea</em> L.)</p>


<div style="text-align:justify"><strong>Background: </strong>Seventy breeding lines of mung bean were evaluated for 20 different characters and mean values were worked for genetic diversity by Mahalanobis D<sup>2</sup> statistic.<br />
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<strong>Methods:</strong> The experiments included 70 mung bean breeding lines which were collected from Plant Breeding Unit, Agricultural Research Station, Badnapur. They were grown during <em>Kharif </em>2016 at experimental research farm Badnapur of Vasantrao Naik Marathwada Krishi Vidyapeeth, Parbhani. The data were recorded for 20 different characters on 10 randomly selected plants. The statistical analysis were done by Mahalanobis D2 statistics.<br />
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<strong>Result:</strong> The results of D<sup>2</sup> analysis indicated the presence of considerable genetic divergence among these breeding lines. In the present study, inter-cluster distances were worked out considering 20 characters and these distances ranged from 240.96 (between cluster V and cluster VII) to 1080.72 (between cluster V and cluster VIII). The inter-specific derivatives were grouped into eight clusters. The maximum inter-cluster distance was between cluster V and cluster VIII. The maximum inter-cluster distance was between cluster V and cluster VIII (1080.72), followed by cluster II and cluster III (932.25), cluster IV and cluster VIII (910.11), cluster VII and cluster VIII (738.30), cluster I and cluster VIII (732.61), cluster VI and cluster V (660.49) and cluster II and cluster VI (494.93). This suggested that there is wide genetic diversity between these clusters. Based on these studies, crosses can be made between breeding lines of these clusters to obtain desirable results either in transgressive breeding or in heterosis breeding. Cluster VIII and cluster IV showed high mean values for most of the yield contributing traits like100-seed weight, shelling %, harvest index, pod length, primary branches per plant, days to 50% flowering, days to maturity, leaf width and days to shattering. So the lines from cluster IV and cluster VIII can be used for mung bean yield improvement programme.</div>


<div>Multivariate Analysis using D<sup>2</sup> and Principal Component Analysis in Mung bean [<em>Vigna radiata</em> (L.) Wilczek] for Study of Genetic Diversity</div>


<div><strong>Background: </strong>Principal component analysis and Finlay-Wilkinson stability analysis were carried out at research farm of ICAR-Indian Institute of Vegetable Research, Varanasi to identify diverse french bean genotypes for green pod yield and suitable genotypes for stable yield and yield related parameters.<br />
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<strong>Methods:</strong> All the 24 genotypes were laid out in randomized block design with two replications during winter, 2017 and 2018. Principal component analysis and stability analysis was done to identify the diverse and stable genotypes.<br />
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<strong>Result:</strong> Eight principal components were observed and the maximum variability was concentrated in the first three principal components PC1, PC2 and PC3 which contributed to 68.61% variance. Cluster analysis from principal component scores formed three clusters with a maximum of seventeen genotypes in cluster I followed by six genotypes in cluster II and one genotype in cluster III. High heritability was observed for 10 pod weight, number of pods per cluster and number of seeds per pod and moderate heritability was observed for yield per plant. Finlay-Wilkinson stability analysis identified the &nbsp;stable &nbsp;genotypes <em>viz</em>., FMGCV 1378, FMGCV 0958, Arka Suvidha, Valentino, Banoa and VRFBB-14-2 for green pod yield per plant, Cartagenta for pod length (cm) and Paulista,&nbsp;Slender Pack, Arka Suvidha, Valentino, FMGCV 0958, Banoa, FORC 6V 1136, VRFBB-14-1, VRFBB-14-2 for number of pods per plant.</div>


<div>Principal Component Analysis and Stability of Genotypes in French Bean (<em>Phaseolus vulgaris</em> L.)</div>


<div style="text-align: justify;"><strong>Background: </strong>Chickpea (<em>Cicer arietinum</em> L.) is the important Rabi season food legume crop ranked third after common bean and field pea. The degree and extent of trait variability, genetic relationship and genetic diversity in promising chickpea genotypes should be known for possible yield improvement. Realizing the importance of genetic relationships and genetic diversity in key economic traits, the present investigation envisaged to measure the genetic relationships in the yield component traits to make effective selection for yield improvement in chickpea breeding programme.<br /><strong>Methods:</strong> The experimental material comprised of 60 chickpea genotypes (both <em>Desi</em> and <em>Kabuli</em> types), grown in RBD with three replications at Pulses Section of Department of Genetics and Plant Breeding, CCSHAU, Hisar during <em>Rabi</em> 2014-15. The data on 11 quantitative traits was analysed for ANOVA, correlation coefficient, path coefficient, regression and principle component analysis.<br />Result: The present study has led the understanding of many inter-related traits involved in the genetic variation of chickpea seed yield. This would certainly provide guidelines for selection of parents as well as effective selection of promising chickpea genotypes, and also have paramount importance in formulating plant model for selection of segregating generations in chickpea breeding programmes for development of high yielding varieties.</div>

<div style="text-align: justify;">Genetic Relationships and Principal Component Analysis in Elite Chickpea (<em>Cicer arietinum</em> L.) Genotypes for Seed Yield and Its Component Traits</div>

<div><strong>Background: </strong>Principal component analysis is a multivariate technique that transforms a number of possibly correlated variables into smaller number of uncorrelated variables leads to dimension reduction. Various economic traits of layer chicken are used for selection of parent birds which need to adjust for selection strategies to augment the genetic improvement.&nbsp;<br />
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<strong>Methods:</strong> The data was collected from 2020-2021 which includes weekly body weight (g) from 0 day to 20<sup>th</sup> week and 40<sup>th</sup> week, Body weight (g) at first egg production, age at sexual maturity (days), weight of first egg (g), egg numbers at 40<sup>th</sup> week, egg weight at 40<sup>th</sup> week (g), egg numbers at 52<sup>nd</sup> week, egg weight at 52<sup>nd </sup>week (g). The least squares mean was estimated considering three different genetic groups of layer chicken (N=450, 150 each group). The main focus of this study was to identify the principal components for economic traits in layer chicken. Further varimax rotation method was applied for the transformation of components to approximate simple structure.<br />
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<strong>Result:</strong> The genetic group Desi cross 1 performed better than Desi cross 2 followed by Punjab Red layer chicken. A total of three principal components were obtained which explained a total variance of 75.524%. Principal component 1 had high loads on body weight 10<sup>th</sup> week to 20<sup>th</sup> week (BW 10-BW 20) and BWSM and had a variance of 38.892%. Similarly, PC2 and PC3 explained variance of 27.072% and 9.560% respectively and had high loads on 1 week body weight to 9-week body weight (BW1-BW9) and age at sexual maturity, 40-week egg production, 52-week egg production respectively. From this study it was included that PCA can be used for selecting the economic traits for breeding purpose of layer chicken.</div>


<p>Principal Component Analysis of Different Economic Traits in Layer Chicken</p>


<div><strong>Background: </strong>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).<br />
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<strong>Methods: </strong>The experiment was laid out in a randomized block design with three replications during <em>kharif</em> 2018 and 2019 at the experimental field of Agricultural Research Station, Navgaon (Alwar) under rainfed condition.&nbsp;<br />
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<strong>Result: </strong>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 <em>i.e</em>., 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.</div>


<p>Genetic Diversity and Principal Component Analysis in Mungbean [<em>Vigna radiata</em> (L.) Wilczek] under Rainfed Condition</p>


<div style="text-align: justify;">Principal component analysis (PCA) was employed on 12 biometric traits of Rampur-Bushair sheep of Himachal Pradesh. Morphological and biometrical observations were recorded on 162 young and 566 adult animals. Multivariate statistics and principal component analysis revealed that body measurements except for peripheral traits were mostly positively and significantly correlated. The correlation among conformation traits ranged from -0.08 to 0.79 and &ldquo;0.18 to 0.71 in young and adult sheep respectively. Three and four factors were extracted in young and adult sheep respectively, which accounted for 57% and 61% of variation. The principal component extracted contributed effectively to explain general body conformation. The regression analysis suggested that use of principal component was more appropriate than the use of original correlated variable in estimating body weights. Therefore, factor extracted could be helpful in breeding programme with sufficient reduction in the number of biometric traits to be recorded to explain the body conformation.&nbsp;</div>

Morphological structuring using principal component analysis of Rampur-Bushair sheep under transhumance production in western Himalayan region, India&nbsp;

<div><strong>Background:</strong> The principal component analysis is applied to identify minimum number of combined variables that account for maximum portion of the variance existing in all variables studied. Chitarangi is a lesser known carpet type wool sheep distributed in Fazilka and Muktsar districts of Punjab, Sri Ganganagar district of Rajasthan and the adjoining areas. The information on body biometry is a prerequisite to characterize the lesser known sheep population available in the country. Hence, it is important to describe the body conformation by recording minimum number of biometric traits.<br />
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<strong>Methods:</strong> Body biometry traits of Chitarangi sheep, a lesser known carpet quality wool producing sheep population were studied using Principal Component Analysis. The traits studied were body length (BL), height at wither (HW), chest girth (CG), paunch girth (PG), ear length (EL), face length (FL), face width (FW), tail length (TL) and adult body weight (BW). The data were collected on 297 ewes in the breeding tract of Chitarangi sheep. The descriptive statistics were determined for all the traits. The phenotypic correlations between different body biometric traits were estimated using partial correlations. Principal components were estimated using correlation matrix. Principal component analysis (PCA), a multivariate approach, is used when the recorded traits are highly correlated. Rotation of principal components was through the transformation of the components to approximate a simple structure. Factor analysis using oblique (promax) rotation was used. All the analysis was carried out using the SPSS statistical package.<br />
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<strong>Result:</strong> The averages for body weight and biometry traits confirmed large size of Chitarangi animals. Most of the phenotypic correlations amongst the studied traits were positive and significant (p&lt;0.01). The three components extracted from nine principal components accounted for 69.06% of the total variance. The first component, which described body size of ewes, accounted for 43.68% of the total variation with high loading for BW, CG, PG, HW, BL and FL. The components two and three explained 13.54 and 11.83% of total variance, respectively. The communalities ranged from 0.490 (FL) to 0.888 (PG). The lower communalities for face length indicated lower contribution of the trait to explain the total variation than others. The study indicates that principal components provided a means of reduction in number of biometric traits to explain body confirmation of adult female Chitarangi sheep.</div>


Genetic Diversity Assessment in Dolichos Bean (Lablab purpureus L.) Based on Principal Component Analysis and Single Linkage Cluster Analysis

Principal Component and Genetic Diversity Analysis for Seed Yield and Its Related Components in the Genotypes of Chickpea (Cicer arietinum L.)

Principal Component Analysis and Path Coefficient Analysis for Groundnut Yield and Seed Quality Attributes (Arachis hypogaea L.)

Multivariate Analysis using D² and Principal Component Analysis in Mung bean [Vigna radiata (L.) Wilczek] for Study of Genetic Diversity

Principal Component Analysis and Stability of Genotypes in French Bean (Phaseolus vulgaris L.)

Genetic Relationships and Principal Component Analysis in Elite Chickpea (Cicer arietinum L.) Genotypes for Seed Yield and Its Component Traits

Principal Component Analysis of Different Economic Traits in Layer Chicken

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

Morphological structuring using principal component analysis of Rampur-Bushair sheep under transhumance production in western Himalayan region, India

Study of Body Conformation of Carpet Wool Type Chitarangi Sheep of India using Principal Component Analysis