scholarly journals Principal component analyses in mungbean genotypes under summer season

2021 ◽  
Vol 17 (2) ◽  
pp. 287-292
Author(s):  
Priya Tiwari ◽  
Stuti Sharma
Keyword(s):  
Principal Component ◽  
Complex Trait ◽  
Yield Component ◽  
Plant Seed ◽  
Eigen Value ◽  
Eigen Values ◽  
Component Traits ◽  
Total Variability ◽  
Yield Component Traits ◽  
Number Of Seeds

Yield is a complex trait subjective to several components and environmental factors. Therefore, it becomes necessary to apply such technique which can identify and prioritize the key traits to lessen the number of traits for valuable selection and genetic gain. Principal component analysis is primarily a renowned data reduction technique which identifies the least number of components and explain maximum variability, it also rank genotypes on the basis of PC scores. PCA was calculated using Ingebriston and Lyon (1985) method. In present study, PCA performed for phenological and yield component traits presented that out of thirteen, only five principal components (PCs) exhibited more than 1.00 eigen value, and showed about 80.28 per cent of total variability among the traits. Scree plot explained the percentage of variance associated with each principal component obtained by illustrating a graph between eigen values and principal component numbers. PC1 showed 26.12 per cent variability with eigen value 3.40. Graph depicted that the maximum variation was observed in PC1 in contrast to other four PCs. The PC1 was further associated with the phenological and yield attributing traits viz., number of nodes per plant, number of pod cluster per plant, number of pod per plant. PC2 exhibited positive effect for harvest index. The PC3 was more related to yield related traits i.e., number of seeds per pod, number of seeds per plant and biological yield per plant, whereas PC4 was more loaded with phenological traits. PC5 was further related to yield and yield contributing traits i.e. number of primary branches per plant, seed yield per plant and 100 seed weight. A high value of PC score of a particular genotype in a particular PC denotes high value for those variables falling under that specific principal component. Pusa Vishal found in PC 2, in PC 3, PC 4 and PC 5, can be considered as an ideal breeding material for selection and for further deployment in defined breeding programme.

scholarly journals Genetic Diversity of Butterfly Pea [Clitoria ternatea] from Indonesia Based on Flower and Yield Component Traits in Two Land Conditions

2020 ◽  
Vol 16 (1) ◽  
pp. 1
Author(s):  
Trixie A. Ulimaz ◽  
Debby Ustari ◽  
Virda Aziza ◽  
Tarkus Suganda ◽  
Vergel Concibido ◽  
...  
Keyword(s):  
Genetic Diversity ◽  
Principal Component ◽  
Yield Component ◽  
Breeding Program ◽  
Clitoria Ternatea ◽  
Fresh Flower ◽  
Underutilized Crop ◽  
Component Traits ◽  
Butterfly Pea ◽  
Yield Component Traits

<p>Genetic diversity among the butterfly pea genotypes is important information to support breeding program of this underutilized crop. The important characters to be targeted in the breeding program of this crop included yield and yield components of flowers that are strongly affected by the environment and have not been previously reported. This study aimed to determine the genetic diversity of butterfly pea (Clitoria ternatea L.) from Indonesia tested in two land conditions, namely dryland and former paddy fields, based on flower character and yield component traits. The results showed that butterfly pea accessions were divided into two main clusters with dissimilarity coefficient of 0.01–3.99 indicating wide genetic diversity across  accessions. The Mantel correlation showed that the genetic distance among accessions studied were not significantly correlated (r = 0.044, P = 0.8709). Based on principal component analysis (PCA), the eigenvalue ranged from 1.69 to 3.34 with a cumulative contribution of 72.64%. The traits that influenced genetic diversity in this study were flower length, weight of one fresh flower, total weight of pods, and weight of 100 seeds. The results of this study should be useful to support future butterfly pea breeding program.</p>

scholarly journals Selection of parental lines among round fruited brinjal for hybridization aiming at export promotion

2017 ◽  
Vol 9 (1) ◽  
pp. 451-455
Author(s):  
Varun Durwas Shende ◽  
Tania Seth ◽  
Subhra Mukherjee ◽  
Arup Chattopadhyay
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Yield Component ◽  
Fruit Yield ◽  
Component Traits ◽  
Parental Lines ◽  
Yield Component Traits ◽  
Marketable Fruit ◽  
Selection Of

Selection of parental lines for considering export trade in hybridization programme is a new approach in brinjal breeding. Eight quantitative characters were taken to estimate genetic variation and relationships among twenty seven genotypes of round fruited brinjal, and to identify potential donors for the development of recombinants suitable for export. Analysis of variation revealed considerable level of variability among the genotypes. High broad sense heritability (˃80 %) and genetic advance as per cent of mean (˃20 %) were observed for the maximum number of traits under study. Among the yield components, only number of marketable fruits per plant showed positive and significant correlation (r = 0.771 and 0.725 at genotypic and phenotypic level, respectively) with marketable fruit yield per plant. However, number of marketable fruits per plant (1.24) followed by average fruit weight (0.834) ex-hibited maximum positive direct effects on marketable fruit yield per plant suggesting to give emphasis on these traits while imposing selection for amenability in fruit yield of round fruited brinjal. Principal component analysis showed the amount of variation by principal components 1 to 4 viz., 26.75, 49.98, 69.81 and 84.28 %, respectively. Divergence analysis based on various yield component traits grouped 27 brinjal genotypes into nine main clusters. Dendrogram based on hierarchal clustering grouped genotypes based on their yield component traits rather than their geographic origin. Based on averages and principal component analysis, six genotypes (BCB-30, Deshi Makra, Gujrat Brinjal Round, 09/BRBWRes-3, BCB-10, 10/BRRVar-2) appeared to be promising donors for use in export oriented breeding programme.

scholarly journals Application of machine learning and genetic optimization algorithms for modeling and optimizing soybean yield using its component traits

PLoS ONE ◽  
2021 ◽  
Vol 16 (4) ◽  
pp. e0250665
Author(s):  
Mohsen Yoosefzadeh-Najafabadi ◽  
Dan Tulpan ◽  
Milad Eskandari
Keyword(s):  
Machine Learning ◽  
Yield Components ◽  
Soybean Seed ◽  
Complex Trait ◽  
Yield Component ◽  
Yield Potential ◽  
Coefficient Of Determination ◽  
Soybean Yield ◽  
Component Traits ◽  
Yield Component Traits

Improving genetic yield potential in major food grade crops such as soybean (Glycine max L.) is the most sustainable way to address the growing global food demand and its security concerns. Yield is a complex trait and reliant on various related variables called yield components. In this study, the five most important yield component traits in soybean were measured using a panel of 250 genotypes grown in four environments. These traits were the number of nodes per plant (NP), number of non-reproductive nodes per plant (NRNP), number of reproductive nodes per plant (RNP), number of pods per plant (PP), and the ratio of number of pods to number of nodes per plant (P/N). These data were used for predicting the total soybean seed yield using the Multilayer Perceptron (MLP), Radial Basis Function (RBF), and Random Forest (RF), machine learning (ML) algorithms, individually and collectively through an ensemble method based on bagging strategy (E-B). The RBF algorithm with highest Coefficient of Determination (R2) value of 0.81 and the lowest Mean Absolute Errors (MAE) and Root Mean Square Error (RMSE) values of 148.61 kg.ha-1, and 185.31 kg.ha-1, respectively, was the most accurate algorithm and, therefore, selected as the metaClassifier for the E-B algorithm. Using the E-B algorithm, we were able to increase the prediction accuracy by improving the values of R2, MAE, and RMSE by 0.1, 0.24 kg.ha-1, and 0.96 kg.ha-1, respectively. Furthermore, for the first time in this study, we allied the E-B with the genetic algorithm (GA) to model the optimum values of yield components in an ideotype genotype in which the yield is maximized. The results revealed a better understanding of the relationships between soybean yield and its components, which can be used for selecting parental lines and designing promising crosses for developing cultivars with improved genetic yield potential.

scholarly journals Evaluating Genotype × Environment Interactions of Yield Traits and Adaptability in Rice Cultivars Grown under Temperate, Subtropical and Tropical Environments

Agriculture ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 558
Author(s):  
Xing Huang ◽  
Su Jang ◽  
Backki Kim ◽  
Zhongze Piao ◽  
Edilberto Redona ◽  
...  
Keyword(s):  
Yield Component ◽  
Yield Potential ◽  
High Yield ◽  
Environment Interaction ◽  
Accurate Identification ◽  
Rice Varieties ◽  
Panicle Number ◽  
Tropical Conditions ◽  
Component Traits ◽  
Yield Component Traits

Rice yield is a complex trait that is strongly affected by environment and genotype × environment interaction (GEI) effects. Consideration of GEI in diverse environments facilitates the accurate identification of optimal genotypes with high yield performance, which are adaptable to specific or diverse environments. In this study, multiple environment trials were conducted to evaluate grain yield (GY) and four yield-component traits: panicle length, panicle number, spikelet number per panicle, and thousand-grain weight. Eighty-nine rice varieties were cultivated in temperate, subtropical, and tropical regions for two years. The effects of both GEI (12.4–19.6%) and environment (23.6–69.6%) significantly contributed to the variation of all yield-component traits. In addition, 37.1% of GY variation was explained by GEI, indicating that GY performance was strongly affected by the different environmental conditions. GY performance and genotype stability were evaluated using simultaneous selection indexing, and 19 desirable genotypes were identified with high productivity and broad adaptability across temperate, subtropical, and tropical conditions. These optimal genotypes could be recommended for cultivation and as elite parents for rice breeding programs to improve yield potential and general adaptability to climates.

scholarly journals Genetic imprints of domestication for disease resistance, oil quality, and yield component traits in groundnut (Arachis hypogaea L.)

2018 ◽  
Vol 294 (2) ◽  
pp. 365-378 ◽  
Author(s):  
Pawan Khera ◽  
Manish K. Pandey ◽  
Nalini Mallikarjuna ◽  
Manda Sriswathi ◽  
Manish Roorkiwal ◽  
...  
Keyword(s):  
Disease Resistance ◽  
Arachis Hypogaea ◽  
Oil Quality ◽  
Yield Component ◽  
Arachis Hypogaea L ◽  
Component Traits ◽  
Yield Component Traits

scholarly journals Evaluation of Yield Component Traits of Honeybee-Pollinated (Apis mellifera L.)Rapeseed Canola (Brassica napus L.)

2010 ◽  
Vol 70 (2) ◽  
pp. 309-314 ◽  
Author(s):  
Ximena Araneda Durán ◽  
Rodrigo Breve Ulloa ◽  
José Aguilera Carrillo ◽  
Jorge Lavín Contreras ◽  
Marcelo Toneatti Bastidas
Keyword(s):  
Apis Mellifera ◽  
Brassica Napus ◽  
Yield Component ◽  
Brassica Napus L ◽  
Component Traits ◽  
Yield Component Traits
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