scholarly journals Study of principal component analyses for pod traits in soybean

2021 ◽  
Vol 17 (2) ◽  
pp. 341-349
Author(s):  
Shivani Uikey ◽  
Stuti Sharma ◽  
M.K. Shrivastava ◽  
Pawan K. Amrate
Keyword(s):  
Principal Component ◽  
Large Set ◽  
Eigen Value ◽  
Total Variability ◽  
Small Set ◽  
Biological Yield ◽  
Advanced Line ◽  
Ideotype Breeding ◽  
Yield Factor ◽  
Positive Effect

Yield being a complex entity influenced by several components and environments. PCA is a well-known method of dimension reduction that can be used to reduce a large set of variables to a small set that still contains most of the information in the large set (Massay, 1965 and Jolliffie, 1986). In present study, PCA preformed for pod and yield traits revealed that out of fourteen, only five principal components (PCs) exhibited more than 1.0 eigen value and showed about 70.44% total variability among the traits. PC1 showed 23.83% variability with eigen value 3.33 indicating the maximum variation in comparison to other four PCs. The PC1 was more related to traits viz., days to 50% flowering, total number of pods per plant, number of seeds per plant, biological yield per plant and seed yield per plant. Thus, PC1 allowed for simultaneous selection of yield related traits and it can be regarded as yield factor. PC2 exhibited positive effect for days to maturity, number of primary branches per plant and number of nodes per plant, The PC3 was more related to number of two seeded pods per plant, 100 seed weight and harvest index traits, whereas PC4 was more loaded with three seeded pods per plant. PC5 was more related to plant height and number of one seeded pods per plant. A high value of PC score of a particular advanced line in a particular PC denotes high value for those variables. Genotypes namely KS 103, JS 20-15, PS 1423, Cat 1957, Cat 1958, JS 20-06 and JS 20-66 can be considered an ideotype breeding material for selection and for further utilization in precise breeding programme.

scholarly journals Principal Component Analysis for Assessment of Genetic Diversity in Rainfed Shallow Lowland Rice (Oryza sativa L)

2020 ◽  
pp. 256-261
Author(s):  
Prince Raj ◽  
Anand Kumar ◽  
. Satyendra ◽  
S. P. Singh ◽  
Mankesh Kumar ◽  
...  
Keyword(s):  
Total Variation ◽  
Seed Weight ◽  
Harvest Index ◽  
Quantitative Traits ◽  
Principal Component ◽  
Panicle Length ◽  
Total Carbohydrate ◽  
Total Variability ◽  
Biological Yield ◽  
Selection Of

The genetic diversity was estimated using seventy two genotypes of rice in a randomized block design with three replications at the rice research farm of Bihar Agricultural University, Sabour (Bhagalpur) during Kharif, 2019-20 to determine the contribution of fifteen quantitative traits to the total variability in rice using Principal component analysis. In the present investigation PCA was performed for fifteen quantitative traits of rice. All the 3PCs exhibited more than 1.0 Eigen value and showed about 95.00% variability. Therefore, these PCs were given due important for the further explanation. The PC1 showed 77.28 per cent variation of total variation followed by second to third components which accounted 15.65 and 2.05 per cent of total variation presented among the genotypes, respectively. PC1 contributed 77.28% of the total variation and correlated with total carbohydrate, generation of H2O2, days to 50% flowering, biological yield, number of fertile grains per panicle, panicle length and flag leaf area while PC2 explained an additional 15.65% of the total variation and dominated by total carbohydrate, days to 50% flowering, harvest index, biological yield, total number of spikelet’s and plant height. PC III accounted 2.05 per cent of the total variability and correlated with the traits like days to 50% flowering, biological yield, total number of spikelet’s, 1000-seed weight, plant height, harvest index, generation of H2O2 and panicle length had maximum positive contribution Since, a total of 95.00% of the total variation was contributed by PC1 and PC2, therefore, these two principal components can be allowed for simultaneous selection of yield contributing traits in desi chickpea. Genotype usually found in more PC, were CR3933-13-2-1-4-1-2-1, TTB1011-14-171-2-2-1-2-1, TTB1032-45-937-2-3-3-1-1, (Santepheap3/IR49830-7/RajendraMahsuri)-1-3-1, (BR11/IR8041OB)-2-1-1, (RajendraMhasuri/CN1039)-4-2-1, TTB1011-14-243-1-2-2-2-1, TTB1032-45-937-2-3-3-1-1, CR4138-3-1-1, CR4139-9-2-1, CR4139-9-2- and CR4128-9-1-1. Genotypes fall in a common principal component were observed to be the most important factor for seed yield. These genotypes may further be utilized in breeding programmes for improving seed yield and these genotypes can be considered an ideotype breeding material for selection of traits viz. more total number of seed per plant and 100-seed weight further utilization in precise breeding programme.

scholarly journals Principal component analysis of JNPT lines of rice for the important traits responsible for yield and quality

2021 ◽  
Vol 81 (01) ◽  
pp. 127-131
Author(s):  
Shivangi Rahangdale ◽  
Yogendra Singh ◽  
P. K. Upadhyay ◽  
G. K. Koutu
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Spikelet Fertility ◽  
Quality Traits ◽  
Plant Type ◽  
Yield And Quality ◽  
Eigen Value ◽  
Total Variability ◽  
Panicle Weight

In present study, 67 JNPT (Jawahar New Plant Type) lines were evaluated for 28 morphological and quality traits planted in RCBD with three replications. Principal Component Analysis (PCA) revealed that out of 28, only eight PCs exhibited more than 1.0 eigen value and showed about 81.84% total variability. For selecting the high yielding genotypes in rice, the characters viz., spikelet density, spikelet fertility, number of tillers plant–1and panicle weight plant–1 may be considered. On the basis of high PC score ten most prominent lines namely JNPT-1059-9, JNPT-1059- 10, JNPT1062-1, JNPT-1062-2, JNPT-1064-9, JNPT-1065-1, JNPT-1065-2, JNPT1065-3, JNPT-1066-52 and JNPT-1068- 65 were identified for yield and quality traits.

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.

Genetic Evaluation and Bi-plot Analysis in Okra Hybrids for Yield, Quality Components and YVMV Resistance

2018 ◽  
Vol 5 (04) ◽  
Author(s):  
SS SOLANKEY ◽  
ANIL K SINGH
Keyword(s):  
Genetic Divergence ◽  
Principal Component ◽  
F1 Hybrids ◽  
Genetic Advance ◽  
Economic Traits ◽  
Cross Combination ◽  
Plot Analysis ◽  
Eigen Value ◽  
Coefficient Of Variability ◽  
Yield Quality

Fifty one okra F1 hybrids (using 17 lines as female and 3 testers as male parent) were evaluated in RCBD design during two different consecutive seasons (summer and rainy). Phenotypic coefficient of variability (PCV) was higher than genotypic coefficient of variability (GCV) for all studied character exhibiting environmental effects on the expression of characters. Heritability (h2b) along with genetic advance per cent of mean was found highest for character YVMV (86.95% and 150.61%). All the 51 okra hybrids were grouped into 4 distinct clusters in which Cluster II was the largest cluster having 28 F1s (54.90% of total F1s) followed by Cluster I with 14 F1s (27.45% of total F1s). Out of the major 6 PCs, 4 principal components (PC1, PC2, PC3 and PC4) accounted with proportionate values of 22.61, 17.22, 11.87 and 10.63%, respectively and contributed 62.33 % of the cumulative variation having Eigen value more than one. Moreover, based on PCs and genetic divergence in Cluster I and Cluster IV for plant height, YVMV and number of fruit per plant is important to identify the best cross combination (Arka Abhay × Arka Anamika) in okra. Therefore, the best cross combinations for improvement in various economic traits can be recommended on the basis of genetic divergence and principal component analysis in okra.

Modelling spatial reasoning systems with shape algebras and formal logic

1997 ◽  
Vol 11 (4) ◽  
pp. 273-285 ◽  
Author(s):  
Scott C. Chase
Keyword(s):  
Spatial Reasoning ◽  
Predicate Logic ◽  
Spatial Relations ◽  
Large Set ◽  
First Order ◽  
Reasoning Systems ◽  
Geometric Objects ◽  
Small Set ◽  
Definition Of ◽  
High Level

AbstractThe combination of the paradigms of shape algebras and predicate logic representations, used in a new method for describing designs, is presented. First-order predicate logic provides a natural, intuitive way of representing shapes and spatial relations in the development of complete computer systems for reasoning about designs. Shape algebraic formalisms have advantages over more traditional representations of geometric objects. Here we illustrate the definition of a large set of high-level design relations from a small set of simple structures and spatial relations, with examples from the domains of geographic information systems and architecture.

scholarly journals Feed efficiency of lactating Holstein cows is less reproducible when changing dietary starch and fibre concentrations than within diet over subsequent lactation stages

2021 ◽  
Author(s):  
Amélie Fischer ◽  
Philippe Gasnier ◽  
Philippe Faverdin
Keyword(s):  
Principal Components ◽  
Feed Efficiency ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Holstein Cows ◽  
Diet Change ◽  
Total Variability ◽  
The One ◽  
The Individual ◽  
Starch Diet

ABSTRACTBackgroundImproving feed efficiency has become a common target for dairy farmers to meet the requirement of producing more milk with fewer resources. To improve feed efficiency, a prerequisite is to ensure that the cows identified as most or least efficient will remain as such, independently of diet composition. Therefore, the current research analysed the ability of lactating dairy cows to maintain their feed efficiency while changing the energy density of the diet by changing its concentration in starch and fibre. A total of 60 lactating Holstein cows, including 33 primiparous cows, were first fed a high starch diet (diet E+P+), then switched over to a low starch diet (diet E−P−). Near infra-red (NIR) spectroscopy was performed on each individual feed ingredient, diet and individual refusals to check for sorting behaviour. A principal component analysis (PCA) was performed to analyse if the variability in NIR spectra of the refusals was explained by the differences in feed efficiency.ResultsThe error of reproducibility of feed efficiency across diets was 2.95 MJ/d. This error was significantly larger than the errors of repeatability estimated within diet over two subsequent lactation stages, which were 2.01 MJ/d within diet E−P− and 2.40 MJ/d within diet E+P+. The coefficient of correlation of concordance (CCC) was 0.64 between feed efficiency estimated within diet E+P+ and feed efficiency estimated within diet E−P−. This CCC was smaller than the one observed for feed efficiency estimated within diet between two subsequent lactation stages (CCC = 0.72 within diet E+P+ and 0.85 within diet E−P−). The first two principal components of the PCA explained 90% of the total variability of the NIR spectra of the individual refusals. Feed efficiency was poorly correlated to those principal components, which suggests that feed sorting behaviour did not explain differences in feed efficiency.ConclusionsFeed efficiency was significantly less reproducible across diets than repeatable within the same diet over subsequent lactation stages, but cow’s ranking for feed efficiency was not significantly affected by diet change. The differences in sorting behaviour between cows were not associated to feed efficiency differences in this trial neither with the E+P+ diet nor with the E−P− diet. Those results have to be confirmed with cows fed with more extreme diets (for example roughage only) to ensure that the least and most efficient cows will not change.

scholarly journals Karabuğday (Fagopyrum esculentum Moench)’da Farklı Azotlu Gübre Formlarının Etkinliği

2016 ◽  
Vol 4 (6) ◽  
pp. 515 ◽  
Author(s):  
Burhan Kara ◽  
Fatoş Güllü Çelebi ◽  
Nimet Kara ◽  
Bekir Atar
Keyword(s):  
Ammonium Nitrate ◽  
Ammonium Sulfate ◽  
Nutrient Content ◽  
Quality Parameters ◽  
Mineral Nutrient ◽  
First Year ◽  
Nitrogen Forms ◽  
Biological Yield ◽  
Fagopyrum Esculentum Moench ◽  
Positive Effect

The research was carried out with aim to determination the efficient of nitrogen forms (ammonium sulfate, ammonium nitrate and urea) on nitrogen use efficient for buckwheat in Isparta during 2014 and 2015 years. All the examined characteristics were determined higher values in applied nitrogen forms according to non-nitrogen parcel. In compared to nitrogen forms, the highest grain yield (1456 and 1325 kg ha-1), biological yield (4873 and 4512 kg ha-1), 1000 grain weight (24.9 and 24.8 g), agronomic efficient (24.96% and 24.25%), recycling efficient (0.24% and 0.22%) and utilization efficient (0.25% and 0.18%) were obtained from ammonium sulfate, the highest protein content (11.37% and 12.44%) and agro-physiological efficient (0.27% and 0.24%) from ammonium nitrate in both years. Among the nitrogen forms weren’t significant differently in physiological efficient in both years, recycling and utilization efficient in the first year. The mineral nutrient content varied according to nitrogen forms. Generally, ammonium sulfate was positive effect to yield and some quality parameters.

scholarly journals Assessment of Physicochemical Properties of Tequila Brands: Authentication and Quality

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Alejandra Carreon-Alvarez ◽  
Amaury Suárez-Gómez ◽  
Florentina Zurita ◽  
Sergio Gómez-Salazar ◽  
J. Felix Armando Soltero ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Refractive Index ◽  
Physicochemical Properties ◽  
Analysis Of Variance ◽  
Principal Component ◽  
Component Analysis ◽  
Total Variability ◽  
And Cluster Analysis ◽  
Physicochemical Data

Several physicochemical properties were measured in commercial tequila brands: conductivity, density, pH, sound velocity, viscosity, and refractive index. Physicochemical data were analyzed by Principal Component Analysis (PCA), cluster analysis, and the one-way analysis of variance to identify the quality and authenticity of tequila brands. According to the Principal Component Analysis, the existence of 3 main components was identified, explaining the 87.76% of the total variability of physicochemical measurements. In general, all tequila brands appeared together in the plane of the first two principal components. In the cluster analysis, four groups showing similar characteristics were identified. In particular, one of the clusters contains some tequila brands that are not identified by the Regulatory Council of Tequila and do not meet the quality requirements established in the Mexican Official Standard 006. These tequila brands are characterized by having higher conductivity and density and lower viscosity and refractive index, determined by one-way analysis of variance. Therefore, these economical measurements, PCA, and cluster analysis can be used to determinate the authenticity of a tequila brand.

The Effect of Public Capital on Aggregate Output

2013 ◽  
Vol 64 (1) ◽  
pp. 51-72
Author(s):  
Jan-Erik Wesselhöft
Keyword(s):  
Dynamic Effect ◽  
High Growth ◽  
Public Capital ◽  
Oecd Countries ◽  
Crowding Out ◽  
Private Capital ◽  
Large Set ◽  
Public And Private ◽  
Long Run ◽  
Positive Effect

Abstract Based on new estimates of public and private capital stocks for 22 OECD countries we study the dynamic effect of public capital on the real gross domestic product using a vector autoregression approach. Whereas most former studies put effort on examining the effects of public capital in a single country, this paper covers a large set of OECD countries. The results show that public capital has a positive effect on output in the short-, medium- and long-run in most countries. In countries where the effect is negative, possible explanations as the different productivities of investments, crowding out or high growth rates of government debt are analyzed.

scholarly journals A data-driven approach to measuring epidemiological susceptibility risk around the world

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Alessandro Bitetto ◽  
Paola Cerchiello ◽  
Charilaos Mertzanis
Keyword(s):  
Factor Model ◽  
Principal Component ◽  
Data Driven ◽  
Dynamic Factor ◽  
Country Level ◽  
Epidemiological Risk ◽  
Data Driven Approach ◽  
Managerial Implications ◽  
Total Variability ◽  
Pca Method

AbstractEpidemic outbreaks are extreme events that become more frequent and severe, associated with large social and real costs. It is therefore important to assess whether countries are prepared to manage epidemiological risks. We use a fully data-driven approach to measure epidemiological susceptibility risk at the country level using time-varying information. We apply both principal component analysis (PCA) and dynamic factor model (DFM) to deal with the presence of strong cross-section dependence in the data. We conduct extensive in-sample model evaluations of 168 countries covering 17 indicators for the 2010–2019 period. The results show that the robust PCA method accounts for about 90% of total variability, whilst the DFM accounts for about 76% of the total variability. Our index could therefore provide the basis for developing risk assessments of epidemiological risk contagion. It could be also used by organizations to assess likely real consequences of epidemics with useful managerial implications.

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