Effective dimensionality and factors affecting crop-livestock integration in West African savannas: a combination of principal component analysis and Tobit approaches

2006 ◽  
Vol 35 (2) ◽  
pp. 145-155 ◽  
Author(s):  
V. M. Manyong ◽  
I. Okike ◽  
T. O. Williams
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
West African ◽  
Factors Affecting ◽  
Effective Dimensionality

scholarly journals Principal Component Analysis of Morphometric Traits of West African Dwarf Goats

2019 ◽  
Vol 1 ◽  
pp. 26-32 ◽  
Author(s):  
I O Dudusola ◽  
S O Oseni ◽  
M A Popoola ◽  
A Jenyo
Keyword(s):  
Principal Component Analysis ◽  
Body Weight ◽  
Total Variation ◽  
Age Groups ◽  
Principal Component ◽  
Component Analysis ◽  
West African ◽  
Age Group ◽  
Morphometric Traits ◽  
West African Dwarf

The study was conducted to evaluate the principal component analysis of phenotypic attributes of West African Dwarf (WAD) goat. Data collected on the live body weight and twelve morphometric traits of the goats which were categorised into four age groups based on their dentition. The age groups were: less than 2years old, 2- 3years old, 3-4 years old and 4 years old. The data were subjected to a PCA and Cluster analyses using the multivariate procedure components of SAS (2003). Result revealed that highest values of morphometric traits were obtained in goats that of 4 years old. The rate of increase in body weight and other morphometric traits was high in age group of ˂2 years to age 2-3years compared to differences observed in others across the age group. Heart Girth had the highest correlation with body weight. Foreleg, neck, ear and hind leg lengths; wither height and rump height were weakly correlated with the body weight of the goats. Result revealed that two Principal components were retained in the first age group (age group˂2years) which accounted for 72.99% of the total variation. The first PC alone accounted for 63.13% of the total variation while PC2 accounted for the remaining 9.86%. From this study, it was concluded that there is interdependence among body weight and morphometric traits and that morphometric traits can be used in predicting live weight of WAD goats; PCA and Cluster could be exploited in breeding and selection programmes to acquire highly coordinated animal bodies using fewer measurements.

scholarly journals Diversity of entomopathogenic fungi associated with Mediterranean fruit fly (Ceratitis capitata (Diptera: Tephritidae)) in Moroccan Argan forests and nearby area: impact of soil factors on their distribution

BMC Ecology ◽  
2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Ayoub Hallouti ◽  
Mohamed Ait Hamza ◽  
Abdelaziz Zahidi ◽  
Rachid Ait Hammou ◽  
Rachid Bouharroud ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Entomopathogenic Fungi ◽  
Ceratitis Capitata ◽  
Chemical Properties ◽  
Principal Component ◽  
Fruit Fly ◽  
Component Analysis ◽  
Organic Content ◽  
Pathogenicity Test ◽  
Factors Affecting

Abstract Background Studying the ecology of biocontrol-agents is a prerequisite to effectively control medfly (Ceratitis capitata (Diptera: Tephritidae)) with entomopathogenic fungi. In this context, factors affecting the occurrence and distribution of medfly-associated entomopathogenic-fungi were studied. Soil samples (22) were collected from natural and cultivated areas of Souss-region Morocco. Results A total of 260 fungal isolates belonging to 22 species and 10 genera were obtained by using medfly pupae as bait. Medfly-associated fungi were detected in all studied soils and pupae infection percentages ranged from 3.33% to 48%. Two genera, Fusarium and Beauveria were the most frequent with 83 isolates (32%) and 50 isolates (19.23%) respectively. Pathogenicity test of isolated species against medfly pupae showed high mortality rates up to 91% for some strains. Principal component analysis (PCA) demonstrated a strong influence of origin, physical, and chemical properties of soil on the abundance of these fungi. In general, medfly-associated fungi were more abundant in soils with moderate pH (7.5 to 8) having high sand and organic content. High relative humidity negatively influenced the abundance of these fungi. Both factors directly affected the fungal infection percentages in pupae. The response of fungi to these parameters varied among species. According to principal component analysis (PCA), the soils of argan fields and forests were more suitable for the development of medfly-associated fungi than citrus orchards. Conclusions These results guide identifying suitable soils for the effective application of entomopathogenic fungi as biological control agents. In summary, isolated indigenous strains seem to be a promising option to control C. capitata.

Effective dimensionality for principal component analysis of time series expression data

Biosystems ◽  
2003 ◽  
Vol 71 (3) ◽  
pp. 311-317 ◽  
Author(s):  
Michael Hörnquist ◽  
John Hertz ◽  
Mattias Wahde
Keyword(s):  
Principal Component Analysis ◽  
Time Series ◽  
Principal Component ◽  
Component Analysis ◽  
Expression Data ◽  
Series Expression ◽  
Effective Dimensionality ◽  
Analysis Of Time Series

scholarly journals Diversity of entomopathogenic fungi associated with Mediterranean fruit fly (C. capitata) in Moroccan Argan forests and nearby area: impact of soil factors on their distribution

2020 ◽  
Author(s):  
Ayoub HALLOUTI ◽  
Mohamed AIT HAMZA ◽  
Abdelaziz ZAHIDI ◽  
Rachid AIT HAMMOU ◽  
Rachid BOUHARROUD ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Entomopathogenic Fungi ◽  
Chemical Properties ◽  
Principal Component ◽  
Fruit Fly ◽  
Component Analysis ◽  
Organic Content ◽  
Pathogenicity Test ◽  
Factors Affecting ◽  
Physical And Chemical

Abstract Background: Studying the ecology of biocontrol-agents is a prerequisite to effectively control medfly (C. capitata) with entomopathogenic fungi. In this context, factors affecting the occurrence and distribution of medfly-associated entomopathogenic-fungi were studied. Soil samples (22) were collected from natural and cultivated areas of Souss-region Morocco. Results: A total of 260 fungal isolates belonging to 22 species and 10 genera were obtained by using medfly pupae as bait. Medfly-associated fungi were detected in all studied soils and pupae infection percentages ranged from 3.33% to 48%. Two genera, Fusarium and Beauveria were the most frequent with 83 isolates (32%) and 50 isolates (19.23%) respectively. Pathogenicity test of isolated species against medfly pupae showed high mortality rates up to 91% for some strains. Principal component analysis (PCA) demonstrated strong influence of origin, physical and chemical properties of soil on the abundance of these fungi. In general, medfly-associated fungi were more abundant in soils with moderate pH (7.5 to 8) having high sand and organic content. High relative humidity negatively influenced the abundance of these fungi. Both factors directly affected the fungal infection percentages in pupae. The response of fungi to these parameters varied among species. According to principal component analysis (PCA) the soils of argan fields and forests were more suitable for the development of medfly-associated fungi than citrus orchards. Conclusions: These results provide guidance on identifying suitable soils for effective application of entomopathogenic fungi as biological control agents. In summary, isolated indigenous strains seem to be a promising option to control C. capitata.

scholarly journals Different Crime Types in Western Cape Province: Principal Component Analysis

2016 ◽  
Vol 8 (5(J)) ◽  
pp. 18-26
Author(s):  
Kyei KA ◽  
Tshisikhawe TH ◽  
Dube LM
Keyword(s):  
Principal Component Analysis ◽  
Urban Areas ◽  
Secondary Data ◽  
Principal Component ◽  
Component Analysis ◽  
Western Cape ◽  
Western Cape Province ◽  
Factors Affecting ◽  
Crime Types ◽  
High Crime

South Africa has a very high crime rate compared to most countries. Crime affects the society, business and psychology of the people. It compels people to move out or come into a particular area. It is most prevalent in the urban areas where poverty gap is conspicuous. Western Cape and Gauteng Provinces are the best developed provinces in the country and therefore have higher crime levels. But the question is: what types of crime are prevalent in the Western Cape Province? And what are the major causes of these crimes? The purpose of this paper is to identify the different types of crimes committed in the Western Cape Province which are prominent. Principal Component analysis (PCA) has been use in this study to gauge the patterns of crime and the distinct important factors affecting the level of crime. Secondary data from a website have been used in the analysis. The results show that violence and vehicle thefts are the most committed crimes in the province. The areas where crime occurs most frequently are Bellville, Cape Town Central, Gugulethu, Harare, Khayelisha, Mitchells Plain, Nyanga and Parow. Firearms have been identified as major means for committing crime. The paper recommends that attempts be made by the provincial government to clamp down unlicensed fire arm holders/dealers. Amnesty should be granted to encourage holders of unlicensed fire arms to surrender without punishment and the public should report to the police all those dealing in unlicensed firearms in order to root out crime in the province.

Evaluation of Regional Water Resources Carrying Capacity in Yan'an Based on Principal Component Analysis

2013 ◽  
Vol 864-867 ◽  
pp. 2331-2334
Author(s):  
Xiao Jun Liu ◽  
Jia Wang
Keyword(s):  
Principal Component Analysis ◽  
Water Resources ◽  
Sewage Treatment ◽  
Temporal Distribution ◽  
Principal Component ◽  
Environmental Water ◽  
Component Analysis ◽  
Treatment Rate ◽  
Factors Affecting ◽  
Development And Utilization

Yan'an is the center of political, economic, cultural and military in Shanbei, and is also a city of water resources extremely shortage. This paper by using the principal component analysis to select 13 indicators and establish the indicators system, two main ingredients reflect Yan'an WRCC, thus evaluate Yan'an WRCC in 2006 - 2011 timing data. Results show that: rate of urbanization, ecological and environmental water use rate, per capita GDP, rate of water resources development and utilization and sewage treatment rate are the main factors affecting Yan'an WRCC; 2006-2011 Yan'an in the temporal distribution of WRCC overall downward trend, the development and utilization of water resources potential in Yan'an weaker. Finally put forward reasonable suggestions aim at future sustainable development and utilization of water resources in Yan'an.

scholarly journals Geometrical Approximated Principal Component Analysis for Hyperspectral Image Analysis

2020 ◽  
Vol 12 (11) ◽  
pp. 1698 ◽  
Author(s):  
Alina L. Machidon ◽  
Fabio Del Frate ◽  
Matteo Picchiani ◽  
Octavian M. Machidon ◽  
Petre L. Ogrutan
Keyword(s):  
Principal Component Analysis ◽  
Dimensionality Reduction ◽  
Hyperspectral Image ◽  
Principal Component ◽  
Component Analysis ◽  
Hyperspectral Images ◽  
Land Classification ◽  
Hyperspectral Image Processing ◽  
Hyperspectral Image Analysis ◽  
Effective Dimensionality

Principal Component Analysis (PCA) is a method based on statistics and linear algebra techniques, used in hyperspectral satellite imagery for data dimensionality reduction required in order to speed up and increase the performance of subsequent hyperspectral image processing algorithms. This paper introduces the PCA approximation method based on a geometric construction approach (gaPCA) method, an alternative algorithm for computing the principal components based on a geometrical constructed approximation of the standard PCA and presents its application to remote sensing hyperspectral images. gaPCA has the potential of yielding better land classification results by preserving a higher degree of information related to the smaller objects of the scene (or to the rare spectral objects) than the standard PCA, being focused not on maximizing the variance of the data, but the range. The paper validates gaPCA on four distinct datasets and performs comparative evaluations and metrics with the standard PCA method. A comparative land classification benchmark of gaPCA and the standard PCA using statistical-based tools is also described. The results show gaPCA is an effective dimensionality-reduction tool, with performance similar to, and in several cases, even higher than standard PCA on specific image classification tasks. gaPCA was shown to be more suitable for hyperspectral images with small structures or objects that need to be detected or where preponderantly spectral classes or spectrally similar classes are present.

Factors Affecting the Variability of Summertime Sulfate in a Rural Area Using Principal Component Analysis

1982 ◽  
Vol 32 (10) ◽  
pp. 1043-1047 ◽  
Author(s):  
Paul J. Lioy ◽  
R. Peter Mallon ◽  
Morton Lippmann ◽  
Theo J. Kneip ◽  
Perry J. Samson
Keyword(s):  
Principal Component Analysis ◽  
Rural Area ◽  
Principal Component ◽  
Component Analysis ◽  
Factors Affecting
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