Subfield management class delineation using cluster analysis from spatial principal components of soil variables

2013 ◽  
Vol 97 ◽  
pp. 6-14 ◽  
Author(s):  
M. Córdoba ◽  
C. Bruno ◽  
J. Costa ◽  
M. Balzarini
Keyword(s):  
Cluster Analysis ◽  
Principal Components ◽  
Soil Variables ◽  
Spatial Principal Components

Ecological interpretation of plant communities by classification and ordination of quantitative soil characteristics

Bothalia ◽  
1983 ◽  
Vol 14 (3/4) ◽  
pp. 691-699 ◽  
Author(s):  
G. J. Bredenkamp ◽  
G. K. Theron ◽  
D. R. J. Van Vuuren
Keyword(s):  
Cluster Analysis ◽  
Principal Components Analysis ◽  
Plant Communities ◽  
Principal Components ◽  
Soil Characteristics ◽  
Soil Variables ◽  
Game Reserve ◽  
Components Analysis

An agglomerative cluster analysis and a principal components analysis of habitat, based on 27 quantitative soil variables, are compared with a Braun-Blanquet classification of the vegetation of the Manyeleti Game Reserve in the eastern Transvaal. The results indicate that these techniques can be successfully used to obtain relatively homogeneous habitat classes, characterized by sets of environmental (soil) variables and not only single variables individually, and which are furthermore significantly correlated with the recognized plant communities of the area.

Application of PCA with georeferenced data in the tourism industry: A case study in the province of Córdoba, Argentina

2021 ◽  
pp. 135481662098768
Author(s):  
Laura I Luna
Keyword(s):  
Decision Making ◽  
Principal Components ◽  
Spatial Data ◽  
Local Development ◽  
Value Added ◽  
Tourism Industry ◽  
Methodological Innovation ◽  
Spatial Principal Components ◽  
Components Analysis

The spatial analysis of tourism industries provides information about their structure, which is necessary for decision-making. In this work, tourism industries in the departments of Córdoba province, Argentina, for the 2001–2014 period were mapped. Multivariate methods with and without spatial restrictions (spatial principal components (sPCs) analysis, MULTISPATI-PCA, and principal components analysis (PCA), respectively) were applied and their performance was compared. MULTISPATI-PCA yielded a higher degree of spatial structuring of the components that summarize tourism activities than PCA. The methodological innovation lies in the generation of statistics for multidimensional spatial data. The departments were classified according to the participation of tourism activities in the value added of tourism using the sPCs obtained as input of the cluster fuzzy k-means analysis. This information provides elements necessary for appropriately defining local development strategies and, therefore, is useful to improve decision-making.

Chemistry research in Europe: A publication analysis (2006–2016)

2019 ◽  
pp. 016555151986549
Author(s):  
Hakan Kaygusuz
Keyword(s):  
Cluster Analysis ◽  
Principal Components Analysis ◽  
Principal Components ◽  
Hierarchical Cluster Analysis ◽  
Hierarchical Cluster ◽  
Polymer Science ◽  
Chemistry Research ◽  
Research Outcome ◽  
Components Analysis ◽  
Number Of Publications

In this article, chemistry research in 51 different European countries between years 2006 and 2016 was studied using statistical methods. This study consists of two parts: In the first part, different economical, institutional and citation parameters were correlated with the number of publications, citations and chemical industry numbers using principal components analysis and hierarchical cluster analysis. The results of the first part indicated that economical and geographical parameters directly affect the chemistry research outcome. In the second part, research in branches of chemistry and related disciplines such as analytical chemistry, polymer science and physical chemistry were analysed using principal components analysis and hierarchical cluster analysis for each country. Publication data were collected as the number of chemistry publications (in Science Citation Index–Expanded (SCI-E)) between years 2006 and 2016 in different chemistry subdisciplines and related scientific areas. Results of the second part of the study produced geographical and economical clusters of countries, interestingly, without addition of any geographical data.

Potential of random amplified microsatellites (RAMS) to typify and discriminate varieties of Physalis ixocarpa Brot. ex Hornem.

2018 ◽  
pp. 396
Author(s):  
Eli Amanda Delgado-Alvarado, Norma Almaraz-Abarca ◽  
Cirenio Escamirosa- Tinoco ◽  
Jose Natividad Uribe-Soto, Jose Antonio Avila-Reyes ◽  
Rene Torres-Ricario, Ana Isabel Chaidez-Ayala
Keyword(s):  
Cluster Analysis ◽  
Genetic Variability ◽  
South America ◽  
Principal Components ◽  
Genetic Relationships ◽  
Important Species ◽  
Physalis Peruviana ◽  
Edible Species ◽  
Components Analysis ◽  
Genetic Closeness

Physalis ixocarpa is an edible species of Solanaceae. This is one of the few cultivated and economically important species of the genus in Mesoamerica. In Mexico, several varieties and landraces have been developed, which have not been molecularly characterized. In the current study, five RAMS primers were used to characterize and assess the genetic variability of two varieties and three landraces of this species. The capacity of these markers to discriminate between them was also evaluated. With comparative aims, Physalis peruviana, the most economically important species of the genus in South America, was analyzed in the same manner. The results revealed that the varieties and landraces of P. ixocarpa conserve important levels of genetic variability (21.75% > Polymorphism < 42.75%), which were higher than that found for P. peruviana (10.75% Polymorphism). RAMS were useful specific markers, as P. peruviana and P. ixocarpa were clearly distinguished one from each other by both cluster analysis and principal components analysis. Close genetic relationships were found between the landraces San Isidro Chihuiro and Verde Puebla, and between the varieties Diamante and Rendidora. In spite of the genetic closeness, the RAMS amplification profiles had a clear varietal-specific tendency, in such a way that they may represent varietal fingerprints, which can be used as authentication tool for varieties and landraces of P. ixocarpa.

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