BALZARINI MONICA GRACIELA
Artículos
Título:
Subfield management class delineation using cluster analysis from spatial principal components of soil variables
Autor/es:
CÓRDOBA, M.; BRUNO, C.; COSTA, J.L.; BALZARINI, M.
Revista:
COMPUTERS AND ELETRONICS IN AGRICULTURE
Editorial:
ELSEVIER SCI LTD
Referencias:
Lugar: Amsterdam; Año: 2013 vol. 97 p. 6 - 6
ISSN:
0168-1699
Resumen:
nderstanding spatial variation within a field is essential for site-specific crop management, which requires the delineation of management areas. Several soil and terrain variables are used to classify the field points into classes. Fuzzy k-means cluster analysis is a widely used tool to delineate management classes in the multivariate context. However, this clustering method does not consider the presence of spatial correlations in the data. The MULTISPATI-PCA algorithm is an extension of principal component analysis that considers spatial autocorrelation in the original variables to produce synthetic variables. We propose and illustrate the implementation of a new method (KM-sPC) for subfield management class delineation based on the joint use of MULTISPATI-PCA and fuzzy k-means cluster. To assess the performance of KM-sPC, we performed clustering of the original soil variables and of both spatial and classical principal components on three field data sets. KM-sPC algorithm improved