Agroclimatic classification: numerical- taxonomic procedures - a review.

Detalhes bibliográficos
Autor(a) principal: REDDY, S. J.
Data de Publicação: 1983
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice)
Texto Completo: http://www.alice.cnptia.embrapa.br/alice/handle/doc/132990
Resumo: The paper catalogues the procedures and steps involved in agroclimatic classification. These vary from conventional descriptive methods to modern computer-based numerical techniques. There are three mutually independent numerical classification techniques, namely Ordination, Cluster analysis, and Minimum spanning tree; and under each technique there are several forms of grouping techniques existing. The vhoice of numerical classification procedure differs with the type of data set. In the case of numerical continuous data sets with booth positive and negative values, the simple and least controversial procedures are unweighted pair group method (UPGMA) and weighted pair group method (WPGMA) under clustering techniques with similarity measure obtained either from Gower metric or standardized Euclidean metric. Where the number of attributes are large, these could be reduced to fewer new attributes defined by the principal components or coordinates by ordination technique. The first few components or coodinates explain the maximum variance in the data matrix. These revided attributes are less affected by noise in the data set. It is possible to check misclassifications using minimum spanning tree.
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spelling Agroclimatic classification: numerical- taxonomic procedures - a review.ClassificacaoAgroclimaticClimaticClimaAgriculturaClimatologiaclassificationThe paper catalogues the procedures and steps involved in agroclimatic classification. These vary from conventional descriptive methods to modern computer-based numerical techniques. There are three mutually independent numerical classification techniques, namely Ordination, Cluster analysis, and Minimum spanning tree; and under each technique there are several forms of grouping techniques existing. The vhoice of numerical classification procedure differs with the type of data set. In the case of numerical continuous data sets with booth positive and negative values, the simple and least controversial procedures are unweighted pair group method (UPGMA) and weighted pair group method (WPGMA) under clustering techniques with similarity measure obtained either from Gower metric or standardized Euclidean metric. Where the number of attributes are large, these could be reduced to fewer new attributes defined by the principal components or coordinates by ordination technique. The first few components or coodinates explain the maximum variance in the data matrix. These revided attributes are less affected by noise in the data set. It is possible to check misclassifications using minimum spanning tree.S. JEEVANANDA REDDY, CPATSA - Consultor.REDDY, S. J.2016-05-31T11:13:34Z2016-05-31T11:13:34Z1996-07-0919832016-05-31T11:13:34Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlePesquisa Agropecuária Brasileira, Brasília, DF, v. 18, n. 5, p. 435-457, 1983.http://www.alice.cnptia.embrapa.br/alice/handle/doc/132990enginfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice)instname:Empresa Brasileira de Pesquisa Agropecuária (Embrapa)instacron:EMBRAPA2017-08-16T02:57:50Zoai:www.alice.cnptia.embrapa.br:doc/132990Repositório InstitucionalPUBhttps://www.alice.cnptia.embrapa.br/oai/requestopendoar:21542017-08-16T02:57:50falseRepositório InstitucionalPUBhttps://www.alice.cnptia.embrapa.br/oai/requestcg-riaa@embrapa.bropendoar:21542017-08-16T02:57:50Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) - Empresa Brasileira de Pesquisa Agropecuária (Embrapa)false
dc.title.none.fl_str_mv Agroclimatic classification: numerical- taxonomic procedures - a review.
title Agroclimatic classification: numerical- taxonomic procedures - a review.
spellingShingle Agroclimatic classification: numerical- taxonomic procedures - a review.
REDDY, S. J.
Classificacao
Agroclimatic
Climatic
Clima
Agricultura
Climatologia
classification
title_short Agroclimatic classification: numerical- taxonomic procedures - a review.
title_full Agroclimatic classification: numerical- taxonomic procedures - a review.
title_fullStr Agroclimatic classification: numerical- taxonomic procedures - a review.
title_full_unstemmed Agroclimatic classification: numerical- taxonomic procedures - a review.
title_sort Agroclimatic classification: numerical- taxonomic procedures - a review.
author REDDY, S. J.
author_facet REDDY, S. J.
author_role author
dc.contributor.none.fl_str_mv S. JEEVANANDA REDDY, CPATSA - Consultor.
dc.contributor.author.fl_str_mv REDDY, S. J.
dc.subject.por.fl_str_mv Classificacao
Agroclimatic
Climatic
Clima
Agricultura
Climatologia
classification
topic Classificacao
Agroclimatic
Climatic
Clima
Agricultura
Climatologia
classification
description The paper catalogues the procedures and steps involved in agroclimatic classification. These vary from conventional descriptive methods to modern computer-based numerical techniques. There are three mutually independent numerical classification techniques, namely Ordination, Cluster analysis, and Minimum spanning tree; and under each technique there are several forms of grouping techniques existing. The vhoice of numerical classification procedure differs with the type of data set. In the case of numerical continuous data sets with booth positive and negative values, the simple and least controversial procedures are unweighted pair group method (UPGMA) and weighted pair group method (WPGMA) under clustering techniques with similarity measure obtained either from Gower metric or standardized Euclidean metric. Where the number of attributes are large, these could be reduced to fewer new attributes defined by the principal components or coordinates by ordination technique. The first few components or coodinates explain the maximum variance in the data matrix. These revided attributes are less affected by noise in the data set. It is possible to check misclassifications using minimum spanning tree.
publishDate 1983
dc.date.none.fl_str_mv 1983
1996-07-09
2016-05-31T11:13:34Z
2016-05-31T11:13:34Z
2016-05-31T11:13:34Z
dc.type.driver.fl_str_mv info:eu-repo/semantics/publishedVersion
info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv Pesquisa Agropecuária Brasileira, Brasília, DF, v. 18, n. 5, p. 435-457, 1983.
http://www.alice.cnptia.embrapa.br/alice/handle/doc/132990
identifier_str_mv Pesquisa Agropecuária Brasileira, Brasília, DF, v. 18, n. 5, p. 435-457, 1983.
url http://www.alice.cnptia.embrapa.br/alice/handle/doc/132990
dc.language.iso.fl_str_mv eng
language eng
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instname:Empresa Brasileira de Pesquisa Agropecuária (Embrapa)
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instname_str Empresa Brasileira de Pesquisa Agropecuária (Embrapa)
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reponame_str Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice)
collection Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice)
repository.name.fl_str_mv Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) - Empresa Brasileira de Pesquisa Agropecuária (Embrapa)
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