Classification of areas associated with soybean yield and agrometeorological variables through fuzzy clustering

E.C. Araújo, J.A. Johann, M.A. Uribe-Opazo, and E.C.G. Camargo. 2013. Classification of areas associated with soybean yield and agrometeorological variables through fuzzy clustering. Cien. Inv. Agr. 40(3): 617-627. This study aimed to apply an approach based on fuzzy clustering for the classificatio...

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Autores principales:	Coimbra de Araújo,Everton, Johann,Jerry A, Uribe-Opazo,Miguel A, Camargo,Eduardo C.G
Lenguaje:	English
Publicado:	Pontificia Universidad Católica de Chile. Facultad de Agronomía e Ingeniería Forestal 2013
Materias:	Agrometeorological variables classification of areas Fuzzy c-Means methods of decision similarity index soybean yield
Acceso en línea:	http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0718-16202013000300014
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spelling	oai:scielo:S0718-162020130003000142014-09-08Classification of areas associated with soybean yield and agrometeorological variables through fuzzy clusteringCoimbra de Araújo,EvertonJohann,Jerry AUribe-Opazo,Miguel ACamargo,Eduardo C.G Agrometeorological variables classification of areas Fuzzy c-Means methods of decision similarity index soybean yield E.C. Araújo, J.A. Johann, M.A. Uribe-Opazo, and E.C.G. Camargo. 2013. Classification of areas associated with soybean yield and agrometeorological variables through fuzzy clustering. Cien. Inv. Agr. 40(3): 617-627. This study aimed to apply an approach based on fuzzy clustering for the classification of areas associated with soybean yield combined with the following agrometeorological variables: rainfall, average air temperature and average global solar radiation. The study was conducted with 48 municipalities in the western region of Paraná State, Brazil, with data from the crop-year 2007/2008. Through the fuzzy c-means algorithm, it was possible to form groups of municipalities that were similar in soybean yield using the Method of Decision by the Higher Degree of Relevance (MDMGP) and Method of Decision by Threshold β (β MDL). Subsequently, the identification of the appropriate number of clusters was obtained using Modified Partition Entropy (MPE). To measure the degree of similarity for each cluster, the Cluster Similarity Index (ISCl) was constructed and implemented. From the perspective of this study, the method used was adequate, allowing the identification of clusters of municipalities with degrees of similarities between 63 and 94%.info:eu-repo/semantics/openAccessPontificia Universidad Católica de Chile. Facultad de Agronomía e Ingeniería ForestalCiencia e investigación agraria v.40 n.3 20132013-12-01text/htmlhttp://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0718-16202013000300014en10.4067/S0718-16202013000300014
institution	Scielo Chile
collection	Scielo Chile
language	English
topic	Agrometeorological variables classification of areas Fuzzy c-Means methods of decision similarity index soybean yield
spellingShingle	Agrometeorological variables classification of areas Fuzzy c-Means methods of decision similarity index soybean yield Coimbra de Araújo,Everton Johann,Jerry A Uribe-Opazo,Miguel A Camargo,Eduardo C.G Classification of areas associated with soybean yield and agrometeorological variables through fuzzy clustering
description	E.C. Araújo, J.A. Johann, M.A. Uribe-Opazo, and E.C.G. Camargo. 2013. Classification of areas associated with soybean yield and agrometeorological variables through fuzzy clustering. Cien. Inv. Agr. 40(3): 617-627. This study aimed to apply an approach based on fuzzy clustering for the classification of areas associated with soybean yield combined with the following agrometeorological variables: rainfall, average air temperature and average global solar radiation. The study was conducted with 48 municipalities in the western region of Paraná State, Brazil, with data from the crop-year 2007/2008. Through the fuzzy c-means algorithm, it was possible to form groups of municipalities that were similar in soybean yield using the Method of Decision by the Higher Degree of Relevance (MDMGP) and Method of Decision by Threshold β (β MDL). Subsequently, the identification of the appropriate number of clusters was obtained using Modified Partition Entropy (MPE). To measure the degree of similarity for each cluster, the Cluster Similarity Index (ISCl) was constructed and implemented. From the perspective of this study, the method used was adequate, allowing the identification of clusters of municipalities with degrees of similarities between 63 and 94%.
author	Coimbra de Araújo,Everton Johann,Jerry A Uribe-Opazo,Miguel A Camargo,Eduardo C.G
author_facet	Coimbra de Araújo,Everton Johann,Jerry A Uribe-Opazo,Miguel A Camargo,Eduardo C.G
author_sort	Coimbra de Araújo,Everton
title	Classification of areas associated with soybean yield and agrometeorological variables through fuzzy clustering
title_short	Classification of areas associated with soybean yield and agrometeorological variables through fuzzy clustering
title_full	Classification of areas associated with soybean yield and agrometeorological variables through fuzzy clustering
title_fullStr	Classification of areas associated with soybean yield and agrometeorological variables through fuzzy clustering
title_full_unstemmed	Classification of areas associated with soybean yield and agrometeorological variables through fuzzy clustering
title_sort	classification of areas associated with soybean yield and agrometeorological variables through fuzzy clustering
publisher	Pontificia Universidad Católica de Chile. Facultad de Agronomía e Ingeniería Forestal
publishDate	2013
url	http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0718-16202013000300014
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Classification of areas associated with soybean yield and agrometeorological variables through fuzzy clustering

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