Pedology-based management class establishment: a study case in Brazilian coffee crops

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MetadadosDescriçãoIdioma
Autor(es): dc.creatorGonçalves, Mariana Gabriele Marcolino-
Autor(es): dc.creatorAvalos, Fabio Arnaldo Pomar-
Autor(es): dc.creatorReis, Josimar Vieira dos-
Autor(es): dc.creatorCosta, Milton Verdade-
Autor(es): dc.creatorSilva, Sérgio Henrique Godinho-
Autor(es): dc.creatorPoggere, Giovana Clarice-
Autor(es): dc.creatorCuri, Nilton-
Autor(es): dc.creatorMenezes, Michele Duarte de-
Data de aceite: dc.date.accessioned2026-02-09T11:15:28Z-
Data de disponibilização: dc.date.available2026-02-09T11:15:28Z-
Data de envio: dc.date.issued2022-07-01-
Data de envio: dc.date.issued2022-07-01-
Data de envio: dc.date.issued2021-12-
Fonte completa do material: dc.identifierhttps://repositorio.ufla.br/handle/1/50428-
Fonte completa do material: dc.identifierhttps://link.springer.com/article/10.1007/s11119-021-09873-0-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/capes/1136993-
Descrição: dc.descriptionThis work proposes an approach for establishing coffee management classes mainly supported by pedological information (soil survey) and land parcels, taking into account peculiarities of Brazilian coffee crops (land parcels already implemented with different crop ages, cultivars and density) and inspired by some management zone concepts. Two initial datasets were used based on soil survey and/or coffee crop management information. Eight sequences of tests were developed, involving: ranking of the most important variables for coffee yield modeling by random forest, reduction of data dimensionality through principal component analysis (PCA) or factorial analysis of mixed data (FAMD), generation of clusters with the hierarchical cluster on principal component (HCPC), applying hierarchical tree by using Ward's minimum variance method and improved by k-means classification. Cluster effectiveness was assessed by statistical difference in coffee yield. A total of 3 clusters were considered the most proper number of management classes, composed by the most accurate random forest model (crop age, crop density, silt fraction and soil organic matter content ranked as most important variables) and highest % of variables explanation by PCA. Although not well explored for such a purpose, HCPC applied in this study case was effective on generating homogeneous management classes, differing statistically from each other by means of coffee yield.-
Idioma: dc.languageen-
Publicador: dc.publisherSpringer-
Direitos: dc.rightsrestrictAccess-
???dc.source???: dc.sourcePrecision Agriculture-
Palavras-chave: dc.subjectHierarchical cluster on principal components-
Palavras-chave: dc.subjectFactor analysis for mixed data-
Palavras-chave: dc.subjectPrincipal component analysis-
Palavras-chave: dc.subjectRandom forest-
Palavras-chave: dc.subjectCoffee yield-
Palavras-chave: dc.subjectLand parcel-
Palavras-chave: dc.subjectSoil fertility-
Palavras-chave: dc.subjectCrop density-
Título: dc.titlePedology-based management class establishment: a study case in Brazilian coffee crops-
Tipo de arquivo: dc.typeArtigo-
Aparece nas coleções:Repositório Institucional da Universidade Federal de Lavras (RIUFLA)

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