Delineation of management zones dealing with low sampling and outliers

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Autor(es): dc.contributorUniversidade Estadual de Campinas (UNICAMP)-
Autor(es): dc.contributorEmpresa Brasileira de Pesquisa Agropecuária (EMBRAPA)-
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.creatorSilva, Cesar de Oliveira Ferreira-
Autor(es): dc.creatorGrego, Celia Regina-
Autor(es): dc.creatorManzione, Rodrigo Lilla-
Autor(es): dc.creatorOliveira, Stanley Robson de Medeiros-
Autor(es): dc.creatorRodrigues, Gustavo Costa-
Autor(es): dc.creatorRodrigues, Cristina Aparecida Gonçalves-
Data de aceite: dc.date.accessioned2025-08-21T23:36:58Z-
Data de disponibilização: dc.date.available2025-08-21T23:36:58Z-
Data de envio: dc.date.issued2025-04-29-
Data de envio: dc.date.issued2025-01-31-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1007/s11119-024-10218-w-
Fonte completa do material: dc.identifierhttps://hdl.handle.net/11449/307980-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/307980-
Descrição: dc.descriptionPurpose: Management zones (MZs) are the subdivision of a field into a few contiguous homogeneous zones to guide variable-rate application. Delineating MZs can be based on geostatistical or clustering approaches, however, the joint use of these approaches is not usual. Here, we show a joint use of both techniques. The objective of this manuscript is twofold: (1) compare different procedures for creating management zones and (2) determine the relation of the MZs delineated with i) coffee yield maps and ii) the summarizing power of each method for each input variable inside the MZs delineated. Methods: The techniques compared to summary spatial data were: (1) summarizing the variables into a soil fertility index (SFI), (2) the MULTISPATI-PCA technique, and (3) the multivariate Min/Max autocorrelation factors (MAF) approach. Then, clustering methods were applied to perform field partition into binary MZs (grouping lower and higher values of input variables). Results and discussion: The MAF approach achieved the best field partition regarding clustering metrics (McNemar’s test, Silhouette Score Coefficient, and variance reduction). In this paper we did not use yields as a cluster variable but as a measure of success. MAF also was the best one for separating low- from high-yielding areas over the MZs. The results show that the proposed approach could be effectively used for management zone delineation. Conclusions: This methodology facilitates evaluating innovative approaches in challenging spatial modeling scenarios, such as low-sampled fields with outliers. A wide range of summarization methods and clustering techniques are available, making this agnostic approach quite interesting for delivering MZ maps. This flexible approach can guide precision nutrient management in low-sampled areas, allowing the joint use of data science tools and agronomical knowledge to delineate variable rate application strategies.-
Descrição: dc.descriptionCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)-
Descrição: dc.descriptionConsórcio Pesquisa Café-
Descrição: dc.descriptionFaculdade de Engenharia Agrícola (FEAGRI) Universidade de Campinas (UNICAMP)-
Descrição: dc.descriptionEmbrapa Agricultura Digital-
Descrição: dc.descriptionFaculdade de Ciências Tecnologia e Educação Universidade Estadual Paulista (UNESP)-
Descrição: dc.descriptionEmbrapa Territorial-
Descrição: dc.descriptionFaculdade de Ciências Tecnologia e Educação Universidade Estadual Paulista (UNESP)-
Descrição: dc.descriptionCAPES: Finance Code 001-
Descrição: dc.descriptionConsórcio Pesquisa Café: Seg number 10 18 20 01200000-
Idioma: dc.languageen-
Relação: dc.relationPrecision Agriculture-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectClustering-
Palavras-chave: dc.subjectCoffea arabica L-
Palavras-chave: dc.subjectCokriging-
Palavras-chave: dc.subjectData fusion-
Palavras-chave: dc.subjectMultivariate kriging-
Palavras-chave: dc.subjectSpecialty coffee-
Título: dc.titleDelineation of management zones dealing with low sampling and outliers-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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