Please use this identifier to cite or link to this item: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1140307
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dc.contributor.authorVASQUES, G. de M.
dc.contributor.authorRODRIGUES, H. M.
dc.contributor.authorTAVARES, S. R. de L.
dc.contributor.authorHERNANI, L. C.
dc.contributor.authorOLIVEIRA, R. P. de
dc.date.accessioned2022-02-22T19:01:21Z-
dc.date.available2022-02-22T19:01:21Z-
dc.date.created2022-02-22
dc.date.issued2022
dc.identifier.citationIn: PEDOMETRICS BRAZIL, 2., 2021, Rio de Janeiro. Annals [...]. Rio de Janeiro: Embrapa Solos, 2022. Não paginado. Evento online.
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1140307-
dc.descriptionAn optimized sampling design to assess soil property variation across the field and within management zones is proposed and validated in a 72-ha crop field in southeastern Brazil. An optimized sample (18 sites) was derived by spatial simulated annealing from proximal sensor covariates. Soil properties were measured at 0-10 cm and validated against those measured at 72 sites on a regular grid. The optimized and regular grid samples had equal global spatial trend models and means for soil clay, pH and exchangeable Ca, Mg and K, and different ones for organic C and available P. Within zones, equal means between sampling designs were found for all soil properties in the ?North? zone, and for most properties in the other two zones. Soil property correlations against proximal sensor variables were honored by the optimized samples in most cases, both globally and within zones. The optimized soil sample reduces costs while keeping most soil information for guiding management decisions.
dc.language.isoeng
dc.rightsopenAccess
dc.subjectProximal soil sensing
dc.subjectSpatial simulated annealing
dc.subjectSpatial trends
dc.titleAn optimized sample for assessing soil property variations across the field and within management zones efficiently.
dc.typeArtigo em anais e proceedings
dc.subject.thesagroSensoriamento Remoto
dc.subject.thesagroAgricultura de Precisão
dc.subject.nalthesaurusPrecision agriculture
dc.subject.nalthesaurusRemote sensing
riaa.ainfo.id1140307
riaa.ainfo.lastupdate2022-02-22
dc.contributor.institutionGUSTAVO DE MATTOS VASQUES, CNPS; HUGO MACHADO RODRIGUES, UFRRJ; SILVIO ROBERTO DE LUCENA TAVARES, CNPS; LUIS CARLOS HERNANI, CNPS; RONALDO PEREIRA DE OLIVEIRA, CNPS.
Appears in Collections:Artigo em anais de congresso (CNPS)

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