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dc.contributor.authorMartínez Casasnovas, José Antonio
dc.contributor.authorEscolà i Agustí, Alexandre
dc.contributor.authorArnó Satorra, Jaume
dc.date.accessioned2018-06-15T11:13:22Z
dc.date.available2018-06-15T11:13:22Z
dc.date.issued2018-06-13
dc.identifier.issn2077-0472
dc.identifier.urihttp://hdl.handle.net/10459.1/64519
dc.description.abstractOne of the fields of research in precision agriculture (PA) is the delineation of potential management zones (PMZs, also known as site-specific management zones, or simply management zones). To delineate PMZs, cluster analysis is the main used and recommended methodology. For cluster analysis, mainly yield maps, remote sensing multispectral indices, apparent soil electrical conductivity (ECa), and topography data are used. Nevertheless, there is still no accepted protocol or guidelines for establishing PMZs, and different solutions exist. In addition, the farmer's expert knowledge is not usually taken into account in the delineation process. The objective of the present work was to propose a methodology to delineate potential management zones for differential crop management that expresses the productive potential of the soil within a field. The Management Zone Analyst (MZA) software, which implements a fuzzy c-means algorithm, was used to create different alternatives of PMZ that were validated with yield data in a maize (Zea mays L.) field. The farmers' expert knowledge was then taken into account to improve the resulting PMZs that best fitted to the yield spatial variability pattern. This knowledge was considered highly valuable information that could be also very useful for deciding management actions to be taken to reduce within-field variability.
dc.description.sponsorshipThis research was funded by the contract C16022 between the University of Lleida and Ventafarinas, S.L. (Lleida, Spain).
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherMDPI
dc.relation.isformatofReproducció del document publicat a: https://doi.org/10.3390/agriculture8060084
dc.relation.ispartofAgriculture, 2018, vol. 8, (6), 84, p. 1-18
dc.rightscc-by (c) Martínez et al., 2018
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectSentinel-2
dc.subjectaccumulated NDVI
dc.subjectApparent electrical conductivity
dc.subjectTopography
dc.subjectcluster analysis
dc.titleUse of farmer knowledge in the delineation of potential management zones in precision agriculture: a case study in maize (Zea mays L.)
dc.typeinfo:eu-repo/semantics/article
dc.date.updated2018-06-15T11:13:28Z
dc.identifier.idgrec027157
dc.type.versionPublishedVersion
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess
dc.identifier.doihttps://doi.org/10.3390/agriculture8060084


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cc-by (c) Martínez et al., 2018
Except where otherwise noted, this item's license is described as cc-by (c) Martínez et al., 2018