Analysis of vegetation indices to determine nitrogen application and yield prediction in maize (Zea mays L.) from a standard UAV service

dc.contributor.authorMaresma Galindo, Ángel
dc.contributor.authorAriza, Mar
dc.contributor.authorMartínez, Elías
dc.contributor.authorLloveras Vilamanyà, Jaume
dc.contributor.authorMartínez Casasnovas, José Antonio
dc.date.accessioned2017-01-25T09:31:19Z
dc.date.issued2016
dc.description.abstractThe growing use of commercial unmanned aerial vehicles (UAV) and the need to adjust N fertilization rates in maize (Zea mays L.) currently constitute a key research issue. In this study, different multispectral vegetation indices (green-band and red-band based indices), SPAD and crop height (derived from a multispectral compact camera mounted on a UAV) were analysed to predict grain yield and determine whether an additional sidedress application of N fertilizer was required just before flowering. Seven different inorganic N rates (0, 100, 150, 200, 250, 300, 400 kg·N·ha−1), two different pig slurry manure rates (Ps) (150 or 250 kg·N·ha−1) and four different inorganic-organic N combinations (N100Ps150, N100Ps250, N200Ps150, N200Ps250) were applied to maize experimental plots. The spectral index that best explained final grain yield for the N treatments was the Wide Dynamic Range Vegetation Index (WDRVI). It identified a key threshold above/below 250–300 kg·N·ha−1. WDRVI, NDVI and crop height showed no significant response to extra N application at the economic optimum rate of fertilization (239.8 kg·N·ha−1), for which a grain yield of 16.12 Mg·ha−1 was obtained. This demonstrates their potential as yield predictors at V12 stage. Finally, a ranking of different vegetation indices and crop height is proposed to overcome the uncertainty associated with basing decisions on a single index.ca_ES
dc.description.sponsorshipThis work was funded by the Spanish Ministry of Science and Innovation (Project AGL2012-35122). The authors also would like to thank the IRTA Research Station (Gimenells, Lleida) for allowing the research to take place, the GIS & Remote Sensing Laboratory of the University of Lleida for the facilities to carry out the image processing and analysis, and the University of Lleida for the PhD scholarship of Ángel Maresma.ca_ES
dc.identifier.doihttps://doi.org/10.3390/rs8120973
dc.identifier.idgrec025042
dc.identifier.issn2072-4292
dc.identifier.urihttp://hdl.handle.net/10459.1/59106
dc.language.isoengca_ES
dc.publisherMDPIca_ES
dc.relationMICINN/PN2008-2011/AGL2012-35122
dc.relation.isformatofReproducció del document publicat a https://doi.org/10.3390/rs8120973ca_ES
dc.relation.ispartofRemote Sensing, 2016, vol. 8, núm. 12, p. 1-15ca_ES
dc.relation.isreferencedbyhttp://hdl.handle.net/10459.1/63101
dc.rightscc-by (c) Maresma, et al., 2016ca_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessca_ES
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectMaizeca_ES
dc.subjectNitrogenca_ES
dc.subjectMultispectral vegetation indicesca_ES
dc.subjectCrop heightca_ES
dc.subjectUAVca_ES
dc.titleAnalysis of vegetation indices to determine nitrogen application and yield prediction in maize (Zea mays L.) from a standard UAV serviceca_ES
dc.typearticleca_ES
dc.type.versionpublishedVersionca_ES
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