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dc.contributor.authorSola Guirado, Rafael Rubén
dc.contributor.authorCastillo-Ruiz, Francisco J.
dc.contributor.authorJiménez Jiménez, Francisco
dc.contributor.authorBlanco Roldán, Gregorio L.
dc.contributor.authorGil Ribes, Jesús
dc.contributor.authorCastro García, Sergio
dc.date.accessioned2017-11-17T11:06:06Z
dc.date.available2017-11-17T11:06:06Z
dc.date.issued2017
dc.identifier.urihttp://hdl.handle.net/10396/15461
dc.description.abstractOlive has a notable importance in countries of Mediterranean basin and its profitability depends on several factors such as actual yield, production cost or product price. Actual “on year” Yield (AY) is production (kg tree-1) in “on years”, and this research attempts to relate it with geometrical parameters of the tree canopy. Regression equation to forecast AY based on manual canopy volume was determined based on data acquired from different orchard categories and cultivars during different harvesting seasons in southern Spain. Orthoimages were acquired with unmanned aerial systems (UAS) imagery calculating individual crown for relating to canopy volume and AY. Yield levels did not vary between orchard categories; however, it did between irrigated orchards (7000–17,000 kg ha-1) and rainfed ones (4000–7000 kg ha-1). After that, manual canopy volume was related with the individual crown area of trees that were calculated by orthoimages acquired with UAS imagery. Finally, AY was forecasted using both manual canopy volume and individual tree crown area as main factors for olive productivity. AY forecast only by using individual crown area made it possible to get a simple and cheap forecast tool for a wide range of olive orchards. Finally, the acquired information was introduced in a thematic map describing spatial AY variability obtained from orthoimage analysis that may be a powerful tool for farmers, insurance systems, market forecasts or to detect agronomical problems.es_ES
dc.format.mimetypeapplication/pdfes_ES
dc.language.isoenges_ES
dc.publisherMDPIes_ES
dc.rightshttps://creativecommons.org/licenses/by/4.0/es_ES
dc.sourceSensors 17(8), 1743 (2017)es_ES
dc.subjectOlive treees_ES
dc.subjectProduction forecastes_ES
dc.subjectManual canopy volumees_ES
dc.subjectIndividual crown areaes_ES
dc.subjectTree mappinges_ES
dc.titleOlive Actual “on Year” Yield Forecast Tool Based on the Tree Canopy Geometry Using UAS Imageryes_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.relation.publisherversionhttp://dx.doi.org/10.3390/s17081743es_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES


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