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dc.contributor.authorUribeetxebarria Alonso de Armiño, Asier
dc.contributor.authorMartínez Casasnovas, José Antonio
dc.contributor.authorEscolà i Agustí, Alexandre
dc.contributor.authorRosell Polo, Joan Ramon
dc.contributor.authorArnó Satorra, Jaume
dc.date.accessioned2019-03-28T10:04:57Z
dc.date.available2019-11-13T23:22:58Z
dc.date.issued2019-03-25
dc.identifier.issn1385-2256
dc.identifier.urihttp://hdl.handle.net/10459.1/66090
dc.description.abstractEstimation of yield or other fruit quality parameter is of great interest to farmers to decide on management actions just before harvesting and, in any case, to anticipate and plan harvesting operations. Making accurate and reliable estimates often requires systematic sampling that, when covering the whole plot, can result in the use of a large number of samples and a significant effort in time and cost for fruit growers. Faced with this whole area sampling strategy, simple random sampling (SRS) using reduced sample sizes is currently a widely used technique despite the less precise estimates that it provides. In this work, different stratified sampling schemes have been tested to estimate yield (kg/tree), fruit firmness (kg/cm2) and the refractometric index (ºBaumé) in a peach orchard located in Gimenells (Lleida, Catalonia, Spain). In contrast to SRS, the use of ancillary information (NDVI and apparent electrical conductivity, ECa) allowed sampling units or trees to be stratified according to two or three classes (strata) within the plot. The classes or homogeneous stratification zones were delimited by cluster analysis using, either separately or in combination, a multispectral airborne image (NDVI) and a ECa survey map acquired by means of a soil resistivity sensor (Veris 3100). Sampling schemes were then compared in terms of efficiency. In general, stratified sampling showed better results than SRS. Regarding yield estimates, stratified sampling according to two strata of NDVI allowed the sample size to be reduced by 17% compared to the SRS for the same precision. On the other hand, quality parameters may require different stratification strategies concerning the number of strata to be used. While ºBaumé was better-estimated using also stratified samples based on two strata of NDVI, fruit firmness showed better results when stratifying by three classes or strata of NDVI. In any case, neither the ECa nor the combined use of NDVI + ECa have improved sampling efficiency when used as ancillary maps for stratification.
dc.description.sponsorshipThis work was funded by the Spanish Ministry of Economy and Competitiveness through the project AgVANCE (AGL2013-48297-C2-2-R). The authors also thank the IRTA Experimental Station in Gimenells (Lleida, Spain) for the possibility of carrying out this sampling study on a peach orchard.
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherSpringer
dc.relationMINECO/PN2013-2016/AGL2013-48297-C2-2-R
dc.relation.isformatofVersió postprint del document publicat a: https://doi.org/10.1007/s11119-018-9619-9
dc.relation.ispartofPrecision Agriculture, 2019, vol. 20, núm. 2, p. 179-192
dc.rights(c) Springer Science+Business Media, 2018
dc.subjectSampling efficiency
dc.subjectFruit yield and quality
dc.subjectPeach
dc.subjectNDVI
dc.subjectApparent electrical conductivity
dc.titleStratified sampling in fruit orchards using cluster-based ancillary information maps: a comparative analysis to improve yield and quality estimates
dc.typeinfo:eu-repo/semantics/article
dc.date.updated2019-03-28T10:04:58Z
dc.identifier.idgrec028055
dc.type.versioninfo:eu-repo/semantics/acceptedVersion
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess
dc.identifier.doihttps://doi.org/10.1007/s11119-018-9619-9


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