Leafiness-LiDAR index and NDVI for identification of temporal patterns in super-intensive almond orchards as response to different management strategies

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dc.contributor.authorSandonís-Pozo, Leire
dc.contributor.authorOger, Baptiste
dc.contributor.authorTisseyre, Bruno
dc.contributor.authorLlorens Calveras, Jordi
dc.contributor.authorEscolà i Agustí, Alexandre
dc.contributor.authorPascual Roca, Miquel
dc.contributor.authorMartínez Casasnovas, José Antonio
dc.date.accessioned2024-07-17T10:22:47Z
dc.date.available2024-07-17T10:22:47Z
dc.date.issued2024-07-13
dc.date.updated2024-07-17T10:22:47Z
dc.description.abstractThe use of super-intensive orchards is a growing trend in fruit production. The present study aims to improve management of these cropping systems by focusing on how agronomic decisions impact orchard dynamics in the short to medium term and by providing a decision-support approach based on stable temporal patterns from previous seasons. A multitemporal study using remote sensing and LiDAR was conducted in a commercial almond orchard over four growing seasons (2019-2022) to determine the optimal timing of image acquisition for variable pre-harvest treatments. A model-based clustering (mclust) was applied to optimal Sentinel-2 NDVI maps and apparent soil electrical conductivity (ECa) data, interpolated to the pixel centroids of Sentinel-2 image grids, to delineate potential management zones (PMZs). The leafiness-LiDAR index (LLI), a leaf area index (LAI) estimator, was obtained as ground truth after summer pruning and before harvesting, showing a significant influence of fertigation and pruning on the LAI, with summer pruning particularly influencing orchard dynamics. The optimal time for NDVI mapping was found to be two months after summer pruning in productive years and two weeks after in unproductive years. The delineated PMZs were consistent across seasons and corresponded to significant LAI differences. This method could contribute to improving resource management and sustainability in super-intensive commercial orchards.
dc.description.sponsorshipThis research was funded by Grant RTI2018–094222-B-I00 (PAgFRUIT project) and PID2021–126648OB-I00 (PAgPROTECT project) by MCIN/AEI/10.13039/501100011033 and by “ERDF, a way of making Europe” of the European Union. The authors wish to thank Alrasa Agraria S.L. (Raimat, Lleida), the Centre for Technological and Industrial Development (Ministry of Science and Innovation, Government of Spain) and EuroChem Iberia S.A. for providing funds and experimental facilities, and to the members of the Research Group in AgroICT and Precision Agriculture (GRAP) of the Universitat de Lleida for their collaboration in data acquisition.
dc.format.mimetypeapplication/pdf
dc.identifier.doihttps://doi.org/10.1016/j.eja.2024.127278
dc.identifier.idgrec034490
dc.identifier.issn1161-0301
dc.identifier.urihttps://repositori.udl.cat/handle/10459.1/466158
dc.language.isoeng
dc.publisherElsevier
dc.relationinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica, Técnica y de Innovación 2021-2023/PID2021-126648OB-I00/ES/PROTECCION DE CULTIVOS DE PRECISION PARA CONSEGUIR OBJETIVOS DEL PACTO VERDE EUROPEO EN USO EFICIENTE Y REDUCCION DE FITOSANITARIOS MEDIANTE AGRICULTURA DE PRECISION/
dc.relation.isformatofReproducció del document publicat a: https://doi.org/10.1016/j.eja.2024.127278
dc.relation.ispartofEuropean Journal of Agronomy, 2024, vol. 159 (2024), num. 127278, p. 1-12
dc.rightscc by-nc, (c) Sandonís-Pozo et al., 2024
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttps://creativecommons.org/licenses/by-nc/4.0/
dc.subjectPerennial crop
dc.subjectSummer pruning
dc.subjectCanopy monitoring
dc.subjectOrchard management
dc.subjectPrecision horticulture
dc.subjectPotential management zones (PMZs)
dc.titleLeafiness-LiDAR index and NDVI for identification of temporal patterns in super-intensive almond orchards as response to different management strategies
dc.typeinfo:eu-repo/semantics/article
dc.type.versioninfo:eu-repo/semantics/publishedVersion
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