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dc.contributor.authorPagani, Valentina
dc.contributor.authorGuarneri, Tommaso
dc.contributor.authorBusetto, Lorenzo
dc.contributor.authorRanghetti, Luigi
dc.contributor.authorBOSCHETTI, MIRCO
dc.contributor.authorMovedi, Ermes
dc.contributor.authorCampos-Taberner, Manuel
dc.contributor.authorGarcía Haro, Francisco Javier
dc.contributor.authorKatsantonis, Dimitrios
dc.contributor.authorStavrakoudis, Dimitris
dc.contributor.authorRicciardelli, Elisabetta
dc.contributor.authorRomano, Filomena
dc.contributor.authorHolecz, Francesco
dc.contributor.authorCollivignarelli, Francesco
dc.contributor.authorGranell, Carlos
dc.contributor.authorCasteleyn, Sven
dc.contributor.authorConfalonieri, Roberto
dc.date.accessioned2019-04-09T09:25:16Z
dc.date.available2019-04-09T09:25:16Z
dc.date.issued2019-01
dc.identifier.citationPAGANI, Valentina, et al. A high-resolution, integrated system for rice yield forecasting at district level. Agricultural Systems, 2019, vol. 168, p. 181-190ca_CA
dc.identifier.issn0308-521X
dc.identifier.urihttp://hdl.handle.net/10234/182258
dc.description.abstractTo meet the growing demands from public and private stakeholders for early yield estimates, a high-resolution (2 km × 2 km) rice yield forecasting system based on the integration of the WARM model and remote sensing (RS) technologies was developed. RS was used to identify rice-cropped area and to derive spatially distributed sowing dates, and for the dynamic assimilation of RS-derived leaf area index (LAI) data within the crop model. The system—tested for the main European rice production districts in Italy, Greece, and Spain—performed satisfactorily; >66% of the inter-annual yield variability was explained in six out of eight combinations of ecotype × district, with a maximum of 89% of the variability explained for the ‘Tropical Japonica’ cultivars in the Vercelli district (Italy). In seven out of eight cases, the assimilation of RS-derived LAI improved the forecasting capability, with minor differences due to the assimilation technology used (updating or recalibration). In particular, RS data reduced uncertainty by capturing factors that were not properly reproduced by the simulation model (given the uncertainty due to large-area simulations). The system, which is an extension of the one used for rice within the EC-JRC-MARS forecasting system, was used pre-operationally in 2015 and 2016 to provide early yield estimates to private companies and institutional stakeholders within the EU-FP7 ERMES project.ca_CA
dc.format.extent10 p.ca_CA
dc.format.mimetypeapplication/pdfca_CA
dc.language.isoengca_CA
dc.publisherElsevier Massonca_CA
dc.relation.isPartOfAgricultural Systems, 2019, vol. 168ca_CA
dc.rights© Elsevier Ltd. All rights reserved.ca_CA
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/*
dc.subjectassimilationca_CA
dc.subjectblast diseaseca_CA
dc.subjectOryza sativa L.ca_CA
dc.subjectremote sensingca_CA
dc.subjectWARM modelca_CA
dc.titleA high-resolution, integrated system for rice yield forecasting at district levelca_CA
dc.typeinfo:eu-repo/semantics/articleca_CA
dc.identifier.doihttps://doi.org/10.1016/j.agsy.2018.05.007
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessca_CA
dc.relation.publisherVersionhttps://www.sciencedirect.com/science/article/pii/S0308521X17305048ca_CA
dc.contributor.funderThis study was partially funded under the EU FP7 Collaborative Project, grant agreement no 606983, ERMES: An Earth obseRvation Model based RicE information Service (ERMES). Carlos Granell and Sven Casteleyn were partly funded by the Ramón y Cajal Programme of the Spanish government (grant numbers RYC-2014-16913 and RYC-2014-16606 respectively)ca_CA
dc.type.versioninfo:eu-repo/semantics/submittedVersionca_CA


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