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dc.contributorKehel, Zakariaen_US
dc.contributorBenomar, Azizaen_US
dc.contributorBellafkih, Mostafaen_US
dc.contributorAmri, Ahmeden_US
dc.creatorAzough, Zainaben_US
dc.date2019-07-24en_US
dc.date.accessioned2019-12-29T18:28:08Z
dc.date.available2019-12-29T18:28:08Z
dc.identifierhttps://mel.cgiar.org/reporting/download/hash/a2e2ff45a619c7c8e2492a9715ab5378en_US
dc.identifier.citationZainab Azough, Zakaria Kehel, Aziza Benomar, Mostafa Bellafkih, Ahmed Amri. (24/7/2019). Predictive Characterization of ICARDA Genebank Barley Accessions sing FIGS and Machine Learning. Morocco.en_US
dc.identifier.urihttps://hdl.handle.net/20.500.11766/10493
dc.description.abstractThe International Center for Agricultural Research in the Dry Areas (ICARDA) has a unique germplasm collection of barley, among many other crops that it holds in its genebank. This collection contains landraces and barley wild relatives and most of them are georeferenced. Distribution of genetic resources is a core genebank activity aiming at responding to requests from various users including breeders, researchers, farmers, etc. ICARDA has developed over the last decade an efficient approach for better targeting adaptive traits called the Focused Identification of Germplasm Strategy (FIGS). FIGS approach links adaptive traits to environments (and associated selection pressures) through filtering and machine learning and it focuses on accessions that are most likely to possess trait specific genetic variation. In this paper, we present a work of predictive characterization on ICARDA barley collection using the FIGS approach and its algorithms combining several machine learning methods, and using several characterization traits. Most of the studied traits have shown a high predictability. Outcomes from this analysis are then used to make a predictive characterization of the entire ICARDA barley collection by assigning probabilities of each trait to the non-evaluated accessions.en_US
dc.formatPDFen_US
dc.languageenen_US
dc.publisherIOS Pressen_US
dc.rightsCC-BY-NC-4.0en_US
dc.subjectcharacterizationen_US
dc.titlePredictive Characterization of ICARDA Genebank Barley Accessions sing FIGS and Machine Learningen_US
dc.typeConference Paperen_US
cg.creator.idKehel, Zakaria: 0000-0002-1625-043Xen_US
cg.creator.idAmri, Ahmed: 0000-0003-0997-0276en_US
cg.creator.ID-typeORCIDen_US
cg.creator.ID-typeORCIDen_US
cg.subject.agrovocbarleyen_US
cg.subject.agrovocfigsen_US
cg.subject.agrovocmachine learningen_US
cg.contributor.centerInternational Center for Agricultural Research in the Dry Areas - ICARDAen_US
cg.contributor.centerNational Institute of Posts and Telecommunications - INPTen_US
cg.contributor.crpCGIAR Research Program on Genebanksen_US
cg.contributor.funderGlobal Crop Diversity Trust - GCDTen_US
cg.contributor.projectApplication of Focused Identification of Germplasm Strategy (FIGS) in Australian Environmenten_US
cg.contributor.project-lead-instituteInternational Center for Agricultural Research in the Dry Areas - ICARDAen_US
cg.coverage.regionGlobalen_US
cg.contactzainab.azough@gmail.comen_US
cg.identifier.doihttps://dx.doi.org/10.3233/AISE190031en_US
dc.identifier.statusOpen accessen_US
mel.project.openhttps://mel.cgiar.org/projects/australiafigsen_US
mel.funder.grant#Grains Research and Development Corporation - GRDC :ICA00014en_US


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