High-throughput characterization and phenotyping of resistance and tolerance to virus infection in sweetpotato

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cg.authorship.typesCGIAR and developing country instituteen_US
cg.authorship.typesCGIAR and advanced research instituteen_US
cg.contributor.affiliationInternational Potato Centeren_US
cg.contributor.affiliationUniversidad de Huánucoen_US
cg.contributor.donorBill & Melinda Gates Foundationen_US
cg.contributor.donorCGIAR Trust Funden_US
cg.contributor.initiativePlant Healthen_US
cg.coverage.regionAfricaen_US
cg.coverage.regionAsiaen_US
cg.coverage.regionLatin Americaen_US
cg.creator.identifierJan Kreuze: 0000-0002-6116-9200en_US
cg.creator.identifierDavid Ramirez: 0000-0003-4546-9745en_US
cg.creator.identifierSegundo Fuentes: 0000-0001-8433-809Xen_US
cg.creator.identifierHildo Loayza: 0000-0002-4145-5453en_US
cg.creator.identifierJohan Ninanya: 0000-0001-9499-2503en_US
cg.creator.identifierJavier Rinza Díaz: 0000-0001-9320-3146en_US
cg.creator.identifierMaria David: 0000-0002-8190-2836en_US
cg.creator.identifierSoledad Gamboa: 0000-0003-1223-9900en_US
cg.creator.identifierBert De Boeck: 0000-0001-5087-2622en_US
cg.creator.identifierFederico Celedonio Diaz Trujillo: 0000-0001-5299-8181en_US
cg.creator.identifierAna Perez: 0000-0001-5314-0160en_US
cg.creator.identifierLuis Silva: 0000-0002-3660-7344en_US
cg.creator.identifierHugo Campos: 0000-0003-0070-1336en_US
cg.howPublishedFormally Publisheden_US
cg.identifier.doihttps://doi.org/10.1016/j.virusres.2023.199276en_US
cg.isijournalISI Journalen_US
cg.issn1872-7492en_US
cg.journalVirus Researchen_US
cg.reviewStatusPeer Reviewen_US
cg.subject.cipSWEETPOTATOESen_US
cg.subject.cipSWEETPOTATO AGRI-FOOD SYSTEMSen_US
cg.volume339en_US
dc.contributor.authorKreuze, Jan F.en_US
dc.contributor.authorRamírez, D.en_US
dc.contributor.authorFuentes, S.en_US
dc.contributor.authorLoayza, H.en_US
dc.contributor.authorNinanya, J.en_US
dc.contributor.authorRinza, J.en_US
dc.contributor.authorDavid, M.en_US
dc.contributor.authorGamboa, S.en_US
dc.contributor.authorBoeck, B. deen_US
dc.contributor.authorDíaz, F.en_US
dc.contributor.authorPérez, A.en_US
dc.contributor.authorSilva, L.en_US
dc.contributor.authorCampos, Hugoen_US
dc.date.accessioned2023-12-01T16:29:33Zen_US
dc.date.available2023-12-01T16:29:33Zen_US
dc.identifier.urihttps://hdl.handle.net/10568/134915en_US
dc.titleHigh-throughput characterization and phenotyping of resistance and tolerance to virus infection in sweetpotatoen_US
dcterms.abstractBreeders have made important efforts to develop genotypes able to resist virus attacks in sweetpotato, a major crop providing food security and poverty alleviation to smallholder farmers in many regions of Sub-Saharan Africa, Asia and Latin America. However, a lack of accurate objective quantitative methods for this selection target in sweetpotato prevents a consistent and extensive assessment of large breeding populations. In this study, an approach to characterize and classify resistance in sweetpotato was established by assessing total yield loss and virus load after the infection of the three most common viruses (SPFMV, SPCSV, SPLCV). Twelve sweetpotato genotypes with contrasting reactions to virus infection were grown in the field under three different treatments: pre-infected by the three viruses, un-infected and protected from re-infection, and un-infected but exposed to natural infection. Virus loads were assessed using ELISA, (RT-)qPCR, and loop-mediated isothermal amplification (LAMP) methods, and also through multispectral reflectance and canopy temperature collected using an unmanned aerial vehicle. Total yield reduction compared to control and the arithmetic sum of (RT-)qPCR relative expression ratios were used to classify genotypes into four categories: resistant, tolerant, susceptible, and sensitives. Using 14 remote sensing predictors, machine learning algorithms were trained to classify all plots under the said categories. The study found that remotely sensed predictors were effective in discriminating the different virus response categories. The results suggest that using machine learning and remotely sensed data, further complemented by fast and sensitive LAMP assays to confirm results of predicted classifications could be used as a high throughput approach to support virus resistance phenotyping in sweetpotato breeding.en_US
dcterms.accessRightsOpen Accessen_US
dcterms.audienceAcademicsen_US
dcterms.audienceCGIARen_US
dcterms.audienceDevelopment Practitionersen_US
dcterms.audienceDonorsen_US
dcterms.audienceExtensionen_US
dcterms.audienceFarmersen_US
dcterms.audienceGeneral Publicen_US
dcterms.audienceNGOsen_US
dcterms.audiencePolicy Makersen_US
dcterms.audienceScientistsen_US
dcterms.available2023-11-25en_US
dcterms.bibliographicCitationKreuze, J.F.; Ramírez, D.; Fuentes, S.; Loayza, H.; Ninanya, J.; David, M.; Gamboa, S.; Boeck, B. de; Pérez, A.; Silva, L.; Campos, H. 2023. High-throughput characterization and phenotyping of resistance and tolerance to virus infection in sweetpotato. Virus Research.en_US
dcterms.extent13 p.en_US
dcterms.issued2023-11-25en_US
dcterms.languageenen_US
dcterms.licenseCC-BY-4.0en_US
dcterms.publisherElsevieren_US
dcterms.subjectsweet potatoesen_US
dcterms.subjectipomoea batatasen_US
dcterms.subjectmachine learningen_US
dcterms.subjectremote sensingen_US
dcterms.typeJournal Articleen_US

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