Wheat yield and protein estimation with handheld- and UAV-based reflectance measurements

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dc.contributor.authorWalsh, Olga S.en
dc.contributor.authorMarshall, Julieten
dc.contributor.authorJackson, Chaden
dc.contributor.authorNambi, Evaen
dc.contributor.authorShafian, Sanazen
dc.contributor.authorJayawardena, Dileepa M.en
dc.contributor.authorLamichhane, Ritikaen
dc.contributor.authorOwusu Ansah, Emmanuellaen
dc.contributor.authorMcClintick-Chess, Jordan R.en
dc.date.accessioned2023-01-18T13:20:18Zen
dc.date.available2023-01-18T13:20:18Zen
dc.date.issued2022-09-27en
dc.date.updated2023-01-16T16:04:27Zen
dc.description.abstractPrecision agriculture provides efficient means of obtaining real-time data to guide nitrogen (N) management based on predicted crop profitability. This study was conducted to assess the efficacy of using in-season measurements (plant height, biomass weight, biomass N, soil plant analysis development [SPAD], GreenSeeker [GS] normalized difference vegetative index [NDVI], and unmanned aerial vehicle [UAV] NDVI) at Feekes 5 (tillering) and Feekes 10 (anthesis) to estimate wheat (Triticum aestivum L.) yield and protein. The secondary aim was to determine whether the accuracy of yield and protein prediction varies by wheat class and cultivar. Six cultivars—hard red spring (HRS) wheat ‘Jefferson’ and ‘SY Basalt’, hard white spring (HWS) wheat ‘Dayn’ and ‘UI Platinum’, and soft white spring (SWS) wheat ‘Seahawk’ and ‘UI Stone’—were planted at two locations in Idaho in 2018–2020. Plots were arranged in a randomized complete block design with four replications with each cultivar evaluated at seven N rates (0, 50, 100, 150, 200, 250, and 300 kg N ha–1). The determination of the Pearson correlation coefficients revealed that all parameters were linearly correlated with yield except for SPAD at Feekes 5 and biomass weight at Feekes 10. Although estimation of in-season grain protein remains a challenge, NDVI was strongly correlated with yield especially at Feekes 5. The accuracy of yield prediction was similar for all wheat classes. Comparable accuracy of yield estimation was achieved with GS NDVI and UAV NDVI. Both hand-held and aerial-based spectral measurements could be used to prescribe N rates to be applied during tiller formation when wheat yield can be optimized.en
dc.description.versionPublished versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1002/agg2.20309en
dc.identifier.eissn2639-6696en
dc.identifier.issn2639-6696en
dc.identifier.issue4en
dc.identifier.urihttp://hdl.handle.net/10919/113225en
dc.identifier.volume5en
dc.language.isoenen
dc.publisherWileyen
dc.rightsCreative Commons Attribution-NonCommercial-NoDerivatives 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/en
dc.subject2 Zero Hungeren
dc.titleWheat yield and protein estimation with handheld- and UAV-based reflectance measurementsen
dc.title.serialAgrosystems, Geosciences and Environmenten
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten
dc.type.otherJournal Articleen
pubs.organisational-group/Virginia Techen
pubs.organisational-group/Virginia Tech/Agriculture & Life Sciencesen
pubs.organisational-group/Virginia Tech/All T&R Facultyen
pubs.organisational-group/Virginia Tech/Agriculture & Life Sciences/CALS T&R Facultyen
pubs.organisational-group/Virginia Tech/Agriculture & Life Sciences/School of Plant and Environmental Sciencesen

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