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dc.contributor.authorThorp, Kelly
dc.contributor.authorThompson, Alison
dc.contributor.authorHarders, Sara
dc.contributor.authorFrench, Andrew
dc.contributor.authorWard, Richard
dc.date.accessioned2020-10-12T21:57:26Z
dc.date.available2020-10-12T21:57:26Z
dc.date.issued2018-10-25
dc.identifier.citationThorp, K. R., Thompson, A. L., Harders, S. J., French, A. N., & Ward, R. W. (2018). High-throughput phenotyping of crop water use efficiency via multispectral drone imagery and a daily soil water balance model. Remote Sensing, 10(11), 1682.en_US
dc.identifier.issn2072-4292
dc.identifier.doi10.3390/rs10111682
dc.identifier.urihttp://hdl.handle.net/10150/647670
dc.description.abstractImprovement of crop water use efficiency (CWUE), defined as crop yield per volume of water used, is an important goal for both crop management and breeding. While many technologies have been developed for measuring crop water use in crop management studies, rarely have these techniques been applied at the scale of breeding plots. The objective was to develop a high-throughput methodology for quantifying water use in a cotton breeding trial at Maricopa, AZ, USA in 2016 and 2017, using evapotranspiration (ET) measurements from a co-located irrigation management trial to evaluate the approach. Approximately weekly overflights with an unmanned aerial system provided multispectral imagery from which plot-level fractional vegetation cover (f(c)) was computed. The f(c) data were used to drive a daily ET-based soil water balance model for seasonal crop water use quantification. A mixed model statistical analysis demonstrated that differences in ET and CWUE could be discriminated among eight cotton varieties (p < 0.05), which were sown at two planting dates and managed with four irrigation levels. The results permitted breeders to identify cotton varieties with more favorable water use characteristics and higher CWUE, indicating that the methodology could become a useful tool for breeding selection.en_US
dc.description.sponsorshipCotton Incorporateden_US
dc.language.isoenen_US
dc.publisherMDPIen_US
dc.rightsCopyright © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).en_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_US
dc.subjectbreedingen_US
dc.subjectdroughten_US
dc.subjectevapotranspirationen_US
dc.subjectmodelingen_US
dc.subjectphenomicsen_US
dc.subjectremote sensingen_US
dc.subjectunmanned aerial systemen_US
dc.titleHigh-Throughput Phenotyping of Crop Water Use Efficiency via Multispectral Drone Imagery and a Daily Soil Water Balance Modelen_US
dc.typeArticleen_US
dc.identifier.eissn2072-4292
dc.contributor.departmentUniv Arizona, Maricopa Agr Ctren_US
dc.identifier.journalREMOTE SENSINGen_US
dc.description.noteOpen access journalen_US
dc.description.collectioninformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.en_US
dc.eprint.versionFinal published versionen_US
dc.identifier.piirs10111682
dc.source.journaltitleRemote Sensing
dc.source.volume10
dc.source.issue11
dc.source.beginpage1682
refterms.dateFOA2020-10-12T21:57:27Z


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Copyright © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Except where otherwise noted, this item's license is described as Copyright © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).