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Assessment of water and nitrogen use efficiencies through UAV-based multispectral phenotyping in winter wheat

Author: Mengjiao Yang
Author: Hassan, M.A.
Author: Kaijie Xu
Author: Chengyan Zheng
Author: Rasheed, A.
Author: Yong Zhang
Author: Xiuliang Jin
Author: Xianchun Xia
Author: Yonggui Xiao
Author: He Zhonghu
Year: 2020
ISSN: 1664-462X (Print)
URI: https://hdl.handle.net/10883/20944
Format: PDF
Language: English
Publisher: Frontiers
Copyright: CIMMYT manages Intellectual Assets as International Public Goods. The user is free to download, print, store and share this work. In case you want to translate or create any other derivative work and share or distribute such translation/derivative work, please contact CIMMYT-Knowledge-Center@cgiar.org indicating the work you want to use and the kind of use you intend; CIMMYT will contact you with the suitable license for that purpose.
Type: Article
Place of Publication: Switzerland
Issue: art. 927
Volume: 11
DOI: 10.3389/fpls.2020.00927
Description: Unmanned aerial vehicle (UAV) based remote sensing is a promising approach for non-destructive and high-throughput assessment of crop water and nitrogen (N) efficiencies. In this study, UAV was used to evaluate two field trials using four water (T0 = 0 mm, T1 = 80 mm, T2 = 120 mm, and T3 = 160 mm), and four N (T0 = 0, T1 = 120 kg ha–1, T2 = 180 kg ha–1, and T3 = 240 kg ha–1) treatments, respectively, conducted on three wheat genotypes at two locations. Ground-based destructive data of water and N indictors such as biomass and N contents were also measured to validate the aerial surveillance results. Multispectral traits including red normalized difference vegetation index (RNDVI), green normalized difference vegetation index (GNDVI), normalized difference red-edge index (NDRE), red-edge chlorophyll index (RECI) and normalized green red difference index (NGRDI) were recorded using UAV as reliable replacement of destructive measurements by showing high r values up to 0.90. NGRDI was identified as the most efficient non-destructive indicator through strong prediction values ranged from R2 = 0.69 to 0.89 for water use efficiencies (WUE) calculated from biomass (WUE.BM), and R2 = 0.80 to 0.86 from grain yield (WUE.GY). RNDVI was better in predicting the phenotypic variations for N use efficiency calculated from nitrogen contents of plant samples (NUE.NC) with high R2 values ranging from 0.72 to 0.94, while NDRE was consistent in predicting both NUE.NC and NUE.GY by 0.73 to 0.84 with low root mean square errors. UAV-based remote sensing demonstrates that treatment T2 in both water 120 mm and N 180 kg ha–1 supply trials was most appropriate dosages for optimum uptake of water and N with high GY. Among three cultivars, Zhongmai 895 was highly efficient in WUE and NUE across the water and N treatments. Conclusively, UAV can be used to predict time-series WUE and NUE across the season for selection of elite genotypes, and to monitor crop efficiency under varying N and water dosages.
Agrovoc: NITROGEN CONTENT
Agrovoc: PHENOTYPES
Agrovoc: VEGETATION INDEX
Agrovoc: UNMANNED AERIAL VEHICLES
Agrovoc: USE EFFICIENCY
Agrovoc: MOISTURE CONTENT
Agrovoc: WHEAT
Related Datasets: https://figshare.com/collections/Assessment_of_Water_and_Nitrogen_Use_Efficiencies_Through_UAV-Based_Multispectral_Phenotyping_in_Winter_Wheat/5041037
Journal: Frontiers in Plant Science


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  • Wheat
    Wheat - breeding, phytopathology, physiology, quality, biotech

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