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Assessment of Water and Nitrogen Use Efficiencies Through UAV-Based Multispectral Phenotyping in Winter Wheat

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),...

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Autores principales: Yang, Mengjiao, Hassan, Muhammad Adeel, Xu, Kaijie, Zheng, Chengyan, Rasheed, Awais, Zhang, Yong, Jin, Xiuliang, Xia, Xianchun, Xiao, Yonggui, He, Zhonghu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7333459/
https://www.ncbi.nlm.nih.gov/pubmed/32676089
http://dx.doi.org/10.3389/fpls.2020.00927
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author Yang, Mengjiao
Hassan, Muhammad Adeel
Xu, Kaijie
Zheng, Chengyan
Rasheed, Awais
Zhang, Yong
Jin, Xiuliang
Xia, Xianchun
Xiao, Yonggui
He, Zhonghu
author_facet Yang, Mengjiao
Hassan, Muhammad Adeel
Xu, Kaijie
Zheng, Chengyan
Rasheed, Awais
Zhang, Yong
Jin, Xiuliang
Xia, Xianchun
Xiao, Yonggui
He, Zhonghu
author_sort Yang, Mengjiao
collection PubMed
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 R(2) = 0.69 to 0.89 for water use efficiencies (WUE) calculated from biomass (WUE.BM), and R(2) = 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 R(2) 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.
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spelling pubmed-73334592020-07-15 Assessment of Water and Nitrogen Use Efficiencies Through UAV-Based Multispectral Phenotyping in Winter Wheat Yang, Mengjiao Hassan, Muhammad Adeel Xu, Kaijie Zheng, Chengyan Rasheed, Awais Zhang, Yong Jin, Xiuliang Xia, Xianchun Xiao, Yonggui He, Zhonghu Front Plant Sci Plant Science 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 R(2) = 0.69 to 0.89 for water use efficiencies (WUE) calculated from biomass (WUE.BM), and R(2) = 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 R(2) 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. Frontiers Media S.A. 2020-06-26 /pmc/articles/PMC7333459/ /pubmed/32676089 http://dx.doi.org/10.3389/fpls.2020.00927 Text en Copyright © 2020 Yang, Hassan, Xu, Zheng, Rasheed, Zhang, Jin, Xia, Xiao and He. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Plant Science
Yang, Mengjiao
Hassan, Muhammad Adeel
Xu, Kaijie
Zheng, Chengyan
Rasheed, Awais
Zhang, Yong
Jin, Xiuliang
Xia, Xianchun
Xiao, Yonggui
He, Zhonghu
Assessment of Water and Nitrogen Use Efficiencies Through UAV-Based Multispectral Phenotyping in Winter Wheat
title Assessment of Water and Nitrogen Use Efficiencies Through UAV-Based Multispectral Phenotyping in Winter Wheat
title_full Assessment of Water and Nitrogen Use Efficiencies Through UAV-Based Multispectral Phenotyping in Winter Wheat
title_fullStr Assessment of Water and Nitrogen Use Efficiencies Through UAV-Based Multispectral Phenotyping in Winter Wheat
title_full_unstemmed Assessment of Water and Nitrogen Use Efficiencies Through UAV-Based Multispectral Phenotyping in Winter Wheat
title_short Assessment of Water and Nitrogen Use Efficiencies Through UAV-Based Multispectral Phenotyping in Winter Wheat
title_sort assessment of water and nitrogen use efficiencies through uav-based multispectral phenotyping in winter wheat
topic Plant Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7333459/
https://www.ncbi.nlm.nih.gov/pubmed/32676089
http://dx.doi.org/10.3389/fpls.2020.00927
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