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Canopy Chlorophyll Density Based Index for Estimating Nitrogen Status and Predicting Grain Yield in Rice

Canopy chlorophyll density (Chl) has a pivotal role in diagnosing crop growth and nutrition status. The purpose of this study was to develop Chl based models for estimating N status and predicting grain yield of rice (Oryza sativa L.) with Leaf area index (LAI) and Chlorophyll concentration of the u...

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Autores principales: Liu, Xiaojun, Zhang, Ke, Zhang, Zeyu, Cao, Qiang, Lv, Zunfu, Yuan, Zhaofeng, Tian, Yongchao, Cao, Weixing, Zhu, Yan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5663930/
https://www.ncbi.nlm.nih.gov/pubmed/29163568
http://dx.doi.org/10.3389/fpls.2017.01829
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author Liu, Xiaojun
Zhang, Ke
Zhang, Zeyu
Cao, Qiang
Lv, Zunfu
Yuan, Zhaofeng
Tian, Yongchao
Cao, Weixing
Zhu, Yan
author_facet Liu, Xiaojun
Zhang, Ke
Zhang, Zeyu
Cao, Qiang
Lv, Zunfu
Yuan, Zhaofeng
Tian, Yongchao
Cao, Weixing
Zhu, Yan
author_sort Liu, Xiaojun
collection PubMed
description Canopy chlorophyll density (Chl) has a pivotal role in diagnosing crop growth and nutrition status. The purpose of this study was to develop Chl based models for estimating N status and predicting grain yield of rice (Oryza sativa L.) with Leaf area index (LAI) and Chlorophyll concentration of the upper leaves. Six field experiments were conducted in Jiangsu Province of East China during 2007, 2008, 2009, 2013, and 2014. Different N rates were applied to generate contrasting conditions of N availability in six Japonica cultivars (9915, 27123, Wuxiangjing 14, Wuyunjing 19, Yongyou 8, and Wuyunjing 24) and two Indica cultivars (Liangyoupei 9, YLiangyou 1). The SPAD values of the four uppermost leaves and LAI were measured from tillering to flowering growth stages. Two N indicators, leaf N accumulation (LNA) and plant N accumulation (PNA) were measured. The LAI estimated by LAI-2000 and LI-3050C were compared and calibrated with a conversion equation. A linear regression analysis showed significant relationships between Chl value and N indicators, the equations were as follows: PNA = (0.092 × Chl) − 1.179 (R(2) = 0.94, P < 0.001, relative root mean square error (RRMSE) = 0.196), LNA = (0.052 × Chl) − 0.269 (R(2) = 0.93, P < 0.001, RRMSE = 0.185). Standardized method was used to quantity the correlation between Chl value and grain yield, normalized yield = (0.601 × normalized Chl) + 0.400 (R(2) = 0.81, P < 0.001, RRMSE = 0.078). Independent experimental data also validated the use of Chl value to accurately estimate rice N status and predict grain yield.
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spelling pubmed-56639302017-11-21 Canopy Chlorophyll Density Based Index for Estimating Nitrogen Status and Predicting Grain Yield in Rice Liu, Xiaojun Zhang, Ke Zhang, Zeyu Cao, Qiang Lv, Zunfu Yuan, Zhaofeng Tian, Yongchao Cao, Weixing Zhu, Yan Front Plant Sci Plant Science Canopy chlorophyll density (Chl) has a pivotal role in diagnosing crop growth and nutrition status. The purpose of this study was to develop Chl based models for estimating N status and predicting grain yield of rice (Oryza sativa L.) with Leaf area index (LAI) and Chlorophyll concentration of the upper leaves. Six field experiments were conducted in Jiangsu Province of East China during 2007, 2008, 2009, 2013, and 2014. Different N rates were applied to generate contrasting conditions of N availability in six Japonica cultivars (9915, 27123, Wuxiangjing 14, Wuyunjing 19, Yongyou 8, and Wuyunjing 24) and two Indica cultivars (Liangyoupei 9, YLiangyou 1). The SPAD values of the four uppermost leaves and LAI were measured from tillering to flowering growth stages. Two N indicators, leaf N accumulation (LNA) and plant N accumulation (PNA) were measured. The LAI estimated by LAI-2000 and LI-3050C were compared and calibrated with a conversion equation. A linear regression analysis showed significant relationships between Chl value and N indicators, the equations were as follows: PNA = (0.092 × Chl) − 1.179 (R(2) = 0.94, P < 0.001, relative root mean square error (RRMSE) = 0.196), LNA = (0.052 × Chl) − 0.269 (R(2) = 0.93, P < 0.001, RRMSE = 0.185). Standardized method was used to quantity the correlation between Chl value and grain yield, normalized yield = (0.601 × normalized Chl) + 0.400 (R(2) = 0.81, P < 0.001, RRMSE = 0.078). Independent experimental data also validated the use of Chl value to accurately estimate rice N status and predict grain yield. Frontiers Media S.A. 2017-10-27 /pmc/articles/PMC5663930/ /pubmed/29163568 http://dx.doi.org/10.3389/fpls.2017.01829 Text en Copyright © 2017 Liu, Zhang, Zhang, Cao, Lv, Yuan, Tian, Cao and Zhu. 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) or licensor 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
Liu, Xiaojun
Zhang, Ke
Zhang, Zeyu
Cao, Qiang
Lv, Zunfu
Yuan, Zhaofeng
Tian, Yongchao
Cao, Weixing
Zhu, Yan
Canopy Chlorophyll Density Based Index for Estimating Nitrogen Status and Predicting Grain Yield in Rice
title Canopy Chlorophyll Density Based Index for Estimating Nitrogen Status and Predicting Grain Yield in Rice
title_full Canopy Chlorophyll Density Based Index for Estimating Nitrogen Status and Predicting Grain Yield in Rice
title_fullStr Canopy Chlorophyll Density Based Index for Estimating Nitrogen Status and Predicting Grain Yield in Rice
title_full_unstemmed Canopy Chlorophyll Density Based Index for Estimating Nitrogen Status and Predicting Grain Yield in Rice
title_short Canopy Chlorophyll Density Based Index for Estimating Nitrogen Status and Predicting Grain Yield in Rice
title_sort canopy chlorophyll density based index for estimating nitrogen status and predicting grain yield in rice
topic Plant Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5663930/
https://www.ncbi.nlm.nih.gov/pubmed/29163568
http://dx.doi.org/10.3389/fpls.2017.01829
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