Leaf area index based nitrogen diagnosis in irrigated lowland rice

Leaf area index (LAI) is used for crop growth monitoring in agronomic research, and is promising to diagnose the nitrogen (N) status of crops. This study was conducted to develop appropriate LAI-based N diagnostic models in irrigated lowland rice. Four field experiments were carried out in Jiangsu P...

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Main Authors: Xiao-jun LIU, Qiang CAO, Zhao-feng YUAN, Xia LIU, Xiao-ling WANG, Yong-chao TIAN, Wei-xing CAO, Yan ZHU
Format: Article
Language:English
Published: Elsevier 2018-01-01
Series:Journal of Integrative Agriculture
Subjects:
Online Access:http://www.sciencedirect.com/science/article/pii/S2095311917617143
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record_format Article
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language English
format Article
sources DOAJ
author Xiao-jun LIU
Qiang CAO
Zhao-feng YUAN
Xia LIU
Xiao-ling WANG
Yong-chao TIAN
Wei-xing CAO
Yan ZHU
spellingShingle Xiao-jun LIU
Qiang CAO
Zhao-feng YUAN
Xia LIU
Xiao-ling WANG
Yong-chao TIAN
Wei-xing CAO
Yan ZHU
Leaf area index based nitrogen diagnosis in irrigated lowland rice
Journal of Integrative Agriculture
leaf area index
rice
LAI-2000
nitrogen diagnosis
plant characters
author_facet Xiao-jun LIU
Qiang CAO
Zhao-feng YUAN
Xia LIU
Xiao-ling WANG
Yong-chao TIAN
Wei-xing CAO
Yan ZHU
author_sort Xiao-jun LIU
title Leaf area index based nitrogen diagnosis in irrigated lowland rice
title_short Leaf area index based nitrogen diagnosis in irrigated lowland rice
title_full Leaf area index based nitrogen diagnosis in irrigated lowland rice
title_fullStr Leaf area index based nitrogen diagnosis in irrigated lowland rice
title_full_unstemmed Leaf area index based nitrogen diagnosis in irrigated lowland rice
title_sort leaf area index based nitrogen diagnosis in irrigated lowland rice
publisher Elsevier
series Journal of Integrative Agriculture
issn 2095-3119
publishDate 2018-01-01
description Leaf area index (LAI) is used for crop growth monitoring in agronomic research, and is promising to diagnose the nitrogen (N) status of crops. This study was conducted to develop appropriate LAI-based N diagnostic models in irrigated lowland rice. Four field experiments were carried out in Jiangsu Province of East China from 2009 to 2014. Different N application rates and plant densities were used to generate contrasting conditions of N availability or population densities in rice. LAI was determined by LI-3000, and estimated indirectly by LAI-2000 during vegetative growth period. Group and individual plant characters (e.g., tiller number (TN) and plant height (H)) were investigated simultaneously. Two N indicators of plant N accumulation (NA) and N nutrition index (NNI) were measured as well. A calibration equation (LAI=1.7787LAI2000–0.8816, R2=0.870**) was developed for LAI-2000. The linear regression analysis showed a significant relationship between NA and actual LAI (R2=0.863**). For the NNI, the relative LAI (R2=0.808**) was a relatively unbiased variable in the regression than the LAI (R2=0.33**). The results were used to formulate two LAI-based N diagnostic models for irrigated lowland rice (NA=29.778LAI–5.9397; NNI=0.7705RLAI+0.2764). Finally, a simple LAI deterministic model was developed to estimate the actual LAI using the characters of TN and H (LAI=–0.3375(TH×H×0.01)2+3.665(TH×H×0.01)–1.8249, R2=0.875**). With these models, the N status of rice can be diagnosed conveniently in the field.
topic leaf area index
rice
LAI-2000
nitrogen diagnosis
plant characters
url http://www.sciencedirect.com/science/article/pii/S2095311917617143
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spelling doaj-d82cb5fd4bc143138a095aa89072b46e2021-06-08T04:38:59ZengElsevierJournal of Integrative Agriculture2095-31192018-01-01171111121Leaf area index based nitrogen diagnosis in irrigated lowland riceXiao-jun LIU0Qiang CAO1Zhao-feng YUAN2Xia LIU3Xiao-ling WANG4Yong-chao TIAN5Wei-xing CAO6Yan ZHU7National Engineering and Technology Center for Information Agriculture/Key Laboratory for Crop System Analysis and Decision Making, Ministry of Agriculture/Jiangsu Key Laboratory for Information Agriculture/Jiangsu Collaborative Innovation Center for Modern Crop Production/Nanjing Agricultural University, Nanjing 210095, P.R.ChinaNational Engineering and Technology Center for Information Agriculture/Key Laboratory for Crop System Analysis and Decision Making, Ministry of Agriculture/Jiangsu Key Laboratory for Information Agriculture/Jiangsu Collaborative Innovation Center for Modern Crop Production/Nanjing Agricultural University, Nanjing 210095, P.R.ChinaNational Engineering and Technology Center for Information Agriculture/Key Laboratory for Crop System Analysis and Decision Making, Ministry of Agriculture/Jiangsu Key Laboratory for Information Agriculture/Jiangsu Collaborative Innovation Center for Modern Crop Production/Nanjing Agricultural University, Nanjing 210095, P.R.ChinaNational Engineering and Technology Center for Information Agriculture/Key Laboratory for Crop System Analysis and Decision Making, Ministry of Agriculture/Jiangsu Key Laboratory for Information Agriculture/Jiangsu Collaborative Innovation Center for Modern Crop Production/Nanjing Agricultural University, Nanjing 210095, P.R.ChinaNational Engineering and Technology Center for Information Agriculture/Key Laboratory for Crop System Analysis and Decision Making, Ministry of Agriculture/Jiangsu Key Laboratory for Information Agriculture/Jiangsu Collaborative Innovation Center for Modern Crop Production/Nanjing Agricultural University, Nanjing 210095, P.R.ChinaNational Engineering and Technology Center for Information Agriculture/Key Laboratory for Crop System Analysis and Decision Making, Ministry of Agriculture/Jiangsu Key Laboratory for Information Agriculture/Jiangsu Collaborative Innovation Center for Modern Crop Production/Nanjing Agricultural University, Nanjing 210095, P.R.ChinaNational Engineering and Technology Center for Information Agriculture/Key Laboratory for Crop System Analysis and Decision Making, Ministry of Agriculture/Jiangsu Key Laboratory for Information Agriculture/Jiangsu Collaborative Innovation Center for Modern Crop Production/Nanjing Agricultural University, Nanjing 210095, P.R.ChinaCorrespondence ZHU Yan, Tel: +86-25-84396598, Fax: +86-25-84396672; National Engineering and Technology Center for Information Agriculture/Key Laboratory for Crop System Analysis and Decision Making, Ministry of Agriculture/Jiangsu Key Laboratory for Information Agriculture/Jiangsu Collaborative Innovation Center for Modern Crop Production/Nanjing Agricultural University, Nanjing 210095, P.R.ChinaLeaf area index (LAI) is used for crop growth monitoring in agronomic research, and is promising to diagnose the nitrogen (N) status of crops. This study was conducted to develop appropriate LAI-based N diagnostic models in irrigated lowland rice. Four field experiments were carried out in Jiangsu Province of East China from 2009 to 2014. Different N application rates and plant densities were used to generate contrasting conditions of N availability or population densities in rice. LAI was determined by LI-3000, and estimated indirectly by LAI-2000 during vegetative growth period. Group and individual plant characters (e.g., tiller number (TN) and plant height (H)) were investigated simultaneously. Two N indicators of plant N accumulation (NA) and N nutrition index (NNI) were measured as well. A calibration equation (LAI=1.7787LAI2000–0.8816, R2=0.870**) was developed for LAI-2000. The linear regression analysis showed a significant relationship between NA and actual LAI (R2=0.863**). For the NNI, the relative LAI (R2=0.808**) was a relatively unbiased variable in the regression than the LAI (R2=0.33**). The results were used to formulate two LAI-based N diagnostic models for irrigated lowland rice (NA=29.778LAI–5.9397; NNI=0.7705RLAI+0.2764). Finally, a simple LAI deterministic model was developed to estimate the actual LAI using the characters of TN and H (LAI=–0.3375(TH×H×0.01)2+3.665(TH×H×0.01)–1.8249, R2=0.875**). With these models, the N status of rice can be diagnosed conveniently in the field.http://www.sciencedirect.com/science/article/pii/S2095311917617143leaf area indexriceLAI-2000nitrogen diagnosisplant characters