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A Mechanistic Model for Estimating Rice Photosynthetic Capacity and Stomatal Conductance from Sun-Induced Chlorophyll Fluorescence

Enhancing the photosynthetic rate is one of the effective ways to increase rice yield, given that photosynthesis is the basis of crop productivity. At the leaf level, crops’ photosynthetic rate is mainly determined by photosynthetic functional traits including the maximum carboxylation rate (V(cmax)...

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Detalles Bibliográficos
Autores principales: Ding, Hao, Wang, Zihao, Zhang, Yongguang, Li, Ji, Jia, Li, Chen, Qiting, Ding, Yanfeng, Wang, Songhan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: AAAS 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10204737/
https://www.ncbi.nlm.nih.gov/pubmed/37228514
http://dx.doi.org/10.34133/plantphenomics.0047
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author Ding, Hao
Wang, Zihao
Zhang, Yongguang
Li, Ji
Jia, Li
Chen, Qiting
Ding, Yanfeng
Wang, Songhan
author_facet Ding, Hao
Wang, Zihao
Zhang, Yongguang
Li, Ji
Jia, Li
Chen, Qiting
Ding, Yanfeng
Wang, Songhan
author_sort Ding, Hao
collection PubMed
description Enhancing the photosynthetic rate is one of the effective ways to increase rice yield, given that photosynthesis is the basis of crop productivity. At the leaf level, crops’ photosynthetic rate is mainly determined by photosynthetic functional traits including the maximum carboxylation rate (V(cmax)) and stomatal conductance (gs). Accurate quantification of these functional traits is important to simulate and predict the growth status of rice. In recent studies, the emerging sun-induced chlorophyll fluorescence (SIF) provides us an unprecedented opportunity to estimate crops’ photosynthetic traits, owing to its direct and mechanistic links to photosynthesis. Therefore, in this study, we proposed a practical semimechanistic model to estimate the seasonal V(cmax) and gs time-series based on SIF. We firstly generated the coupling relationship between the open ratio of photosystem II (qL) and photosynthetically active radiation (PAR), then estimate the electron transport rate (ETR) based on the proposed mechanistic relationship between SIF and ETR. Finally, V(cmax) and gs were estimated by linking to ETR based on the principle of evolutionary optimality and the photosynthetic pathway. Validation with field observations showed that our proposed model can estimate V(cmax) and gs with high accuracy (R(2) > 0.8). Compared to simple linear regression model, the proposed model could increase the accuracy of V(cmax) estimates by >40%. Therefore, the proposed method effectively enhanced the estimation accuracy of crops’ functional traits, which sheds new light on developing high-throughput monitoring techniques to estimate plant functional traits, and also can improve our understating of crops’ physiological response to climate change.
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spelling pubmed-102047372023-05-24 A Mechanistic Model for Estimating Rice Photosynthetic Capacity and Stomatal Conductance from Sun-Induced Chlorophyll Fluorescence Ding, Hao Wang, Zihao Zhang, Yongguang Li, Ji Jia, Li Chen, Qiting Ding, Yanfeng Wang, Songhan Plant Phenomics Research Article Enhancing the photosynthetic rate is one of the effective ways to increase rice yield, given that photosynthesis is the basis of crop productivity. At the leaf level, crops’ photosynthetic rate is mainly determined by photosynthetic functional traits including the maximum carboxylation rate (V(cmax)) and stomatal conductance (gs). Accurate quantification of these functional traits is important to simulate and predict the growth status of rice. In recent studies, the emerging sun-induced chlorophyll fluorescence (SIF) provides us an unprecedented opportunity to estimate crops’ photosynthetic traits, owing to its direct and mechanistic links to photosynthesis. Therefore, in this study, we proposed a practical semimechanistic model to estimate the seasonal V(cmax) and gs time-series based on SIF. We firstly generated the coupling relationship between the open ratio of photosystem II (qL) and photosynthetically active radiation (PAR), then estimate the electron transport rate (ETR) based on the proposed mechanistic relationship between SIF and ETR. Finally, V(cmax) and gs were estimated by linking to ETR based on the principle of evolutionary optimality and the photosynthetic pathway. Validation with field observations showed that our proposed model can estimate V(cmax) and gs with high accuracy (R(2) > 0.8). Compared to simple linear regression model, the proposed model could increase the accuracy of V(cmax) estimates by >40%. Therefore, the proposed method effectively enhanced the estimation accuracy of crops’ functional traits, which sheds new light on developing high-throughput monitoring techniques to estimate plant functional traits, and also can improve our understating of crops’ physiological response to climate change. AAAS 2023-05-09 /pmc/articles/PMC10204737/ /pubmed/37228514 http://dx.doi.org/10.34133/plantphenomics.0047 Text en Copyright © 2023 Hao Ding et al. https://creativecommons.org/licenses/by/4.0/Exclusive licensee Nanjing Agricultural University. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution License 4.0 (CC BY 4.0) (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Research Article
Ding, Hao
Wang, Zihao
Zhang, Yongguang
Li, Ji
Jia, Li
Chen, Qiting
Ding, Yanfeng
Wang, Songhan
A Mechanistic Model for Estimating Rice Photosynthetic Capacity and Stomatal Conductance from Sun-Induced Chlorophyll Fluorescence
title A Mechanistic Model for Estimating Rice Photosynthetic Capacity and Stomatal Conductance from Sun-Induced Chlorophyll Fluorescence
title_full A Mechanistic Model for Estimating Rice Photosynthetic Capacity and Stomatal Conductance from Sun-Induced Chlorophyll Fluorescence
title_fullStr A Mechanistic Model for Estimating Rice Photosynthetic Capacity and Stomatal Conductance from Sun-Induced Chlorophyll Fluorescence
title_full_unstemmed A Mechanistic Model for Estimating Rice Photosynthetic Capacity and Stomatal Conductance from Sun-Induced Chlorophyll Fluorescence
title_short A Mechanistic Model for Estimating Rice Photosynthetic Capacity and Stomatal Conductance from Sun-Induced Chlorophyll Fluorescence
title_sort mechanistic model for estimating rice photosynthetic capacity and stomatal conductance from sun-induced chlorophyll fluorescence
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10204737/
https://www.ncbi.nlm.nih.gov/pubmed/37228514
http://dx.doi.org/10.34133/plantphenomics.0047
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