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Li Y, Zhang P, Sheng W, Zhang Z, Rose RJ, Song Y. Securing maize reproductive success under drought stress by harnessing CO 2 fertilization for greater productivity. FRONTIERS IN PLANT SCIENCE 2023; 14:1221095. [PMID: 37860252 PMCID: PMC10582713 DOI: 10.3389/fpls.2023.1221095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Accepted: 09/19/2023] [Indexed: 10/21/2023]
Abstract
Securing maize grain yield is crucial to meet food and energy needs for the future growing population, especially under frequent drought events and elevated CO2 (eCO2) due to climate change. To maximize the kernel setting rate under drought stress is a key strategy in battling against the negative impacts. Firstly, we summarize the major limitations to leaf source and kernel sink in maize under drought stress, and identified that loss in grain yield is mainly attributed to reduced kernel set. Reproductive drought tolerance can be realized by collective contribution with a greater assimilate import into ear, more available sugars for ovary and silk use, and higher capacity to remobilize assimilate reserve. As such, utilization of CO2 fertilization by improved photosynthesis and greater reserve remobilization is a key strategy for coping with drought stress under climate change condition. We propose that optimizing planting methods and mining natural genetic variation still need to be done continuously, meanwhile, by virtue of advanced genetic engineering and plant phenomics tools, the breeding program of higher photosynthetic efficiency maize varieties adapted to eCO2 can be accelerated. Consequently, stabilizing maize production under drought stress can be achieved by securing reproductive success by harnessing CO2 fertilization.
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Affiliation(s)
- Yangyang Li
- College of Agronomy, Anhui Agricultural University, Hefei, Anhui, China
| | - Pengpeng Zhang
- College of Agronomy, Anhui Agricultural University, Hefei, Anhui, China
| | - Wenjing Sheng
- College of Agronomy, Anhui Agricultural University, Hefei, Anhui, China
| | - Zixiang Zhang
- College of Agronomy, Anhui Agricultural University, Hefei, Anhui, China
| | - Ray J. Rose
- School of Environmental and Life Sciences, The University of Newcastle, Newcastle, NSW, Australia
| | - Youhong Song
- College of Agronomy, Anhui Agricultural University, Hefei, Anhui, China
- Centre for Crop Science, Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, QLD, Australia
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2
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Varghese R, Cherukuri AK, Doddrell NH, Doss CGP, Simkin AJ, Ramamoorthy S. Machine learning in photosynthesis: Prospects on sustainable crop development. PLANT SCIENCE : AN INTERNATIONAL JOURNAL OF EXPERIMENTAL PLANT BIOLOGY 2023; 335:111795. [PMID: 37473784 DOI: 10.1016/j.plantsci.2023.111795] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Revised: 07/10/2023] [Accepted: 07/13/2023] [Indexed: 07/22/2023]
Abstract
Improving photosynthesis is a promising avenue to increase food security. Studying photosynthetic traits with the aim to improve efficiency has been one of many strategies to increase crop yield but analyzing large data sets presents an ongoing challenge. Machine learning (ML) represents a ubiquitous tool that can provide a more elaborate data analysis. Here we review the application of ML in various domains of photosynthetic research, as well as in photosynthetic pigment studies. We highlight how correlating hyperspectral data with photosynthetic parameters to improve crop yield could be achieved through various ML algorithms. We also propose strategies to employ ML in promoting photosynthetic pigment research for furthering crop yield.
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Affiliation(s)
- Ressin Varghese
- School of Bio Sciences and Technology, VIT University, Vellore 632014, Tamil Nadu, India
| | - Aswani Kumar Cherukuri
- School of Information Technology and Engineering, VIT University, Vellore 632014, Tamil Nadu, India
| | | | - C George Priya Doss
- School of Bio Sciences and Technology, VIT University, Vellore 632014, Tamil Nadu, India
| | - Andrew J Simkin
- School of Biosciences, University of Kent, Canterbury CT2 7NJ, UK; School of Life Sciences, University of Essex, Wivenhoe Park, Colchester CO4 3SQ, UK
| | - Siva Ramamoorthy
- School of Bio Sciences and Technology, VIT University, Vellore 632014, Tamil Nadu, India.
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3
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Wong CYS. Plant optics: underlying mechanisms in remotely sensed signals for phenotyping applications. AOB PLANTS 2023; 15:plad039. [PMID: 37560760 PMCID: PMC10407989 DOI: 10.1093/aobpla/plad039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Accepted: 07/04/2023] [Indexed: 08/11/2023]
Abstract
Optical-based remote sensing offers great potential for phenotyping vegetation traits and functions for a range of applications including vegetation monitoring and assessment. A key strength of optical-based approaches is the underlying mechanistic link to vegetation physiology, biochemistry, and structure that influences a spectral signal. By exploiting spectral variation driven by plant physiological response to environment, remotely sensed products can be used to estimate vegetation traits and functions. However, oftentimes these products are proxies based on covariance, which can lead to misinterpretation and decoupling under certain scenarios. This viewpoint will discuss (i) the optical properties of vegetation, (ii) applications of vegetation indices, solar-induced fluorescence, and machine-learning approaches, and (iii) how covariance can lead to good empirical proximation of plant traits and functions. Understanding and acknowledging the underlying mechanistic basis of plant optics must be considered as remotely sensed data availability and applications continue to grow. Doing so will enable appropriate application and consideration of limitations for the use of optical-based remote sensing for phenotyping applications.
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Sun Y, Wen J, Gu L, Joiner J, Chang CY, van der Tol C, Porcar-Castell A, Magney T, Wang L, Hu L, Rascher U, Zarco-Tejada P, Barrett CB, Lai J, Han J, Luo Z. From remotely-sensed solar-induced chlorophyll fluorescence to ecosystem structure, function, and service: Part II-Harnessing data. GLOBAL CHANGE BIOLOGY 2023; 29:2893-2925. [PMID: 36802124 DOI: 10.1111/gcb.16646] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Revised: 02/09/2023] [Accepted: 02/14/2023] [Indexed: 05/03/2023]
Abstract
Although our observing capabilities of solar-induced chlorophyll fluorescence (SIF) have been growing rapidly, the quality and consistency of SIF datasets are still in an active stage of research and development. As a result, there are considerable inconsistencies among diverse SIF datasets at all scales and the widespread applications of them have led to contradictory findings. The present review is the second of the two companion reviews, and data oriented. It aims to (1) synthesize the variety, scale, and uncertainty of existing SIF datasets, (2) synthesize the diverse applications in the sector of ecology, agriculture, hydrology, climate, and socioeconomics, and (3) clarify how such data inconsistency superimposed with the theoretical complexities laid out in (Sun et al., 2023) may impact process interpretation of various applications and contribute to inconsistent findings. We emphasize that accurate interpretation of the functional relationships between SIF and other ecological indicators is contingent upon complete understanding of SIF data quality and uncertainty. Biases and uncertainties in SIF observations can significantly confound interpretation of their relationships and how such relationships respond to environmental variations. Built upon our syntheses, we summarize existing gaps and uncertainties in current SIF observations. Further, we offer our perspectives on innovations needed to help improve informing ecosystem structure, function, and service under climate change, including enhancing in-situ SIF observing capability especially in "data desert" regions, improving cross-instrument data standardization and network coordination, and advancing applications by fully harnessing theory and data.
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Affiliation(s)
- Ying Sun
- School of Integrative Plant Science, Soil and Crop Sciences Section, Cornell University, Ithaca, New York, USA
| | - Jiaming Wen
- School of Integrative Plant Science, Soil and Crop Sciences Section, Cornell University, Ithaca, New York, USA
| | - Lianhong Gu
- Environmental Sciences Division and Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA
| | - Joanna Joiner
- National Aeronautics and Space Administration (NASA) Goddard Space Flight Center (GSFC), Greenbelt, Maryland, USA
| | - Christine Y Chang
- US Department of Agriculture, Agricultural Research Service, Adaptive Cropping Systems Laboratory, Beltsville, Maryland, USA
| | - Christiaan van der Tol
- Affiliation Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, Enschede, The Netherlands
| | - Albert Porcar-Castell
- Optics of Photosynthesis Laboratory, Institute for Atmospheric and Earth System Research (INAR)/Forest Sciences, Viikki Plant Science Center (ViPS), University of Helsinki, Helsinki, Finland
| | - Troy Magney
- Department of Plant Sciences, University of California, Davis, Davis, California, USA
| | - Lixin Wang
- Department of Earth Sciences, Indiana University-Purdue University Indianapolis (IUPUI), Indianapolis, Indiana, USA
| | - Leiqiu Hu
- Department of Atmospheric and Earth Science, University of Alabama in Huntsville, Huntsville, Alabama, USA
| | - Uwe Rascher
- Institute of Bio- and Geosciences, Forschungszentrum Jülich GmbH, Jülich, Germany
| | - Pablo Zarco-Tejada
- School of Agriculture and Food (SAF-FVAS) and Faculty of Engineering and Information Technology (IE-FEIT), University of Melbourne, Melbourne, Victoria, Australia
| | - Christopher B Barrett
- Charles H. Dyson School of Applied Economics and Management, Cornell University, Ithaca, New York, USA
| | - Jiameng Lai
- School of Integrative Plant Science, Soil and Crop Sciences Section, Cornell University, Ithaca, New York, USA
| | - Jimei Han
- School of Integrative Plant Science, Soil and Crop Sciences Section, Cornell University, Ithaca, New York, USA
| | - Zhenqi Luo
- School of Integrative Plant Science, Soil and Crop Sciences Section, Cornell University, Ithaca, New York, USA
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Ding H, Wang Z, Zhang Y, Li J, Jia L, Chen Q, Ding Y, Wang S. A Mechanistic Model for Estimating Rice Photosynthetic Capacity and Stomatal Conductance from Sun-Induced Chlorophyll Fluorescence. PLANT PHENOMICS (WASHINGTON, D.C.) 2023; 5:0047. [PMID: 37228514 PMCID: PMC10204737 DOI: 10.34133/plantphenomics.0047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/23/2022] [Accepted: 04/12/2023] [Indexed: 05/27/2023]
Abstract
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 (Vcmax) 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 Vcmax 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, Vcmax 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 Vcmax and gs with high accuracy (R2 > 0.8). Compared to simple linear regression model, the proposed model could increase the accuracy of Vcmax 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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Affiliation(s)
- Hao Ding
- Jiangsu Collaborative Innovation Center for Modern Crop Production/Key Laboratory of Crop Physiology and Ecology in Southern China,
Nanjing Agricultural University, Nanjing, China
| | - Zihao Wang
- Jiangsu Collaborative Innovation Center for Modern Crop Production/Key Laboratory of Crop Physiology and Ecology in Southern China,
Nanjing Agricultural University, Nanjing, China
| | - Yongguang Zhang
- International Institute for Earth System Sciences, Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application,
Nanjing University, Nanjing, China
| | - Ji Li
- International Institute for Earth System Sciences, Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application,
Nanjing University, Nanjing, China
| | - Li Jia
- State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute,
Chinese Academy of Sciences, Beijing 100101, China
| | - Qiting Chen
- State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute,
Chinese Academy of Sciences, Beijing 100101, China
| | - Yanfeng Ding
- Jiangsu Collaborative Innovation Center for Modern Crop Production/Key Laboratory of Crop Physiology and Ecology in Southern China,
Nanjing Agricultural University, Nanjing, China
| | - Songhan Wang
- Jiangsu Collaborative Innovation Center for Modern Crop Production/Key Laboratory of Crop Physiology and Ecology in Southern China,
Nanjing Agricultural University, Nanjing, China
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6
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Peng H, Cendrero-Mateo MP, Bendig J, Siegmann B, Acebron K, Kneer C, Kataja K, Muller O, Rascher U. HyScreen: A Ground-Based Imaging System for High-Resolution Red and Far-Red Solar-Induced Chlorophyll Fluorescence. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22239443. [PMID: 36502141 PMCID: PMC9740991 DOI: 10.3390/s22239443] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Revised: 11/28/2022] [Accepted: 11/28/2022] [Indexed: 05/14/2023]
Abstract
Solar-induced chlorophyll fluorescence (SIF) is used as a proxy of photosynthetic efficiency. However, interpreting top-of-canopy (TOC) SIF in relation to photosynthesis remains challenging due to the distortion introduced by the canopy's structural effects (i.e., fluorescence re-absorption, sunlit-shaded leaves, etc.) and sun-canopy-sensor geometry (i.e., direct radiation infilling). Therefore, ground-based, high-spatial-resolution data sets are needed to characterize the described effects and to be able to downscale TOC SIF to the leafs where the photosynthetic processes are taking place. We herein introduce HyScreen, a ground-based push-broom hyperspectral imaging system designed to measure red (F687) and far-red (F760) SIF and vegetation indices from TOC with single-leaf spatial resolution. This paper presents measurement protocols, the data processing chain and a case study of SIF retrieval. Raw data from two imaging sensors were processed to top-of-canopy radiance by dark-current correction, radiometric calibration, and empirical line correction. In the next step, the improved Fraunhofer line descrimination (iFLD) and spectral-fitting method (SFM) were used for SIF retrieval, and vegetation indices were calculated. With the developed protocol and data processing chain, we estimated a signal-to-noise ratio (SNR) between 50 and 200 from reference panels with reflectance from 5% to 95% and noise equivalent radiance (NER) of 0.04 (5%) to 0.18 (95%) mW m-2 sr-1 nm-1. The results from the case study showed that non-vegetation targets had SIF values close to 0 mW m-2 sr-1 nm-1, whereas vegetation targets had a mean F687 of 1.13 and F760 of 1.96 mW m-2 sr-1 nm-1 from the SFM method. HyScreen showed good performance for SIF retrievals at both F687 and F760; nevertheless, we recommend further adaptations to correct for the effects of noise, varying illumination and sensor optics. In conclusion, due to its high spatial resolution, Hyscreen is a promising tool for investigating the relationship between leafs and TOC SIF as well as their relationship with plants' photosynthetic capacity.
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Affiliation(s)
- Huaiyue Peng
- Institute of Bio- and Geosciences, IBG-2: Plant Sciences, Forschungszentrum Jülich GmbH, 52428 Jülich, Germany
- Correspondence: ; Tel.: +49-(0)-2461-61-4514
| | - Maria Pilar Cendrero-Mateo
- Laboratory of Earth Observation, Image Processing Laboratory, University of Valencia, 46980 Paterna, Spain
| | - Juliane Bendig
- Institute of Bio- and Geosciences, IBG-2: Plant Sciences, Forschungszentrum Jülich GmbH, 52428 Jülich, Germany
| | - Bastian Siegmann
- Institute of Bio- and Geosciences, IBG-2: Plant Sciences, Forschungszentrum Jülich GmbH, 52428 Jülich, Germany
| | - Kelvin Acebron
- Institute of Bio- and Geosciences, IBG-2: Plant Sciences, Forschungszentrum Jülich GmbH, 52428 Jülich, Germany
| | - Caspar Kneer
- Institute of Bio- and Geosciences, IBG-2: Plant Sciences, Forschungszentrum Jülich GmbH, 52428 Jülich, Germany
| | - Kari Kataja
- Specim Spectral Imaging Ltd., 90590 Oulu, Finland
| | - Onno Muller
- Institute of Bio- and Geosciences, IBG-2: Plant Sciences, Forschungszentrum Jülich GmbH, 52428 Jülich, Germany
| | - Uwe Rascher
- Institute of Bio- and Geosciences, IBG-2: Plant Sciences, Forschungszentrum Jülich GmbH, 52428 Jülich, Germany
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7
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Pandiyan S, Govindjee G, Meenatchi S, Prasanna S, Gunasekaran G, Guo Y. Evaluating the Impact of Summer Drought on Vegetation Growth Using Space-Based Solar-Induced Chlorophyll Fluorescence Across Extensive Spatial Measures. BIG DATA 2022; 10:230-245. [PMID: 33983846 DOI: 10.1089/big.2020.0350] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Drought is the primary and dominant natural cause of stress on vegetation, and thus, it needs our full attention. Current understanding of drought across extensive spatial measures, around the world, is considerably limited. As case studies to evaluate the feasibility of utilizing space-based solar-induced chlorophyll fluorescence (SIF) across extensive spatial measures, here, we have used data from 2007 to 2017 in Heilongjiang and Jiangsu provinces of China. The onset of the 2015 drought was accompanied by a substantial response of SIF from vegetation in both the provinces; these data were associated with changes in soil moisture, standardized precipitation evapotranspiration index, and emissivity. Our findings suggest that SIF can effectively provide the spatial and temporal progress of drought, as inferred through substantial associations with SIF normalized by absorbed photosynthetically active radiation (related to ΦF) and by photosynthetically active radiation (SIFpar). For the depiction of onset to drought, SIF, ΦF, and SIFpar provide a significant association and a quicker response than the leaf area index and the normalized difference vegetation index. Furthermore, we found that the correlation between gross primary productivity and SIF is highly substantial in both Heilongjiang (R2 = 0.85, p < 0.001) and Jiangsu (R2 = 0.75, p < 0.001) during the drought period. Our results indicate that continuing evaluation from space-based SIF can indeed provide an understanding of the seasonal differences in vegetation for evaluating the impact of drought across extensive spatial measures.
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Affiliation(s)
- Sanjeevi Pandiyan
- Key Laboratory of Advanced Process Control for Light Industry, Ministry of Education, Jiangnan University, Wuxi, China
| | - Govindjee Govindjee
- Department of Plant Biology, Department of Biochemistry, and Center of Biophysics & Quantitative Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | - S Meenatchi
- School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, India
| | - S Prasanna
- School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, India
| | - G Gunasekaran
- School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, India
| | - Ya Guo
- Key Laboratory of Advanced Process Control for Light Industry, Ministry of Education, Jiangnan University, Wuxi, China
- Department of Bioengineering, University of Missouri, Columbia, Missouri, USA
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8
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Fu P, Montes CM, Siebers MH, Gomez-Casanovas N, McGrath JM, Ainsworth EA, Bernacchi CJ. Advances in field-based high-throughput photosynthetic phenotyping. JOURNAL OF EXPERIMENTAL BOTANY 2022; 73:3157-3172. [PMID: 35218184 PMCID: PMC9126737 DOI: 10.1093/jxb/erac077] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Accepted: 02/23/2022] [Indexed: 05/22/2023]
Abstract
Gas exchange techniques revolutionized plant research and advanced understanding, including associated fluxes and efficiencies, of photosynthesis, photorespiration, and respiration of plants from cellular to ecosystem scales. These techniques remain the gold standard for inferring photosynthetic rates and underlying physiology/biochemistry, although their utility for high-throughput phenotyping (HTP) of photosynthesis is limited both by the number of gas exchange systems available and the number of personnel available to operate the equipment. Remote sensing techniques have long been used to assess ecosystem productivity at coarse spatial and temporal resolutions, and advances in sensor technology coupled with advanced statistical techniques are expanding remote sensing tools to finer spatial scales and increasing the number and complexity of phenotypes that can be extracted. In this review, we outline the photosynthetic phenotypes of interest to the plant science community and describe the advances in high-throughput techniques to characterize photosynthesis at spatial scales useful to infer treatment or genotypic variation in field-based experiments or breeding trials. We will accomplish this objective by presenting six lessons learned thus far through the development and application of proximal/remote sensing-based measurements and the accompanying statistical analyses. We will conclude by outlining what we perceive as the current limitations, bottlenecks, and opportunities facing HTP of photosynthesis.
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Affiliation(s)
- Peng Fu
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Champaign, IL, USA
- Department of Plant Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Department of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Christopher M Montes
- Department of Plant Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Department of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- United States Department of Agriculture, Global Change and Photosynthesis Research Unit, Agricultural Research Service, Urbana, IL, USA
| | - Matthew H Siebers
- Department of Plant Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Department of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- United States Department of Agriculture, Global Change and Photosynthesis Research Unit, Agricultural Research Service, Urbana, IL, USA
| | - Nuria Gomez-Casanovas
- Department of Plant Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Department of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Institute for Sustainability, Energy & Environment, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Justin M McGrath
- Department of Plant Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Department of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- United States Department of Agriculture, Global Change and Photosynthesis Research Unit, Agricultural Research Service, Urbana, IL, USA
| | - Elizabeth A Ainsworth
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Champaign, IL, USA
- Department of Plant Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Department of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- United States Department of Agriculture, Global Change and Photosynthesis Research Unit, Agricultural Research Service, Urbana, IL, USA
- Institute for Sustainability, Energy & Environment, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Carl J Bernacchi
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Champaign, IL, USA
- Department of Plant Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Department of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- United States Department of Agriculture, Global Change and Photosynthesis Research Unit, Agricultural Research Service, Urbana, IL, USA
- Institute for Sustainability, Energy & Environment, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Correspondence:
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9
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Han J, Gu L, Wen J, Sun Y. Inference of photosynthetic capacity parameters from chlorophyll a fluorescence is affected by redox state of PSII reaction centers. PLANT, CELL & ENVIRONMENT 2022; 45:1298-1314. [PMID: 35098552 DOI: 10.1111/pce.14271] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/28/2021] [Accepted: 12/29/2021] [Indexed: 06/14/2023]
Abstract
Solar-induced chlorophyll fluorescence (SIF) has been used to infer photosynthetic capacity parameters (e.g., the maximum carboxylation rate Vcmax , and the maximum electron transport rate Jmax ). However, the precise mechanism and practical utility of such approach under dynamic environments remain unclear. We used the balance between the light and carbon reactions to derive theoretical equations relating chlorophyll a fluorescence (ChlF) emission and photosynthetic capacity parameters, and formulated testable hypotheses regarding the dynamic relationships between the true total ChlF emitted from PSII (SIFPSII ) and Vcmax and Jmax . We employed concurrent measurements of gas exchanges and ChlF parameters for 15 species from six biomes to test the formulated hypotheses across species, temperatures, and limitation state of carboxylation. Our results revealed that SIFPSII alone is incapable of informing the variations in Vcmax and Jmax across species, even when SIFPSII is determined under the same environmental conditions. In contrast, the product of SIFPSII and the fraction of open PSII reactions qL , which indicates the redox state of PSII, is a strong predictor of both Vcmax and Jmax , although their precise relationships vary somewhat with environmental conditions. Our findings suggest the redox state of PSII strongly influences the relationship between SIFPSII and Vcmax and Jmax .
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Affiliation(s)
- Jimei Han
- College of Agriculture and Life Sciences, School of Integrative Plant Science, Soil and Crop Science Section, Cornell University, Ithaca, New York, USA
| | - Lianhong Gu
- Environmental Sciences Division and Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA
| | - Jiaming Wen
- College of Agriculture and Life Sciences, School of Integrative Plant Science, Soil and Crop Science Section, Cornell University, Ithaca, New York, USA
| | - Ying Sun
- College of Agriculture and Life Sciences, School of Integrative Plant Science, Soil and Crop Science Section, Cornell University, Ithaca, New York, USA
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10
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Emerging approaches to measure photosynthesis from the leaf to the ecosystem. Emerg Top Life Sci 2021; 5:261-274. [PMID: 33527993 PMCID: PMC8166339 DOI: 10.1042/etls20200292] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2020] [Revised: 01/12/2021] [Accepted: 01/14/2021] [Indexed: 12/03/2022]
Abstract
Measuring photosynthesis is critical for quantifying and modeling leaf to regional scale productivity of managed and natural ecosystems. This review explores existing and novel advances in photosynthesis measurements that are certain to provide innovative directions in plant science research. First, we address gas exchange approaches from leaf to ecosystem scales. Leaf level gas exchange is a mature method but recent improvements to the user interface and environmental controls of commercial systems have resulted in faster and higher quality data collection. Canopy chamber and micrometeorological methods have also become more standardized tools and have an advanced understanding of ecosystem functioning under a changing environment and through long time series data coupled with community data sharing. Second, we review proximal and remote sensing approaches to measure photosynthesis, including hyperspectral reflectance- and fluorescence-based techniques. These techniques have long been used with aircraft and orbiting satellites, but lower-cost sensors and improved statistical analyses are allowing these techniques to become applicable at smaller scales to quantify changes in the underlying biochemistry of photosynthesis. Within the past decade measurements of chlorophyll fluorescence from earth-orbiting satellites have measured Solar Induced Fluorescence (SIF) enabling estimates of global ecosystem productivity. Finally, we highlight that stronger interactions of scientists across disciplines will benefit our capacity to accurately estimate productivity at regional and global scales. Applying the multiple techniques outlined in this review at scales from the leaf to the globe are likely to advance understanding of plant functioning from the organelle to the ecosystem.
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