1
|
Whippo CW, Saliendra NZ, Liebig MA. Cover crop inclusion and residue retention improves soybean production and physiology in drought conditions. Heliyon 2024; 10:e29838. [PMID: 38699707 PMCID: PMC11063448 DOI: 10.1016/j.heliyon.2024.e29838] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Revised: 04/15/2024] [Accepted: 04/16/2024] [Indexed: 05/05/2024] Open
Abstract
Soybean (Glycine max (L.) Merr.) planting has increased in central and western North Dakota despite frequent drought occurrences that limit productivity. Soybean plants need high photosynthetic and transpiration rates to be productive, but they also need high water use efficiency when water is limited. Crop residues and cover crops in crop rotations may improve soybean drought tolerance in northern Great Plains. We aimed to examine how a management practice that included cover crops and residue retention impacts agronomic, ecosystem water and carbon dioxide flux, and canopy-scale physiological attributes of soybeans in the northern Great Plains under drought conditions. The experiment consisted of two soybean fields over two years with business-as-usual (no-cover crops and spring wheat residue removal) and aspirational management (cover crops and spring wheat residue retention) during a drought year. We compared yield; aboveground biomass; green chromatic coordinates, and CO2 and H2O fluxes from eddy covariance, Phenocam images, and ancillary micrometeorological measurements. These measurements were used to derive ecosystem-scale physical, and physiological attributes with the 'big leaf' framework to diagnose underlying processes. Soybean yields were 29 % higher under drought conditions in the field managed in a system that included cover crops and residue retention. This yield increase was associated with a 5 day increase in the green-chromatic-coordinate defined maturity phenophase, increasing agronomic and intrinsic water use efficiency by 27 % and 33 %, respectively, increasing water uptake, and increasing the rubisco-limited photosynthetic capacity (Vcmax25) by 42 %. The inclusion of cover crops and residue retention into a cropping system improved soybean productivity because of differences in water use, phenology timing, and photosynthetic capacity. These results suggest that farmers can improve soybean productivity and yield stability by incorporating cover crops and residue retention into their management suite because these practices to facilitate more aggressive water uptake.
Collapse
Affiliation(s)
- Craig W. Whippo
- USDA-ARS, Northern Great Plains Research Laboratory, P.O. Box 459, Mandan, ND, 58554, USA
| | - Nicanor Z. Saliendra
- USDA-ARS, Northern Great Plains Research Laboratory, P.O. Box 459, Mandan, ND, 58554, USA
| | - Mark A. Liebig
- USDA-ARS, Northern Great Plains Research Laboratory, P.O. Box 459, Mandan, ND, 58554, USA
| |
Collapse
|
2
|
Hu X, Shi L, Lin L, Li S, Deng X, Li L, Bian J, Lian X. A novel hybrid modelling framework for GPP estimation: Integrating a multispectral surface reflectance based V cmax25 simulator into the process-based model. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 921:171182. [PMID: 38402983 DOI: 10.1016/j.scitotenv.2024.171182] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Revised: 02/20/2024] [Accepted: 02/20/2024] [Indexed: 02/27/2024]
Abstract
Terrestrial gross primary productivity (GPP) is the key element in the carbon cycle process. Accurate GPP estimation hinges on the maximum carboxylation rate (Vcmax,025). The high uncertainty in deriving ecosystem-level Vcmax,025 has long hampered efforts toward the performance of the GPP model. Recently studies suggest the strong relationship between spectral reflectance and Vcmax,025. We proposed the multispectral surface reflectance-driven Vcmax,025 simulator using the fully connected deep neural network and built the hybrid modelling framework for GPP estimation by integrating the data-driven Vcmax,025 simulator in the process-based model. The performance of hybrid GPP model was evaluated at 95 flux sites. The result shows that the multispectral surface reflectance-driven Vcmax,025 simulator acquires the satisfactory estimation, with correlation coefficient (R), root mean square error (RMSE) and median absolute percentage error (MdAPE) ranging from 0.34 to 0.80, 14 to 43 μmol m-2 s-1 and 21 % to 66 % across different land cover types, respectively. The hybrid framework generates good GPP estimates with R, RMSE and MdAPE varying from 0.76 to 0.89, 1.79 to 6.16 μmol m-2 s-1 and 27 % to 90 %, respectively. Compared with EVI-driven method, the multispectral surface reflectance significantly improves the Vcmax,025 and GPP estimates, with MdAPE declining by 0.6 %-18 % and 1 % to 21 %, respectively. The Shapley value analysis reveals that red (620-670 nm), near-infrared (841-876 nm) and shortwave infrared (1628-1652 nm and 2105-2155 nm) are the key bands for Vcmax,025 estimation. This study highlights the potential of multispectral surface reflectance for quantifying ecosystem-level Vcmax,025. The new hybrid framework fully extracts the information of all available spectral bands using deep learning to reduce parameter uncertainty while maintains the description of photosynthetic process to ensure its physical reasonability. It can serve as a powerful tool for accurate global GPP estimation.
Collapse
Affiliation(s)
- Xiaolong Hu
- State Key Laboratory of Water Resources Engineering and Management, Wuhan University, Wuhan, Hubei 430072, China
| | - Liangsheng Shi
- State Key Laboratory of Water Resources Engineering and Management, Wuhan University, Wuhan, Hubei 430072, China.
| | - Lin Lin
- State Key Laboratory of Water Resources Engineering and Management, Wuhan University, Wuhan, Hubei 430072, China
| | - Shenji Li
- Urban Operation Management Center of Hengsha Township, Shanghai 201914, China
| | - Xianzhi Deng
- State Key Laboratory of Water Resources Engineering and Management, Wuhan University, Wuhan, Hubei 430072, China
| | - Li Li
- Shanghai Tramy Green Food (Group) Co., Ltd, Shanghai 201201, China
| | - Jiang Bian
- State Key Laboratory of Water Resources Engineering and Management, Wuhan University, Wuhan, Hubei 430072, China
| | - Xie Lian
- State Key Laboratory of Water Resources Engineering and Management, Wuhan University, Wuhan, Hubei 430072, China
| |
Collapse
|
3
|
Wu G, Guan K, Ainsworth EA, Martin DG, Kimm H, Yang X. Solar-induced chlorophyll fluorescence captures the effects of elevated ozone on canopy structure and acceleration of senescence in soybean. JOURNAL OF EXPERIMENTAL BOTANY 2024; 75:350-363. [PMID: 37702411 DOI: 10.1093/jxb/erad356] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Accepted: 09/11/2023] [Indexed: 09/14/2023]
Abstract
Solar-induced chlorophyll fluorescence (SIF) provides an opportunity to rapidly and non-destructively investigate how plants respond to stress. Here, we explored the potential of SIF to detect the effects of elevated O3 on soybean in the field where soybean was subjected to ambient and elevated O3 throughout the growing season in 2021. Exposure to elevated O3 resulted in a significant decrease in canopy SIF at 760 nm (SIF760), with a larger decrease in the late growing season (36%) compared with the middle growing season (13%). Elevated O3 significantly decreased the fraction of absorbed photosynthetically active radiation by 8-15% in the middle growing season and by 35% in the late growing stage. SIF760 escape ratio (fesc) was significantly increased under elevated O3 by 5-12% in the late growth stage due to a decrease of leaf chlorophyll content and leaf area index. Fluorescence yield of the canopy was reduced by 5-11% in the late growing season depending on the fesc estimation method, during which leaf maximum carboxylation rate and maximum electron transport were significantly reduced by 29% and 20% under elevated O3. These results demonstrated that SIF could capture the elevated O3 effect on canopy structure and acceleration of senescence in soybean and provide empirical support for using SIF for soybean stress detection and phenotyping.
Collapse
Affiliation(s)
- Genghong Wu
- Agroecosystem Sustainability Center, Institute for Sustainability, Energy, and Environment, University of Illinois Urbana Champaign, Urbana, IL 61801, USA
- Department of Natural Resources and Environmental Sciences, College of Agricultural, Consumers, and Environmental Sciences, University of Illinois Urbana Champaign, Urbana, IL 61801, USA
- Institute of Bio- and Geosciences, IBG-2: Plant Sciences, Forschungszentrum Jülich GmbH, Jülich 52425, Germany
| | - Kaiyu Guan
- Agroecosystem Sustainability Center, Institute for Sustainability, Energy, and Environment, University of Illinois Urbana Champaign, Urbana, IL 61801, USA
- Department of Natural Resources and Environmental Sciences, College of Agricultural, Consumers, and Environmental Sciences, University of Illinois Urbana Champaign, Urbana, IL 61801, USA
- National Center for Supercomputing Applications, University of Illinois Urbana Champaign, Urbana, IL 61801, USA
| | - Elizabeth A Ainsworth
- Agroecosystem Sustainability Center, Institute for Sustainability, Energy, and Environment, University of Illinois Urbana Champaign, Urbana, IL 61801, USA
- Department of Plant Biology, University of Illinois Urbana Champaign, Urbana, IL 61801, USA
- USDA-ARS, Global Change and Photosynthesis Research Unit, Urbana, IL 61801, USA
| | - Duncan G Martin
- Department of Plant Biology, University of Illinois Urbana Champaign, Urbana, IL 61801, USA
| | - Hyungsuk Kimm
- Department of Natural Resources and Environmental Sciences, College of Agricultural, Consumers, and Environmental Sciences, University of Illinois Urbana Champaign, Urbana, IL 61801, USA
- Research Institute of Agriculture and Life Sciences, Seoul National University, Seoul 08826, South Korea
| | - Xi Yang
- Department of Environmental Sciences, University of Virginia, Charlottesville, VA 22903, USA
| |
Collapse
|
4
|
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.
Collapse
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
| |
Collapse
|
5
|
Zhang P, Huang J, Ma Y, Wang X, Kang M, Song Y. Crop/Plant Modeling Supports Plant Breeding: II. Guidance of Functional Plant Phenotyping for Trait Discovery. PLANT PHENOMICS (WASHINGTON, D.C.) 2023; 5:0091. [PMID: 37780969 PMCID: PMC10538623 DOI: 10.34133/plantphenomics.0091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Accepted: 08/26/2023] [Indexed: 10/03/2023]
Abstract
Observable morphological traits are widely employed in plant phenotyping for breeding use, which are often the external phenotypes driven by a chain of functional actions in plants. Identifying and phenotyping inherently functional traits for crop improvement toward high yields or adaptation to harsh environments remains a major challenge. Prediction of whole-plant performance in functional-structural plant models (FSPMs) is driven by plant growth algorithms based on organ scale wrapped up with micro-environments. In particular, the models are flexible for scaling down or up through specific functions at the organ nexus, allowing the prediction of crop system behaviors from the genome to the field. As such, by virtue of FSPMs, model parameters that determine organogenesis, development, biomass production, allocation, and morphogenesis from a molecular to the whole plant level can be profiled systematically and made readily available for phenotyping. FSPMs can provide rich functional traits representing biological regulatory mechanisms at various scales in a dynamic system, e.g., Rubisco carboxylation rate, mesophyll conductance, specific leaf nitrogen, radiation use efficiency, and source-sink ratio apart from morphological traits. High-throughput phenotyping such traits is also discussed, which provides an unprecedented opportunity to evolve FSPMs. This will accelerate the co-evolution of FSPMs and plant phenomics, and thus improving breeding efficiency. To expand the great promise of FSPMs in crop science, FSPMs still need more effort in multiscale, mechanistic, reproductive organ, and root system modeling. In summary, this study demonstrates that FSPMs are invaluable tools in guiding functional trait phenotyping at various scales and can thus provide abundant functional targets for phenotyping toward crop improvement.
Collapse
Affiliation(s)
- Pengpeng Zhang
- School of Agronomy, Anhui Agricultural University, Hefei, Anhui Province 230036, China
| | - Jingyao Huang
- School of Agronomy, Anhui Agricultural University, Hefei, Anhui Province 230036, China
| | - Yuntao Ma
- College of Land Science and Technology, China Agricultural University, Beijing 100094, China
| | - Xiujuan Wang
- The State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Mengzhen Kang
- The State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Youhong Song
- School of Agronomy, Anhui Agricultural University, Hefei, Anhui Province 230036, China
- Centre for Crop Science, Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, QLD 4350, Australia
- Centre for Crop Science, Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, QLD 4350, Australia
| |
Collapse
|
6
|
Bernacchi CJ, Ruiz-Vera UM, Siebers MH, DeLucia NJ, Ort DR. Short- and long-term warming events on photosynthetic physiology, growth, and yields of field grown crops. Biochem J 2023; 480:999-1014. [PMID: 37418286 PMCID: PMC10422931 DOI: 10.1042/bcj20220433] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Revised: 06/15/2023] [Accepted: 06/16/2023] [Indexed: 07/08/2023]
Abstract
Global temperatures are rising from increasing concentrations of greenhouse gases in the atmosphere associated with anthropogenic activities. Global warming includes a warmer shift in mean temperatures as well as increases in the probability of extreme heating events, termed heat waves. Despite the ability of plants to cope with temporal variations in temperature, global warming is increasingly presenting challenges to agroecosystems. The impact of warming on crop species has direct consequences on food security, therefore understanding impacts and opportunities to adapt crops to global warming necessitates experimentation that allows for modification of growth environments to represent global warming scenarios. Published studies addressing crop responses to warming are extensive, however, in-field studies where growth temperature is manipulated to mimic global warming are limited. Here, we provide an overview of in-field heating techniques employed to understand crop responses to warmer growth environments. We then focus on key results associated with season-long warming, as expected with rising global mean temperatures, and with heat waves, as a consequence of increasing temperature variability and rising global mean temperatures. We then discuss the role of rising temperatures on atmospheric water vapor pressure deficit and potential implications for crop photosynthesis and productivity. Finally, we review strategies by which crop photosynthetic processes might be optimized to adapt crops to the increasing temperatures and frequencies of heat waves. Key findings from this review are that higher temperatures consistently reduce photosynthesis and yields of crops even as atmospheric carbon dioxide increases, yet potential strategies to minimize losses from high-temperature exist.
Collapse
Affiliation(s)
- Carl J. Bernacchi
- Global Change and Photosynthesis Research Unit, USDA-ARS, Urbana, IL, U.S.A
- Department of Plant Biology, University of Illinois Urbana-Champaign, Urbana, IL, U.S.A
- Carl R Woese Institute for Genomic Biology, University of Illinois Urbana-Champaign, Urbana, IL, U.S.A
| | | | - Matthew H. Siebers
- Global Change and Photosynthesis Research Unit, USDA-ARS, Urbana, IL, U.S.A
- Department of Plant Biology, University of Illinois Urbana-Champaign, Urbana, IL, U.S.A
| | - Nicholas J. DeLucia
- Global Change and Photosynthesis Research Unit, USDA-ARS, Urbana, IL, U.S.A
- Department of Plant Biology, University of Illinois Urbana-Champaign, Urbana, IL, U.S.A
| | - Donald R. Ort
- Department of Plant Biology, University of Illinois Urbana-Champaign, Urbana, IL, U.S.A
- Carl R Woese Institute for Genomic Biology, University of Illinois Urbana-Champaign, Urbana, IL, U.S.A
| |
Collapse
|
7
|
Sanaeifar A, Yang C, de la Guardia M, Zhang W, Li X, He Y. Proximal hyperspectral sensing of abiotic stresses in plants. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 861:160652. [PMID: 36470376 DOI: 10.1016/j.scitotenv.2022.160652] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Revised: 11/27/2022] [Accepted: 11/29/2022] [Indexed: 06/17/2023]
Abstract
Recent attempts, advances and challenges, as well as future perspectives regarding the application of proximal hyperspectral sensing (where sensors are placed within 10 m above plants, either on land-based platforms or in controlled environments) to assess plant abiotic stresses have been critically reviewed. Abiotic stresses, caused by either physical or chemical reasons such as nutrient deficiency, drought, salinity, heavy metals, herbicides, extreme temperatures, and so on, may be more damaging than biotic stresses (affected by infectious agents such as bacteria, fungi, insects, etc.) on crop yields. The proximal hyperspectral sensing provides images at a sub-millimeter spatial resolution for doing an in-depth study of plant physiology and thus offers a global view of the plant's status and allows for monitoring spatio-temporal variations from large geographical areas reliably and economically. The literature update has been based on 362 research papers in this field, published from 2010, most of which are from four years ago and, in our knowledge, it is the first paper that provides a comprehensive review of the applications of the technique for the detection of various types of abiotic stresses in plants.
Collapse
Affiliation(s)
- Alireza Sanaeifar
- College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China.
| | - Ce Yang
- Department of Bioproducts and Biosystems Engineering, University of Minnesota, Saint Paul, MN 55108, United States.
| | - Miguel de la Guardia
- Department of Analytical Chemistry, University of Valencia, Dr. Moliner 50, 46100 Burjassot, Valencia, Spain.
| | - Wenkai Zhang
- College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China.
| | - Xiaoli Li
- College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China.
| | - Yong He
- College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China.
| |
Collapse
|
8
|
Estrada F, Flexas J, Araus JL, Mora-Poblete F, Gonzalez-Talice J, Castillo D, Matus IA, Méndez-Espinoza AM, Garriga M, Araya-Riquelme C, Douthe C, Castillo B, del Pozo A, Lobos GA. Exploring plant responses to abiotic stress by contrasting spectral signature changes. FRONTIERS IN PLANT SCIENCE 2023; 13:1026323. [PMID: 36777544 PMCID: PMC9910286 DOI: 10.3389/fpls.2022.1026323] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Accepted: 12/23/2022] [Indexed: 06/18/2023]
Abstract
In this study, daily changes over a short period and diurnal progression of spectral reflectance at the leaf level were used to identify spring wheat genotypes (Triticum aestivum L.) susceptible to adverse conditions. Four genotypes were grown in pots experiments under semi-controlled conditions in Chile and Spain. Three treatments were applied: i) control (C), ii) water stress (WS), and iii) combined water and heat shock (WS+T). Spectral reflectance, gas exchange and chlorophyll fluorescence measurements were performed on flag leaves for three consecutive days at anthesis. High canopy temperature ( H CT ) genotypes showed less variability in their mean spectral reflectance signature and chlorophyll fluorescence, which was related to weaker responses to environmental fluctuations. While low canopy temperature ( L CT ) genotypes showed greater variability. The genotypes spectral signature changes, in accordance with environmental fluctuation, were associated with variations in their stomatal conductance under both stress conditions (WS and WS+T); L CT genotypes showed an anisohydric response compared that of H CT , which was isohydric. This approach could be used in breeding programs for screening a large number of genotypes through proximal or remote sensing tools and be a novel but simple way to identify groups of genotypes with contrasting performances.
Collapse
Affiliation(s)
- Félix Estrada
- Plant Breeding and Phenomics Center, Faculty of Agricultural Sciences, University of Talca, Talca, Chile
- Instituto de Investigaciones Agropecuarias INIA-Quilamapu, Chillán, Chile
| | - Jaume Flexas
- Instituto de Investigaciones Agropecuarias INIA-Remehue, Osorno, Chile
| | - Jose Luis Araus
- Research Group on Plant Biology Under Mediterranean Conditions, Departament de Biologia, Institute of Agro-Environmental Research and Water Economy, Universitat de les Illes Balears, Illes Balears, Spain
| | - Freddy Mora-Poblete
- Department of Evolutive Biology Ecology, and Environmental Sciences, University of Barcelona, Barcelona, Spain
| | | | - Dalma Castillo
- Departamento de Producción Forestal y Tecnología de la Madera, Facultad de Agronomía, Universidad de la República, Montevideo, Uruguay
| | - Ivan A. Matus
- Instituto de Investigaciones Agropecuarias INIA-Quilamapu, Chillán, Chile
| | | | - Miguel Garriga
- Departamento de Producción Vegetal, Facultad de Agronomía, Universidad de Concepción, Concepción, Chile
| | - Carlos Araya-Riquelme
- Plant Breeding and Phenomics Center, Faculty of Agricultural Sciences, University of Talca, Talca, Chile
| | - Cyril Douthe
- Research Group on Plant Biology Under Mediterranean Conditions, Departament de Biologia, Institute of Agro-Environmental Research and Water Economy, Universitat de les Illes Balears, Illes Balears, Spain
| | - Benjamin Castillo
- Plant Breeding and Phenomics Center, Faculty of Agricultural Sciences, University of Talca, Talca, Chile
| | - Alejandro del Pozo
- Plant Breeding and Phenomics Center, Faculty of Agricultural Sciences, University of Talca, Talca, Chile
| | - Gustavo A. Lobos
- Plant Breeding and Phenomics Center, Faculty of Agricultural Sciences, University of Talca, Talca, Chile
| |
Collapse
|
9
|
Song G, Wang Q, Jin J. Temporal instability of partial least squares regressions for estimating leaf photosynthetic traits from hyperspectral information. JOURNAL OF PLANT PHYSIOLOGY 2022; 279:153831. [PMID: 36252398 DOI: 10.1016/j.jplph.2022.153831] [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: 06/20/2021] [Revised: 09/09/2022] [Accepted: 09/25/2022] [Indexed: 06/16/2023]
Abstract
Partial least squares regression (PLSR) is applied increasingly often to predict plant photosynthesis from reflectance spectra. While its applicability across different areas has been examined in previous studies, its stability across time has yet to be evaluated. In this study, we assessed a series of PLSR models built upon three different band selection approaches (iterative stepwise, genetic algorithm, and uninformative variable elimination), in combination with different spectral transforms (original and first-order derivative spectra), for their stabilities in predicting the maximum carboxylation rate (Vcmax) and maximum electron transport rate (Jmax) from hyperspectral reflectance spectra at different temporal scales (seasonal and interannual). The results showed that both photosynthetic parameters can be estimated from leaf hyperspectral reflectance with moderate to good accuracy across different growing stages (R2 = 0.45-0.84) and years (R2 = 0.37-0.97). We further found that the iterative stepwise selection of informative bands when building PLSR models could greatly improve its predictive capacity compared with that of other PLSR models, especially those based on first-order derivative spectra. However, the selected bands of the models for both photosynthetic parameters were, unfortunately not consistent. Furthermore, we could not have identified any model with fixed spectra performed consistently across different seasonal stages and across different years. However, the blue spectral regions were popularly selected throughout the growing stages and in different years. The results demonstrate that leaf spectra-trait estimation using PLSR models varies with time and thus cast doubt over the use of a specific PLSR model to infer leaf traits across different temporal-spatial contexts. The development of a general applicable PLSR model is still in the works.
Collapse
Affiliation(s)
- Guangman Song
- Graduate School of Science and Technology, Shizuoka University, Shizuoka, 422-8529, Japan
| | - Quan Wang
- Faculty of Agriculture, Shizuoka University, Shizuoka, 422-8529, Japan.
| | - Jia Jin
- Faculty of Agriculture, Shizuoka University, Shizuoka, 422-8529, Japan
| |
Collapse
|
10
|
Khruschev SS, Plyusnina TY, Antal TK, Pogosyan SI, Riznichenko GY, Rubin AB. Machine learning methods for assessing photosynthetic activity: environmental monitoring applications. Biophys Rev 2022; 14:821-842. [PMID: 36124273 PMCID: PMC9481805 DOI: 10.1007/s12551-022-00982-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Accepted: 07/08/2022] [Indexed: 10/15/2022] Open
Abstract
Monitoring of the photosynthetic activity of natural and artificial biocenoses is of crucial importance. Photosynthesis is the basis for the existence of life on Earth, and a decrease in primary photosynthetic production due to anthropogenic influences can have catastrophic consequences. Currently, great efforts are being made to create technologies that allow continuous monitoring of the state of the photosynthetic apparatus of terrestrial plants and microalgae. There are several sources of information suitable for assessing photosynthetic activity, including gas exchange and optical (reflectance and fluorescence) measurements. The advent of inexpensive optical sensors makes it possible to collect data locally (manually or using autonomous sea and land stations) and globally (using aircraft and satellite imaging). In this review, we consider machine learning methods proposed for determining the functional parameters of photosynthesis based on local and remote optical measurements (hyperspectral imaging, solar-induced chlorophyll fluorescence, local chlorophyll fluorescence imaging, and various techniques of fast and delayed chlorophyll fluorescence induction). These include classical and novel (such as Partial Least Squares) regression methods, unsupervised cluster analysis techniques, various classification methods (support vector machine, random forest, etc.) and artificial neural networks (multilayer perceptron, long short-term memory, etc.). Special aspects of time-series analysis are considered. Applicability of particular information sources and mathematical methods for assessment of water quality and prediction of algal blooms, for estimation of primary productivity of biocenoses, stress tolerance of agricultural plants, etc. is discussed.
Collapse
Affiliation(s)
- S. S. Khruschev
- Department of Biophysics, Faculty of Biology, Lomonosov Moscow State University, Moscow, 119234 Russia
| | - T. Yu. Plyusnina
- Department of Biophysics, Faculty of Biology, Lomonosov Moscow State University, Moscow, 119234 Russia
| | - T. K. Antal
- Laboratory of Integrated Environmental Research, Pskov State University, Pskov, 180000 Russia
| | - S. I. Pogosyan
- Department of Biophysics, Faculty of Biology, Lomonosov Moscow State University, Moscow, 119234 Russia
| | - G. Yu. Riznichenko
- Department of Biophysics, Faculty of Biology, Lomonosov Moscow State University, Moscow, 119234 Russia
| | - A. B. Rubin
- Department of Biophysics, Faculty of Biology, Lomonosov Moscow State University, Moscow, 119234 Russia
| |
Collapse
|
11
|
Ren Y, Zhu J, Zhang H, Lin B, Hao P, Hua S. Leaf Carbohydrate Metabolism Variation Caused by Late Planting in Rapeseed (Brassica napus L.) at Reproductive Stage. PLANTS 2022; 11:plants11131696. [PMID: 35807649 PMCID: PMC9268982 DOI: 10.3390/plants11131696] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/22/2022] [Revised: 06/20/2022] [Accepted: 06/22/2022] [Indexed: 11/16/2022]
Abstract
Delayed planting date of rapeseed is an important factor affecting seed yield. However, regulation of the leaf carbohydrate metabolism in rapeseed by a late planting date at the reproductive stage is scarcely investigated. A two-year field experiment was conducted to assess the effect of planting dates, including early (15 September), optimal (1 October), late (15 October), and very late (30 October), on leaf growth and carbohydrate biosynthetic and catabolic metabolism at the reproductive stage. The results showed that leaf dry matter decreased linearly on average from 7.48 to 0.62 g plant−1 with an early planting date, whereas it increased at first and peaked at 14 days after anthesis (DAA) with other planting dates. Leaf dry matter was the lowest at the very late planting date during the reproductive stage. For leaf chlorophyll content, rapeseed planted at an optimal date maximized at 14 DAA with an average content of 1.51 mg g−1 fresh weight, whereas it kept high and stable at a very late planting date after 28 DAA. For the carbohydrate catabolic system, acid and neutral invertase (AI and NI, respectively) showed higher activity before 14 DAA, whereas both sucrose synthase (SS) and starch phosphorylase (SP) showed higher activity after 14 DAA. For the carbohydrate biosynthetic system, the activity of sucrose phosphate synthase (SPS) was the highest at the late planting date after 14 DAA, whereas it was at the lowest at the very late planting date. However, the activity of ADP-glucose pyrophosphorylase (AGPase) at the late and very late planting dates was significantly higher than that of the early and optimal plant dates after 21 DAA, which is in accordance with the leaf total soluble sugar content, suggesting that leaf carbohydrate metabolism is governed by a biosynthetic system. The current study provides new insights on leaf carbohydrate metabolism regulation by late planting in rapeseed at the reproductive stage.
Collapse
Affiliation(s)
- Yun Ren
- Huzhou Agricultural Science and Technology Development Center, Huzhou Academy of Agricultural Sciences, Huzhou 313000, China; (Y.R.); (J.Z.)
| | - Jianfang Zhu
- Huzhou Agricultural Science and Technology Development Center, Huzhou Academy of Agricultural Sciences, Huzhou 313000, China; (Y.R.); (J.Z.)
| | - Hui Zhang
- Zhejiang Agro-Tech Extension and Service Center, Hangzhou 310020, China;
| | - Baogang Lin
- Institute of Crop and Nuclear Technology Utilization, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China; (B.L.); (P.H.)
| | - Pengfei Hao
- Institute of Crop and Nuclear Technology Utilization, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China; (B.L.); (P.H.)
| | - Shuijin Hua
- Institute of Crop and Nuclear Technology Utilization, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China; (B.L.); (P.H.)
- Correspondence:
| |
Collapse
|
12
|
Montes CM, Fox C, Sanz-Sáez Á, Serbin SP, Kumagai E, Krause MD, Xavier A, Specht JE, Beavis WD, Bernacchi CJ, Diers BW, Ainsworth EA. High-throughput characterization, correlation, and mapping of leaf photosynthetic and functional traits in the soybean (Glycine max) nested association mapping population. Genetics 2022; 221:iyac065. [PMID: 35451475 PMCID: PMC9157091 DOI: 10.1093/genetics/iyac065] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2021] [Accepted: 04/03/2022] [Indexed: 11/14/2022] Open
Abstract
Photosynthesis is a key target to improve crop production in many species including soybean [Glycine max (L.) Merr.]. A challenge is that phenotyping photosynthetic traits by traditional approaches is slow and destructive. There is proof-of-concept for leaf hyperspectral reflectance as a rapid method to model photosynthetic traits. However, the crucial step of demonstrating that hyperspectral approaches can be used to advance understanding of the genetic architecture of photosynthetic traits is untested. To address this challenge, we used full-range (500-2,400 nm) leaf reflectance spectroscopy to build partial least squares regression models to estimate leaf traits, including the rate-limiting processes of photosynthesis, maximum Rubisco carboxylation rate, and maximum electron transport. In total, 11 models were produced from a diverse population of soybean sampled over multiple field seasons to estimate photosynthetic parameters, chlorophyll content, leaf carbon and leaf nitrogen percentage, and specific leaf area (with R2 from 0.56 to 0.96 and root mean square error approximately <10% of the range of calibration data). We explore the utility of these models by applying them to the soybean nested association mapping population, which showed variability in photosynthetic and leaf traits. Genetic mapping provided insights into the underlying genetic architecture of photosynthetic traits and potential improvement in soybean. Notably, the maximum Rubisco carboxylation rate mapped to a region of chromosome 19 containing genes encoding multiple small subunits of Rubisco. We also mapped the maximum electron transport rate to a region of chromosome 10 containing a fructose 1,6-bisphosphatase gene, encoding an important enzyme in the regeneration of ribulose 1,5-bisphosphate and the sucrose biosynthetic pathway. The estimated rate-limiting steps of photosynthesis were low or negatively correlated with yield suggesting that these traits are not influenced by the same genetic mechanisms and are not limiting yield in the soybean NAM population. Leaf carbon percentage, leaf nitrogen percentage, and specific leaf area showed strong correlations with yield and may be of interest in breeding programs as a proxy for yield. This work is among the first to use hyperspectral reflectance to model and map the genetic architecture of the rate-limiting steps of photosynthesis.
Collapse
Affiliation(s)
| | - Carolyn Fox
- Department of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Álvaro Sanz-Sáez
- Department of Crop, Soil, and Environmental Sciences, Auburn, AL 36849, USA
| | - Shawn P Serbin
- Environmental and Climate Sciences Department, Brookhaven National Laboratory, Upton, NY 11973, USA
| | - Etsushi Kumagai
- Institute of Agro-environmental Sciences, National Agriculture and Food Research Organization, Tsukuba, Ibaraki 305-8604, Japan
| | - Matheus D Krause
- Department of Agronomy, Iowa State University, Agronomy Hall, Ames, IA 50011, USA
| | - Alencar Xavier
- Department of Agronomy, Purdue University, West Lafayette, IN 47907, USA
- Department of Biostatistics, Corteva Agrisciences, Johnston, IA 50131, USA
| | - James E Specht
- Department of Agronomy and Horticulture, University of Nebraska, Lincoln, NE 68583, USA
| | - William D Beavis
- Department of Agronomy, Iowa State University, Agronomy Hall, Ames, IA 50011, USA
| | - Carl J Bernacchi
- Global Change and Photosynthesis Research Unit, USDA ARS, Urbana, IL 61801, USA
- Carl R. Woese Institute for Genomic Biology, Urbana, IL 61801, USA
- Department of Plant Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Brian W Diers
- Department of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Elizabeth A Ainsworth
- Global Change and Photosynthesis Research Unit, USDA ARS, Urbana, IL 61801, USA
- Carl R. Woese Institute for Genomic Biology, Urbana, IL 61801, USA
- Department of Plant Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| |
Collapse
|
13
|
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.
Collapse
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
| |
Collapse
|