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Eragam A, Mohapatra A, Shukla V, Kadumuri RV, George AP, Putta L, Akkareddy S, Chavali S, Vemireddy LR, Ramireddy E. Panicle transcriptome of high-yield mutant indica rice reveals physiological mechanisms and novel candidate regulatory genes for yield under reproductive stage drought stress. BMC PLANT BIOLOGY 2023; 23:493. [PMID: 37833626 PMCID: PMC10571340 DOI: 10.1186/s12870-023-04507-1] [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: 03/21/2023] [Accepted: 10/03/2023] [Indexed: 10/15/2023]
Abstract
BACKGROUND Reproductive stage drought stress (RDS) is a major global threat to rice production. Due to climate change, water scarcity is becoming an increasingly common phenomenon in major rice-growing areas worldwide. Understanding RDS mechanisms will allow candidate gene identification to generate novel rice genotypes tolerant to RDS. RESULTS To generate novel rice genotypes that can sustain yield under RDS, we performed gamma-irradiation mediated mutation breeding in the drought stress susceptible mega rice variety, MTU1010. One of the mutant MM11 (MTU1010 derived mutant11) shows consistently increased performance in yield-related traits under field conditions consecutively for four generations. In addition, compared to MTU1010, the yield of MM11 is sustained in prolonged drought imposed during the reproductive stage under field and in pot culture conditions. A comparative emerged panicle transcriptome analysis of the MTU1010 and MM11 suggested metabolic adjustment, enhanced photosynthetic ability, and hormone interplay in regulating yield under drought responses during emerged panicle development. Regulatory network analysis revealed few putative significant transcription factor (TF)-target interactions involved in integrated signalling between panicle development, yield and drought stress. CONCLUSIONS A gamma-irradiate rice mutant MM11 was identified by mutation breeding, and it showed higher potential to sustain yield under reproductive stage drought stress in field and pot culture conditions. Further, a comparative panicle transcriptome revealed significant biological processes and molecular regulators involved in emerged panicle development, yield and drought stress integration. The study extends our understanding of the physiological mechanisms and candidate genes involved in sustaining yield under drought stress.
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Affiliation(s)
- Aparna Eragam
- Department of Biology, Indian Institute of Science Education and Research (IISER) Tirupati, Tirupati, 517507, Andhra Pradesh, India
- Department of Molecular Biology and Biotechnology, S.V. Agricultural College, Acharya NG Ranga Agricultural University (ANGRAU), Tirupati, 517502, India
| | - Ankita Mohapatra
- Department of Biology, Indian Institute of Science Education and Research (IISER) Tirupati, Tirupati, 517507, Andhra Pradesh, India
| | - Vishnu Shukla
- Department of Biology, Indian Institute of Science Education and Research (IISER) Tirupati, Tirupati, 517507, Andhra Pradesh, India
| | - Rajashekar Varma Kadumuri
- Department of Biology, Indian Institute of Science Education and Research (IISER) Tirupati, Tirupati, 517507, Andhra Pradesh, India
| | - Abin Panackal George
- Department of Biology, Indian Institute of Science Education and Research (IISER) Tirupati, Tirupati, 517507, Andhra Pradesh, India
| | - Latha Putta
- Regional Agricultural Research Station (RARS), ANGRAU, Tirupati, India
| | | | - Sreenivas Chavali
- Department of Biology, Indian Institute of Science Education and Research (IISER) Tirupati, Tirupati, 517507, Andhra Pradesh, India
| | - Lakshminarayana R Vemireddy
- Department of Molecular Biology and Biotechnology, S.V. Agricultural College, Acharya NG Ranga Agricultural University (ANGRAU), Tirupati, 517502, India.
| | - Eswarayya Ramireddy
- Department of Biology, Indian Institute of Science Education and Research (IISER) Tirupati, Tirupati, 517507, Andhra Pradesh, India.
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Ting TC, Souza ACM, Imel RK, Guadagno CR, Hoagland C, Yang Y, Wang DR. Quantifying physiological trait variation with automated hyperspectral imaging in rice. FRONTIERS IN PLANT SCIENCE 2023; 14:1229161. [PMID: 37799551 PMCID: PMC10548215 DOI: 10.3389/fpls.2023.1229161] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Accepted: 08/21/2023] [Indexed: 10/07/2023]
Abstract
Advancements in hyperspectral imaging (HSI) together with the establishment of dedicated plant phenotyping facilities worldwide have enabled high-throughput collection of plant spectral images with the aim of inferring target phenotypes. Here, we test the utility of HSI-derived canopy data, which were collected as part of an automated plant phenotyping system, to predict physiological traits in cultivated Asian rice (Oryza sativa). We evaluated 23 genetically diverse rice accessions from two subpopulations under two contrasting nitrogen conditions and measured 14 leaf- and canopy-level parameters to serve as ground-reference observations. HSI-derived data were used to (1) classify treatment groups across multiple vegetative stages using support vector machines (≥ 83% accuracy) and (2) predict leaf-level nitrogen content (N, %, n=88) and carbon to nitrogen ratio (C:N, n=88) with Partial Least Squares Regression (PLSR) following RReliefF wavelength selection (validation: R 2 = 0.797 and RMSEP = 0.264 for N; R 2 = 0.592 and RMSEP = 1.688 for C:N). Results demonstrated that models developed using training data from one rice subpopulation were able to predict N and C:N in the other subpopulation, while models trained on a single treatment group were not able to predict samples from the other treatment. Finally, optimization of PLSR-RReliefF hyperparameters showed that 300-400 wavelengths generally yielded the best model performance with a minimum calibration sample size of 62. Results support the use of canopy-level hyperspectral imaging data to estimate leaf-level N and C:N across diverse rice, and this work highlights the importance of considering calibration set design prior to data collection as well as hyperparameter optimization for model development in future studies.
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Affiliation(s)
- To-Chia Ting
- Agronomy Department, Purdue University, West Lafayette, IN, United States
| | - Augusto C. M. Souza
- Institute for Plant Sciences, Purdue University, West Lafayette, IN, United States
| | - Rachel K. Imel
- Agronomy Department, Purdue University, West Lafayette, IN, United States
| | | | - Chris Hoagland
- Institute for Plant Sciences, Purdue University, West Lafayette, IN, United States
| | - Yang Yang
- Institute for Plant Sciences, Purdue University, West Lafayette, IN, United States
| | - Diane R. Wang
- Agronomy Department, Purdue University, West Lafayette, IN, United States
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Wijewardane NK, Zhang H, Yang J, Schnable JC, Schachtman DP, Ge Y. A leaf-level spectral library to support high throughput plant phenotyping: Predictive accuracy and model transfer. JOURNAL OF EXPERIMENTAL BOTANY 2023:erad129. [PMID: 37018460 PMCID: PMC10400152 DOI: 10.1093/jxb/erad129] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Indexed: 06/19/2023]
Abstract
Leaf-level hyperspectral reflectance has become an effective tool for high-throughput phenotyping of plant leaf traits due to its rapid, low-cost, multi-sensing, and non-destructive nature. However, collecting samples for model calibration can still be expensive; and models show poor transferability among different datasets. This study had three specific objectives: (i) assemble a large library of leaf hyperspectral data (n=2460) from maize and sorghum, (ii) evaluate two machine-learning approaches to estimate nine leaf properties (chlorophyll, thickness, water content, nitrogen, phosphorus, potassium, calcium, magnesium, and sulfur), and (iii) investigate the usefulness of this spectral library for predicting external datasets (n=445) including soybean and camelina using extra-weighted spiking. Internal cross-validation showed satisfactory performance of the spectral library to estimate all nine traits (average R 2 0.688), with Partial Least Squares Regression outperforming Deep Neural Network models. Models calibrated solely using the spectral library showed degraded performance on external datasets (average R 2 0.159 for camelina, 0.337 for soybean). Models improved significantly when a small portion of external samples (n=20) was added to the library via extra-weighted spiking (average R 2 0.574 for camelina, 0.536 for soybean). The leaf-level spectral library greatly benefits plant physiological and biochemical phenotyping; whereas extra-weight spiking improves model transferability and extends its utility.
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Affiliation(s)
- Nuwan K Wijewardane
- Department of Agricultural and Biological Engineering, Mississippi State University, Starkville, MS, USA
| | - Huichun Zhang
- College of Mechanical and Electrical Engineering, Nanjing Forestry University, China
- Jiangsu Co-Innovation Center of Efficient Processing and Utilization of Forest Resources, Nanjing Forestry University, Nanjing, China
| | - Jinliang Yang
- Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE, USA
- Center for Plant Science Innovation, University of Nebraska-Lincoln, Lincoln, NE, USA
| | - James C Schnable
- Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE, USA
- Center for Plant Science Innovation, University of Nebraska-Lincoln, Lincoln, NE, USA
| | - Daniel P Schachtman
- Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE, USA
- Center for Plant Science Innovation, University of Nebraska-Lincoln, Lincoln, NE, USA
| | - Yufeng Ge
- Department of Biological Systems Engineering, University of Nebraska-Lincoln, Lincoln, NE, USA
- Center for Plant Science Innovation, University of Nebraska-Lincoln, Lincoln, NE, USA
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4
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Yang X, Jiang P, Luo Y, Shi Y. Non-destructive Detection of Fatty Acid Content of Camellia Seed Based on Hyperspectral. J Oleo Sci 2023; 72:69-77. [PMID: 36504187 DOI: 10.5650/jos.ess22139] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
As a unique traditional vegetable oil in China, camellia seed oil has very high edible value. Camellia seed kernel is mainly composed of fatty acids, which not only determines the oil yield of camellia seed, but also exert an important impact on the storage performance of camellia seed. In order to quickly and accurately determine the fatty acid content of camellia seed, this paper took camellia seed as the research object, used hyperspectral technology to determine the fatty acid content of camellia seed, and establishes a spectral model. 8 pretreatment methods, such as Savitzky-Golay smoothing, normalization, baseline correction, multivariate scattering correction, standard normal variable transformation, detrending algorithm, first derivative and second derivative, were adopted in this paper. The spectral prediction model of fatty acid content in camellia seed was established by combining 4 modeling methods: principal components regression (PCR), partial least square regression (PLSR), back propagation neural network (BP), radial basis function neural network (RBF). The optimal prediction model was selected by comparing the coefficient of determination (R2) and root mean square error (RMSE) of various models. The results showed that the spectral sensitive bands with high correlation coefficients (r) were 410-420 nm, 450-460 nm, 490-510 nm, 545-580 nm, 845-870 nm and 905-925 nm, respectively. The r obtained by MSC pretreatment of spectral data was the largest. The data obtained by 8 different pretreatment methods combined with RBF neural network model was the best, in which the average value of coefficient of determination (RC2) in the calibration set was 0.8654, and the root mean square error of calibration (RMSEC) was 0.0777; the average value of coefficient of determination (RP2) and root mean square error of prediction (RMSEP) in the prediction set model were 0.8437 and 0.0827, respectively. It could be seen that the best accuracy could be achieved by MSC pretreatment combined with RBF neural network modeling. This paper can provide reference for rapid nondestructive detection of fatty acid content in camellia seed by hyperspectral technology.
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Affiliation(s)
- Xiwen Yang
- College of Mechanical and Electrical Engineering, Hunan Agricultural University
| | - Ping Jiang
- College of Mechanical and Electrical Engineering, Hunan Agricultural University
| | - Yahui Luo
- College of Mechanical and Electrical Engineering, Hunan Agricultural University
| | - Yixin Shi
- College of Mechanical and Electrical Engineering, Hunan Agricultural University
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5
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Brugger A, Yamati FI, Barreto A, Paulus S, Schramowsk P, Kersting K, Steiner U, Neugart S, Mahlein AK. Hyperspectral Imaging in the UV Range Allows for Differentiation of Sugar Beet Diseases Based on Changes in Secondary Plant Metabolites. PHYTOPATHOLOGY 2023; 113:44-54. [PMID: 35904439 DOI: 10.1094/phyto-03-22-0086-r] [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: 06/15/2023]
Abstract
Fungal infections trigger defense or signaling responses in plants, leading to various changes in plant metabolites. The changes in metabolites, for example chlorophyll or flavonoids, have long been detectable using time-consuming destructive analytical methods including high-performance liquid chromatography or photometric determination. Recent plant phenotyping studies have revealed that hyperspectral imaging (HSI) in the UV range can be used to link spectral changes with changes in plant metabolites. To compare established destructive analytical methods with new nondestructive hyperspectral measurements, the interaction between sugar beet leaves and the pathogens Cercospora beticola, which causes Cercospora leaf spot disease (CLS), and Uromyces betae, which causes sugar beet rust (BR), was investigated. With the help of destructive analyses, we showed that both diseases have different effects on chlorophylls, carotenoids, flavonoids, and several phenols. Nondestructive hyperspectral measurements in the UV range revealed different effects of CLS and BR on plant metabolites resulting in distinct reflectance patterns. Both diseases resulted in specific spectral changes that allowed differentiation between the two diseases. Machine learning algorithms enabled the differentiation between the symptom classes and recognition of the two sugar beet diseases. Feature importance analysis identified specific wavelengths important to the classification, highlighting the utility of the UV range. The study demonstrates that HSI in the UV range is a promising, nondestructive tool to investigate the influence of plant diseases on plant physiology and biochemistry.
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Affiliation(s)
- Anna Brugger
- University of Bonn, Institute for Crop Science and Resource Conservation (INRES)-Plant Pathology, Bonn, 53115, Germany
| | | | - Abel Barreto
- Institute of Sugar Beet Research, Goettingen, 37079, Germany
| | - Stefan Paulus
- Institute of Sugar Beet Research, Goettingen, 37079, Germany
| | - Patrick Schramowsk
- Technical University Darmstadt, Computer Science Department and Centre for Cognitive Science, Darmstadt, 64289, Germany
| | - Kristian Kersting
- Technical University Darmstadt, Computer Science Department and Centre for Cognitive Science, Darmstadt, 64289, Germany
| | - Ulrike Steiner
- University of Bonn, Institute for Crop Science and Resource Conservation (INRES)-Plant Pathology, Bonn, 53115, Germany
| | - Susanne Neugart
- University of Goettingen, Division of Quality and Sensory of Plant Products, Goettingen, 37075, Germany
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Tao H, Xu S, Tian Y, Li Z, Ge Y, Zhang J, Wang Y, Zhou G, Deng X, Zhang Z, Ding Y, Jiang D, Guo Q, Jin S. Proximal and remote sensing in plant phenomics: 20 years of progress, challenges, and perspectives. PLANT COMMUNICATIONS 2022; 3:100344. [PMID: 35655429 PMCID: PMC9700174 DOI: 10.1016/j.xplc.2022.100344] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Revised: 05/08/2022] [Accepted: 05/27/2022] [Indexed: 06/01/2023]
Abstract
Plant phenomics (PP) has been recognized as a bottleneck in studying the interactions of genomics and environment on plants, limiting the progress of smart breeding and precise cultivation. High-throughput plant phenotyping is challenging owing to the spatio-temporal dynamics of traits. Proximal and remote sensing (PRS) techniques are increasingly used for plant phenotyping because of their advantages in multi-dimensional data acquisition and analysis. Substantial progress of PRS applications in PP has been observed over the last two decades and is analyzed here from an interdisciplinary perspective based on 2972 publications. This progress covers most aspects of PRS application in PP, including patterns of global spatial distribution and temporal dynamics, specific PRS technologies, phenotypic research fields, working environments, species, and traits. Subsequently, we demonstrate how to link PRS to multi-omics studies, including how to achieve multi-dimensional PRS data acquisition and processing, how to systematically integrate all kinds of phenotypic information and derive phenotypic knowledge with biological significance, and how to link PP to multi-omics association analysis. Finally, we identify three future perspectives for PRS-based PP: (1) strengthening the spatial and temporal consistency of PRS data, (2) exploring novel phenotypic traits, and (3) facilitating multi-omics communication.
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Affiliation(s)
- Haiyu Tao
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, National Engineering and Technology Center for Information Agriculture, Collaborative Innovation Centre for Modern Crop Production co-sponsored by Province and Ministry, Nanjing Agricultural University, Address: No. 1 Weigang, Xuanwu District, Nanjing 210095, China
| | - Shan Xu
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, National Engineering and Technology Center for Information Agriculture, Collaborative Innovation Centre for Modern Crop Production co-sponsored by Province and Ministry, Nanjing Agricultural University, Address: No. 1 Weigang, Xuanwu District, Nanjing 210095, China
| | - Yongchao Tian
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, National Engineering and Technology Center for Information Agriculture, Collaborative Innovation Centre for Modern Crop Production co-sponsored by Province and Ministry, Nanjing Agricultural University, Address: No. 1 Weigang, Xuanwu District, Nanjing 210095, China
| | - Zhaofeng Li
- The Key Laboratory of Oasis Eco-agriculture, Xinjiang Production and Construction Corps, Agriculture College, Shihezi University, Shihezi 832003, China
| | - Yan Ge
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, National Engineering and Technology Center for Information Agriculture, Collaborative Innovation Centre for Modern Crop Production co-sponsored by Province and Ministry, Nanjing Agricultural University, Address: No. 1 Weigang, Xuanwu District, Nanjing 210095, China
| | - Jiaoping Zhang
- State Key Laboratory of Crop Genetics and Germplasm Enhancement, National Center for Soybean Improvement, Key Laboratory for Biology and Genetic Improvement of Soybean (General, Ministry of Agriculture), Nanjing Agricultural University, Nanjing 210095, China
| | - Yu Wang
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, National Engineering and Technology Center for Information Agriculture, Collaborative Innovation Centre for Modern Crop Production co-sponsored by Province and Ministry, Nanjing Agricultural University, Address: No. 1 Weigang, Xuanwu District, Nanjing 210095, China
| | - Guodong Zhou
- Sanya Research Institute of Nanjing Agriculture University, Sanya 572024, China
| | - Xiong Deng
- Key Laboratory of Plant Molecular Physiology, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China
| | - Ze Zhang
- The Key Laboratory of Oasis Eco-agriculture, Xinjiang Production and Construction Corps, Agriculture College, Shihezi University, Shihezi 832003, China
| | - Yanfeng Ding
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, National Engineering and Technology Center for Information Agriculture, Collaborative Innovation Centre for Modern Crop Production co-sponsored by Province and Ministry, Nanjing Agricultural University, Address: No. 1 Weigang, Xuanwu District, Nanjing 210095, China; Hainan Yazhou Bay Seed Laboratory, Sanya 572025, China; Sanya Research Institute of Nanjing Agriculture University, Sanya 572024, China
| | - Dong Jiang
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, National Engineering and Technology Center for Information Agriculture, Collaborative Innovation Centre for Modern Crop Production co-sponsored by Province and Ministry, Nanjing Agricultural University, Address: No. 1 Weigang, Xuanwu District, Nanjing 210095, China; Hainan Yazhou Bay Seed Laboratory, Sanya 572025, China; Sanya Research Institute of Nanjing Agriculture University, Sanya 572024, China
| | - Qinghua Guo
- Institute of Ecology, College of Urban and Environmental Science, Peking University, Beijing 100871, China
| | - Shichao Jin
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, National Engineering and Technology Center for Information Agriculture, Collaborative Innovation Centre for Modern Crop Production co-sponsored by Province and Ministry, Nanjing Agricultural University, Address: No. 1 Weigang, Xuanwu District, Nanjing 210095, China; Hainan Yazhou Bay Seed Laboratory, Sanya 572025, China; Sanya Research Institute of Nanjing Agriculture University, Sanya 572024, China; Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, International Institute for Earth System Sciences, Nanjing University, Nanjing, Jiangsu 210023, China.
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7
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Rodrigues M, Berti de Oliveira R, Leboso Alemparte Abrantes Dos Santos G, Mayara de Oliveira K, Silveira Reis A, Herrig Furlanetto R, Antônio Yanes Bernardo Júnior L, Silva Coelho F, Rafael Nanni M. Rapid quantification of alkaloids, sugar and yield of tobacco (Nicotiana tabacum L.) varieties by using Vis-NIR-SWIR spectroradiometry. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2022; 274:121082. [PMID: 35248861 DOI: 10.1016/j.saa.2022.121082] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Revised: 02/03/2022] [Accepted: 02/24/2022] [Indexed: 05/27/2023]
Abstract
Tobacco genetic improvement programs, as well as the tobacco industry, require techniques that allow the estimation of its attributes in a fast and cheap way. The use of remote sensing through visible, near infrared and short-wave spectroscopy (Vis-NIR-SWIR) has been studied aiming to meet such demand. Thus, the aim of this work was to evaluate the use of Vis-NIR-SWIR spectroradiometer as a rapid tool to estimate alkaloids, sugars and yield of tobacco varieties. For that purpose, a study was carried out in a greenhouse with plants grown in pots (18 dm-3) containing nutrient solutions. The experimental design was completely randomized, with 30 treatments (tobacco varieties) and 10 repetitions. Tobacco leaf reflectance was collected at 13, 34 and 68 days after transplantation (DAT) with a plant-probe device connected to the spectroradiometer by an optical fiber. Subsequently, leaf analysis of alkaloids, sugars and yield were performed, and such attributes were estimated by using the Partial Least Squares Regression (PLSR), combined with the following pre-processing (PP) techniques: multiplicative scatter correction (MSC), Savitzky-Golay (SG) and standard normal variate (SNV). The results showed presence of typical inflections of chemical and structural components of the plants, which allowed obtaining PLSR models with R2p and RPDp superior to 0.71 and 2.27, respectively, for all PP techniques and attributes evaluated. The most important wavelengths were well distributed within the three operating ranges of the spectroradiometer (Vis-NIR-SWIR). Thus, the methodology proposed by this research was able to simultaneously determine all the three attributes (alkaloids, sugars and yield) with excellent predictive capacity. This is a promising result for genetic improvement and processing of tobacco (as well as other crops), since it is necessary to evaluate a large number of samples within a short period and at a low cost.
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Affiliation(s)
- Marlon Rodrigues
- Department of Agronomy, State University of Maringá, Maringá, Brazil.
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Kyaw TY, Siegert CM, Dash P, Poudel KP, Pitts JJ, Renninger HJ. Using hyperspectral leaf reflectance to estimate photosynthetic capacity and nitrogen content across eastern cottonwood and hybrid poplar taxa. PLoS One 2022; 17:e0264780. [PMID: 35271605 PMCID: PMC8912144 DOI: 10.1371/journal.pone.0264780] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Accepted: 02/16/2022] [Indexed: 02/05/2023] Open
Abstract
Eastern cottonwood (Populus deltoides W. Bartram ex Marshall) and hybrid poplars are well-known bioenergy crops. With advances in tree breeding, it is increasingly necessary to find economical ways to identify high-performing Populus genotypes that can be planted under different environmental conditions. Photosynthesis and leaf nitrogen content are critical parameters for plant growth, however, measuring them is an expensive and time-consuming process. Instead, these parameters can be quickly estimated from hyperspectral leaf reflectance if robust statistical models can be developed. To this end, we measured photosynthetic capacity parameters (Rubisco-limited carboxylation rate (Vcmax), electron transport-limited carboxylation rate (Jmax), and triose phosphate utilization-limited carboxylation rate (TPU)), nitrogen per unit leaf area (Narea), and leaf reflectance of seven taxa and 62 genotypes of Populus from two study plantations in Mississippi. For statistical modeling, we used least absolute shrinkage and selection operator (LASSO) and principal component analysis (PCA). Our results showed that the predictive ability of LASSO and PCA models was comparable, except for Narea in which LASSO was superior. In terms of model interpretability, LASSO outperformed PCA because the LASSO models needed 2 to 4 spectral reflectance wavelengths to estimate parameters. The LASSO models used reflectance values at 758 and 935 nm for estimating Vcmax (R2 = 0.51 and RMSPE = 31%) and Jmax (R2 = 0.54 and RMSPE = 32%); 687, 746, and 757 nm for estimating TPU (R2 = 0.56 and RMSPE = 31%); and 304, 712, 921, and 1021 nm for estimating Narea (R2 = 0.29 and RMSPE = 21%). The PCA model also identified 935 nm as a significant wavelength for estimating Vcmax and Jmax. Therefore, our results suggest that hyperspectral leaf reflectance modeling can be used as a cost-effective means for field phenotyping and rapid screening of Populus genotypes because of its capacity to estimate these physicochemical parameters.
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Affiliation(s)
- Thu Ya Kyaw
- Department of Forestry, Forest and Wildlife Research Center, Mississippi State University, Starkville, Mississippi, United States of America
- * E-mail:
| | - Courtney M. Siegert
- Department of Forestry, Forest and Wildlife Research Center, Mississippi State University, Starkville, Mississippi, United States of America
| | - Padmanava Dash
- Department of Geosciences, Mississippi State University, Starkville, Mississippi, United States of America
| | - Krishna P. Poudel
- Department of Forestry, Forest and Wildlife Research Center, Mississippi State University, Starkville, Mississippi, United States of America
| | - Justin J. Pitts
- Department of Forestry, Forest and Wildlife Research Center, Mississippi State University, Starkville, Mississippi, United States of America
| | - Heidi J. Renninger
- Department of Forestry, Forest and Wildlife Research Center, Mississippi State University, Starkville, Mississippi, United States of America
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Robles-Zazueta CA, Pinto F, Molero G, Foulkes MJ, Reynolds MP, Murchie EH. Prediction of Photosynthetic, Biophysical, and Biochemical Traits in Wheat Canopies to Reduce the Phenotyping Bottleneck. FRONTIERS IN PLANT SCIENCE 2022; 13:828451. [PMID: 35481146 PMCID: PMC9036448 DOI: 10.3389/fpls.2022.828451] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Accepted: 01/25/2022] [Indexed: 06/14/2023]
Abstract
To achieve food security, it is necessary to increase crop radiation use efficiency (RUE) and yield through the enhancement of canopy photosynthesis to increase the availability of assimilates for the grain, but its study in the field is constrained by low throughput and the lack of integrative measurements at canopy level. In this study, partial least squares regression (PLSR) was used with high-throughput phenotyping (HTP) data in spring wheat to build predictive models of photosynthetic, biophysical, and biochemical traits for the top, middle, and bottom layers of wheat canopies. The combined layer model predictions performed better than individual layer predictions with a significance as follows for photosynthesis R 2 = 0.48, RMSE = 5.24 μmol m-2 s-1 and stomatal conductance: R 2 = 0.36, RMSE = 0.14 mol m-2 s-1. The predictions of these traits from PLSR models upscaled to canopy level compared to field observations were statistically significant at initiation of booting (R 2 = 0.3, p < 0.05; R 2 = 0.29, p < 0.05) and at 7 days after anthesis (R 2 = 0.15, p < 0.05; R 2 = 0.65, p < 0.001). Using HTP allowed us to increase phenotyping capacity 30-fold compared to conventional phenotyping methods. This approach can be adapted to screen breeding progeny and genetic resources for RUE and to improve our understanding of wheat physiology by adding different layers of the canopy to physiological modeling.
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Affiliation(s)
- Carlos A. Robles-Zazueta
- Division of Plant and Crop Sciences, School of Biosciences, University of Nottingham, Leicestershire, United Kingdom
- Global Wheat Program, International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Francisco Pinto
- Global Wheat Program, International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Gemma Molero
- Global Wheat Program, International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - M. John Foulkes
- Division of Plant and Crop Sciences, School of Biosciences, University of Nottingham, Leicestershire, United Kingdom
| | - Matthew P. Reynolds
- Global Wheat Program, International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Erik H. Murchie
- Division of Plant and Crop Sciences, School of Biosciences, University of Nottingham, Leicestershire, United Kingdom
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Buchaillot ML, Soba D, Shu T, Liu J, Aranjuelo I, Araus JL, Runion GB, Prior SA, Kefauver SC, Sanz-Saez A. Estimating peanut and soybean photosynthetic traits using leaf spectral reflectance and advance regression models. PLANTA 2022; 255:93. [PMID: 35325309 PMCID: PMC8948130 DOI: 10.1007/s00425-022-03867-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Accepted: 03/03/2022] [Indexed: 06/14/2023]
Abstract
MAIN CONCLUSION By combining hyperspectral signatures of peanut and soybean, we predicted Vcmax and Jmax with 70 and 50% accuracy. The PLS was the model that better predicted these photosynthetic parameters. One proposed key strategy for increasing potential crop stability and yield centers on exploitation of genotypic variability in photosynthetic capacity through precise high-throughput phenotyping techniques. Photosynthetic parameters, such as the maximum rate of Rubisco catalyzed carboxylation (Vc,max) and maximum electron transport rate supporting RuBP regeneration (Jmax), have been identified as key targets for improvement. The primary techniques for measuring these physiological parameters are very time-consuming. However, these parameters could be estimated using rapid and non-destructive leaf spectroscopy techniques. This study compared four different advanced regression models (PLS, BR, ARDR, and LASSO) to estimate Vc,max and Jmax based on leaf reflectance spectra measured with an ASD FieldSpec4. Two leguminous species were tested under different controlled environmental conditions: (1) peanut under different water regimes at normal atmospheric conditions and (2) soybean under high [CO2] and high night temperature. Model sensitivities were assessed for each crop and treatment separately and in combination to identify strengths and weaknesses of each modeling approach. Regardless of regression model, robust predictions were achieved for Vc,max (R2 = 0.70) and Jmax (R2 = 0.50). Field spectroscopy shows promising results for estimating spatial and temporal variations in photosynthetic capacity based on leaf and canopy spectral properties.
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Affiliation(s)
- Ma Luisa Buchaillot
- Integrative Crop Ecophysiology Group, Plant Physiology Section, Faculty of Biology, University of Barcelona, 08028, Barcelona, Spain
- AGROTECNIO (Center for Research in Agrotechnology), Av. Rovira Roure 191, 25198, Lleida, Spain
| | - David Soba
- Instituto de Agrobiotecnología (IdAB), Consejo Superior de Investigaciones Científicas (CSIC)-Gobierno de Navarra, Av. Pamplona 123, 31192, Mutilva, Spain
| | - Tianchu Shu
- Department of Crop, Soil, and Environmental Sciences, Auburn University, Alabama, USA
| | - Juan Liu
- Industrial Crops Research Institute, Henan Academy of Agricultural Sciences, Henan, China
| | - Iker Aranjuelo
- Instituto de Agrobiotecnología (IdAB), Consejo Superior de Investigaciones Científicas (CSIC)-Gobierno de Navarra, Av. Pamplona 123, 31192, Mutilva, Spain
| | - José Luis Araus
- Integrative Crop Ecophysiology Group, Plant Physiology Section, Faculty of Biology, University of Barcelona, 08028, Barcelona, Spain
- AGROTECNIO (Center for Research in Agrotechnology), Av. Rovira Roure 191, 25198, Lleida, Spain
| | - G Brett Runion
- U.S. Department of Agriculture-Agricultural Research Service, National Soil Dynamics Laboratory, Auburn, AL, 36832, USA
| | - Stephen A Prior
- U.S. Department of Agriculture-Agricultural Research Service, National Soil Dynamics Laboratory, Auburn, AL, 36832, USA
| | - Shawn C Kefauver
- Integrative Crop Ecophysiology Group, Plant Physiology Section, Faculty of Biology, University of Barcelona, 08028, Barcelona, Spain.
- AGROTECNIO (Center for Research in Agrotechnology), Av. Rovira Roure 191, 25198, Lleida, Spain.
| | - Alvaro Sanz-Saez
- Department of Crop, Soil, and Environmental Sciences, Auburn University, Alabama, USA.
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11
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Sarić R, Nguyen VD, Burge T, Berkowitz O, Trtílek M, Whelan J, Lewsey MG, Čustović E. Applications of hyperspectral imaging in plant phenotyping. TRENDS IN PLANT SCIENCE 2022; 27:301-315. [PMID: 34998690 DOI: 10.1016/j.tplants.2021.12.003] [Citation(s) in RCA: 39] [Impact Index Per Article: 19.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/07/2021] [Revised: 11/30/2021] [Accepted: 12/06/2021] [Indexed: 06/14/2023]
Abstract
Our ability to interrogate and manipulate the genome far exceeds our capacity to measure the effects of genetic changes on plant traits. Much effort has been made recently by the plant science research community to address this imbalance. The responses of plants to environmental conditions can now be defined using a variety of imaging approaches. Hyperspectral imaging (HSI) has emerged as a promising approach to measure traits using a wide range of wavebands simultaneously in 3D to capture information in lab, glasshouse, or field settings. HSI has been applied to define abiotic, biotic, and quality traits for optimisation of crop management.
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Affiliation(s)
- Rijad Sarić
- Department of Animal, Plant and Soil Science, AgriBio Building, La Trobe University, Bundoora, VIC 3086, Australia; Department of Engineering, School of Engineering and Mathematical Sciences, La Trobe University, Bundoora, VIC 3086, Australia
| | - Viet D Nguyen
- Department of Animal, Plant and Soil Science, AgriBio Building, La Trobe University, Bundoora, VIC 3086, Australia; Department of Engineering, School of Engineering and Mathematical Sciences, La Trobe University, Bundoora, VIC 3086, Australia
| | - Timothy Burge
- Australian Research Council Research Hub for Medicinal Agriculture, AgriBio Building, La Trobe University, Bundoora, VIC 3086, Australia
| | - Oliver Berkowitz
- Department of Animal, Plant and Soil Science, AgriBio Building, La Trobe University, Bundoora, VIC 3086, Australia; Australian Research Council Research Hub for Medicinal Agriculture, AgriBio Building, La Trobe University, Bundoora, VIC 3086, Australia
| | - Martin Trtílek
- Photon Systems Instruments plant phenotyping research centre, Photon System Instruments, 664 24 Drasov, Brno, Czech Republic
| | - James Whelan
- Department of Animal, Plant and Soil Science, AgriBio Building, La Trobe University, Bundoora, VIC 3086, Australia; Australian Research Council Research Hub for Medicinal Agriculture, AgriBio Building, La Trobe University, Bundoora, VIC 3086, Australia.
| | - Mathew G Lewsey
- Department of Animal, Plant and Soil Science, AgriBio Building, La Trobe University, Bundoora, VIC 3086, Australia; Australian Research Council Research Hub for Medicinal Agriculture, AgriBio Building, La Trobe University, Bundoora, VIC 3086, Australia
| | - Edhem Čustović
- Department of Engineering, School of Engineering and Mathematical Sciences, La Trobe University, Bundoora, VIC 3086, Australia
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12
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Vatter T, Gracia-Romero A, Kefauver SC, Nieto-Taladriz MT, Aparicio N, Araus JL. Preharvest phenotypic prediction of grain quality and yield of durum wheat using multispectral imaging. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2022; 109:1507-1518. [PMID: 34951491 DOI: 10.1111/tpj.15648] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/14/2021] [Revised: 12/02/2021] [Accepted: 12/14/2021] [Indexed: 05/08/2023]
Abstract
Durum wheat is an important cereal that is widely grown in the Mediterranean basin. In addition to high yield, grain quality traits are of high importance for farmers. The strong influence of climatic conditions makes the improvement of grain quality traits, like protein content, vitreousness, and test weight, a challenging task. Evaluation of quality traits post-harvest is time- and labor-intensive and requires expensive equipment, such as near-infrared spectroscopes or hyperspectral imagers. Predicting not only yield but also important quality traits in the field before harvest is of high value for breeders aiming to optimize resource allocation. Implementation of efficient approaches for trait prediction, such as the use of high-resolution spectral data acquired by a multispectral camera mounted on unmanned aerial vehicles (UAVs), needs to be explored. In this study, we have acquired multispectral image data with an 11-band multispectral camera mounted on a UAV and analyzed the data with machine learning (ML) models to predict grain yield and important quality traits in breeding micro-plots. Combining 11-band multispectral data for 34 cultivars and 16 environments allowed to develop ML models with good prediction capability. Applying the trained models to test sets explained a considerable degree of phenotypic variance with good accuracy showing r squared values of 0.84, 0.69, 0.64, and 0.61 and normalized root mean squared errors of 0.17, 0.07, 0.14, and 0.03 for grain yield, protein content, vitreousness, and test weight, respectively.
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Affiliation(s)
- Thomas Vatter
- Integrative Crop Ecophysiology Group, Plant Physiology Section, Faculty of Biology, University of Barcelona, Diagonal 643, 08028, Barcelona, Spain
- AGROTECNIO (Center of Research in Agrotechnology), Av. Rovira Roure 191, 25198, Lleida, Spain
| | - Adrian Gracia-Romero
- Integrative Crop Ecophysiology Group, Plant Physiology Section, Faculty of Biology, University of Barcelona, Diagonal 643, 08028, Barcelona, Spain
- AGROTECNIO (Center of Research in Agrotechnology), Av. Rovira Roure 191, 25198, Lleida, Spain
| | - Shawn Carlisle Kefauver
- Integrative Crop Ecophysiology Group, Plant Physiology Section, Faculty of Biology, University of Barcelona, Diagonal 643, 08028, Barcelona, Spain
- AGROTECNIO (Center of Research in Agrotechnology), Av. Rovira Roure 191, 25198, Lleida, Spain
| | - María Teresa Nieto-Taladriz
- INIA-CSIC (Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria), Ctra. de la Coruña Km. 7.5, 28040, Madrid, Spain
| | - Nieves Aparicio
- Technological and Agrarian Institute of Castilla y León (ITACyL), Agricultural Research, Ctra Burgos km 119, 47041, Valladolid, Spain
| | - José Luis Araus
- Integrative Crop Ecophysiology Group, Plant Physiology Section, Faculty of Biology, University of Barcelona, Diagonal 643, 08028, Barcelona, Spain
- AGROTECNIO (Center of Research in Agrotechnology), Av. Rovira Roure 191, 25198, Lleida, Spain
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13
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Martínez-Peña R, Schlereth A, Höhne M, Encke B, Morcuende R, Nieto-Taladriz MT, Araus JL, Aparicio N, Vicente R. Source-Sink Dynamics in Field-Grown Durum Wheat Under Contrasting Nitrogen Supplies: Key Role of Non-Foliar Organs During Grain Filling. FRONTIERS IN PLANT SCIENCE 2022; 13:869680. [PMID: 35574116 PMCID: PMC9100808 DOI: 10.3389/fpls.2022.869680] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Accepted: 03/31/2022] [Indexed: 05/08/2023]
Abstract
The integration of high-throughput phenotyping and metabolic approaches is a suitable strategy to study the genotype-by-environment interaction and identify novel traits for crop improvement from canopy to an organ level. Our aims were to study the phenotypic and metabolic traits that are related to grain yield and quality at canopy and organ levels, with a special focus on source-sink coordination under contrasting N supplies. Four modern durum wheat varieties with contrasting grain yield were grown in field conditions under two N fertilization levels in north-eastern Spain. We evaluated canopy vegetation indices taken throughout the growing season, physiological and metabolic traits in different photosynthetic organs (flag leaf blade, sheath, peduncle, awn, glume, and lemma) at anthesis and mid-grain filling stages, and agronomic and grain quality traits at harvest. Low N supply triggered an imbalance of C and N coordination at the whole plant level, leading to a reduction of grain yield and nutrient composition. The activities of key enzymes in C and N metabolism as well as the levels of photoassimilates showed that each organ plays an important role during grain filling, some with a higher photosynthetic capacity, others for nutrient storage for later stages of grain filling, or N assimilation and recycling. Interestingly, the enzyme activities and sucrose content of the ear organs were positively associated with grain yield and quality, suggesting, together with the regression models using isotope signatures, the potential contribution of these organs during grain filling. This study highlights the use of holistic approaches to the identification of novel targets to improve grain yield and quality in C3 cereals and the key role of non-foliar organs at late-growth stages.
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Affiliation(s)
- Raquel Martínez-Peña
- Group of Cereals, Section of Herbaceous, Instituto Tecnológico Agrario de Castilla y León (ITACyL), Junta de Castilla y León, Valladolid, Spain
| | - Armin Schlereth
- Max Planck Institute of Molecular Plant Physiology, Potsdam, Germany
| | - Melanie Höhne
- Max Planck Institute of Molecular Plant Physiology, Potsdam, Germany
| | - Beatrice Encke
- Max Planck Institute of Molecular Plant Physiology, Potsdam, Germany
| | - Rosa Morcuende
- Institute of Natural Resources and Agrobiology of Salamanca (IRNASA), Consejo Superior de Investigaciones Científicas (CSIC), Salamanca, Spain
| | | | - José Luis Araus
- Integrative Crop Ecophysiology Group, Section of Plant Physiology, Faculty of Biology, University of Barcelona, Barcelona, Spain
| | - Nieves Aparicio
- Group of Cereals, Section of Herbaceous, Instituto Tecnológico Agrario de Castilla y León (ITACyL), Junta de Castilla y León, Valladolid, Spain
| | - Rubén Vicente
- Max Planck Institute of Molecular Plant Physiology, Potsdam, Germany
- Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa (ITQB NOVA), Plant Ecophysiology and Metabolism Group, Oeiras, Portugal
- *Correspondence: Rubén Vicente
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14
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Melandri G, Thorp KR, Broeckling C, Thompson AL, Hinze L, Pauli D. Assessing Drought and Heat Stress-Induced Changes in the Cotton Leaf Metabolome and Their Relationship With Hyperspectral Reflectance. FRONTIERS IN PLANT SCIENCE 2021; 12:751868. [PMID: 34745185 PMCID: PMC8569624 DOI: 10.3389/fpls.2021.751868] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Accepted: 09/30/2021] [Indexed: 06/13/2023]
Abstract
The study of phenotypes that reveal mechanisms of adaptation to drought and heat stress is crucial for the development of climate resilient crops in the face of climate uncertainty. The leaf metabolome effectively summarizes stress-driven perturbations of the plant physiological status and represents an intermediate phenotype that bridges the plant genome and phenome. The objective of this study was to analyze the effect of water deficit and heat stress on the leaf metabolome of 22 genetically diverse accessions of upland cotton grown in the Arizona low desert over two consecutive years. Results revealed that membrane lipid remodeling was the main leaf mechanism of adaptation to drought. The magnitude of metabolic adaptations to drought, which had an impact on fiber traits, was found to be quantitatively and qualitatively associated with different stress severity levels during the two years of the field trial. Leaf-level hyperspectral reflectance data were also used to predict the leaf metabolite profiles of the cotton accessions. Multivariate statistical models using hyperspectral data accurately estimated (R 2 > 0.7 in ∼34% of the metabolites) and predicted (Q 2 > 0.5 in 15-25% of the metabolites) many leaf metabolites. Predicted values of metabolites could efficiently discriminate stressed and non-stressed samples and reveal which regions of the reflectance spectrum were the most informative for predictions. Combined together, these findings suggest that hyperspectral sensors can be used for the rapid, non-destructive estimation of leaf metabolites, which can summarize the plant physiological status.
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Affiliation(s)
- Giovanni Melandri
- School of Plant Sciences, University of Arizona, Tucson, AZ, United States
| | - Kelly R. Thorp
- United States Department of Agriculture-Agricultural Research Service, Arid Land Agricultural Research Center, Maricopa, AZ, United States
| | - Corey Broeckling
- Analytical Resources Core: Bioanalysis and Omics Center, Colorado State University, Fort Collins, CO, United States
- Department of Agricultural Biology, Colorado State University, Fort Collins, CO, United States
| | - Alison L. Thompson
- United States Department of Agriculture-Agricultural Research Service, Arid Land Agricultural Research Center, Maricopa, AZ, United States
| | - Lori Hinze
- United States Department of Agriculture-Agricultural Research Service, Southern Plains Agricultural Research Center, College Station, TX, United States
| | - Duke Pauli
- School of Plant Sciences, University of Arizona, Tucson, AZ, United States
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15
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Chen J, Xue M, Liu H, Fernie AR, Chen W. Exploring the genic resources underlying metabolites through mGWAS and mQTL in wheat: From large-scale gene identification and pathway elucidation to crop improvement. PLANT COMMUNICATIONS 2021; 2:100216. [PMID: 34327326 PMCID: PMC8299079 DOI: 10.1016/j.xplc.2021.100216] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Revised: 06/04/2021] [Accepted: 06/28/2021] [Indexed: 05/23/2023]
Abstract
Common wheat (Triticum aestivum L.) is a leading cereal crop, but has lagged behind with respect to the interpretation of the molecular mechanisms of phenotypes compared with other major cereal crops such as rice and maize. The recently available genome sequence of wheat affords the pre-requisite information for efficiently exploiting the potential molecular resources for decoding the genetic architecture of complex traits and identifying valuable breeding targets. Meanwhile, the successful application of metabolomics as an emergent large-scale profiling methodology in several species has demonstrated this approach to be accessible for reaching the above goals. One such productive avenue is combining metabolomics approaches with genetic designs. However, this trial is not as widespread as that for sequencing technologies, especially when the acquisition, understanding, and application of metabolic approaches in wheat populations remain more difficult and even arguably underutilized. In this review, we briefly introduce the techniques used in the acquisition of metabolomics data and their utility in large-scale identification of functional candidate genes. Considerable progress has been made in delivering improved varieties, suggesting that the inclusion of information concerning these metabolites and genes and metabolic pathways enables a more explicit understanding of phenotypic traits and, as such, this procedure could serve as an -omics-informed roadmap for executing similar improvement strategies in wheat and other species.
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Affiliation(s)
- Jie Chen
- National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research (Wuhan), Huazhong Agricultural University, Wuhan 430070, China
- College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Mingyun Xue
- National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research (Wuhan), Huazhong Agricultural University, Wuhan 430070, China
- College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Hongbo Liu
- National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research (Wuhan), Huazhong Agricultural University, Wuhan 430070, China
| | - Alisdair R. Fernie
- Max-Planck-Institute of Molecular Plant Physiology, Potsdam-Golm 14476, Germany
| | - Wei Chen
- National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research (Wuhan), Huazhong Agricultural University, Wuhan 430070, China
- College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
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16
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Grzybowski M, Wijewardane NK, Atefi A, Ge Y, Schnable JC. Hyperspectral reflectance-based phenotyping for quantitative genetics in crops: Progress and challenges. PLANT COMMUNICATIONS 2021; 2:100209. [PMID: 34327323 PMCID: PMC8299078 DOI: 10.1016/j.xplc.2021.100209] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/18/2020] [Revised: 04/23/2021] [Accepted: 05/24/2021] [Indexed: 05/05/2023]
Abstract
Many biochemical and physiological properties of plants that are of interest to breeders and geneticists have extremely low throughput and/or can only be measured destructively. This has limited the use of information on natural variation in nutrient and metabolite abundance, as well as photosynthetic capacity in quantitative genetic contexts where it is necessary to collect data from hundreds or thousands of plants. A number of recent studies have demonstrated the potential to estimate many of these traits from hyperspectral reflectance data, primarily in ecophysiological contexts. Here, we summarize recent advances in the use of hyperspectral reflectance data for plant phenotyping, and discuss both the potential benefits and remaining challenges to its application in plant genetics contexts. The performances of previously published models in estimating six traits from hyperspectral reflectance data in maize were evaluated on new sample datasets, and the resulting predicted trait values shown to be heritable (e.g., explained by genetic factors) were estimated. The adoption of hyperspectral reflectance-based phenotyping beyond its current uses may accelerate the study of genes controlling natural variation in biochemical and physiological traits.
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Affiliation(s)
- Marcin Grzybowski
- Center for Plant Science Innovation and Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE, USA
- Department of Plant Molecular Ecophysiology, Institute of Plant Experimental Biology and Biotechnology, Faculty of Biology, University of Warsaw, Warsaw, Poland
| | - Nuwan K. Wijewardane
- Department of Biological Systems Engineering, University of Nebraska-Lincoln, Lincoln, NE, USA
- Department of Agricultural Biological Engineering, Mississippi State University, Starkville, MS, USA
| | - Abbas Atefi
- Department of Biological Systems Engineering, University of Nebraska-Lincoln, Lincoln, NE, USA
| | - Yufeng Ge
- Department of Biological Systems Engineering, University of Nebraska-Lincoln, Lincoln, NE, USA
| | - James C. Schnable
- Center for Plant Science Innovation and Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE, USA
- Corresponding author
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17
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Fernie AR, Alseekh S, Liu J, Yan J. Using precision phenotyping to inform de novo domestication. PLANT PHYSIOLOGY 2021; 186:1397-1411. [PMID: 33848336 PMCID: PMC8260140 DOI: 10.1093/plphys/kiab160] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Accepted: 03/22/2021] [Indexed: 05/09/2023]
Abstract
An update on the use of precision phenotyping to assess the potential of lesser cultivated species as candidates for de novo domestication or similar development for future agriculture.
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Affiliation(s)
- Alisdair R Fernie
- Max Planck Institute for Molecular Plant Physiology, Am Mühlenberg 1, 14476 Potsdam-Golm, Germany
- Centre of Plant Systems Biology and Biotechnology, 4000 Plovdiv, Bulgaria
- Author for communication: (A.R.F.)
| | - Saleh Alseekh
- Max Planck Institute for Molecular Plant Physiology, Am Mühlenberg 1, 14476 Potsdam-Golm, Germany
- Centre of Plant Systems Biology and Biotechnology, 4000 Plovdiv, Bulgaria
| | - Jie Liu
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, 430070 Wuhan, Hubei, China
| | - Jianbing Yan
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, 430070 Wuhan, Hubei, China
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18
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Early Identification of Root Rot Disease by Using Hyperspectral Reflectance: The Case of Pathosystem Grapevine/Armillaria. REMOTE SENSING 2021. [DOI: 10.3390/rs13132436] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Armillaria genus represents one of the most common causes of chronic root rot disease in woody plants. Prompt recognition of diseased plants is crucial to control the pathogen. However, the current disease detection methods are limited at a field scale. Therefore, an alternative approach is needed. In this study, we investigated the potential of hyperspectral techniques to identify fungi-infected vs. healthy plants of Vitis vinifera. We used the hyperspectral imaging sensor Specim-IQ to acquire leaves’ reflectance data of the Teroldego Rotaliano grapevine cultivar. We analyzed three different groups of plants: healthy, asymptomatic, and diseased. Highly significant differences were found in the near-infrared (NIR) spectral region with a decreasing pattern from healthy to diseased plants attributable to the leaf mesophyll changes. Asymptomatic plants emerged from the other groups due to a lower reflectance in the red edge spectrum (around 705 nm), ascribable to an accumulation of secondary metabolites involved in plant defense strategies. Further significant differences were observed in the wavelengths close to 550 nm in diseased vs. asymptomatic plants. We evaluated several machine learning paradigms to differentiate the plant groups. The Naïve Bayes (NB) algorithm, combined with the most discriminant variables among vegetation indices and spectral narrow bands, provided the best results with an overall accuracy of 90% and 75% in healthy vs. diseased and healthy vs. asymptomatic plants, respectively. To our knowledge, this study represents the first report on the possibility of using hyperspectral data for root rot disease diagnosis in woody plants. Although further validation studies are required, it appears that the spectral reflectance technique, possibly implemented on unmanned aerial vehicles (UAVs), could be a promising tool for a cost-effective, non-invasive method of Armillaria disease diagnosis and mapping in-field, contributing to a significant step forward in precision viticulture.
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19
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Cavender-Bares J, Reich P, Townsend P, Banerjee A, Butler E, Desai A, Gevens A, Hobbie S, Isbell F, Laliberté E, Meireles JE, Menninger H, Pavlick R, Pinto-Ledezma J, Potter C, Schuman M, Springer N, Stefanski A, Trivedi P, Trowbridge A, Williams L, Willis C, Yang Y. BII-Implementation: The causes and consequences of plant biodiversity across scales in a rapidly changing world. RESEARCH IDEAS AND OUTCOMES 2021. [DOI: 10.3897/rio.7.e63850] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
The proposed Biology Integration Institute will bring together two major research institutions in the Upper Midwest—the University of Minnesota (UMN) and University of Wisconsin-Madison (UW)—to investigate the causes and consequences of plant biodiversity across scales in a rapidly changing world—from genes and molecules within cells and tissues to communities, ecosystems, landscapes and the biosphere. The Institute focuses on plant biodiversity, defined broadly to encompass the heterogeneity within life that occurs from the smallest to the largest biological scales. A premise of the Institute is that life is envisioned as occurring at different scales nested within several contrasting conceptions of biological hierarchies, defined by the separate but related fields of physiology, evolutionary biology and ecology. The Institute will emphasize the use of ‘spectral biology’—detection of biological properties based on the interaction of light energy with matter—and process-oriented predictive models to investigate the processes by which biological components at one scale give rise to emergent properties at higher scales. Through an iterative process that harnesses cutting edge technologies to observe a suite of carefully designed empirical systems—including the National Ecological Observatory Network (NEON) and some of the world’s longest running and state-of-the-art global change experiments—the Institute will advance biological understanding and theory of the causes and consequences of changes in biodiversity and at the interface of plant physiology, ecology and evolution.
INTELLECTUAL MERIT
The Institute brings together a diverse, gender-balanced and highly productive team with significant leadership experience that spans biological disciplines and career stages and is poised to integrate biology in new ways. Together, the team will harness the potential of spectral biology, experiments, observations and synthetic modeling in a manner never before possible to transform understanding of how variation within and among biological scales drives plant and ecosystem responses to global change over diurnal, seasonal and millennial time scales. In doing so, it will use and advance state-of-the-art theory. The institute team posits that the designed projects will unearth transformative understanding and biological rules at each of the various scales that will enable an unprecedented capacity to discern the linkages between physiological, ecological and evolutionary processes in relation to the multi-dimensional nature of biodiversity in this time of massive planetary change. A strength of the proposed Institute is that it leverages prior federal investments in research and formalizes partnerships with foreign institutions heavily invested in related biodiversity research. Most of the planned projects leverage existing research initiatives, infrastructure, working groups, experiments, training programs, and public outreach infrastructure, all of which are already highly synergistic and collaborative, and will bring together members of the overall research and training team.
BROADER IMPACTS
A central goal of the proposed Institute is to train the next generation of diverse integrative biologists. Post-doctoral, graduate student and undergraduate trainees, recruited from non-traditional and underrepresented groups, including through formal engagement with Native American communities, will receive a range of mentoring and training opportunities. Annual summer training workshops will be offered at UMN and UW as well as training experiences with the Global Change and Biodiversity Research Priority Program (URPP-GCB) at the University of Zurich (UZH) and through the Canadian Airborne Biodiversity Observatory (CABO). The Institute will engage diverse K-12 audiences, the general public and Native American communities through Market Science modules, Minute Earth videos, a museum exhibit and public engagement and educational activities through the Bell Museum of Natural History, the Cedar Creek Ecosystem Science Reserve (CCESR) and the Wisconsin Tribal Conservation Association.
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20
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Scossa F, Alseekh S, Fernie AR. Integrating multi-omics data for crop improvement. JOURNAL OF PLANT PHYSIOLOGY 2021; 257:153352. [PMID: 33360148 DOI: 10.1016/j.jplph.2020.153352] [Citation(s) in RCA: 48] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/01/2020] [Revised: 12/13/2020] [Accepted: 12/14/2020] [Indexed: 05/26/2023]
Abstract
Our agricultural systems are now in urgent need to secure food for a growing world population. To meet this challenge, we need a better characterization of plant genetic and phenotypic diversity. The combination of genomics, transcriptomics and metabolomics enables a deeper understanding of the mechanisms underlying the complex architecture of many phenotypic traits of agricultural relevance. We review the recent advances in plant genomics to see how these can be integrated with broad molecular profiling approaches to improve our understanding of plant phenotypic variation and inform crop breeding strategies.
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Affiliation(s)
- Federico Scossa
- Max-Planck-Institut für Molekulare Pflanzenphysiologie, 14476, Potsdam, Golm, Germany; Council for Agricultural Research and Economics (CREA), Research Centre for Genomics and Bioinformatics (CREA-GB), 00178, Rome, Italy.
| | - Saleh Alseekh
- Max-Planck-Institut für Molekulare Pflanzenphysiologie, 14476, Potsdam, Golm, Germany; Center of Plant Systems Biology and Biotechnology (CPSBB), Plovdiv, Bulgaria
| | - Alisdair R Fernie
- Max-Planck-Institut für Molekulare Pflanzenphysiologie, 14476, Potsdam, Golm, Germany; Center of Plant Systems Biology and Biotechnology (CPSBB), Plovdiv, Bulgaria.
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Correction. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2020; 103:930. [PMID: 32681614 DOI: 10.1111/tpj.14866] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
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Druege U. Overcoming Physiological Bottlenecks of Leaf Vitality and Root Development in Cuttings: A Systemic Perspective. FRONTIERS IN PLANT SCIENCE 2020; 11:907. [PMID: 32714348 PMCID: PMC7340085 DOI: 10.3389/fpls.2020.00907] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/24/2020] [Accepted: 06/03/2020] [Indexed: 05/09/2023]
Abstract
Each year, billions of ornamental young plants are produced worldwide from cuttings that are harvested from stock plants and planted to form adventitious roots. Depending on the plant genotype, the maturation of the cutting, and the particular environment, which is complex and often involves intermediate storage of cuttings under dark conditions and shipping between different climate regions, induced senescence or abscission of leaves and insufficient root development can impair the success of propagation and the quality of generated young plants. Recent findings on the molecular and physiological control of leaf vitality and adventitious root formation are integrated into a systemic perspective on improved physiologically-based control of cutting propagation. The homeostasis and signal transduction of the wound responsive plant hormones ethylene and jasmonic acid, of auxin, cytokinins and strigolactones, and the carbon-nitrogen source-sink balance in cuttings are considered as important processes that are both, highly responsive to environmental inputs and decisive for the development of cuttings. Important modules and bottlenecks of cutting function are identified. Critical environmental inputs at stock plant and cutting level are highlighted and physiological outputs that can be used as quality attributes to monitor the functional capacity of cuttings and as response parameters to optimize the cutting environment are discussed. Facing the great genetic diversity of ornamental crops, a physiologically targeted approach is proposed to define bottleneck-specific plant groups. Components from the field of machine learning may help to mathematically describe the complex environmental response of specific plant species.
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