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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.
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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
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Combining Hyperspectral Reflectance and Multivariate Regression Models to Estimate Plant Biomass of Advanced Spring Wheat Lines in Diverse Phenological Stages under Salinity Conditions. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12041983] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
An area of growing interest in wheat-breeding programs for abiotic stresses is the accurate and expeditious phenotyping of large genotype collections using nondestructive hyperspectral sensing tools. The main goal of this study was to use data from canopy spectral signatures (CSS) in the full-spectrum range (400–2500 nm) to estimate and predict the plant biomass dry weight at booting (BDW-BT) and anthesis (BDW-AN) growth stages, and biological yield (BY) of 64 spring wheat germplasms exposed to 150 mM NaCl using 13 spectral reflectance indices (SRIs, consisting of seven vegetation-related SRIs and six water-related SRIs) and partial least squares regression (PLSR). SRI and PLSR performance in estimating plant traits was evaluated during two years at BT, AN, and early milk grain (EMG) growth stages. Results showed significant genotypic differences between the three traits and SRIs, with highly significant two-way and three-way interactions between genotypes, years, and growth stages for all SRIs. Genotypic differences in CSS and the relationships between the three traits and a single wavelength over the full-spectrum range depended on the growth stage. Water-related SRIs were more strongly correlated with the three traits compared with vegetation-related SRIs at the BT stage; the opposite was found at the EMG stage. Both types of SRIs exhibited comparable associations with the three traits at the AN stage. Principal component analysis indicated that it is possible to assess plant biomass variations at an early stage (BT) through published and modified SRIs. SRIs coupled with PLSR models at the BT stage exhibited good prediction capacity of BDW-BT (57%), BDW-AN (82%), and BY (55%). Overall, results demonstrated that the integration of SRIs and multivariate models may present a feasible tool for plant breeders to increase the efficiency of the evaluation process and to improve the genetics for salt tolerance in wheat-breeding programs.
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Lobos GA, Estrada F, Del Pozo A, Romero-Bravo S, Astudillo CA, Mora-Poblete F. Challenges for a Massive Implementation of Phenomics in Plant Breeding Programs. Methods Mol Biol 2022; 2539:135-157. [PMID: 35895202 DOI: 10.1007/978-1-0716-2537-8_13] [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] [Indexed: 06/15/2023]
Abstract
Due to climate change and expected food shortage in the coming decades, not only will it be necessary to develop cultivars with greater tolerance to environmental stress, but it is also imperative to reduce breeding cycle time. In addition to yield evaluation, plant breeders resort to many sensory assessments and some others of intermediate complexity. However, to develop cultivars better adapted to current/future constraints, it is necessary to incorporate a new set of traits, such as morphophysiological and physicochemical attributes, information relevant to the successful selection of genotypes or parents. Unfortunately, because of the large number of genotypes to be screened, measurements with conventional equipment are unfeasible, especially under field conditions. High-throughput plant phenotyping (HTPP) facilitates collecting a significant amount of data quickly; however, it is necessary to transform all this information (e.g., plant reflectance) into helpful descriptors to the breeder. To the extent that a holistic characterization of the plant (phenomics) is performed in challenging environments, it will be possible to select the best genotypes (forward phenomics) objectively but also understand why the said individual differs from the rest (reverse phenomics). Unfortunately, several elements had prevented phenomics from developing as desired. Consequently, a new set of prediction/validation methodologies, seasonal ambient information, and the fusion of data matrices (e.g., genotypic and phenotypic information) need to be incorporated into the modeling. In this sense, for the massive implementation of phenomics in plant breeding, it will be essential to count an interdisciplinary team that responds to the urgent need to release material with greater capacity to tolerate environmental stress. Therefore, breeding programs should (i) be more efficient (e.g., early discarding of unsuitable material), (ii) have shorter breeding cycles (fewer crosses to achieve the desired cultivar), and (iii) be more productive, increasing the probability of success at the end of the breeding process (percentage of cultivars released to the number of initial crosses).
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Affiliation(s)
- Gustavo A Lobos
- Plant Breeding and Phenomics Center, Faculty of Agricultural Sciences, Universidad de Talca, Talca, Chile.
| | - Félix Estrada
- Plant Breeding and Phenomics Center, Faculty of Agricultural Sciences, Universidad de Talca, Talca, Chile
| | - Alejandro Del Pozo
- Plant Breeding and Phenomics Center, Faculty of Agricultural Sciences, Universidad de Talca, Talca, Chile
| | | | - Cesar A Astudillo
- Department of Computer Science, Faculty of Engineering, Universidad de Talca, Curico, Chile
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El-Hendawy S, Al-Suhaibani N, Mubushar M, Tahir MU, Refay Y, Tola E. Potential Use of Hyperspectral Reflectance as a High-Throughput Nondestructive Phenotyping Tool for Assessing Salt Tolerance in Advanced Spring Wheat Lines under Field Conditions. PLANTS (BASEL, SWITZERLAND) 2021; 10:plants10112512. [PMID: 34834875 PMCID: PMC8624136 DOI: 10.3390/plants10112512] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Revised: 11/07/2021] [Accepted: 11/16/2021] [Indexed: 06/01/2023]
Abstract
The incorporation of stress tolerance indices (STIs) with the early estimation of grain yield (GY) in an expeditious and nondestructive manner can enable breeders for ensuring the success of genotype development for a wide range of environmental conditions. In this study, the relative performance of GY for sixty-four spring wheat germplasm under the control and 15.0 dS m-1 NaCl were compared through different STIs, and the ability of a hyperspectral reflectance tool for the early estimation of GY and STIs was assessed using twenty spectral reflectance indices (SRIs; 10 vegetation SRIs and 10 water SRIs). The results showed that salinity treatments, genotypes, and their interactions had significant effects on the GY and nearly all SRIs. Significant genotypic variations were also observed for all STIs. Based on the GY under the control (GYc) and salinity (GYs) conditions and all STIs, the tested genotypes were classified into three salinity tolerance groups (salt-tolerant, salt-sensitive, and moderately salt-tolerant groups). Most vegetation and water SRIs showed strong relationships with the GYc, stress tolerance index (STI), and geometric mean productivity (GMP); moderate relationships with GYs and sometimes with the tolerance index (TOL); and weak relationships with the yield stability index (YSI) and stress susceptibility index (SSI). Obvious differences in the spectral reflectance curves were found among the three salinity tolerance groups under the control and salinity conditions. Stepwise multiple linear regressions identified three SRIs from each vegetation and water SRI as the most influential indices that contributed the most variation in the GY. These SRIs were much more effective in estimating the GYc (R2 = 0.64 - 0.79) than GYs (R2 = 0.38 - 0.47). They also provided a much accurate estimation of the GYc and GYs for the moderately salt-tolerant genotype group; YSI, SSI, and TOL for the salt-sensitive genotypes group; and STI and GMP for all the three salinity tolerance groups. Overall, the results of this study highlight the potential of using a hyperspectral reflectance tool in breeding programs for phenotyping a sufficient number of genotypes under a wide range of environmental conditions in a cost-effective, noninvasive, and expeditious manner. This will aid in accelerating the development of genotypes for salinity conditions in breeding programs.
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Affiliation(s)
- Salah El-Hendawy
- Department of Plant Production, College of Food and Agriculture Sciences, King Saud University, Riyadh 11451, Saudi Arabia; (N.A.-S.); (M.M.); (M.U.T.); (Y.R.)
- Department of Agronomy, Faculty of Agriculture, Suez Canal University, Ismailia 41522, Egypt
| | - Nasser Al-Suhaibani
- Department of Plant Production, College of Food and Agriculture Sciences, King Saud University, Riyadh 11451, Saudi Arabia; (N.A.-S.); (M.M.); (M.U.T.); (Y.R.)
| | - Muhammad Mubushar
- Department of Plant Production, College of Food and Agriculture Sciences, King Saud University, Riyadh 11451, Saudi Arabia; (N.A.-S.); (M.M.); (M.U.T.); (Y.R.)
| | - Muhammad Usman Tahir
- Department of Plant Production, College of Food and Agriculture Sciences, King Saud University, Riyadh 11451, Saudi Arabia; (N.A.-S.); (M.M.); (M.U.T.); (Y.R.)
| | - Yahya Refay
- Department of Plant Production, College of Food and Agriculture Sciences, King Saud University, Riyadh 11451, Saudi Arabia; (N.A.-S.); (M.M.); (M.U.T.); (Y.R.)
| | - ElKamil Tola
- Precision Agriculture Research Chair (PARC), College of Food and Agriculture Sciences, King Saud University, Riyadh 11451, Saudi Arabia;
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Combining Thermal and RGB Imaging Indices with Multivariate and Data-Driven Modeling to Estimate the Growth, Water Status, and Yield of Potato under Different Drip Irrigation Regimes. REMOTE SENSING 2021. [DOI: 10.3390/rs13091679] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Advances in proximal hyperspectral sensing tools, chemometric techniques, and data-driven modeling have enhanced precision irrigation management by facilitating the monitoring of several plant traits. This study investigated the performance of remote sensing indices derived from thermal and red-green-blue (RGB) images combined with stepwise multiple linear regression (SMLR) and an integrated adaptive neuro-fuzzy inference system with a genetic algorithm (ANFIS-GA) for monitoring the biomass fresh weight (BFW), biomass dry weight (BDW), biomass water content (BWC), and total tuber yield (TTY) of two potato varieties under 100%, 75%, and 50% of the estimated crop evapotranspiration (ETc). Results showed that the plant traits and indices varied significantly between the three irrigation regimes. Furthermore, all of the indices exhibited strong relationships with BFW, CWC, and TTY (R2 = 0.80–0.92) and moderate to weak relationships with BDW (R2 = 0.25–0.65) when considered for each variety across the irrigation regimes, for each season across the varieties and irrigation regimes, and across all data combined, but none of the indices successfully assessed any of the plant traits when considered for each irrigation regime across the two varieties. The SMLR and ANFIS-GA models gave the best predictions for the four plant traits in the calibration and testing stages, with the exception of the SMLR testing model for BDW. Thus, the use of thermal and RGB imaging indices with ANFIS-GA models could be a practical tool for managing the growth and production of potato crops under deficit irrigation regimes.
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Upreti P, Narayan S, Khan F, Tewari LM, Shirke PA. Physiological attributes associated with leaf spectral alterations in guar [ Cyamopsis tetragonoloba (L.) Taub.] under drought. 3 Biotech 2021; 11:125. [PMID: 33643760 DOI: 10.1007/s13205-021-02674-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2020] [Accepted: 02/01/2021] [Indexed: 11/25/2022] Open
Abstract
Two contrasting guar varieties [Cyamopsis tetragonoloba (L.) Taub.], RGC-1002-a better drought tolerant than RGC-936-a comparatively sensitive variety, were subjected to 8 days of water stress. The physiological characteristics of gas exchange, pigments and the spectral properties of the leaves were monitored and assessed. The guar variety, RGC-1002 exhibited higher relative water content (RWC) and biomass accumulation in water stress condition as compared to RGC-936. The RGC-1002 variety also showed a higher rate of photosynthesis, stomatal conductance, and lesser reduction in chlorophyll content as compared to RGC-936 variety. On the 8th day of drought, the leaf anthocyanin levels were also higher in RGC-1002 than RGC-936. Increased levels of anthocyanin result in decrease in absorption of light, and an increase in reflectance, and transmittance. The scattering coefficient of leaf spectra was significantly increased in RGC-936 than in RGC-1002 leaves; this is due to an increase in the size of intercellular air spaces and shrinkage of cells as a result of water loss. The leaf reflectance was also observed to be significantly increased in RGC-936 than in RGC-1002 leaves. The physiological and leaf optical observations substantiate that RGC-1002 was better adapted to water stress than RGC-936 variety. SUPPLEMENTARY INFORMATION The online version contains supplementary material available at 10.1007/s13205-021-02674-2.
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Affiliation(s)
- Priyanka Upreti
- Plant Physiology Laboratory, Council of Scientific and Industrial Research-National Botanical Research Institute, Rana Pratap Marg, Lucknow, U.P. 226001 India
- Department of Botany, Kumaun University, D. S. B. Campus, Nainital, 263001 India
| | - Shiv Narayan
- Plant Physiology Laboratory, Council of Scientific and Industrial Research-National Botanical Research Institute, Rana Pratap Marg, Lucknow, U.P. 226001 India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002 India
| | - Furqan Khan
- Plant Physiology Laboratory, Council of Scientific and Industrial Research-National Botanical Research Institute, Rana Pratap Marg, Lucknow, U.P. 226001 India
- Present Address: KPHEI, Jhalwa College, Affiliated to Prof. Rajendra Singh (Rajju Bhaiya) University, Prayagraj, U.P. India
| | - Lalit Mohan Tewari
- Department of Botany, Kumaun University, D. S. B. Campus, Nainital, 263001 India
| | - Pramod Arvind Shirke
- Plant Physiology Laboratory, Council of Scientific and Industrial Research-National Botanical Research Institute, Rana Pratap Marg, Lucknow, U.P. 226001 India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002 India
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Use of Hyperspectral Reflectance Sensing for Assessing Growth and Chlorophyll Content of Spring Wheat Grown under Simulated Saline Field Conditions. PLANTS 2021; 10:plants10010101. [PMID: 33418974 PMCID: PMC7825289 DOI: 10.3390/plants10010101] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Revised: 12/31/2020] [Accepted: 01/02/2021] [Indexed: 11/26/2022]
Abstract
The application of proximal hyperspectral sensing, using simple vegetation indices, offers an easy, fast, and non-destructive approach for assessing various plant variables related to salinity tolerance. Because most existing indices are site- and species-specific, published indices must be further validated when they are applied to other conditions and abiotic stress. This study compared the performance of various published and newly constructed indices, which differ in algorithm forms and wavelength combinations, for remotely assessing the shoot dry weight (SDW) as well as chlorophyll a (Chla), chlorophyll b (Chlb), and chlorophyll a+b (Chlt) content of two wheat genotypes exposed to three salinity levels. Stepwise multiple linear regression (SMLR) was used to extract the most influential indices within each spectral reflectance index (SRI) type. Linear regression based on influential indices was applied to predict plant variables in distinct conditions (genotypes, salinity levels, and seasons). The results show that salinity levels, genotypes, and their interaction had significant effects (p ≤ 0.05 and 0.01) on all plant variables and nearly all indices. Almost all indices within each SRI type performed favorably in estimating the plant variables under both salinity levels (6.0 and 12.0 dS m−1) and for the salt-sensitive genotype Sakha 61. The most effective indices extracted from each SRI type by SMLR explained 60%–81% of the total variability in four plant variables. The various predictive models provided a more accurate estimation of Chla and Chlt content than of SDW and Chlb under both salinity levels. They also provided a more accurate estimation of SDW than of Chl content for salt-tolerant genotype Sakha 93, exhibited strong performance for predicting the four variables for Sakha 61, and failed to predict any variables under control and Chlb for Sakha 93. The overall results indicate that the simple form of indices can be used in practice to remotely assess the growth and chlorophyll content of distinct wheat genotypes under saline field conditions.
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Elmetwalli AH, El-Hendawy S, Al-Suhaibani N, Alotaibi M, Tahir MU, Mubushar M, Hassan WM, Elsayed S. Potential of Hyperspectral and Thermal Proximal Sensing for Estimating Growth Performance and Yield of Soybean Exposed to Different Drip Irrigation Regimes Under Arid Conditions. SENSORS (BASEL, SWITZERLAND) 2020; 20:E6569. [PMID: 33213009 PMCID: PMC7698533 DOI: 10.3390/s20226569] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/12/2020] [Revised: 11/09/2020] [Accepted: 11/12/2020] [Indexed: 11/16/2022]
Abstract
Proximal hyperspectral sensing tools could complement and perhaps replace destructive traditional methods for accurate estimation and monitoring of various morpho-physiological plant indicators. In this study, we assessed the potential of thermal imaging (TI) criteria and spectral reflectance indices (SRIs) to monitor different vegetative growth traits (biomass fresh weight, biomass dry weight, and canopy water mass) and seed yield (SY) of soybean exposed to 100%, 75%, and 50% of estimated crop evapotranspiration (ETc). These different plant traits were evaluated and related to TI criteria and SRIs at the beginning bloom (R1) and full seed (R6) growth stages. Results showed that all plant traits, TI criteria, and SRIs presented significant variations (p < 0.05) among irrigation regimes at both growth stages. The performance of TI criteria and SRIs for assessment of vegetative growth traits and SY fluctuated when relationships were analyzed for each irrigation regime or growth stage separately or when the data of both conditions were combined together. TI criteria and SRIs exhibited a moderate to strong relationship with vegetative growth traits when data from different irrigation regimes were pooled together at each growth stage or vice versa. The R6 and R1 growth stages are suitable for assessing SY under full (100% ETc) and severe (50% ETc) irrigation regimes, respectively, using SRIs. The overall results indicate that the usefulness of the TI and SRIs for assessment of growth, yield, and water status of soybean under arid conditions is limited to the growth stage, the irrigation level, and the combination between them.
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Affiliation(s)
- Adel H. Elmetwalli
- Agricultural Engineering Department, Faculty of Agriculture, Tanta University, Tanta 31527, Egypt;
| | - Salah El-Hendawy
- Department of Plant Production, College of Food and Agriculture Sciences, King Saud University, Riyadh 11451, Saudi Arabia; (N.A.-S.); (M.A.); (M.U.T.); (M.M.)
- Department of Agronomy, Faculty of Agriculture, Suez Canal University, Ismailia 41522, Egypt
| | - Nasser Al-Suhaibani
- Department of Plant Production, College of Food and Agriculture Sciences, King Saud University, Riyadh 11451, Saudi Arabia; (N.A.-S.); (M.A.); (M.U.T.); (M.M.)
| | - Majed Alotaibi
- Department of Plant Production, College of Food and Agriculture Sciences, King Saud University, Riyadh 11451, Saudi Arabia; (N.A.-S.); (M.A.); (M.U.T.); (M.M.)
| | - Muhammad Usman Tahir
- Department of Plant Production, College of Food and Agriculture Sciences, King Saud University, Riyadh 11451, Saudi Arabia; (N.A.-S.); (M.A.); (M.U.T.); (M.M.)
| | - Muhammad Mubushar
- Department of Plant Production, College of Food and Agriculture Sciences, King Saud University, Riyadh 11451, Saudi Arabia; (N.A.-S.); (M.A.); (M.U.T.); (M.M.)
| | - Wael M. Hassan
- Department of Biology, College of Science and Humanities at Quwayiyah, Shaqra University, Riyadh 19257, Saudi Arabia;
- Department of Agricultural Botany, Faculty of Agriculture, Suez Canal University, Ismailia 41522, Egypt
| | - Salah Elsayed
- Agricultural Engineering, Evaluation of Natural Resources Department, Environmental Studies and Research Institute, University of Sadat City, Minufiya 32897, Egypt;
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Sanchez-Bragado R, Vicente R, Molero G, Serret MD, Maydup ML, Araus JL. New avenues for increasing yield and stability in C3 cereals: exploring ear photosynthesis. CURRENT OPINION IN PLANT BIOLOGY 2020; 56:223-234. [PMID: 32088154 DOI: 10.1016/j.pbi.2020.01.001] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/16/2019] [Revised: 01/08/2020] [Accepted: 01/09/2020] [Indexed: 05/22/2023]
Abstract
Small grain cereals such as wheat, rice and barley are among the most important crops worldwide. Any attempt to increase crop productivity and stability through breeding implies developing new strategies for plant phenotyping, including defining ideotype attributes for selection. Recently, the role of non-foliar photosynthetic organs, particularly the inflorescences, has received increasing attention. For example, ear photosynthesis has been reported to be a major contributor to grain filling in wheat and barley under stress and good agronomic conditions. This review provides an overview of the particular characteristics of the ear that makes this photosynthetic organ better adapted to grain filling than the flag leaf and revises potential metabolic and molecular traits that merit further research as targets for cereal improvement. Currently, the absence of high-throughput phenotyping methods limits the inclusion of ear photosynthesis in the breeding agenda. In this regard, a number of different approaches are presented.
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Affiliation(s)
- Rut Sanchez-Bragado
- Department of Crop and Forest Sciences, University of Lleida - AGROTECNIO Center, Av. R. Roure 191, 25198 Lleida, Spain; Secció de Fisiologia Vegetal, Facultat de Biologia, Universitat de Barcelona, Barcelona, and AGROTECNIO Center, Lleida, Spain
| | - Rubén Vicente
- Max Planck Institute of Molecular Plant Physiology, Am Mühlenberg 1, 14476 Potsdam, Germany
| | - Gemma Molero
- Global Wheat Program, International Maize and Wheat Improvement Centre (CIMMYT), Texcoco, Mexico
| | - Maria Dolors Serret
- Secció de Fisiologia Vegetal, Facultat de Biologia, Universitat de Barcelona, Barcelona, and AGROTECNIO Center, Lleida, Spain
| | - María Luján Maydup
- National Council of Scientific and Technological Research, CONICET, La Plata University- Plant Physiology Institute INFIVE, Argentina
| | - José Luis Araus
- Secció de Fisiologia Vegetal, Facultat de Biologia, Universitat de Barcelona, Barcelona, and AGROTECNIO Center, Lleida, Spain.
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Combining Genetic Analysis and Multivariate Modeling to Evaluate Spectral Reflectance Indices as Indirect Selection Tools in Wheat Breeding under Water Deficit Stress Conditions. REMOTE SENSING 2020. [DOI: 10.3390/rs12091480] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Progress in high-throughput tools has enabled plant breeders to increase the rate of genetic gain through multidimensional assessment of previously intractable traits in a fast and nondestructive manner. This study investigates the potential use of spectral reflectance indices (SRIs; 15 vegetation-SRIs; 15 water-SRIs) as alternative selection tools for destructively measured traits in wheat breeding programs. The genetic variability, heritability (h2), genetic gain (GG), and expected genetic advances (GA) of these indices were compared with those of destructively measured traits in 43 F7-8 recombinant inbred lines (RILs) grown under limited water conditions. The performance of SRIs to estimate the destructively measured traits directly was also evaluated using the partial least squares regression (PLSR) and stepwise multiple linear regression (SMLR) models. Most vegetation-SRIs exhibited high genotypic variation, similar to the measured traits, and phenotypic correlations with these traits, compared with the water-SRIs. Most vegetation-SRIs presented comparable values for h2 (>60%) and GG (>20%) as intermediate traits, while about half of water-SRIs exhibited a high h2 (>60%), but low GG (<20%). Principle component analysis revealed that most vegetation-SRIs and seven of 15 water-SRIs were grouped together in a positive direction, had a moderate to strong relationship with measured traits, and could identify the drought-tolerant parent Sakha 93 and several RILs. The PLSR model based on all SRIs as a single index showed moderate to high R2 in calibration (0.53–0.75) and validation (0.46–0.72) datasets, with strong relationships between observed and predicted values of measured traits. The SMLR models identified four and three SRIs from vegetation-SRIs and water-SRIs, respectively, to explain 63–86% of the total variability in measured traits among genotypes. These results demonstrated that vegetation-SRIs can be used individually or combined with water-SRIs as alternative breeding tools to increase genetic gains and selection accuracy in spring wheat breeding.
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Vergara-Diaz O, Vatter T, Kefauver SC, Obata T, Fernie AR, Araus JL. Assessing durum wheat ear and leaf metabolomes in the field through hyperspectral data. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2020; 102:615-630. [PMID: 31808224 DOI: 10.1111/tpj.14636] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/18/2019] [Revised: 11/13/2019] [Accepted: 11/21/2019] [Indexed: 05/24/2023]
Abstract
Hyperspectral techniques are currently used to retrieve information concerning plant biophysical traits, predominantly targeting pigments, water, and nitrogen-protein contents, structural elements, and the leaf area index. Even so, hyperspectral data could be more extensively exploited to overcome the breeding challenges being faced under global climate change by advancing high-throughput field phenotyping. In this study, we explore the potential of field spectroscopy to predict the metabolite profiles in flag leaves and ear bracts in durum wheat. The full-range reflectance spectra (visible (VIS)-near-infrared (NIR)-short wave infrared (SWIR)) of flag leaves, ears and canopies were recorded in a collection of contrasting genotypes grown in four environments under different water regimes. GC-MS metabolite profiles were analyzed in the flag leaves, ear bracts, glumes, and lemmas. The results from regression models exceeded 50% of the explained variation (adj-R2 in the validation sets) for at least 15 metabolites in each plant organ, whereas their errors were considerably low. The best regressions were obtained for malate (82%), glycerate and serine (63%) in leaves; myo-inositol (81%) in lemmas; glycolate (80%) in glumes; sucrose in leaves and glumes (68%); γ-aminobutyric acid (GABA) in leaves and glumes (61% and 71%, respectively); proline and glucose in lemmas (74% and 71%, respectively) and glumes (72% and 69%, respectively). The selection of wavebands in the models and the performance of the models based on canopy and VIS organ spectra and yield prediction are discussed. We feel that this technique will likely to be of interest due to its broad applicability in ecophysiology research, plant breeding programmes, and the agri-food industry.
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Affiliation(s)
- Omar Vergara-Diaz
- Integrative Crop Ecophysiology Group, Plant Physiology Section, Faculty of Biology, University of Barcelona, Diagonal 643, 08028, Barcelona, Spain
| | - Thomas Vatter
- Integrative Crop Ecophysiology Group, Plant Physiology Section, Faculty of Biology, University of Barcelona, Diagonal 643, 08028, Barcelona, Spain
| | - Shawn Carlisle Kefauver
- Integrative Crop Ecophysiology Group, Plant Physiology Section, Faculty of Biology, University of Barcelona, Diagonal 643, 08028, Barcelona, Spain
| | - Toshihiro Obata
- Max Planck Institute of Molecular Plant Physiology, Am Mühlenberg 1, 14476, Potsdam, Germany
| | - Alisdair R Fernie
- Max Planck Institute of Molecular Plant Physiology, Am Mühlenberg 1, 14476, Potsdam, Germany
| | - José Luis Araus
- Integrative Crop Ecophysiology Group, Plant Physiology Section, Faculty of Biology, University of Barcelona, Diagonal 643, 08028, Barcelona, Spain
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Editorial for the Special Issue “Estimation of Crop Phenotyping Traits using Unmanned Ground Vehicle and Unmanned Aerial Vehicle Imagery”. REMOTE SENSING 2020. [DOI: 10.3390/rs12060940] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
High-throughput crop phenotyping is harnessing the potential of genomic resources for the genetic improvement of crop production under changing climate conditions. As global food security is not yet assured, crop phenotyping has received increased attention during the past decade. This spectral issue (SI) collects 30 papers reporting research on estimation of crop phenotyping traits using unmanned ground vehicle (UGV) and unmanned aerial vehicle (UAV) imagery. Such platforms were previously not widely available. The special issue includes papers presenting recent advances in the field, with 22 UAV-based papers and 12 UGV-based articles. The special issue covers 16 RGB sensor papers, 11 papers on multi-spectral imagery, and further 4 papers on hyperspectral and 3D data acquisition systems. A total of 13 plants’ phenotyping traits, including morphological, structural, and biochemical traits are covered. Twenty different data processing and machine learning methods are presented. In this way, the special issue provides a good overview regarding potential applications of the platforms and sensors, to timely provide crop phenotyping traits in a cost-efficient and objective manner. With the fast development of sensors technology and image processing algorithms, we expect that the estimation of crop phenotyping traits supporting crop breeding scientists will gain even more attention in the future.
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El-Hendawy SE, Alotaibi M, Al-Suhaibani N, Al-Gaadi K, Hassan W, Dewir YH, Emam MAEG, Elsayed S, Schmidhalter U. Comparative Performance of Spectral Reflectance Indices and Multivariate Modeling for Assessing Agronomic Parameters in Advanced Spring Wheat Lines Under Two Contrasting Irrigation Regimes. FRONTIERS IN PLANT SCIENCE 2019; 10:1537. [PMID: 31850029 PMCID: PMC6892836 DOI: 10.3389/fpls.2019.01537] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/12/2019] [Accepted: 11/04/2019] [Indexed: 05/06/2023]
Abstract
The incorporation of nondestructive and cost-effective tools in genetic drought studies in combination with reliable indirect screening criteria that exhibit high heritability and genetic correlations will be critical for addressing the water deficit challenges of the agricultural sector under arid conditions and ensuring the success of genotype development. In this study, the proximal spectral reflectance data were exploited to assess three destructive agronomic parameters [dry weight (DW) and water content (WC) of the aboveground biomass and grain yield (GY)] in 30 recombinant F7 and F8 inbred lines (RILs) growing under full (FL) and limited (LM) irrigation regimes. The utility of different groups of spectral reflectance indices (SRIs) as an indirect assessment tool was tested based on heritability and genetic correlations. The performance of the SRIs and different models of partial least squares regression (PLSR) and stepwise multiple linear regression (SMLR) in estimating the destructive parameters was considered. Generally, all groups of SRIs, as well as different models of PLSR and SMLR, generated better estimations for destructive parameters under LM and combined FL+LM than under FL. Even though most of the SRIs exhibited a low association with destructive parameters under FL, they exhibited moderate to high genetic correlations and also had high heritability. The SRIs based on near-infrared (NIR)/visible (VIS) and NIR/NIR, especially those developed in this study, spectral band intervals extracted within VIS, red edge, and NIR spectral range, or individual effective wavelengths relevant to green, red, red edge, and middle NIR spectral region, were found to be more effective in estimating the destructive parameters under all conditions. Five models of SMLR and PLSR for each condition explained most of the variation in the three destructive parameters among genotypes. These models explained 42% to 46%, 19% to 30%, and 39% to 46% of the variation in DW, WC, and GY among genotypes under FL, 69% to 72%, 59% to 61%, and 77% to 81% under LM, and 71% to 75%, 61% to 71%, and 74% to 78% under FL+LM, respectively. Overall, these results confirmed that application of hyperspectral reflectance sensing in breeding programs is not only important for evaluating a sufficient number of genotypes in an expeditious and cost-effective manner but also could be exploited to develop indirect breeding traits that aid in accelerating the development of genotypes for application under adverse environmental conditions.
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Affiliation(s)
- Salah E. El-Hendawy
- Department of Plant Production, College of Food and Agriculture Sciences, King Saud University, Riyadh, Saudi Arabia
- Department of Agronomy, Faculty of Agriculture, Suez Canal University, Ismailia, Egypt
| | - Majed Alotaibi
- Department of Plant Production, College of Food and Agriculture Sciences, King Saud University, Riyadh, Saudi Arabia
| | - Nasser Al-Suhaibani
- Department of Plant Production, College of Food and Agriculture Sciences, King Saud University, Riyadh, Saudi Arabia
| | - Khalid Al-Gaadi
- Department of Agricultural Engineering, Precision Agriculture Research Chair, College of Food and Agriculture Sciences, King Saud University, Riyadh, Saudi Arabia
| | - Wael Hassan
- Department of Agricultural Botany, Faculty of Agriculture, Suez Canal University, Ismailia, Egypt
- Department of Biology, College of Science and Humanities at Quwayiah, Shaqra University, Riyadh, Saudi Arabia
| | - Yaser Hassan Dewir
- Department of Plant Production, College of Food and Agriculture Sciences, King Saud University, Riyadh, Saudi Arabia
- Department of Horticulture, Faculty of Agriculture, Kafrelsheikh University, Kafr El Sheikh, Egypt
| | | | - Salah Elsayed
- Evaluation of Natural Resources Department, Environmental Studies and Research Institute, University of Sadat City, Menoufia, Egypt
| | - Urs Schmidhalter
- Department of Plant Sciences, Technische Universität München, Freising, Germany
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El-Hendawy S, Al-Suhaibani N, Alotaibi M, Hassan W, Elsayed S, Tahir MU, Mohamed AI, Schmidhalter U. Estimating growth and photosynthetic properties of wheat grown in simulated saline field conditions using hyperspectral reflectance sensing and multivariate analysis. Sci Rep 2019; 9:16473. [PMID: 31712701 PMCID: PMC6848100 DOI: 10.1038/s41598-019-52802-5] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2019] [Accepted: 10/15/2019] [Indexed: 01/01/2023] Open
Abstract
The timely estimation of growth and photosynthetic-related traits in an easy and nondestructive manner using hyperspectral data will become imperative for addressing the challenges of environmental stresses inherent to the agricultural sector in arid conditions. However, the handling and analysis of these data by exploiting the full spectrum remains the determining factor for refining the estimation of crop variables. The main objective of this study was to estimate growth and traits underpinning photosynthetic efficiency of two wheat cultivars grown under simulated saline field conditions and exposed to three salinity levels using hyperspectral reflectance information from 350–2500 nm obtained at two years. Partial least squares regression (PLSR) based on the full spectrum was applied to develop predictive models for estimating the measured parameters in different conditions (salinity levels, cultivars, and years). Variable importance in projection (VIP) of PLSR in combination with multiple linear regression (MLR) was implemented to identify important waveband regions and influential wavelengths related to the measured parameters. The results showed that the PLSR models exhibited moderate to high coefficients of determination (R2) in both the calibration and validation datasets (0.30–0.95), but that this range of R2 values depended on parameters and conditions. The PLSR models based on the full spectrum accurately and robustly predicted three of four parameters across all conditions. Based on the combination of PLSR-VIP and MLR analysis, the wavelengths selected within the visible (VIS), red-edge, and middle near-infrared (NIR) wavebands were the most sensitive to all parameters in all conditions, whereas those selected within the shortwave infrared (SWIR) waveband were effective for some parameters in particular conditions. Overall, these results indicated that the PLSR analysis and band selection techniques can offer a rapid and nondestructive alternative approach to accurately estimate growth- and photosynthetic-related trait responses to salinity stress.
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Affiliation(s)
- Salah El-Hendawy
- Department of Plant Production, College of Food and Agriculture Sciences, King Saud University, P.O. Box 2460, 11451, Riyadh, Saudi Arabia. .,Department of Agronomy, Faculty of Agriculture, Suez Canal University, Ismailia, 41522, Egypt.
| | - Nasser Al-Suhaibani
- Department of Plant Production, College of Food and Agriculture Sciences, King Saud University, P.O. Box 2460, 11451, Riyadh, Saudi Arabia
| | - Majed Alotaibi
- Department of Plant Production, College of Food and Agriculture Sciences, King Saud University, P.O. Box 2460, 11451, Riyadh, Saudi Arabia
| | - Wael Hassan
- Department of Agricultural Botany, Faculty of Agriculture, Suez Canal University, Ismailia, 41522, Egypt.,Department of Biology, College of Science and Humanities at Quwayiah, Shaqra University, Riyadh, 11961, Saudi Arabia
| | - Salah Elsayed
- Evaluation of Natural Resources Department, Environmental Studies and Research Institute, University of Sadat City, Menoufia, 32897, Egypt
| | - Muhammad Usman Tahir
- Department of Plant Production, College of Food and Agriculture Sciences, King Saud University, P.O. Box 2460, 11451, Riyadh, Saudi Arabia
| | - Ahmed Ibrahim Mohamed
- Department of Soil and Water, Faculty of Agriculture, Suez Canal University, Ismailia, 41522, Egypt
| | - Urs Schmidhalter
- Department of Plant Science, Chair of Plant Nutrition, Technical University of Munich, Freising, Germany
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