1
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Yu R, Cao X, Liu J, Nie R, Zhang C, Yuan M, Huang Y, Liu X, Zheng W, Wang C, Wu T, Su B, Kang Z, Zeng Q, Han D, Wu J. Using UAV-Based Temporal Spectral Indices to Dissect Changes in the Stay-Green Trait in Wheat. PLANT PHENOMICS (WASHINGTON, D.C.) 2024; 6:0171. [PMID: 38694449 PMCID: PMC11062509 DOI: 10.34133/plantphenomics.0171] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/08/2023] [Accepted: 03/17/2024] [Indexed: 05/04/2024]
Abstract
Stay-green (SG) in wheat is a beneficial trait that increases yield and stress tolerance. However, conventional phenotyping techniques limited the understanding of its genetic basis. Spectral indices (SIs) as non-destructive tools to evaluate crop temporal senescence provide an alternative strategy. Here, we applied SIs to monitor the senescence dynamics of 565 diverse wheat accessions from anthesis to maturation stages over 2 field seasons. Four SIs (normalized difference vegetation index, green normalized difference vegetation index, normalized difference red edge index, and optimized soil-adjusted vegetation index) were normalized to develop relative stay-green scores (RSGS) as the SG indicators. An RSGS-based genome-wide association study identified 47 high-confidence quantitative trait loci (QTL) harboring 3,079 single-nucleotide polymorphisms associated with SG and 1,085 corresponding candidate genes. Among them, 15 QTL overlapped or were adjacent to known SG-related QTL/genes, while the remaining QTL were novel. Notably, a set of favorable haplotypes of SG-related candidate genes such as TraesCS2A03G1081100, TracesCS6B03G0356400, and TracesCS2B03G1299500 are increasing following the Green Revolution, further validating the feasibility of the pipeline. This study provided a valuable reference for further quantitative SG and genetic research in diverse wheat panels.
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Affiliation(s)
- Rui Yu
- College of Agronomy,
Northwest A&F University, Yangling, Shaanxi 712100, China
- State Key Laboratory of Crop Stress Resistance and High-Efficiency Production,
Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Xiaofeng Cao
- State Key Laboratory of Crop Stress Resistance and High-Efficiency Production,
Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Jia Liu
- College of Agronomy,
Northwest A&F University, Yangling, Shaanxi 712100, China
- State Key Laboratory of Crop Stress Resistance and High-Efficiency Production,
Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Ruiqi Nie
- College of Agronomy,
Northwest A&F University, Yangling, Shaanxi 712100, China
- State Key Laboratory of Crop Stress Resistance and High-Efficiency Production,
Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Chuanliang Zhang
- College of Agronomy,
Northwest A&F University, Yangling, Shaanxi 712100, China
- State Key Laboratory of Crop Stress Resistance and High-Efficiency Production,
Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Meng Yuan
- College of Agronomy,
Northwest A&F University, Yangling, Shaanxi 712100, China
- State Key Laboratory of Crop Stress Resistance and High-Efficiency Production,
Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Yanchuan Huang
- College of Agronomy,
Northwest A&F University, Yangling, Shaanxi 712100, China
- State Key Laboratory of Crop Stress Resistance and High-Efficiency Production,
Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Xinzhe Liu
- College of Mechanical and Electronic Engineering,
Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Weijun Zheng
- College of Agronomy,
Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Changfa Wang
- College of Agronomy,
Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Tingting Wu
- College of Mechanical and Electronic Engineering,
Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Baofeng Su
- College of Mechanical and Electronic Engineering,
Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Zhensheng Kang
- State Key Laboratory of Crop Stress Resistance and High-Efficiency Production,
Northwest A&F University, Yangling, Shaanxi 712100, China
- College of Plant Protection,
Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Qingdong Zeng
- State Key Laboratory of Crop Stress Resistance and High-Efficiency Production,
Northwest A&F University, Yangling, Shaanxi 712100, China
- College of Plant Protection,
Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Dejun Han
- College of Agronomy,
Northwest A&F University, Yangling, Shaanxi 712100, China
- State Key Laboratory of Crop Stress Resistance and High-Efficiency Production,
Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Jianhui Wu
- College of Agronomy,
Northwest A&F University, Yangling, Shaanxi 712100, China
- State Key Laboratory of Crop Stress Resistance and High-Efficiency Production,
Northwest A&F University, Yangling, Shaanxi 712100, China
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2
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Bhupenchandra I, Chongtham SK, Gangarani Devi A, Dutta P, Lamalakshmi E, Mohanty S, Choudhary AK, Das A, Sarika K, Kumar S, Yumnam S, Sagolsem D, Rupert Anand Y, Bhutia DD, Victoria M, Vinodh S, Tania C, Dhanachandra Sharma A, Deb L, Sahoo MR, Seth CS, Swapnil P, Meena M. Harnessing weedy rice as functional food and source of novel traits for crop improvement. PLANT, CELL & ENVIRONMENT 2024. [PMID: 38436101 DOI: 10.1111/pce.14868] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 02/12/2024] [Accepted: 02/15/2024] [Indexed: 03/05/2024]
Abstract
A relative of cultivated rice (Oryza sativa L.), weedy or red rice (Oryza spp.) is currently recognized as the dominant weed, leading to a drastic loss of yield of cultivated rice due to its highly competitive abilities like producing more tillers, panicles, and biomass with better nutrient uptake. Due to its high nutritional value, antioxidant properties (anthocyanin and proanthocyanin), and nutrient absorption ability, weedy rice is gaining immense research attentions to understand its genetic constitution to augment future breeding strategies and to develop nutrition-rich functional foods. Consequently, this review focuses on the unique gene source of weedy rice to enhance the cultivated rice for its crucial features like water use efficiency, abiotic and biotic stress tolerance, early flowering, and the red pericarp of the seed. It explores the debating issues on the origin and evolution of weedy rice, including its high diversity, signalling aspects, quantitative trait loci (QTL) mapping under stress conditions, the intricacy of the mechanism in the expression of the gene flow, and ecological challenges of nutrient removal by weedy rice. This review may create a foundation for future researchers to understand the gene flow between cultivated crops and weedy traits and support an improved approach for the applicability of several models in predicting multiomics variables.
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Affiliation(s)
- Ingudam Bhupenchandra
- ICAR-Farm Science Centre Tamenglong, ICAR Research Complex for NEH Region, Manipur Centre, Imphal, Manipur, India
| | - Sunil Kumar Chongtham
- Multi Technology Testing Centre and Vocational Training Centre, College of Horticulture, Central Agricultural University, Bermiok, Sikkim, India
| | - Ayam Gangarani Devi
- ICAR Research Complex for North Eastern Hill Region, Tripura Centre Lembucherra, Tripura, India
| | - Pranab Dutta
- School of Crop Protection, College of Post Graduate Studies in Agricultural Sciences, Central Agricultural University (Imphal), Umiam, Meghalaya, India
| | - Elangbam Lamalakshmi
- ICAR Research Complex for North Eastern Hill Region, Sikkim Centre, Tadong, Sikkim, India
| | - Sansuta Mohanty
- Molecular Biology and Biotechnology Department, Faculty of Agricultural Sciences, Siksha O Anusandhan University, Bhubaneswar, Odisha, India
| | - Anil K Choudhary
- Division of Crop Production, ICAR-Central Potato Research Institute, Shimla, Himachal Pradesh, India
| | - Anup Das
- ICAR Research Complex for North Eastern Hill Region, Lembucherra, Tripura, India
| | - Konsam Sarika
- ICAR Research Complex for North Eastern Hill Region, Manipur Centre, Imphal, Manipur, India
| | - Sumit Kumar
- Department of Mycology and Plant Pathology, Institute of Agricultural Sciences, Banaras Hindu University, Varanasi, Uttar Pradesh, India
- Department of Plant Pathology, B.M. College of Agriculture, Khandwa, Rajmata Vijayaraje Scindia Krishi Vishwa Vidyalaya, Gwalior, Madhya Pradesh, India
| | - Sonika Yumnam
- All India Coordinated Research Project on Chickpea, Central Agricultural University, Imphal, Manipur, India
| | - Diana Sagolsem
- Multi Technology Testing Centre and Vocational Training Centre, College of Horticulture, Central Agricultural University, Bermiok, Sikkim, India
| | - Y Rupert Anand
- Multi Technology Testing Centre and Vocational Training Centre, College of Horticulture, Central Agricultural University, Bermiok, Sikkim, India
| | - Dawa Dolma Bhutia
- Multi Technology Testing Centre and Vocational Training Centre, College of Horticulture, Central Agricultural University, Bermiok, Sikkim, India
| | - M Victoria
- Multi Technology Testing Centre and Vocational Training Centre, College of Horticulture, Central Agricultural University, Bermiok, Sikkim, India
| | - S Vinodh
- Multi Technology Testing Centre and Vocational Training Centre, College of Horticulture, Central Agricultural University, Bermiok, Sikkim, India
| | - Chongtham Tania
- ICAR Research Complex for North Eastern Hill Region, Manipur Centre, Imphal, Manipur, India
| | | | - Lipa Deb
- School of Crop Protection, College of Post Graduate Studies in Agricultural Sciences, Central Agricultural University (Imphal), Umiam, Meghalaya, India
| | - Manas Ranjan Sahoo
- ICAR Research Complex for North Eastern Hill Region, Manipur Centre, Imphal, Manipur, India
| | | | - Prashant Swapnil
- Department of Botany, School of Basic Science, Central University of Punjab, Bhatinda, Punjab, India
| | - Mukesh Meena
- Laboratory of Phytopathology and Microbial Biotechnology, Department of Botany, Mohanlal Sukhadia University, Udaipur, Rajasthan, India
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3
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Knopf O, Castro A, Bendig J, Pude R, Kleist E, Poorter H, Rascher U, Muller O. Field phenotyping of ten wheat cultivars under elevated CO 2 shows seasonal differences in chlorophyll fluorescence, plant height and vegetation indices. FRONTIERS IN PLANT SCIENCE 2024; 14:1304751. [PMID: 38259917 PMCID: PMC10800489 DOI: 10.3389/fpls.2023.1304751] [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: 09/29/2023] [Accepted: 12/05/2023] [Indexed: 01/24/2024]
Abstract
In the context of climate change and global sustainable development goals, future wheat cultivation has to master various challenges at a time, including the rising atmospheric carbon dioxide concentration ([CO2]). To investigate growth and photosynthesis dynamics under the effects of ambient (~434 ppm) and elevated [CO2] (~622 ppm), a Free-Air CO2 Enrichment (FACE) facility was combined with an automated phenotyping platform and an array of sensors. Ten modern winter wheat cultivars (Triticum aestivum L.) were monitored over a vegetation period using a Light-induced Fluorescence Transient (LIFT) sensor, ground-based RGB cameras and a UAV equipped with an RGB and multispectral camera. The LIFT sensor enabled a fast quantification of the photosynthetic performance by measuring the operating efficiency of Photosystem II (Fq'/Fm') and the kinetics of electron transport, i.e. the reoxidation rates Fr1' and Fr2'. Our results suggest that elevated [CO2] significantly increased Fq'/Fm' and plant height during the vegetative growth phase. As the plants transitioned to the senescence phase, a pronounced decline in Fq'/Fm' was observed under elevated [CO2]. This was also reflected in the reoxidation rates Fr1' and Fr2'. A large majority of the cultivars showed a decrease in the harvest index, suggesting a different resource allocation and indicating a potential plateau in yield progression under e[CO2]. Our results indicate that the rise in atmospheric [CO2] has significant effects on the cultivation of winter wheat with strong manifestation during early and late growth.
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Affiliation(s)
- Oliver Knopf
- Institute of Bio- and Geosciences: Plant Sciences (IBG-2), Forschungszentrum Jülich GmbH, Jülich, Germany
| | - Antony Castro
- Institute of Bio- and Geosciences: Plant Sciences (IBG-2), Forschungszentrum Jülich GmbH, Jülich, Germany
| | - Juliane Bendig
- Institute of Bio- and Geosciences: Plant Sciences (IBG-2), Forschungszentrum Jülich GmbH, Jülich, Germany
| | - Ralf Pude
- INRES-Renewable Resources, University of Bonn, Rheinbach, Germany
| | - Einhard Kleist
- Institute of Bio- and Geosciences: Plant Sciences (IBG-2), Forschungszentrum Jülich GmbH, Jülich, Germany
| | - Hendrik Poorter
- Institute of Bio- and Geosciences: Plant Sciences (IBG-2), Forschungszentrum Jülich GmbH, Jülich, Germany
- Department of Natural Sciences, Macquarie University, North Ryde, NSW, Australia
| | - Uwe Rascher
- Institute of Bio- and Geosciences: Plant Sciences (IBG-2), Forschungszentrum Jülich GmbH, Jülich, Germany
| | - Onno Muller
- Institute of Bio- and Geosciences: Plant Sciences (IBG-2), Forschungszentrum Jülich GmbH, Jülich, Germany
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4
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Li R, Chen S, Matsumoto H, Gouda M, Gafforov Y, Wang M, Liu Y. Predicting rice diseases using advanced technologies at different scales: present status and future perspectives. ABIOTECH 2023; 4:359-371. [PMID: 38106429 PMCID: PMC10721578 DOI: 10.1007/s42994-023-00126-4] [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: 09/27/2023] [Accepted: 10/30/2023] [Indexed: 12/19/2023]
Abstract
The past few years have witnessed significant progress in emerging disease detection techniques for accurately and rapidly tracking rice diseases and predicting potential solutions. In this review we focus on image processing techniques using machine learning (ML) and deep learning (DL) models related to multi-scale rice diseases. Furthermore, we summarize applications of different detection techniques, including genomic, physiological, and biochemical approaches. In addition, we also present the state-of-the-art in contemporary optical sensing applications of pathogen-plant interaction phenotypes. This review serves as a valuable resource for researchers seeking effective solutions to address the challenges of high-throughput data and model recognition for early detection of issues affecting rice crops through ML and DL models.
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Affiliation(s)
- Ruyue Li
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, 310058 China
- College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, 310058 China
| | - Sishi Chen
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, 310058 China
| | - Haruna Matsumoto
- State Key Laboratory of Rice Biology, and Ministry of Agricultural and Rural Affairs Laboratory of Molecular Biology of Crop Pathogens and Insects, Zhejiang University, Hangzhou, 310058 China
| | - Mostafa Gouda
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, 310058 China
- Department of Nutrition and Food Science, National Research Centre, Giza, 12622 Egypt
| | - Yusufjon Gafforov
- Central Asian Center for Development Studies, New Uzbekistan University, Tashkent, 100000 Uzbekistan
| | - Mengcen Wang
- State Key Laboratory of Rice Biology, and Ministry of Agricultural and Rural Affairs Laboratory of Molecular Biology of Crop Pathogens and Insects, Zhejiang University, Hangzhou, 310058 China
- Global Education Program for AgriScience Frontiers, Graduate School of Agriculture, Hokkaido University, Sapporo, 060-8589 Japan
| | - Yufei Liu
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, 310058 China
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5
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Roth L, Fossati D, Krähenbühl P, Walter A, Hund A. Image-based phenomic prediction can provide valuable decision support in wheat breeding. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2023; 136:162. [PMID: 37368140 DOI: 10.1007/s00122-023-04395-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Accepted: 05/29/2023] [Indexed: 06/28/2023]
Abstract
KEY MESSAGE Genotype-by-environment interactions of secondary traits based on high-throughput field phenotyping are less complex than those of target traits, allowing for a phenomic selection in unreplicated early generation trials. Traditionally, breeders' selection decisions in early generations are largely based on visual observations in the field. With the advent of affordable genome sequencing and high-throughput phenotyping technologies, enhancing breeders' ratings with such information became attractive. In this research, it is hypothesized that G[Formula: see text]E interactions of secondary traits (i.e., growth dynamics' traits) are less complex than those of related target traits (e.g., yield). Thus, phenomic selection (PS) may allow selecting for genotypes with beneficial response-pattern in a defined population of environments. A set of 45 winter wheat varieties was grown at 5 year-sites and analyzed with linear and factor-analytic (FA) mixed models to estimate G[Formula: see text]E interactions of secondary and target traits. The dynamic development of drone-derived plant height, leaf area and tiller density estimations was used to estimate the timing of key stages, quantities at defined time points and temperature dose-response curve parameters. Most of these secondary traits and grain protein content showed little G[Formula: see text]E interactions. In contrast, the modeling of G[Formula: see text]E for yield required a FA model with two factors. A trained PS model predicted overall yield performance, yield stability and grain protein content with correlations of 0.43, 0.30 and 0.34. While these accuracies are modest and do not outperform well-trained GS models, PS additionally provided insights into the physiological basis of target traits. An ideotype was identified that potentially avoids the negative pleiotropic effects between yield and protein content.
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Affiliation(s)
- Lukas Roth
- Institute of Agricultural Sciences, ETH Zurich, Universitätstrasse 2, 8092, Zurich, Switzerland.
| | | | - Patrick Krähenbühl
- Delley Samen und Pflanzen AG, Route de Portalban 40, 1567, Delley, Switzerland
| | - Achim Walter
- Institute of Agricultural Sciences, ETH Zurich, Universitätstrasse 2, 8092, Zurich, Switzerland
| | - Andreas Hund
- Institute of Agricultural Sciences, ETH Zurich, Universitätstrasse 2, 8092, Zurich, Switzerland
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6
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Ganeva D, Roumenina E, Dimitrov P, Gikov A, Jelev G, Dyulgenova B, Valcheva D, Bozhanova V. Remotely Sensed Phenotypic Traits for Heritability Estimates and Grain Yield Prediction of Barley Using Multispectral Imaging from UAVs. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23115008. [PMID: 37299735 DOI: 10.3390/s23115008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Revised: 05/12/2023] [Accepted: 05/20/2023] [Indexed: 06/12/2023]
Abstract
This study tested the potential of parametric and nonparametric regression modeling utilizing multispectral data from two different unoccupied aerial vehicles (UAVs) as a tool for the prediction of and indirect selection of grain yield (GY) in barley breeding experiments. The coefficient of determination (R2) of the nonparametric models for GY prediction ranged between 0.33 and 0.61 depending on the UAV and flight date, where the highest value was achieved with the DJI Phantom 4 Multispectral (P4M) image from 26 May (milk ripening). The parametric models performed worse than the nonparametric ones for GY prediction. Independent of the retrieval method and UAV, GY retrieval was more accurate in milk ripening than dough ripening. The leaf area index (LAI), fraction of absorbed photosynthetically active radiation (fAPAR), fraction vegetation cover (fCover), and leaf chlorophyll content (LCC) were modeled at milk ripening using nonparametric models with the P4M images. A significant effect of the genotype was found for the estimated biophysical variables, which was referred to as remotely sensed phenotypic traits (RSPTs). Measured GY heritability was lower, with a few exceptions, compared to the RSPTs, indicating that GY was more environmentally influenced than the RSPTs. The moderate to strong genetic correlation of the RSPTs to GY in the present study indicated their potential utility as an indirect selection approach to identify high-yield genotypes of winter barley.
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Affiliation(s)
- Dessislava Ganeva
- Space Research and Technology Institute, Bulgarian Academy of Sciences, 1113 Sofia, Bulgaria
| | - Eugenia Roumenina
- Space Research and Technology Institute, Bulgarian Academy of Sciences, 1113 Sofia, Bulgaria
| | - Petar Dimitrov
- Space Research and Technology Institute, Bulgarian Academy of Sciences, 1113 Sofia, Bulgaria
| | - Alexander Gikov
- Space Research and Technology Institute, Bulgarian Academy of Sciences, 1113 Sofia, Bulgaria
| | - Georgi Jelev
- Space Research and Technology Institute, Bulgarian Academy of Sciences, 1113 Sofia, Bulgaria
| | | | - Darina Valcheva
- Institute of Agriculture, Agriculture Academy, 8400 Karnobat, Bulgaria
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7
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Jackson R, Buntjer JB, Bentley AR, Lage J, Byrne E, Burt C, Jack P, Berry S, Flatman E, Poupard B, Smith S, Hayes C, Barber T, Love B, Gaynor RC, Gorjanc G, Howell P, Mackay IJ, Hickey JM, Ober ES. Phenomic and genomic prediction of yield on multiple locations in winter wheat. Front Genet 2023; 14:1164935. [PMID: 37229190 PMCID: PMC10203586 DOI: 10.3389/fgene.2023.1164935] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Accepted: 04/20/2023] [Indexed: 05/27/2023] Open
Abstract
Genomic selection has recently become an established part of breeding strategies in cereals. However, a limitation of linear genomic prediction models for complex traits such as yield is that these are unable to accommodate Genotype by Environment effects, which are commonly observed over trials on multiple locations. In this study, we investigated how this environmental variation can be captured by the collection of a large number of phenomic markers using high-throughput field phenotyping and whether it can increase GS prediction accuracy. For this purpose, 44 winter wheat (Triticum aestivum L.) elite populations, comprising 2,994 lines, were grown on two sites over 2 years, to approximate the size of trials in a practical breeding programme. At various growth stages, remote sensing data from multi- and hyperspectral cameras, as well as traditional ground-based visual crop assessment scores, were collected with approximately 100 different data variables collected per plot. The predictive power for grain yield was tested for the various data types, with or without genome-wide marker data sets. Models using phenomic traits alone had a greater predictive value (R2 = 0.39-0.47) than genomic data (approximately R2 = 0.1). The average improvement in predictive power by combining trait and marker data was 6%-12% over the best phenomic-only model, and performed best when data from one full location was used to predict the yield on an entire second location. The results suggest that genetic gain in breeding programmes can be increased by utilisation of large numbers of phenotypic variables using remote sensing in field trials, although at what stage of the breeding cycle phenomic selection could be most profitably applied remains to be answered.
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Affiliation(s)
- Robert Jackson
- The John Bingham Laboratory, NIAB, Cambridge, United Kingdom
| | - Jaap B. Buntjer
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Scotland, United Kingdom
| | | | - Jacob Lage
- KWS UK Ltd, Thriplow, Royston, Cambridgeshire, United Kingdom
| | - Ed Byrne
- KWS UK Ltd, Thriplow, Royston, Cambridgeshire, United Kingdom
| | - Chris Burt
- RAGT UK, Ickleton, Saffron Walden, Cambridgeshire, United Kingdom
| | - Peter Jack
- RAGT UK, Ickleton, Saffron Walden, Cambridgeshire, United Kingdom
| | - Simon Berry
- Limagrain UK Ltd, Rothwell, Market Rasen, Lincolnshire, United Kingdom
| | - Edward Flatman
- Limagrain UK Ltd, Rothwell, Market Rasen, Lincolnshire, United Kingdom
| | - Bruno Poupard
- Limagrain UK Ltd, Rothwell, Market Rasen, Lincolnshire, United Kingdom
| | - Stephen Smith
- Elsoms Wheat Limited, Spalding, Linconshire, United Kingdom
| | | | - Tobias Barber
- The John Bingham Laboratory, NIAB, Cambridge, United Kingdom
| | - Bethany Love
- The John Bingham Laboratory, NIAB, Cambridge, United Kingdom
| | - R. Chris Gaynor
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Scotland, United Kingdom
| | - Gregor Gorjanc
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Scotland, United Kingdom
| | - Phil Howell
- The John Bingham Laboratory, NIAB, Cambridge, United Kingdom
| | - Ian J. Mackay
- The John Bingham Laboratory, NIAB, Cambridge, United Kingdom
| | - John M. Hickey
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Scotland, United Kingdom
| | - Eric S. Ober
- The John Bingham Laboratory, NIAB, Cambridge, United Kingdom
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8
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Röckel F, Schreiber T, Schüler D, Braun U, Krukenberg I, Schwander F, Peil A, Brandt C, Willner E, Gransow D, Scholz U, Kecke S, Maul E, Lange M, Töpfer R. PhenoApp: A mobile tool for plant phenotyping to record field and greenhouse observations. F1000Res 2022; 11:12. [PMID: 36636476 PMCID: PMC9813448 DOI: 10.12688/f1000research.74239.1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 12/20/2021] [Indexed: 01/21/2023] Open
Abstract
With the ongoing cost decrease of genotyping and sequencing technologies, accurate and fast phenotyping remains the bottleneck in the utilizing of plant genetic resources for breeding and breeding research. Although cost-efficient high-throughput phenotyping platforms are emerging for specific traits and/or species, manual phenotyping is still widely used and is a time- and money-consuming step. Approaches that improve data recording, processing or handling are pivotal steps towards the efficient use of genetic resources and are demanded by the research community. Therefore, we developed PhenoApp, an open-source Android app for tablets and smartphones to facilitate the digital recording of phenotypical data in the field and in greenhouses. It is a versatile tool that offers the possibility to fully customize the descriptors/scales for any possible scenario, also in accordance with international information standards such as MIAPPE (Minimum Information About a Plant Phenotyping Experiment) and FAIR (Findable, Accessible, Interoperable, and Reusable) data principles. Furthermore, PhenoApp enables the use of pre-integrated ready-to-use BBCH (Biologische Bundesanstalt für Land- und Forstwirtschaft, Bundessortenamt und CHemische Industrie) scales for apple, cereals, grapevine, maize, potato, rapeseed and rice. Additional BBCH scales can easily be added. The simple and adaptable structure of input and output files enables an easy data handling by either spreadsheet software or even the integration in the workflow of laboratory information management systems (LIMS). PhenoApp is therefore a decisive contribution to increase efficiency of digital data acquisition in genebank management but also contributes to breeding and breeding research by accelerating the labour intensive and time-consuming acquisition of phenotyping data.
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Affiliation(s)
- Franco Röckel
- Julius Kühn Institute (JKI) - Federal Research Centre for Cultivated Plants, Institute for Grapevine Breeding Geilweilerhof, Siebeldingen, 76833, Germany,
| | - Toni Schreiber
- Julius Kühn Institute (JKI) - Federal Research Centre for Cultivated Plants, Data Processing Department, Erwin-Baur-Straße 27, Quedlinburg, 06484, Germany
| | - Danuta Schüler
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) Gatersleben, Corrensstraße 3, Seeland, 06466, Germany
| | - Ulrike Braun
- Julius Kühn Institute (JKI) - Federal Research Centre for Cultivated Plants, Institute for Grapevine Breeding Geilweilerhof, Siebeldingen, 76833, Germany
| | - Ina Krukenberg
- Julius Kühn Institute (JKI) - Federal Research Centre for Cultivated Plants, Data Processing Department, Königin-Luise-Strasse 19, Berlin, 14195, Germany
| | - Florian Schwander
- Julius Kühn Institute (JKI) - Federal Research Centre for Cultivated Plants, Institute for Grapevine Breeding Geilweilerhof, Siebeldingen, 76833, Germany
| | - Andreas Peil
- Julius Kühn Institute (JKI) - Federal Research Centre for Cultivated Plants, Institute for Breeding Research on Fruit Crops, Pillnitzer Platz 3a, Dresden/Pillnitz, 01326, Germany
| | - Christine Brandt
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), The Satellite Collections North, Parkweg 3a, Sanitz, 18190, Germany
| | - Evelin Willner
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), The Satellite Collections North, Inselstraße 9, Malchow/Poel, 23999, Germany
| | - Daniel Gransow
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), The Satellite Collections North, Inselstraße 9, Malchow/Poel, 23999, Germany
| | - Uwe Scholz
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) Gatersleben, Corrensstraße 3, Seeland, 06466, Germany
| | - Steffen Kecke
- Julius Kühn Institute (JKI) - Federal Research Centre for Cultivated Plants, Data Processing Department, Erwin-Baur-Straße 27, Quedlinburg, 06484, Germany
| | - Erika Maul
- Julius Kühn Institute (JKI) - Federal Research Centre for Cultivated Plants, Institute for Grapevine Breeding Geilweilerhof, Siebeldingen, 76833, Germany
| | - Matthias Lange
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) Gatersleben, Corrensstraße 3, Seeland, 06466, Germany
| | - Reinhard Töpfer
- Julius Kühn Institute (JKI) - Federal Research Centre for Cultivated Plants, Institute for Grapevine Breeding Geilweilerhof, Siebeldingen, 76833, Germany
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9
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Röckel F, Schreiber T, Schüler D, Braun U, Krukenberg I, Schwander F, Peil A, Brandt C, Willner E, Gransow D, Scholz U, Kecke S, Maul E, Lange M, Töpfer R. PhenoApp: A mobile tool for plant phenotyping to record field and greenhouse observations. F1000Res 2022; 11:12. [PMID: 36636476 PMCID: PMC9813448 DOI: 10.12688/f1000research.74239.2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 11/25/2022] [Indexed: 11/29/2022] Open
Abstract
With the ongoing cost decrease of genotyping and sequencing technologies, accurate and fast phenotyping remains the bottleneck in the utilizing of plant genetic resources for breeding and breeding research. Although cost-efficient high-throughput phenotyping platforms are emerging for specific traits and/or species, manual phenotyping is still widely used and is a time- and money-consuming step. Approaches that improve data recording, processing or handling are pivotal steps towards the efficient use of genetic resources and are demanded by the research community. Therefore, we developed PhenoApp, an open-source Android app for tablets and smartphones to facilitate the digital recording of phenotypical data in the field and in greenhouses. It is a versatile tool that offers the possibility to fully customize the descriptors/scales for any possible scenario, also in accordance with international information standards such as MIAPPE (Minimum Information About a Plant Phenotyping Experiment) and FAIR (Findable, Accessible, Interoperable, and Reusable) data principles. Furthermore, PhenoApp enables the use of pre-integrated ready-to-use BBCH (Biologische Bundesanstalt für Land- und Forstwirtschaft, Bundessortenamt und CHemische Industrie) scales for apple, cereals, grapevine, maize, potato, rapeseed and rice. Additional BBCH scales can easily be added. The simple and adaptable structure of input and output files enables an easy data handling by either spreadsheet software or even the integration in the workflow of laboratory information management systems (LIMS). PhenoApp is therefore a decisive contribution to increase efficiency of digital data acquisition in genebank management but also contributes to breeding and breeding research by accelerating the labour intensive and time-consuming acquisition of phenotyping data.
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Affiliation(s)
- Franco Röckel
- Julius Kühn Institute (JKI) - Federal Research Centre for Cultivated Plants, Institute for Grapevine Breeding Geilweilerhof, Siebeldingen, 76833, Germany,
| | - Toni Schreiber
- Julius Kühn Institute (JKI) - Federal Research Centre for Cultivated Plants, Data Processing Department, Erwin-Baur-Straße 27, Quedlinburg, 06484, Germany
| | - Danuta Schüler
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) Gatersleben, Corrensstraße 3, Seeland, 06466, Germany
| | - Ulrike Braun
- Julius Kühn Institute (JKI) - Federal Research Centre for Cultivated Plants, Institute for Grapevine Breeding Geilweilerhof, Siebeldingen, 76833, Germany
| | - Ina Krukenberg
- Julius Kühn Institute (JKI) - Federal Research Centre for Cultivated Plants, Data Processing Department, Königin-Luise-Strasse 19, Berlin, 14195, Germany
| | - Florian Schwander
- Julius Kühn Institute (JKI) - Federal Research Centre for Cultivated Plants, Institute for Grapevine Breeding Geilweilerhof, Siebeldingen, 76833, Germany
| | - Andreas Peil
- Julius Kühn Institute (JKI) - Federal Research Centre for Cultivated Plants, Institute for Breeding Research on Fruit Crops, Pillnitzer Platz 3a, Dresden/Pillnitz, 01326, Germany
| | - Christine Brandt
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), The Satellite Collections North, Parkweg 3a, Sanitz, 18190, Germany
| | - Evelin Willner
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), The Satellite Collections North, Inselstraße 9, Malchow/Poel, 23999, Germany
| | - Daniel Gransow
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), The Satellite Collections North, Inselstraße 9, Malchow/Poel, 23999, Germany
| | - Uwe Scholz
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) Gatersleben, Corrensstraße 3, Seeland, 06466, Germany
| | - Steffen Kecke
- Julius Kühn Institute (JKI) - Federal Research Centre for Cultivated Plants, Data Processing Department, Erwin-Baur-Straße 27, Quedlinburg, 06484, Germany
| | - Erika Maul
- Julius Kühn Institute (JKI) - Federal Research Centre for Cultivated Plants, Institute for Grapevine Breeding Geilweilerhof, Siebeldingen, 76833, Germany
| | - Matthias Lange
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) Gatersleben, Corrensstraße 3, Seeland, 06466, Germany
| | - Reinhard Töpfer
- Julius Kühn Institute (JKI) - Federal Research Centre for Cultivated Plants, Institute for Grapevine Breeding Geilweilerhof, Siebeldingen, 76833, Germany
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10
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Divyanth LG, Marzougui A, González-Bernal MJ, McGee RJ, Rubiales D, Sankaran S. Evaluation of Effective Class-Balancing Techniques for CNN-Based Assessment of Aphanomyces Root Rot Resistance in Pea ( Pisum sativum L.). SENSORS (BASEL, SWITZERLAND) 2022; 22:7237. [PMID: 36236336 PMCID: PMC9572822 DOI: 10.3390/s22197237] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Revised: 09/15/2022] [Accepted: 09/16/2022] [Indexed: 06/16/2023]
Abstract
Aphanomyces root rot (ARR) is a devastating disease that affects the production of pea. The plants are prone to infection at any growth stage, and there are no chemical or cultural controls. Thus, the development of resistant pea cultivars is important. Phenomics technologies to support the selection of resistant cultivars through phenotyping can be valuable. One such approach is to couple imaging technologies with deep learning algorithms that are considered efficient for the assessment of disease resistance across a large number of plant genotypes. In this study, the resistance to ARR was evaluated through a CNN-based assessment of pea root images. The proposed model, DeepARRNet, was designed to classify the pea root images into three classes based on ARR severity scores, namely, resistant, intermediate, and susceptible classes. The dataset consisted of 1581 pea root images with a skewed distribution. Hence, three effective data-balancing techniques were identified to solve the prevalent problem of unbalanced datasets. Random oversampling with image transformations, generative adversarial network (GAN)-based image synthesis, and loss function with class-weighted ratio were implemented during the training process. The result indicated that the classification F1-score was 0.92 ± 0.03 when GAN-synthesized images were added, 0.91 ± 0.04 for random resampling, and 0.88 ± 0.05 when class-weighted loss function was implemented, which was higher than when an unbalanced dataset without these techniques were used (0.83 ± 0.03). The systematic approaches evaluated in this study can be applied to other image-based phenotyping datasets, which can aid the development of deep-learning models with improved performance.
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Affiliation(s)
- L. G. Divyanth
- Department of Biological Systems Engineering, Washington State University, Pullman, WA 99164, USA
- Department of Agricultural and Food Engineering, Indian Institute of Technology Kharagpur, Kharagpur 721302, India
| | - Afef Marzougui
- Department of Biological Systems Engineering, Washington State University, Pullman, WA 99164, USA
| | | | - Rebecca J. McGee
- Grain Legume Genetics and Physiology Research Unit, US Department of Agriculture-Agricultural Research Service (USDA-ARS), Pullman, WA 99164, USA
| | - Diego Rubiales
- The Institute for Sustainable Agriculture, Spanish National Research Council, 14001 Cordova, Spain
| | - Sindhuja Sankaran
- Department of Biological Systems Engineering, Washington State University, Pullman, WA 99164, USA
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11
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Reinert S. Quantitative genetics of pleiotropy and its potential for plant sciences. JOURNAL OF PLANT PHYSIOLOGY 2022; 276:153784. [PMID: 35944292 DOI: 10.1016/j.jplph.2022.153784] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Revised: 07/14/2022] [Accepted: 07/18/2022] [Indexed: 06/15/2023]
Affiliation(s)
- Stephan Reinert
- Friedrich-Alexander-University Erlangen-Nürnberg, Department of Biology, Division of Biochemistry, Biocomputing Lab, Staudtstraße 5, 91058, Erlangen, Germany.
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12
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Yu S, Fan J, Lu X, Wen W, Shao S, Guo X, Zhao C. Hyperspectral Technique Combined With Deep Learning Algorithm for Prediction of Phenotyping Traits in Lettuce. FRONTIERS IN PLANT SCIENCE 2022; 13:927832. [PMID: 35845657 PMCID: PMC9279906 DOI: 10.3389/fpls.2022.927832] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Accepted: 06/13/2022] [Indexed: 06/15/2023]
Abstract
The currently available methods for evaluating most biochemical traits of plant phenotyping are destructive and have extremely low throughput. However, hyperspectral techniques can non-destructively obtain the spectral reflectance characteristics of plants, which can provide abundant biophysical and biochemical information. Therefore, plant spectra combined with machine learning algorithms can be used to predict plant phenotyping traits. However, the raw spectral reflectance characteristics contain noise and redundant information, thus can easily affect the robustness of the models developed via multivariate analysis methods. In this study, two end-to-end deep learning models were developed based on 2D convolutional neural networks (2DCNN) and fully connected neural networks (FCNN; Deep2D and DeepFC, respectively) to rapidly and non-destructively predict the phenotyping traits of lettuces from spectral reflectance. Three linear and two nonlinear multivariate analysis methods were used to develop models to weigh the performance of the deep learning models. The models based on multivariate analysis methods require a series of manual feature extractions, such as pretreatment and wavelength selection, while the proposed models can automatically extract the features in relation to phenotyping traits. A visible near-infrared hyperspectral camera was used to image lettuce plants growing in the field, and the spectra extracted from the images were used to train the network. The proposed models achieved good performance with a determination coefficient of prediction ( R p 2 ) of 0.9030 and 0.8490 using Deep2D for soluble solids content and DeepFC for pH, respectively. The performance of the deep learning models was compared with five multivariate analysis method. The quantitative analysis showed that the deep learning models had higher R p 2 than all the multivariate analysis methods, indicating better performance. Also, wavelength selection and different pretreatment methods had different effects on different multivariate analysis methods, and the selection of appropriate multivariate analysis methods and pretreatment methods increased more time and computational cost. Unlike multivariate analysis methods, the proposed deep learning models did not require any pretreatment or dimensionality reduction and thus are more suitable for application in high-throughput plant phenotyping platforms. These results indicate that the deep learning models can better predict phenotyping traits of plants using spectral reflectance.
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Affiliation(s)
- Shuan Yu
- National Engineering Research Center for Agro-Ecological Big Data Analysis and Application, Anhui University, Hefei, China
- Beijing Key Laboratory of Digital Plant, China National Engineering Research Center for Information Technology in Agriculture, Beijing, China
| | - Jiangchuan Fan
- Beijing Key Laboratory of Digital Plant, China National Engineering Research Center for Information Technology in Agriculture, Beijing, China
| | - Xianju Lu
- Beijing Key Laboratory of Digital Plant, China National Engineering Research Center for Information Technology in Agriculture, Beijing, China
| | - Weiliang Wen
- Beijing Key Laboratory of Digital Plant, China National Engineering Research Center for Information Technology in Agriculture, Beijing, China
| | - Song Shao
- National Engineering Research Center for Agro-Ecological Big Data Analysis and Application, Anhui University, Hefei, China
- Beijing Key Laboratory of Digital Plant, China National Engineering Research Center for Information Technology in Agriculture, Beijing, China
| | - Xinyu Guo
- Beijing Key Laboratory of Digital Plant, China National Engineering Research Center for Information Technology in Agriculture, Beijing, China
| | - Chunjiang Zhao
- National Engineering Research Center for Agro-Ecological Big Data Analysis and Application, Anhui University, Hefei, China
- Beijing Key Laboratory of Digital Plant, China National Engineering Research Center for Information Technology in Agriculture, Beijing, China
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13
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A Review of Integrative Omic Approaches for Understanding Rice Salt Response Mechanisms. PLANTS 2022; 11:plants11111430. [PMID: 35684203 PMCID: PMC9182744 DOI: 10.3390/plants11111430] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Revised: 05/20/2022] [Accepted: 05/24/2022] [Indexed: 01/04/2023]
Abstract
Soil salinity is one of the most serious environmental challenges, posing a growing threat to agriculture across the world. Soil salinity has a significant impact on rice growth, development, and production. Hence, improving rice varieties’ resistance to salt stress is a viable solution for meeting global food demand. Adaptation to salt stress is a multifaceted process that involves interacting physiological traits, biochemical or metabolic pathways, and molecular mechanisms. The integration of multi-omics approaches contributes to a better understanding of molecular mechanisms as well as the improvement of salt-resistant and tolerant rice varieties. Firstly, we present a thorough review of current knowledge about salt stress effects on rice and mechanisms behind rice salt tolerance and salt stress signalling. This review focuses on the use of multi-omics approaches to improve next-generation rice breeding for salinity resistance and tolerance, including genomics, transcriptomics, proteomics, metabolomics and phenomics. Integrating multi-omics data effectively is critical to gaining a more comprehensive and in-depth understanding of the molecular pathways, enzyme activity and interacting networks of genes controlling salinity tolerance in rice. The key data mining strategies within the artificial intelligence to analyse big and complex data sets that will allow more accurate prediction of outcomes and modernise traditional breeding programmes and also expedite precision rice breeding such as genetic engineering and genome editing.
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14
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Sharwood RE, Quick WP, Sargent D, Estavillo GM, Silva-Perez V, Furbank RT. Mining for allelic gold: finding genetic variation in photosynthetic traits in crops and wild relatives. JOURNAL OF EXPERIMENTAL BOTANY 2022; 73:3085-3108. [PMID: 35274686 DOI: 10.1093/jxb/erac081] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Accepted: 02/28/2022] [Indexed: 06/14/2023]
Abstract
Improvement of photosynthetic traits in crops to increase yield potential and crop resilience has recently become a major breeding target. Synthetic biology and genetic technologies offer unparalleled opportunities to create new genetics for photosynthetic traits driven by existing fundamental knowledge. However, large 'gene bank' collections of germplasm comprising historical collections of crop species and their relatives offer a wealth of opportunities to find novel allelic variation in the key steps of photosynthesis, to identify new mechanisms and to accelerate genetic progress in crop breeding programmes. Here we explore the available genetic resources in food and fibre crops, strategies to selectively target allelic variation in genes underpinning key photosynthetic processes, and deployment of this variation via gene editing in modern elite material.
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Affiliation(s)
- Robert E Sharwood
- Hawkesbury Institute for the Environment, Western Sydney University, Richmond, NSW, Australia
- ARC Centre of Excellence for Translational Photosynthesis, Research School of Biology, Australian National University, Canberra, ACT, Australia
| | - W Paul Quick
- ARC Centre of Excellence for Translational Photosynthesis, Research School of Biology, Australian National University, Canberra, ACT, Australia
- International Rice Research Institute, Los Baños, Laguna, Philippines
| | - Demi Sargent
- Hawkesbury Institute for the Environment, Western Sydney University, Richmond, NSW, Australia
| | | | | | - Robert T Furbank
- ARC Centre of Excellence for Translational Photosynthesis, Research School of Biology, Australian National University, Canberra, ACT, Australia
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15
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Rozov SM, Permyakova NV, Sidorchuk YV, Deineko EV. Optimization of Genome Knock-In Method: Search for the Most Efficient Genome Regions for Transgene Expression in Plants. Int J Mol Sci 2022; 23:ijms23084416. [PMID: 35457234 PMCID: PMC9027324 DOI: 10.3390/ijms23084416] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2022] [Revised: 04/01/2022] [Accepted: 04/14/2022] [Indexed: 02/04/2023] Open
Abstract
Plant expression systems are currently regarded as promising alternative platforms for the production of recombinant proteins, including the proteins for biopharmaceutical purposes. However, the accumulation level of a target protein in plant expression systems is still rather low compared with the other existing systems, namely, mammalian, yeast, and E. coli cells. To solve this problem, numerous methods and approaches have been designed and developed. At the same time, the random nature of the distribution of transgenes over the genome can lead to gene silencing, variability in the accumulation of recombinant protein, and also to various insertional mutations. The current research study considered inserting target genes into pre-selected regions of the plant genome (genomic “safe harbors”) using the CRISPR/Cas system. Regions of genes expressed constitutively and at a high transcriptional level in plant cells (housekeeping genes) that are of interest as attractive targets for the delivery of target genes were characterized. The results of the first attempts to deliver target genes to the regions of housekeeping genes are discussed. The approach of “euchromatization” of the transgene integration region using the modified dCas9 associated with transcription factors is considered. A number of the specific features in the spatial chromatin organization allowing individual genes to efficiently transcribe are discussed.
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16
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Flores PC, Yoon JS, Kim DY, Seo YW. Transcriptome Analysis of MYB Genes and Patterns of Anthocyanin Accumulation During Seed Development in Wheat. Evol Bioinform Online 2022; 18:11769343221093341. [PMID: 35444404 PMCID: PMC9014723 DOI: 10.1177/11769343221093341] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Accepted: 03/14/2022] [Indexed: 12/01/2022] Open
Abstract
Plants accumulate key metabolites as a response of biotic/abiotic stress conditions. In seed coats, anthocyanins, carotenoids, and chlorophylls can be found. They have been associated as important antioxidants that affect germination. In wheat, anthocyanins can impart the seed coat color which have been recognized as health-promoting nutrients. Transcription factors act as master regulators of cellular processes. Transcription complexes such as MYB-bHLH-WD40 (MBW) regulate the expression of multiple target genes in various plant species. In this study, the spatiotemporal accumulation of seed coat pigments in different developmental stages (10, 20, 30, and 40 days after pollination) was analyzed using cryo-cuts. Moreover, the accumulation of phenolic, anthocyanin, and chlorophyll contents was quantified, and the expression of flavonoid biosynthetic genes was evaluated. Finally, transcriptome analysis was performed to analyze putative MYB genes related to seed coat color, followed by further characterization of putative genes. TaTCL2, an MYB gene, was cloned and sequenced. It was determined that TaTCL2 contains a SANT domain, which is often present in proteins participating in the response to anthocyanin accumulation. Moreover, TaTCL2 transcript levels were shown to be influenced by anthocyanin accumulation during grain development. Interaction network analysis showed interactions with GL2 (HD-ZIP IV), EGL3 (bHLH), and TTG1 (WD40). The findings of this study elucidate the mechanisms underlying color formation in Triticum aestivum L. seed coats.
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Affiliation(s)
| | - Jin Seok Yoon
- Ojeong Plant Breeding Research Center, Korea University, Seoul, Korea
| | - Dae Yeon Kim
- Department of Biotechnology, Korea University, Seoul, Korea
| | - Yong Weon Seo
- Department of Plant Biotechnology, Korea University, Seoul, Korea
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17
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Tanner F, Tonn S, de Wit J, Van den Ackerveken G, Berger B, Plett D. Sensor-based phenotyping of above-ground plant-pathogen interactions. PLANT METHODS 2022; 18:35. [PMID: 35313920 PMCID: PMC8935837 DOI: 10.1186/s13007-022-00853-7] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Accepted: 02/08/2022] [Indexed: 05/20/2023]
Abstract
Plant pathogens cause yield losses in crops worldwide. Breeding for improved disease resistance and management by precision agriculture are two approaches to limit such yield losses. Both rely on detecting and quantifying signs and symptoms of plant disease. To achieve this, the field of plant phenotyping makes use of non-invasive sensor technology. Compared to invasive methods, this can offer improved throughput and allow for repeated measurements on living plants. Abiotic stress responses and yield components have been successfully measured with phenotyping technologies, whereas phenotyping methods for biotic stresses are less developed, despite the relevance of plant disease in crop production. The interactions between plants and pathogens can lead to a variety of signs (when the pathogen itself can be detected) and diverse symptoms (detectable responses of the plant). Here, we review the strengths and weaknesses of a broad range of sensor technologies that are being used for sensing of signs and symptoms on plant shoots, including monochrome, RGB, hyperspectral, fluorescence, chlorophyll fluorescence and thermal sensors, as well as Raman spectroscopy, X-ray computed tomography, and optical coherence tomography. We argue that choosing and combining appropriate sensors for each plant-pathosystem and measuring with sufficient spatial resolution can enable specific and accurate measurements of above-ground signs and symptoms of plant disease.
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Affiliation(s)
- Florian Tanner
- Australian Plant Phenomics Facility, School of Agriculture, Food and Wine, University of Adelaide, Urrbrae, SA Australia
| | - Sebastian Tonn
- Department of Biology, Plant-Microbe Interactions, Utrecht University, 3584CH Utrecht, The Netherlands
| | - Jos de Wit
- Department of Imaging Physics, Delft University of Technology, Lorentzweg 1, 2628 CJ Delft, The Netherlands
| | - Guido Van den Ackerveken
- Department of Biology, Plant-Microbe Interactions, Utrecht University, 3584CH Utrecht, The Netherlands
| | - Bettina Berger
- Australian Plant Phenomics Facility, School of Agriculture, Food and Wine, University of Adelaide, Urrbrae, SA Australia
| | - Darren Plett
- Australian Plant Phenomics Facility, School of Agriculture, Food and Wine, University of Adelaide, Urrbrae, SA Australia
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18
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Ninomiya S. High-throughput field crop phenotyping: current status and challenges. BREEDING SCIENCE 2022; 72:3-18. [PMID: 36045897 PMCID: PMC8987842 DOI: 10.1270/jsbbs.21069] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Accepted: 12/16/2021] [Indexed: 05/03/2023]
Abstract
In contrast to the rapid advances made in plant genotyping, plant phenotyping is considered a bottleneck in plant science. This has promoted high-throughput plant phenotyping (HTP) studies, resulting in an exponential increase in phenotyping-related publications. The development of HTP was originally intended for use as indoor HTP technologies for model plant species under controlled environments. However, this subsequently shifted to HTP for use in crops in fields. Although HTP in fields is much more difficult to conduct due to unstable environmental conditions compared to HTP in controlled environments, recent advances in HTP technology have allowed these difficulties to be overcome, allowing for rapid, efficient, non-destructive, non-invasive, quantitative, repeatable, and objective phenotyping. Recent HTP developments have been accelerated by the advances in data analysis, sensors, and robot technologies, including machine learning, image analysis, three dimensional (3D) reconstruction, image sensors, laser sensors, environmental sensors, and drones, along with high-speed computational resources. This article provides an overview of recent HTP technologies, focusing mainly on canopy-based phenotypes of major crops, such as canopy height, canopy coverage, canopy biomass, and canopy stressed appearance, in addition to crop organ detection and counting in the fields. Current topics in field HTP are also presented, followed by a discussion on the low rates of adoption of HTP in practical breeding programs.
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Affiliation(s)
- Seishi Ninomiya
- Graduate School of Agriculture and Life Sciences, The University of Tokyo, Nishitokyo, Tokyo 188-0002, Japan
- Plant Phenomics Research Center, Nanjing Agricultural University, Nanjing, China
- Corresponding author (e-mail: )
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19
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Ebersbach J, Khan NA, McQuillan I, Higgins EE, Horner K, Bandi V, Gutwin C, Vail SL, Robinson SJ, Parkin IAP. Exploiting High-Throughput Indoor Phenotyping to Characterize the Founders of a Structured B. napus Breeding Population. FRONTIERS IN PLANT SCIENCE 2022; 12:780250. [PMID: 35069637 PMCID: PMC8767643 DOI: 10.3389/fpls.2021.780250] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/20/2021] [Accepted: 12/10/2021] [Indexed: 06/14/2023]
Abstract
Phenotyping is considered a significant bottleneck impeding fast and efficient crop improvement. Similar to many crops, Brassica napus, an internationally important oilseed crop, suffers from low genetic diversity, and will require exploitation of diverse genetic resources to develop locally adapted, high yielding and stress resistant cultivars. A pilot study was completed to assess the feasibility of using indoor high-throughput phenotyping (HTP), semi-automated image processing, and machine learning to capture the phenotypic diversity of agronomically important traits in a diverse B. napus breeding population, SKBnNAM, introduced here for the first time. The experiment comprised 50 spring-type B. napus lines, grown and phenotyped in six replicates under two treatment conditions (control and drought) over 38 days in a LemnaTec Scanalyzer 3D facility. Growth traits including plant height, width, projected leaf area, and estimated biovolume were extracted and derived through processing of RGB and NIR images. Anthesis was automatically and accurately scored (97% accuracy) and the number of flowers per plant and day was approximated alongside relevant canopy traits (width, angle). Further, supervised machine learning was used to predict the total number of raceme branches from flower attributes with 91% accuracy (linear regression and Huber regression algorithms) and to identify mild drought stress, a complex trait which typically has to be empirically scored (0.85 area under the receiver operating characteristic curve, random forest classifier algorithm). The study demonstrates the potential of HTP, image processing and computer vision for effective characterization of agronomic trait diversity in B. napus, although limitations of the platform did create significant variation that limited the utility of the data. However, the results underscore the value of machine learning for phenotyping studies, particularly for complex traits such as drought stress resistance.
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Affiliation(s)
| | - Nazifa Azam Khan
- Department of Computer Science, University of Saskatchewan, Saskatoon, SK, Canada
| | - Ian McQuillan
- Department of Computer Science, University of Saskatchewan, Saskatoon, SK, Canada
| | | | - Kyla Horner
- Agriculture and Agri-Food Canada, Saskatoon, SK, Canada
| | - Venkat Bandi
- Department of Computer Science, University of Saskatchewan, Saskatoon, SK, Canada
| | - Carl Gutwin
- Department of Computer Science, University of Saskatchewan, Saskatoon, SK, Canada
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20
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Volk GM, Byrne PF, Coyne CJ, Flint-Garcia S, Reeves PA, Richards C. Integrating Genomic and Phenomic Approaches to Support Plant Genetic Resources Conservation and Use. PLANTS (BASEL, SWITZERLAND) 2021; 10:2260. [PMID: 34834625 PMCID: PMC8619436 DOI: 10.3390/plants10112260] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Revised: 10/20/2021] [Accepted: 10/20/2021] [Indexed: 05/17/2023]
Abstract
Plant genebanks provide genetic resources for breeding and research programs worldwide. These programs benefit from having access to high-quality, standardized phenotypic and genotypic data. Technological advances have made it possible to collect phenomic and genomic data for genebank collections, which, with the appropriate analytical tools, can directly inform breeding programs. We discuss the importance of considering genebank accession homogeneity and heterogeneity in data collection and documentation. Citing specific examples, we describe how well-documented genomic and phenomic data have met or could meet the needs of plant genetic resource managers and users. We explore future opportunities that may emerge from improved documentation and data integration among plant genetic resource information systems.
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Affiliation(s)
- Gayle M. Volk
- United States Department of Agriculture, Agricultural Research Service, National Laboratory for Genetic Resources Preservation, Fort Collins, CO 80521, USA; (P.A.R.); (C.R.)
| | - Patrick F. Byrne
- Department of Soil and Crop Sciences, Colorado State University, Fort Collins, CO 80523, USA;
| | - Clarice J. Coyne
- United States Department of Agriculture, Agricultural Research Service, Western Regional Plant Introduction Station, Pullman, WA 99164, USA;
| | - Sherry Flint-Garcia
- Plant Genetics Research Unit, United States Department of Agriculture, Agricultural Research Service, Columbia, MO 65211, USA;
| | - Patrick A. Reeves
- United States Department of Agriculture, Agricultural Research Service, National Laboratory for Genetic Resources Preservation, Fort Collins, CO 80521, USA; (P.A.R.); (C.R.)
| | - Chris Richards
- United States Department of Agriculture, Agricultural Research Service, National Laboratory for Genetic Resources Preservation, Fort Collins, CO 80521, USA; (P.A.R.); (C.R.)
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21
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Zhao Y, Zheng B, Chapman SC, Laws K, George-Jaeggli B, Hammer GL, Jordan DR, Potgieter AB. Detecting Sorghum Plant and Head Features from Multispectral UAV Imagery. PLANT PHENOMICS (WASHINGTON, D.C.) 2021; 2021:9874650. [PMID: 34676373 PMCID: PMC8502246 DOI: 10.34133/2021/9874650] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Accepted: 08/31/2021] [Indexed: 06/03/2023]
Abstract
In plant breeding, unmanned aerial vehicles (UAVs) carrying multispectral cameras have demonstrated increasing utility for high-throughput phenotyping (HTP) to aid the interpretation of genotype and environment effects on morphological, biochemical, and physiological traits. A key constraint remains the reduced resolution and quality extracted from "stitched" mosaics generated from UAV missions across large areas. This can be addressed by generating high-quality reflectance data from a single nadir image per plot. In this study, a pipeline was developed to derive reflectance data from raw multispectral UAV images that preserve the original high spatial and spectral resolutions and to use these for phenotyping applications. Sequential steps involved (i) imagery calibration, (ii) spectral band alignment, (iii) backward calculation, (iv) plot segmentation, and (v) application. Each step was designed and optimised to estimate the number of plants and count sorghum heads within each breeding plot. Using a derived nadir image of each plot, the coefficients of determination were 0.90 and 0.86 for estimates of the number of sorghum plants and heads, respectively. Furthermore, the reflectance information acquired from the different spectral bands showed appreciably high discriminative ability for sorghum head colours (i.e., red and white). Deployment of this pipeline allowed accurate segmentation of crop organs at the canopy level across many diverse field plots with minimal training needed from machine learning approaches.
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Affiliation(s)
- Yan Zhao
- The University of Queensland, Queensland Alliance for Agriculture and Food Innovation, Centre for Crop Science, Gatton, Queensland 4343, Australia
| | - Bangyou Zheng
- CSIRO Agriculture and Food, St. Lucia, Queensland 4072, Australia
| | - Scott C. Chapman
- The University of Queensland, Queensland Alliance for Agriculture and Food Innovation, Centre for Crop Science, Gatton, Queensland 4343, Australia
- The University of Queensland, School of Agriculture and Food Sciences, St. Lucia, Queensland 4072, Australia
| | - Kenneth Laws
- Department of Agriculture and Fisheries, Agri-Science Queensland, Warwick, Queensland 4370, Australia
| | - Barbara George-Jaeggli
- The University of Queensland, Queensland Alliance for Agriculture and Food Innovation, Centre for Crop Science, Gatton, Queensland 4343, Australia
- Department of Agriculture and Fisheries, Agri-Science Queensland, Warwick, Queensland 4370, Australia
| | - Graeme L. Hammer
- The University of Queensland, Queensland Alliance for Agriculture and Food Innovation, Centre for Crop Science, Gatton, Queensland 4343, Australia
| | - David R. Jordan
- The University of Queensland, Queensland Alliance for Agriculture and Food Innovation, Centre for Crop Science, Gatton, Queensland 4343, Australia
- Department of Agriculture and Fisheries, Agri-Science Queensland, Warwick, Queensland 4370, Australia
| | - Andries B. Potgieter
- The University of Queensland, Queensland Alliance for Agriculture and Food Innovation, Centre for Crop Science, Gatton, Queensland 4343, Australia
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22
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Razzaq A, Wani SH, Saleem F, Yu M, Zhou M, Shabala S. Rewilding crops for climate resilience: economic analysis and de novo domestication strategies. JOURNAL OF EXPERIMENTAL BOTANY 2021; 72:6123-6139. [PMID: 34114599 DOI: 10.1093/jxb/erab276] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/05/2021] [Accepted: 06/09/2021] [Indexed: 05/08/2023]
Abstract
To match predicted population growth, annual food production should be doubled by 2050. This is not achievable by current agronomical and breeding practices, due to the impact of climate changes and associated abiotic stresses on agricultural production systems. Here, we analyze the impact of global climate trends on crop productivity and show that the overall loss in crop production from climate-driven abiotic stresses may exceed US$170 billion year-1 and represents a major threat to global food security. We also show that abiotic stress tolerance had been present in wild progenitors of modern crops but was lost during their domestication. We argue for a major shift in our paradigm of crop breeding, focusing on climate resilience, and call for a broader use of wild relatives as a major tool in this process. We argue that, while molecular tools are currently in place to harness the potential of climate-resilient genes present in wild relatives, the complex polygenic nature of tolerance traits remains a major bottleneck in this process. Future research efforts should be focused not only on finding appropriate wild relatives but also on development of efficient cell-based high-throughput phenotyping platforms allowing assessment of the in planta operation of key genes.
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Affiliation(s)
- Ali Razzaq
- Centre of Agricultural Biochemistry and Biotechnology (CABB), University of Agriculture, Faisald 38040,Pakistan
| | - Shabir Hussain Wani
- Mountain Research Center for Field Crops, Khudwani, Sher-e-Kashmir University of Agricultural Sciences and Technology of Kashmir, J&K,India
| | - Fozia Saleem
- Centre of Agricultural Biochemistry and Biotechnology (CABB), University of Agriculture, Faisald 38040,Pakistan
| | - Min Yu
- International Research Centre for Environmental Membrane Biology, Foshan University, Foshan 528000,China
| | - Meixue Zhou
- Tasmanian Institute of Agriculture, University of Tasmania, Hobart, Tas 7001,Australia
| | - Sergey Shabala
- International Research Centre for Environmental Membrane Biology, Foshan University, Foshan 528000,China
- Tasmanian Institute of Agriculture, University of Tasmania, Hobart, Tas 7001,Australia
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23
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Verbyla AP, De Faveri J, Deery DM, Rebetzke GJ. Modelling temporal genetic and spatio‐temporal residual effects for high‐throughput phenotyping data*. AUST NZ J STAT 2021. [DOI: 10.1111/anzs.12336] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- A. P. Verbyla
- Data61 CSIRO 47 Maunds Rd. Atherton QLD4883Australia
| | - J. De Faveri
- Data61 CSIRO 47 Maunds Rd. Atherton QLD4883Australia
| | - D. M. Deery
- Agriculture and Food CSIRO 2 ‐ 40 Clunies Ross Street Acton ACT2601Australia
| | - G. J. Rebetzke
- Agriculture and Food CSIRO 2 ‐ 40 Clunies Ross Street Acton ACT2601Australia
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24
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Reynolds MP, Lewis JM, Ammar K, Basnet BR, Crespo-Herrera L, Crossa J, Dhugga KS, Dreisigacker S, Juliana P, Karwat H, Kishii M, Krause MR, Langridge P, Lashkari A, Mondal S, Payne T, Pequeno D, Pinto F, Sansaloni C, Schulthess U, Singh RP, Sonder K, Sukumaran S, Xiong W, Braun HJ. Harnessing translational research in wheat for climate resilience. JOURNAL OF EXPERIMENTAL BOTANY 2021; 72:5134-5157. [PMID: 34139769 PMCID: PMC8272565 DOI: 10.1093/jxb/erab256] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Accepted: 06/14/2021] [Indexed: 05/24/2023]
Abstract
Despite being the world's most widely grown crop, research investments in wheat (Triticum aestivum and Triticum durum) fall behind those in other staple crops. Current yield gains will not meet 2050 needs, and climate stresses compound this challenge. However, there is good evidence that heat and drought resilience can be boosted through translating promising ideas into novel breeding technologies using powerful new tools in genetics and remote sensing, for example. Such technologies can also be applied to identify climate resilience traits from among the vast and largely untapped reserve of wheat genetic resources in collections worldwide. This review describes multi-pronged research opportunities at the focus of the Heat and Drought Wheat Improvement Consortium (coordinated by CIMMYT), which together create a pipeline to boost heat and drought resilience, specifically: improving crop design targets using big data approaches; developing phenomic tools for field-based screening and research; applying genomic technologies to elucidate the bases of climate resilience traits; and applying these outputs in developing next-generation breeding methods. The global impact of these outputs will be validated through the International Wheat Improvement Network, a global germplasm development and testing system that contributes key productivity traits to approximately half of the global wheat-growing area.
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Affiliation(s)
- Matthew P Reynolds
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Janet M Lewis
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Karim Ammar
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Bhoja R Basnet
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | | | - José Crossa
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Kanwarpal S Dhugga
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | | | - Philomin Juliana
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Hannes Karwat
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Masahiro Kishii
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Margaret R Krause
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Peter Langridge
- School of Agriculture, Food and Wine, University of Adelaide, Waite Campus, PMB1, Glen Osmond SA 5064, Australia
- Wheat Initiative, Julius Kühn-Institute, Königin-Luise-Str. 19, 14195 Berlin, Germany
| | - Azam Lashkari
- CIMMYT-Henan Collaborative Innovation Center, Henan Agricultural University, Zhengzhou, 450002, PR China
| | - Suchismita Mondal
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Thomas Payne
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Diego Pequeno
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Francisco Pinto
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Carolina Sansaloni
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Urs Schulthess
- CIMMYT-Henan Collaborative Innovation Center, Henan Agricultural University, Zhengzhou, 450002, PR China
| | - Ravi P Singh
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Kai Sonder
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | | | - Wei Xiong
- CIMMYT-Henan Collaborative Innovation Center, Henan Agricultural University, Zhengzhou, 450002, PR China
| | - Hans J Braun
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
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25
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Riera LG, Carroll ME, Zhang Z, Shook JM, Ghosal S, Gao T, Singh A, Bhattacharya S, Ganapathysubramanian B, Singh AK, Sarkar S. Deep Multiview Image Fusion for Soybean Yield Estimation in Breeding Applications. PLANT PHENOMICS (WASHINGTON, D.C.) 2021; 2021:9846470. [PMID: 34250507 PMCID: PMC8240512 DOI: 10.34133/2021/9846470] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/16/2020] [Accepted: 05/19/2021] [Indexed: 05/17/2023]
Abstract
Reliable seed yield estimation is an indispensable step in plant breeding programs geared towards cultivar development in major row crops. The objective of this study is to develop a machine learning (ML) approach adept at soybean (Glycine max L. (Merr.)) pod counting to enable genotype seed yield rank prediction from in-field video data collected by a ground robot. To meet this goal, we developed a multiview image-based yield estimation framework utilizing deep learning architectures. Plant images captured from different angles were fused to estimate the yield and subsequently to rank soybean genotypes for application in breeding decisions. We used data from controlled imaging environment in field, as well as from plant breeding test plots in field to demonstrate the efficacy of our framework via comparing performance with manual pod counting and yield estimation. Our results demonstrate the promise of ML models in making breeding decisions with significant reduction of time and human effort and opening new breeding method avenues to develop cultivars.
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Affiliation(s)
- Luis G. Riera
- Department of Mechanical Engineering, Iowa State University, Ames, Iowa, USA
| | | | - Zhisheng Zhang
- Department of Mechanical Engineering, Iowa State University, Ames, Iowa, USA
| | | | - Sambuddha Ghosal
- Department of Mechanical Engineering, Iowa State University, Ames, Iowa, USA
| | - Tianshuang Gao
- Department of Computer Science, Iowa State University, Ames, Iowa, USA
| | - Arti Singh
- Department of Agronomy, Iowa State University, Ames, Iowa, USA
| | | | | | | | - Soumik Sarkar
- Department of Mechanical Engineering, Iowa State University, Ames, Iowa, USA
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26
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Camarillo-Castillo F, Huggins TD, Mondal S, Reynolds MP, Tilley M, Hays DB. High-resolution spectral information enables phenotyping of leaf epicuticular wax in wheat. PLANT METHODS 2021; 17:58. [PMID: 34098962 PMCID: PMC8185930 DOI: 10.1186/s13007-021-00759-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/12/2020] [Accepted: 05/26/2021] [Indexed: 05/02/2023]
Abstract
BACKGROUND Epicuticular wax (EW) is the first line of defense in plants for protection against biotic and abiotic factors in the environment. In wheat, EW is associated with resilience to heat and drought stress, however, the current limitations on phenotyping EW restrict the integration of this secondary trait into wheat breeding pipelines. In this study we evaluated the use of light reflectance as a proxy for EW load and developed an efficient indirect method for the selection of genotypes with high EW density. RESULTS Cuticular waxes affect the light that is reflected, absorbed and transmitted by plants. The narrow spectral regions statistically associated with EW overlap with bands linked to photosynthetic radiation (500 nm), carotenoid absorbance (400 nm) and water content (~ 900 nm) in plants. The narrow spectral indices developed predicted 65% (EWI-13) and 44% (EWI-1) of the variation in this trait utilizing single-leaf reflectance. However, the normalized difference indices EWI-4 and EWI-9 improved the phenotyping efficiency with canopy reflectance across all field experimental trials. Indirect selection for EW with EWI-4 and EWI-9 led to a selection efficiency of 70% compared to phenotyping with the chemical method. The regression model EWM-7 integrated eight narrow wavelengths and accurately predicted 71% of the variation in the EW load (mg·dm-2) with leaf reflectance, but under field conditions, a single-wavelength model consistently estimated EW with an average RMSE of 1.24 mg·dm-2 utilizing ground and aerial canopy reflectance. CONCLUSIONS Overall, the indices EWI-1, EWI-13 and the model EWM-7 are reliable tools for indirect selection for EW based on leaf reflectance, and the indices EWI-4, EWI-9 and the model EWM-1 are reliable for selection based on canopy reflectance. However, further research is needed to define how the background effects and geometry of the canopy impact the accuracy of these phenotyping methods.
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Affiliation(s)
- Fátima Camarillo-Castillo
- Global Wheat Program, International Maize and Wheat Improvement Center (CIMMYT), Apdo. Postal 6-641, Mexico, D.F, 06600, Mexico.
| | - Trevis D Huggins
- USDA ARS, Dale Bumper National Rice Research Center, Stuttgart, AR, 72160, USA
| | - Suchismita Mondal
- Global Wheat Program, International Maize and Wheat Improvement Center (CIMMYT), Apdo. Postal 6-641, Mexico, D.F, 06600, Mexico
| | - Matthew P Reynolds
- Global Wheat Program, International Maize and Wheat Improvement Center (CIMMYT), Apdo. Postal 6-641, Mexico, D.F, 06600, Mexico
| | - Michael Tilley
- Agricultural Research Service, Center for Grain and Animal Health Research, USDA, 1515 College Ave., Manhattan, KS, 66502, USA
| | - Dirk B Hays
- Department of Soil and Crop Sciences, Texas A&M University, College Station, Texas, 77840, USA
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27
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Langridge P, Reynolds M. Breeding for drought and heat tolerance in wheat. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2021; 134:1753-1769. [PMID: 33715017 DOI: 10.1007/s00122-021-03795-1] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/02/2020] [Accepted: 02/16/2021] [Indexed: 05/02/2023]
Abstract
Many approaches have been adopted to enhance the heat and drought tolerance of wheat with mixed success. An assessment of the relative merits of different strategies is presented. Wheat is the most widely grown crop globally and plays a key role in human nutrition. However, it is grown in environments that are prone to heat and drought stress, resulting in severely reduced yield in some seasons. Increased climate variability is expected to have a particularly adverse effect of wheat production. Breeding for stable yield across both good and bad seasons while maintaining high yield under optimal conditions is a high priority for most wheat breeding programs and has been a focus of research activities. Multiple strategies have been explored to enhance the heat and drought tolerance of wheat including extensive genetic analysis and modify the expression of genes involved in stress responses, targeting specific physiological traits and direct selection under a range of stress scenarios. These approaches have been combined with improvements in phenotyping, the development of genetic and genomic resources, and extended screening and analysis techniques. The results have greatly expanded our knowledge and understanding of the factors that influence yield under stress, but not all have delivered the hoped-for progress. Here, we provide an overview of the different strategies and an assessment of the most promising approaches.
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Affiliation(s)
- Peter Langridge
- School of Agriculture Food and Wine, University of Adelaide, Glen Osmond, SA, 5064, Australia.
- Wheat Initiative, Julius-Kühn-Institute, 14195, Berlin, Germany.
| | - Matthew Reynolds
- International Maize and Wheat Improvement Centre (CIMMYT), Int. AP 6-641, 06600, Mexico, D.F., Mexico
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28
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Smith DT, Potgieter AB, Chapman SC. Scaling up high-throughput phenotyping for abiotic stress selection in the field. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2021; 134:1845-1866. [PMID: 34076731 DOI: 10.1007/s00122-021-03864-5] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Accepted: 05/13/2021] [Indexed: 05/18/2023]
Abstract
High-throughput phenotyping (HTP) is in its infancy for deployment in large-scale breeding programmes. With the ability to measure correlated traits associated with physiological ideotypes, in-field phenotyping methods are available for screening of abiotic stress responses. As cropping environments become more hostile and unpredictable due to the effects of climate change, the need to characterise variability across spatial and temporal scales will become increasingly important. The sensor technologies that have enabled HTP from macroscopic through to satellite sensors may also be utilised here to complement spatial characterisation using envirotyping, which can improve estimations of genotypic performance across environments by better accounting for variation at the plot, trial and inter-trial levels. Climate change is leading to increased variation at all physical and temporal scales in the cropping environment. Maintaining yield stability under circumstances with greater levels of abiotic stress while capitalising upon yield potential in good years, requires approaches to plant breeding that target the physiological limitations to crop performance in specific environments. This requires dynamic modelling of conditions within target populations of environments, GxExM predictions, clustering of environments so breeding trajectories can be defined, and the development of screens that enable selection for genetic gain to occur. High-throughput phenotyping (HTP), combined with related technologies used for envirotyping, can help to address these challenges. Non-destructive analysis of the morphological, biochemical and physiological qualities of plant canopies using HTP has great potential to complement whole-genome selection, which is becoming increasingly common in breeding programmes. A range of novel analytic techniques, such as machine learning and deep learning, combined with a widening range of sensors, allow rapid assessment of large breeding populations that are repeatable and objective. Secondary traits underlying radiation use efficiency and water use efficiency can be screened with HTP for selection at the early stages of a breeding programme. HTP and envirotyping technologies can also characterise spatial variability at trial and within-plot levels, which can be used to correct for spatial variations that confound measurements of genotypic values. This review explores HTP for abiotic stress selection through a physiological trait lens and additionally investigates the use of envirotyping and EC to characterise spatial variability at all physical scales in METs.
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Affiliation(s)
- Daniel T Smith
- The University of Queensland, St Lucia, Brisbane, QLD, 4072, Australia
| | - Andries B Potgieter
- Centre for Crop Science, Queensland Alliance for Agriculture and Food Innovation, University of Queensland, Brisbane, QLD, 4072, Australia
| | - Scott C Chapman
- The University of Queensland, St Lucia, Brisbane, QLD, 4072, Australia.
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29
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Yoosefzadeh-Najafabadi M, Tulpan D, Eskandari M. Application of machine learning and genetic optimization algorithms for modeling and optimizing soybean yield using its component traits. PLoS One 2021; 16:e0250665. [PMID: 33930039 PMCID: PMC8087002 DOI: 10.1371/journal.pone.0250665] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2021] [Accepted: 04/12/2021] [Indexed: 12/02/2022] Open
Abstract
Improving genetic yield potential in major food grade crops such as soybean (Glycine max L.) is the most sustainable way to address the growing global food demand and its security concerns. Yield is a complex trait and reliant on various related variables called yield components. In this study, the five most important yield component traits in soybean were measured using a panel of 250 genotypes grown in four environments. These traits were the number of nodes per plant (NP), number of non-reproductive nodes per plant (NRNP), number of reproductive nodes per plant (RNP), number of pods per plant (PP), and the ratio of number of pods to number of nodes per plant (P/N). These data were used for predicting the total soybean seed yield using the Multilayer Perceptron (MLP), Radial Basis Function (RBF), and Random Forest (RF), machine learning (ML) algorithms, individually and collectively through an ensemble method based on bagging strategy (E-B). The RBF algorithm with highest Coefficient of Determination (R2) value of 0.81 and the lowest Mean Absolute Errors (MAE) and Root Mean Square Error (RMSE) values of 148.61 kg.ha-1, and 185.31 kg.ha-1, respectively, was the most accurate algorithm and, therefore, selected as the metaClassifier for the E-B algorithm. Using the E-B algorithm, we were able to increase the prediction accuracy by improving the values of R2, MAE, and RMSE by 0.1, 0.24 kg.ha-1, and 0.96 kg.ha-1, respectively. Furthermore, for the first time in this study, we allied the E-B with the genetic algorithm (GA) to model the optimum values of yield components in an ideotype genotype in which the yield is maximized. The results revealed a better understanding of the relationships between soybean yield and its components, which can be used for selecting parental lines and designing promising crosses for developing cultivars with improved genetic yield potential.
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Affiliation(s)
| | - Dan Tulpan
- Department of Animal Biosciences, University of Guelph, Guelph, Ontario, Canada
| | - Milad Eskandari
- Department of Plant Agriculture, University of Guelph, Guelph, Ontario, Canada
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30
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Jangra S, Chaudhary V, Yadav RC, Yadav NR. High-Throughput Phenotyping: A Platform to Accelerate Crop Improvement. PHENOMICS (CHAM, SWITZERLAND) 2021; 1:31-53. [PMID: 36939738 PMCID: PMC9590473 DOI: 10.1007/s43657-020-00007-6] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Development of high-throughput phenotyping technologies has progressed considerably in the last 10 years. These technologies provide precise measurements of desired traits among thousands of field-grown plants under diversified environments; this is a critical step towards selection of better performing lines as to yield, disease resistance, and stress tolerance to accelerate crop improvement programs. High-throughput phenotyping techniques and platforms help unraveling the genetic basis of complex traits associated with plant growth and development and targeted traits. This review focuses on the advancements in technologies involved in high-throughput, field-based, aerial, and unmanned platforms. Development of user-friendly data management tools and softwares to better understand phenotyping will increase the use of field-based high-throughput techniques, which have potential to revolutionize breeding strategies and meet the future needs of stakeholders.
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Affiliation(s)
- Sumit Jangra
- Department of Molecular Biology, Biotechnology, and Bioinformatics, CCS Haryana Agricultural University, Hisar, 125004 India
| | - Vrantika Chaudhary
- Department of Molecular Biology, Biotechnology, and Bioinformatics, CCS Haryana Agricultural University, Hisar, 125004 India
| | - Ram C. Yadav
- Department of Molecular Biology, Biotechnology, and Bioinformatics, CCS Haryana Agricultural University, Hisar, 125004 India
| | - Neelam R. Yadav
- Department of Molecular Biology, Biotechnology, and Bioinformatics, CCS Haryana Agricultural University, Hisar, 125004 India
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31
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Wang L, Liu F, Hao X, Wang W, Xing G, Luo J, Zhou G, He J, Gai J. Identification of the QTL-allele System Underlying Two High-Throughput Physiological Traits in the Chinese Soybean Germplasm Population. Front Genet 2021; 12:600444. [PMID: 33719333 PMCID: PMC7947801 DOI: 10.3389/fgene.2021.600444] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2020] [Accepted: 02/01/2021] [Indexed: 12/21/2022] Open
Abstract
The QTL-allele system underlying two spectral reflectance physiological traits, NDVI (normalized difference vegetation index) and CHL (chlorophyll index), related to plant growth and yield was studied in the Chinese soybean germplasm population (CSGP), which consisted of 341 wild accessions (WA), farmer landraces (LR), and released cultivars (RC). Samples were evaluated in the Photosynthetic System II imaging platform at Nanjing Agricultural University. The NDVI and CHL data were obtained from hyperspectral reflectance images in a randomized incomplete block design experiment with two replicates. The NDVI and CHL ranged from 0.05–0.18 and 1.20–4.78, had averages of 0.11 and 3.57, and had heritabilities of 78.3% and 69.2%, respectively; the values of NDVI and CHL were both significantly higher in LR and RC than in WA. Using the RTM-GWAS (restricted two-stage multi-locus genome-wide association study) method, 38 and 32 QTLs with 89 and 82 alleles and 2–4 and 2–6 alleles per locus were identified for NDVI and CHL, respectively, which explained 48.36% and 51.35% of the phenotypic variation for NDVI and CHL, respectively. The QTL-allele matrices were established and separated into WA, LR, and RC submatrices. From WA to LR + RC, 4 alleles and 2 new loci emerged, and 1 allele was excluded for NDVI, whereas 6 alleles emerged, and no alleles were excluded, in LR + RC for CHL. Recombination was the major motivation of evolutionary differences. For NDVI and CHL, 39 and 32 candidate genes were annotated and assigned to GO groups, respectively, indicating a complex gene network. The NDVI and CHL were upstream traits that were relatively conservative in their genetic changes compared with those of downstream agronomic traits. High-throughput phenotyping integrated with RTM-GWAS provides an efficient procedure for studying the population genetics of traits.
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Affiliation(s)
- Lei Wang
- Soybean Research Institute, Nanjing Agricultural University, Nanjing, China
| | - Fangdong Liu
- Soybean Research Institute, Nanjing Agricultural University, Nanjing, China
| | - Xiaoshuai Hao
- Soybean Research Institute, Nanjing Agricultural University, Nanjing, China
| | - Wubin Wang
- Soybean Research Institute, Nanjing Agricultural University, Nanjing, China.,MARA National Center for Soybean Improvement, Nanjing Agricultural University, Nanjing, China.,MARA Key Laboratory of Biology and Genetic Improvement of Soybean (General), Nanjing Agricultural University, Nanjing, China.,State Key Laboratory for Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing, China
| | - Guangnan Xing
- Soybean Research Institute, Nanjing Agricultural University, Nanjing, China.,MARA National Center for Soybean Improvement, Nanjing Agricultural University, Nanjing, China.,MARA Key Laboratory of Biology and Genetic Improvement of Soybean (General), Nanjing Agricultural University, Nanjing, China.,State Key Laboratory for Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing, China
| | - Jingjing Luo
- Plant Phenomics Research Center, Nanjing Agricultural University, Nanjing, China
| | - Guodong Zhou
- Plant Phenomics Research Center, Nanjing Agricultural University, Nanjing, China
| | - Jianbo He
- Soybean Research Institute, Nanjing Agricultural University, Nanjing, China.,MARA National Center for Soybean Improvement, Nanjing Agricultural University, Nanjing, China.,MARA Key Laboratory of Biology and Genetic Improvement of Soybean (General), Nanjing Agricultural University, Nanjing, China.,State Key Laboratory for Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing, China
| | - Junyi Gai
- Soybean Research Institute, Nanjing Agricultural University, Nanjing, China.,MARA National Center for Soybean Improvement, Nanjing Agricultural University, Nanjing, China.,MARA Key Laboratory of Biology and Genetic Improvement of Soybean (General), Nanjing Agricultural University, Nanjing, China.,State Key Laboratory for Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing, China.,Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, China
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Volpato L, Pinto F, González-Pérez L, Thompson IG, Borém A, Reynolds M, Gérard B, Molero G, Rodrigues FA. High Throughput Field Phenotyping for Plant Height Using UAV-Based RGB Imagery in Wheat Breeding Lines: Feasibility and Validation. FRONTIERS IN PLANT SCIENCE 2021; 12:591587. [PMID: 33664755 PMCID: PMC7921806 DOI: 10.3389/fpls.2021.591587] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/04/2020] [Accepted: 01/25/2021] [Indexed: 05/07/2023]
Abstract
Plant height (PH) is an essential trait in the screening of most crops. While in crops such as wheat, medium stature helps reduce lodging, tall plants are preferred to increase total above-ground biomass. PH is an easy trait to measure manually, although it can be labor-intense depending on the number of plots. There is an increasing demand for alternative approaches to estimate PH in a higher throughput mode. Crop surface models (CSMs) derived from dense point clouds generated via aerial imagery could be used to estimate PH. This study evaluates PH estimation at different phenological stages using plot-level information from aerial imaging-derived 3D CSM in wheat inbred lines during two consecutive years. Multi-temporal and high spatial resolution images were collected by fixed-wing (P l a t F W ) and multi-rotor (P l a t M R ) unmanned aerial vehicle (UAV) platforms over two wheat populations (50 and 150 lines). The PH was measured and compared at four growth stages (GS) using ground-truth measurements (PHground) and UAV-based estimates (PHaerial). The CSMs generated from the aerial imagery were validated using ground control points (GCPs) as fixed reference targets at different heights. The results show that PH estimations using P l a t F W were consistent with those obtained from P l a t M R , showing some slight differences due to image processing settings. The GCPs heights derived from CSM showed a high correlation and low error compared to their actual heights (R 2 ≥ 0.90, RMSE ≤ 4 cm). The coefficient of determination (R 2) between PHground and PHaerial at different GS ranged from 0.35 to 0.88, and the root mean square error (RMSE) from 0.39 to 4.02 cm for both platforms. In general, similar and higher heritability was obtained using PHaerial across different GS and years and ranged according to the variability, and environmental error of the PHground observed (0.06-0.97). Finally, we also observed high Spearman rank correlations (0.47-0.91) and R 2 (0.63-0.95) of PHaerial adjusted and predicted values against PHground values. This study provides an example of the use of UAV-based high-resolution RGB imagery to obtain time-series estimates of PH, scalable to tens-of-thousands of plots, and thus suitable to be applied in plant wheat breeding trials.
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Affiliation(s)
- Leonardo Volpato
- Department of Agronomy, Federal University of Viçosa, Viçosa, Brazil
| | - Francisco Pinto
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | | | | | - Aluízio Borém
- Department of Agronomy, Federal University of Viçosa, Viçosa, Brazil
| | - Matthew Reynolds
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Bruno Gérard
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Gemma Molero
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
- KWS Momont Recherche, Mons-en-Pevele, France
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Applying RGB- and Thermal-Based Vegetation Indices from UAVs for High-Throughput Field Phenotyping of Drought Tolerance in Forage Grasses. REMOTE SENSING 2021. [DOI: 10.3390/rs13010147] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
The persistence and productivity of forage grasses, important sources for feed production, are threatened by climate change-induced drought. Breeding programs are in search of new drought tolerant forage grass varieties, but those programs still rely on time-consuming and less consistent visual scoring by breeders. In this study, we evaluate whether Unmanned Aerial Vehicle (UAV) based remote sensing can complement or replace this visual breeder score. A field experiment was set up to test the drought tolerance of genotypes from three common forage types of two different species: Festuca arundinacea, diploid Lolium perenne and tetraploid Lolium perenne. Drought stress was imposed by using mobile rainout shelters. UAV flights with RGB and thermal sensors were conducted at five time points during the experiment. Visual-based indices from different colour spaces were selected that were closely correlated to the breeder score. Furthermore, several indices, in particular H and NDLab, from the HSV (Hue Saturation Value) and CIELab (Commission Internationale de l’éclairage) colour space, respectively, displayed a broad-sense heritability that was as high or higher than the visual breeder score, making these indices highly suited for high-throughput field phenotyping applications that can complement or even replace the breeder score. The thermal-based Crop Water Stress Index CWSI provided complementary information to visual-based indices, enabling the analysis of differences in ecophysiological mechanisms for coping with reduced water availability between species and ploidy levels. All species/types displayed variation in drought stress tolerance, which confirms that there is sufficient variation for selection within these groups of grasses. Our results confirmed the better drought tolerance potential of Festuca arundinacea, but also showed which Lolium perenne genotypes are more tolerant.
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Hübner S, Kantar MB. Tapping Diversity From the Wild: From Sampling to Implementation. FRONTIERS IN PLANT SCIENCE 2021; 12:626565. [PMID: 33584776 PMCID: PMC7873362 DOI: 10.3389/fpls.2021.626565] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/06/2020] [Accepted: 01/07/2021] [Indexed: 05/05/2023]
Abstract
The diversity observed among crop wild relatives (CWRs) and their ability to flourish in unfavorable and harsh environments have drawn the attention of plant scientists and breeders for many decades. However, it is also recognized that the benefit gained from using CWRs in breeding is a potential rose between thorns of detrimental genetic variation that is linked to the trait of interest. Despite the increased interest in CWRs, little attention was given so far to the statistical, analytical, and technical considerations that should guide the sampling design, the germplasm characterization, and later its implementation in breeding. Here, we review the entire process of sampling and identifying beneficial genetic variation in CWRs and the challenge of using it in breeding. The ability to detect beneficial genetic variation in CWRs is strongly affected by the sampling design which should be adjusted to the spatial and temporal variation of the target species, the trait of interest, and the analytical approach used. Moreover, linkage disequilibrium is a key factor that constrains the resolution of searching for beneficial alleles along the genome, and later, the ability to deplete linked deleterious genetic variation as a consequence of genetic drag. We also discuss how technological advances in genomics, phenomics, biotechnology, and data science can improve the ability to identify beneficial genetic variation in CWRs and to exploit it in strive for higher-yielding and sustainable crops.
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Affiliation(s)
- Sariel Hübner
- Galilee Research Institute (MIGAL), Tel-Hai College, Qiryat Shemona, Israel
- *Correspondence: Sariel Hübner,
| | - Michael B. Kantar
- Department of Tropical Plant and Soil Sciences, University of Hawai’i at Mânoa, Honolulu, HI, United States
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Deery DM, Jones HG. Field Phenomics: Will It Enable Crop Improvement? PLANT PHENOMICS (WASHINGTON, D.C.) 2021; 2021:9871989. [PMID: 34549194 PMCID: PMC8433881 DOI: 10.34133/2021/9871989] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/08/2020] [Accepted: 08/14/2021] [Indexed: 05/19/2023]
Abstract
Field phenomics has been identified as a promising enabling technology to assist plant breeders with the development of improved cultivars for farmers. Yet, despite much investment, there are few examples demonstrating the application of phenomics within a plant breeding program. We review recent progress in field phenomics and highlight the importance of targeting breeders' needs, rather than perceived technology needs, through developing and enhancing partnerships between phenomics researchers and plant breeders.
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Affiliation(s)
| | - Hamlyn G. Jones
- CSIRO Agriculture and Food, Canberra, ACT, Australia
- Division of Plant Sciences, University of Dundee, UK
- School of Agriculture and Environment, University of Western Australia, Australia
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36
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Advances and Challenges in the Breeding of Salt-Tolerant Rice. Int J Mol Sci 2020; 21:ijms21218385. [PMID: 33182265 PMCID: PMC7664944 DOI: 10.3390/ijms21218385] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2020] [Revised: 11/06/2020] [Accepted: 11/07/2020] [Indexed: 12/16/2022] Open
Abstract
Soil salinization and a degraded ecological environment are challenging agricultural productivity and food security. Rice (Oryza sativa), the staple food of much of the world’s population, is categorized as a salt-susceptible crop. Improving the salt tolerance of rice would increase the potential of saline-alkali land and ensure food security. Salt tolerance is a complex quantitative trait. Biotechnological efforts to improve the salt tolerance of rice hinge on a detailed understanding of the molecular mechanisms underlying salt stress tolerance. In this review, we summarize progress in the breeding of salt-tolerant rice and in the mapping and cloning of genes and quantitative trait loci (QTLs) associated with salt tolerance in rice. Furthermore, we describe biotechnological tools that can be used to cultivate salt-tolerant rice, providing a reference for efforts aimed at rapidly and precisely cultivating salt-tolerance rice varieties.
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37
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Roth L, Camenzind M, Aasen H, Kronenberg L, Barendregt C, Camp KH, Walter A, Kirchgessner N, Hund A. Repeated Multiview Imaging for Estimating Seedling Tiller Counts of Wheat Genotypes Using Drones. PLANT PHENOMICS (WASHINGTON, D.C.) 2020; 2020:3729715. [PMID: 33313553 PMCID: PMC7706335 DOI: 10.34133/2020/3729715] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/16/2019] [Accepted: 07/21/2020] [Indexed: 05/18/2023]
Abstract
Early generation breeding nurseries with thousands of genotypes in single-row plots are well suited to capitalize on high throughput phenotyping. Nevertheless, methods to monitor the intrinsically hard-to-phenotype early development of wheat are yet rare. We aimed to develop proxy measures for the rate of plant emergence, the number of tillers, and the beginning of stem elongation using drone-based imagery. We used RGB images (ground sampling distance of 3 mm pixel-1) acquired by repeated flights (≥ 2 flights per week) to quantify temporal changes of visible leaf area. To exploit the information contained in the multitude of viewing angles within the RGB images, we processed them to multiview ground cover images showing plant pixel fractions. Based on these images, we trained a support vector machine for the beginning of stem elongation (GS30). Using the GS30 as key point, we subsequently extracted plant and tiller counts using a watershed algorithm and growth modeling, respectively. Our results show that determination coefficients of predictions are moderate for plant count (R 2 = 0.52), but strong for tiller count (R 2 = 0.86) and GS30 (R 2 = 0.77). Heritabilities are superior to manual measurements for plant count and tiller count, but inferior for GS30 measurements. Increasing the selection intensity due to throughput may overcome this limitation. Multiview image traits can replace hand measurements with high efficiency (85-223%). We therefore conclude that multiview images have a high potential to become a standard tool in plant phenomics.
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Affiliation(s)
- Lukas Roth
- ETH Zurich, Institute of Agricultural Sciences, Universitätstrasse 2, 8092 Zurich, Switzerland
| | - Moritz Camenzind
- ETH Zurich, Institute of Agricultural Sciences, Universitätstrasse 2, 8092 Zurich, Switzerland
| | - Helge Aasen
- ETH Zurich, Institute of Agricultural Sciences, Universitätstrasse 2, 8092 Zurich, Switzerland
| | - Lukas Kronenberg
- ETH Zurich, Institute of Agricultural Sciences, Universitätstrasse 2, 8092 Zurich, Switzerland
| | | | - Karl-Heinz Camp
- Delley Samen und Pflanzen AG, Route de Portalban 40, 1567 Delley, Switzerland
| | - Achim Walter
- ETH Zurich, Institute of Agricultural Sciences, Universitätstrasse 2, 8092 Zurich, Switzerland
| | - Norbert Kirchgessner
- ETH Zurich, Institute of Agricultural Sciences, Universitätstrasse 2, 8092 Zurich, Switzerland
| | - Andreas Hund
- ETH Zurich, Institute of Agricultural Sciences, Universitätstrasse 2, 8092 Zurich, Switzerland
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Kitavi M, Cashell R, Ferguson M, Lorenzen J, Nyine M, McKeown PC, Spillane C. Heritable epigenetic diversity for conservation and utilization of epigenetic germplasm resources of clonal East African Highland banana (EAHB) accessions. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2020; 133:2605-2625. [PMID: 32719910 PMCID: PMC7419381 DOI: 10.1007/s00122-020-03620-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/15/2019] [Accepted: 05/20/2020] [Indexed: 05/22/2023]
Abstract
KEY MESSAGE Genetically identical East African Highland banana (EAHB) clones are epigenetically diverse with heritable epialleles that can contribute to morphological diversity. Heritable epigenetic variation can contribute to agronomic traits in crops and should be considered in germplasm conservation. Despite the genetic uniformity arising from a genetic bottleneck of one ancestral clone, followed by subsequent vegetative propagation, East African Highland bananas (EAHBs) display significant phenotypic diversity potentially arising from somatic mutations, heritable epialleles and/or genotype-by-environment interactions. Here, we use DNA methylation profiling across EAHB accessions representing most of the primary EAHB genepool to demonstrate that the genetically uniform EAHB genepool harbours significant epigenetic diversity. By analysing 724 polymorphic DNA methylation sites by methylation-sensitive AFLP across 90 EAHB cultivars, we could differentiate the EAHB varieties according to their regions (Kenya and Uganda). In contrast, there was minimal association of DNA methylation variation with the five morphological groups that are used to classify EAHBs. We further analysed DNA methylation patterns in parent-offspring cohort, which were maintained in offspring generated by sexual (seed) and asexual (vegetative) propagation, with higher levels of altered DNA methylation observed in vegetatively generated offspring. Our results indicate that the phenotypic diversity of near-isogenic EAHBs is mirrored by considerable DNA methylation variation, which is transmitted between generations by both vegetative reproduction and seed reproduction. Genetically uniform vegetatively propagated crops such as EAHBs harbour considerable heritable epigenetic variation, where heritable epialleles could arise in offspring and contribute to functional traits. This study provides a basis for developing strategies for conservation of epigenetic resources and for integration of epimarkers into crop breeding programmes.
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Affiliation(s)
- M Kitavi
- Genetics and Biotechnology Lab, Plant and AgriBiosciences Research Centre (PABC), Ryan Institute, National University of Ireland Galway, University Road, Galway, H91 REW4, Ireland
- International Institute for Tropical Agriculture (IITA), P.O. Box 30709-00100, Nairobi, Kenya
| | - R Cashell
- Genetics and Biotechnology Lab, Plant and AgriBiosciences Research Centre (PABC), Ryan Institute, National University of Ireland Galway, University Road, Galway, H91 REW4, Ireland
| | - M Ferguson
- International Institute for Tropical Agriculture (IITA), P.O. Box 30709-00100, Nairobi, Kenya
| | - J Lorenzen
- International Institute for Tropical Agriculture (IITA), P.O. Box 30709-00100, Nairobi, Kenya
- Crop R&D, Agricultural Development, Bill & Melinda Gates Foundation, PO Box 23350, Seattle, WA, 98102, USA
| | - M Nyine
- International Institute for Tropical Agriculture (IITA), P.O. Box 30709-00100, Nairobi, Kenya
| | - P C McKeown
- Genetics and Biotechnology Lab, Plant and AgriBiosciences Research Centre (PABC), Ryan Institute, National University of Ireland Galway, University Road, Galway, H91 REW4, Ireland
| | - C Spillane
- Genetics and Biotechnology Lab, Plant and AgriBiosciences Research Centre (PABC), Ryan Institute, National University of Ireland Galway, University Road, Galway, H91 REW4, Ireland.
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Horgan FG, Jauregui A, Peñalver Cruz A, Crisol Martínez E, Bernal CC. Changes in reflectance of rice seedlings during planthopper feeding as detected by digital camera: Potential applications for high-throughput phenotyping. PLoS One 2020; 15:e0238173. [PMID: 32853293 PMCID: PMC7451558 DOI: 10.1371/journal.pone.0238173] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2020] [Accepted: 08/11/2020] [Indexed: 11/19/2022] Open
Abstract
Damage to grasses and cereals by phloem-feeding herbivores is manifest as nutrient and chlorophyll loss, desiccation, and a gradual decline in host vigour. Chlorophyll loss in particular leads to a succession of colour changes before eventual host death. Depending on the attacking herbivore species, colour changes can be difficult to detect with the human eye. This study used digital images to examine colour changes of rice seedlings during feeding by the brown planthopper, Nilaparvata lugens (Stål) and whitebacked planthopper, Sogatella furcifera (Horváth). Values for red (580 nm), green (540 nm) and blue (550 nm) reflectance for 39 rice varieties during seedling seed-box tests were derived from images captured with a digital camera. Red and blue reflectance gradually increased as herbivore damage progressed until final plant death. Red reflectance was greater from plants attacked by the brown planthopper than plants attacked by the whitebacked planthopper, which had proportionately more green and blue reflectance, indicating distinct impacts by the two planthoppers on their hosts. Analysis of digital images was used to discriminate variety responses to the two planthoppers. Ordination methods based on red-green-blue reflectance and vegetation indices such as the Green Leaf Index (GLI) that included blue reflectance were more successful than two-colour indices or indices based on hue, saturation and brightness in discriminating between damage responses among varieties. We make recommendations to advance seed-box screening methods for cereal resistance to phloem feeders and demonstrate how images from digital cameras can be used to improve the quality of data captured during high-throughput phenotyping.
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Affiliation(s)
- Finbarr G. Horgan
- EcoLaVerna Integral Restoration Ecology, Bridestown, Kildinan, Co. Cork, Ireland
- Environment and Sustainable Resource Management, University College Dublin, Belfield, Dublin, Ireland
| | - Artzai Jauregui
- Unidad de Diseño, Departamento de Branding, Dirección de Comunicaciones Corporativas, Universidad de Talca, Talca, Chile
- International Rice Research Institute, Metro Manila, Philippines
| | - Ainara Peñalver Cruz
- International Rice Research Institute, Metro Manila, Philippines
- Laboratorio de Control Biológico, Instituto de Ciencias Biológicas, Universidad de Talca, Talca, Chile
| | - Eduardo Crisol Martínez
- EcoLaVerna Integral Restoration Ecology, Bridestown, Kildinan, Co. Cork, Ireland
- Department of Agroecology, COEXPHAL (Association of Vegetable and Fruit Growers of Almeria), La Mojonera, Almeria, Spain
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Nguyen GN, Norton SL. Genebank Phenomics: A Strategic Approach to Enhance Value and Utilization of Crop Germplasm. PLANTS (BASEL, SWITZERLAND) 2020; 9:E817. [PMID: 32610615 PMCID: PMC7411623 DOI: 10.3390/plants9070817] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/03/2020] [Revised: 06/25/2020] [Accepted: 06/26/2020] [Indexed: 02/07/2023]
Abstract
Genetically diverse plant germplasm stored in ex-situ genebanks are excellent resources for breeding new high yielding and sustainable crop varieties to ensure future food security. Novel alleles have been discovered through routine genebank activities such as seed regeneration and characterization, with subsequent utilization providing significant genetic gains and improvements for the selection of favorable traits, including yield, biotic, and abiotic resistance. Although some genebanks have implemented cost-effective genotyping technologies through advances in DNA technology, the adoption of modern phenotyping is lagging. The introduction of advanced phenotyping technologies in recent decades has provided genebank scientists with time and cost-effective screening tools to obtain valuable phenotypic data for more traits on large germplasm collections during routine activities. The utilization of these phenotyping tools, coupled with high-throughput genotyping, will accelerate the use of genetic resources and fast-track the development of more resilient food crops for the future. In this review, we highlight current digital phenotyping methods that can capture traits during annual seed regeneration to enrich genebank phenotypic datasets. Next, we describe strategies for the collection and use of phenotypic data of specific traits for downstream research using high-throughput phenotyping technology. Finally, we examine the challenges and future perspectives of genebank phenomics.
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Affiliation(s)
- Giao N. Nguyen
- Australian Grains Genebank, Agriculture Victoria, 110 Natimuk Road, Horsham 3400, Australia;
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Abstract
The complex formation of grain yield (GY) is related to multiple dry matter (DM) traits; however, due to their time-consuming determination, they are not readily accessible. In winter wheat (Triticum aestivum L.), both agronomic treatments and genotypic variation influence GY in interaction with the environment. Spectral proximal sensing is promising for high-throughput non-destructive phenotyping but was rarely evaluated systematically for dissecting yield-related variation in DM traits. Aiming at a temporal, spectral and organ-level optimization, 48 vegetation indices were evaluated in a high-yielding environment in 10 growth stages for the estimation of 31 previously compared traits related to GY formation—influenced by sowing time, fungicide, N fertilization, and cultivar. A quantitative index ranking was evaluated to assess the stage-independent index suitability. GY showed close linear relationships with spectral vegetation indices across and within agronomic treatments (R2 = 0.47–0.67 ***). Water band indices, followed by red edge-based indices, best used at milk or early dough ripeness, were better suited than the widely used normalized difference vegetation index (NDVI). Index rankings for many organ-level DM traits were comparable, but the relationships were often less close. Among yield components, grain number per spike (R2 = 0.24–0.34 ***) and spike density (R2 = 0.23–0.46 ***) were moderately estimated. GY was mainly estimated by detecting total DM rather than the harvest index. Across agronomic treatments and cultivars, seasonal index rankings were the most stable for GY and total DM, whereas traits related to DM allocation and translocation demanded specific index selection. The results suggest using indices with water bands, near infrared/red edge and visible light bands to increase the accuracy of in-season spectral phenotyping for GY, contributing organ-level traits, and yield components, respectively.
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42
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Silva-Pérez V, De Faveri J, Molero G, Deery DM, Condon AG, Reynolds MP, Evans JR, Furbank RT. Genetic variation for photosynthetic capacity and efficiency in spring wheat. JOURNAL OF EXPERIMENTAL BOTANY 2020; 71:2299-2311. [PMID: 31565736 PMCID: PMC7134913 DOI: 10.1093/jxb/erz439] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2019] [Accepted: 09/19/2019] [Indexed: 05/05/2023]
Abstract
One way to increase yield potential in wheat is screening for natural variation in photosynthesis. This study uses measured and modelled physiological parameters to explore genotypic diversity in photosynthetic capacity (Pc, Rubisco carboxylation capacity per unit leaf area at 25 °C) and efficiency (Peff, Pc per unit of leaf nitrogen) in wheat in relation to fertilizer, plant stage, and environment. Four experiments (Aus1, Aus2, Aus3, and Mex1) were carried out with diverse wheat collections to investigate genetic variation for Rubisco capacity (Vcmax25), electron transport rate (J), CO2 assimilation rate, stomatal conductance, and complementary plant functional traits: leaf nitrogen, leaf dry mass per unit area, and SPAD. Genotypes for Aus1 and Aus2 were grown in the glasshouse with two fertilizer levels. Genotypes for Aus3 and Mex1 experiments were grown in the field in Australia and Mexico, respectively. Results showed that Vcmax25 derived from gas exchange measurements is a robust parameter that does not depend on stomatal conductance and was positively correlated with Rubisco content measured in vitro. There was significant genotypic variation in most of the experiments for Pc and Peff. Heritability of Pc reached 0.7 and 0.9 for SPAD. Genotypic variation and heritability of traits show that there is scope for these traits to be used in pre-breeding programmes to improve photosynthesis with the ultimate objective of raising yield potential.
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Affiliation(s)
- Viridiana Silva-Pérez
- CSIRO Agriculture & Food, Canberra, ACT, Australia
- ARC Centre of Excellence for Translational Photosynthesis, Research School of Biology. The Australian National University, Canberra, ACT, Australia
| | | | - Gemma Molero
- International Maize and Wheat Improvement Centre (CIMMYT), México, DF, Mexico
| | | | - Anthony G Condon
- CSIRO Agriculture & Food, Canberra, ACT, Australia
- ARC Centre of Excellence for Translational Photosynthesis, Research School of Biology. The Australian National University, Canberra, ACT, Australia
| | - Matthew P Reynolds
- International Maize and Wheat Improvement Centre (CIMMYT), México, DF, Mexico
| | - John R Evans
- ARC Centre of Excellence for Translational Photosynthesis, Research School of Biology. The Australian National University, Canberra, ACT, Australia
| | - Robert T Furbank
- ARC Centre of Excellence for Translational Photosynthesis, Research School of Biology. The Australian National University, Canberra, ACT, Australia
- Agriculture Victoria, Horsham, VIC, Australia
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43
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Lyra DH, Virlet N, Sadeghi-Tehran P, Hassall KL, Wingen LU, Orford S, Griffiths S, Hawkesford MJ, Slavov GT. Functional QTL mapping and genomic prediction of canopy height in wheat measured using a robotic field phenotyping platform. JOURNAL OF EXPERIMENTAL BOTANY 2020; 71:1885-1898. [PMID: 32097472 PMCID: PMC7094083 DOI: 10.1093/jxb/erz545] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/07/2019] [Accepted: 02/19/2020] [Indexed: 05/08/2023]
Abstract
Genetic studies increasingly rely on high-throughput phenotyping, but the resulting longitudinal data pose analytical challenges. We used canopy height data from an automated field phenotyping platform to compare several approaches to scanning for quantitative trait loci (QTLs) and performing genomic prediction in a wheat recombinant inbred line mapping population based on up to 26 sampled time points (TPs). We detected four persistent QTLs (i.e. expressed for most of the growing season), with both empirical and simulation analyses demonstrating superior statistical power of detecting such QTLs through functional mapping approaches compared with conventional individual TP analyses. In contrast, even very simple individual TP approaches (e.g. interval mapping) had superior detection power for transient QTLs (i.e. expressed during very short periods). Using spline-smoothed phenotypic data resulted in improved genomic predictive abilities (5-8% higher than individual TP prediction), while the effect of including significant QTLs in prediction models was relatively minor (<1-4% improvement). Finally, although QTL detection power and predictive ability generally increased with the number of TPs analysed, gains beyond five or 10 TPs chosen based on phenological information had little practical significance. These results will inform the development of an integrated, semi-automated analytical pipeline, which will be more broadly applicable to similar data sets in wheat and other crops.
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Affiliation(s)
- Danilo H Lyra
- Department of Computational & Analytical Sciences, Rothamsted Research, Harpenden, UK
| | - Nicolas Virlet
- Department of Plant Sciences, Rothamsted Research, Harpenden, UK
| | | | - Kirsty L Hassall
- Department of Computational & Analytical Sciences, Rothamsted Research, Harpenden, UK
| | - Luzie U Wingen
- John Innes Centre, Norwich Research Park, Colney Lane, Norwich, UK
| | - Simon Orford
- John Innes Centre, Norwich Research Park, Colney Lane, Norwich, UK
| | - Simon Griffiths
- John Innes Centre, Norwich Research Park, Colney Lane, Norwich, UK
| | | | - Gancho T Slavov
- Department of Computational & Analytical Sciences, Rothamsted Research, Harpenden, UK
- Scion, Rotorua, New Zealand
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44
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Advancing High-Throughput Phenotyping of Wheat in Early Selection Cycles. REMOTE SENSING 2020. [DOI: 10.3390/rs12030574] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Enhancing plant breeding to ensure global food security requires new technologies. For wheat phenotyping, only limited seeds and resources are available in early selection cycles. This forces breeders to use small plots with single or multiple row plots in order to include the maximum number of genotypes/lines for their assessment. High-throughput phenotyping through remote sensing may meet the requirements for the phenotyping of thousands of genotypes grown in small plots in early selection cycles. Therefore, the aim of this study was to compare the performance of an unmanned aerial vehicle (UAV) for assessing the grain yield of wheat genotypes in different row numbers per plot in the early selection cycles with ground-based spectral sensing. A field experiment consisting of 32 wheat genotypes with four plot designs (1, 2, 3, and 12 rows per plot) was conducted. Near infrared (NIR)-based spectral indices showed significant correlations (p < 0.01) with the grain yield at flowering to grain filling, regardless of row numbers, indicating the potential of spectral indices as indirect selection traits for the wheat grain yield. Compared with terrestrial sensing, aerial-based sensing from UAV showed consistently higher levels of association with the grain yield, indicating that an increased precision may be obtained and is expected to increase the efficiency of high-throughput phenotyping in large-scale plant breeding programs. Our results suggest that high-throughput sensing from UAV may become a convenient and efficient tool for breeders to promote a more efficient selection of improved genotypes in early selection cycles. Such new information may support the calibration of genomic information by providing additional information on other complex traits, which can be ascertained by spectral sensing.
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45
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Deery DM, Rebetzke GJ, Jimenez-Berni JA, Condon AG, Smith DJ, Bechaz KM, Bovill WD. Ground-Based LiDAR Improves Phenotypic Repeatability of Above-Ground Biomass and Crop Growth Rate in Wheat. PLANT PHENOMICS (WASHINGTON, D.C.) 2020; 2020:8329798. [PMID: 33313565 PMCID: PMC7706344 DOI: 10.34133/2020/8329798] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/15/2019] [Accepted: 05/06/2020] [Indexed: 05/19/2023]
Abstract
Highly repeatable, nondestructive, and high-throughput measures of above-ground biomass (AGB) and crop growth rate (CGR) are important for wheat improvement programs. This study evaluates the repeatability of destructive AGB and CGR measurements in comparison to two previously described methods for the estimation of AGB from LiDAR: 3D voxel index (3DVI) and 3D profile index (3DPI). Across three field experiments, contrasting in available water supply and comprising up to 98 wheat genotypes varying for canopy architecture, several concurrent measurements of LiDAR and AGB were made from jointing to anthesis. Phenotypic correlations at discrete events between AGB and the LiDAR-derived biomass indices were significant, ranging from 0.31 (P < 0.05) to 0.86 (P < 0.0001), providing confidence in the LiDAR indices as effective surrogates for AGB. The repeatability of the LiDAR biomass indices at discrete events was at least similar to and often higher than AGB, particularly under water limitation. The correlations between calculated CGR for AGB and the LiDAR indices were moderate to high and varied between experiments. However, across all experiments, the repeatabilities of the CGR derived from the LiDAR indices were appreciably greater than those for AGB, except for the 3DPI in the water-limited environment. In our experiments, the repeatability of either LiDAR index was consistently higher than that of AGB, both at discrete time points and when CGR was calculated. These findings provide promising support for the reliable use of ground-based LiDAR, as a surrogate measure of AGB and CGR, for screening germplasm in research and wheat breeding.
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46
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Belzile F, Abed A, Torkamaneh D. Time for a paradigm shift in the use of plant genetic resources. Genome 2019; 63:189-194. [PMID: 31825685 DOI: 10.1139/gen-2019-0141] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
For all major crops, sizeable genebanks are maintained across the world and serve as repositories of genetic diversity and key sources of novel traits used in breeding. Although molecular markers have been used to characterize diversity in a broad sense, the most common approach to exploring these resources has been through phenotypic characterization of subsets of these large collections. With the advent of affordable large-scale genotyping technologies and the increasing body of candidate genes for traits of interest, we argue here that it is time for a paradigm shift in the way that we explore and exploit these considerable and highly useful resources. By combining dense genotypic information in and around candidate genes, it is possible to classify accessions based on their haplotype, something approximating the actual alleles at these genes of interest.
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Affiliation(s)
- François Belzile
- Département de Phytologie and Institut de Biologie Intégrative et des Systèmes (IBIS), Université Laval, Québec City, QC, Canada
| | - Amina Abed
- Département de Phytologie and Institut de Biologie Intégrative et des Systèmes (IBIS), Université Laval, Québec City, QC, Canada.,Département de Phytologie and Institut de Biologie Intégrative et des Systèmes (IBIS), Université Laval, Québec City, QC, Canada
| | - Davoud Torkamaneh
- Département de Phytologie and Institut de Biologie Intégrative et des Systèmes (IBIS), Université Laval, Québec City, QC, Canada.,Département de Phytologie and Institut de Biologie Intégrative et des Systèmes (IBIS), Université Laval, Québec City, QC, Canada
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47
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Tresch L, Mu Y, Itoh A, Kaga A, Taguchi K, Hirafuji M, Ninomiya S, Guo W. Easy MPE: Extraction of Quality Microplot Images for UAV-Based High-Throughput Field Phenotyping. PLANT PHENOMICS (WASHINGTON, D.C.) 2019; 2019:2591849. [PMID: 33313523 PMCID: PMC7706339 DOI: 10.34133/2019/2591849] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/09/2019] [Accepted: 11/16/2019] [Indexed: 05/05/2023]
Abstract
Microplot extraction (PE) is a necessary image processing step in unmanned aerial vehicle- (UAV-) based research on breeding fields. At present, it is manually using ArcGIS, QGIS, or other GIS-based software, but achieving the desired accuracy is time-consuming. We therefore developed an intuitive, easy-to-use semiautomatic program for MPE called Easy MPE to enable researchers and others to access reliable plot data UAV images of whole fields under variable field conditions. The program uses four major steps: (1) binary segmentation, (2) microplot extraction, (3) production of ∗.shp files to enable further file manipulation, and (4) projection of individual microplots generated from the orthomosaic back onto the raw aerial UAV images to preserve the image quality. Crop rows were successfully identified in all trial fields. The performance of the proposed method was evaluated by calculating the intersection-over-union (IOU) ratio between microplots determined manually and by Easy MPE: the average IOU (±SD) of all trials was 91% (±3).
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Affiliation(s)
- Léa Tresch
- International Field Phenomics Research Laboratory, Institute for Sustainable Agro-Ecosystem Services, Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo, Japan
- Montpellier SupAgro, Montpellier, France
| | - Yue Mu
- Plant Phenomics Research Center, Nanjing Agricultural University, Nanjing, China
| | - Atsushi Itoh
- Memuro Upland Farming Research Station, Hokkaido Agricultural Research Center, National Agriculture and Food Research Organization, Hokkaido, Japan
| | - Akito Kaga
- Institute of Crop Science, National Agriculture and Food Research Organization, Tsukuba City, Ibaraki, Japan
| | - Kazunori Taguchi
- Memuro Upland Farming Research Station, Hokkaido Agricultural Research Center, National Agriculture and Food Research Organization, Hokkaido, Japan
| | - Masayuki Hirafuji
- International Field Phenomics Research Laboratory, Institute for Sustainable Agro-Ecosystem Services, Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo, Japan
| | - Seishi Ninomiya
- International Field Phenomics Research Laboratory, Institute for Sustainable Agro-Ecosystem Services, Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo, Japan
- Plant Phenomics Research Center, Nanjing Agricultural University, Nanjing, China
| | - Wei Guo
- International Field Phenomics Research Laboratory, Institute for Sustainable Agro-Ecosystem Services, Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo, Japan
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48
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Mahlein AK, Kuska MT, Thomas S, Wahabzada M, Behmann J, Rascher U, Kersting K. Quantitative and qualitative phenotyping of disease resistance of crops by hyperspectral sensors: seamless interlocking of phytopathology, sensors, and machine learning is needed! CURRENT OPINION IN PLANT BIOLOGY 2019; 50:156-162. [PMID: 31387067 DOI: 10.1016/j.pbi.2019.06.007] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/25/2019] [Revised: 06/22/2019] [Accepted: 06/24/2019] [Indexed: 05/21/2023]
Abstract
Determination and characterization of resistance reactions of crops against fungal pathogens are essential to select resistant genotypes. In plant breeding, phenotyping of genotypes is realized by time consuming and expensive visual plant ratings. During resistance reactions and during pathogenesis plants initiate different structural and biochemical defence mechanisms, which partly affect the optical properties of plant organs. Recently, intensive research has been conducted to develop innovative optical methods for an assessment of compatible and incompatible plant pathogen interaction. These approaches, combining classical phytopathology or microbiology with technology driven methods - such as sensors, robotics, machine learning, and artificial intelligence - are summarized by the term digital phenotyping. In contrast to common visual rating, detection and assessment methods, optical sensors in combination with advanced data analysis methods are able to retrieve pathogen induced changes in the physiology of susceptible or resistant plants non-invasively and objectively. Phenotyping disease resistance aims different tasks. In an early breeding step, a qualitative assessment and characterization of specific resistance action is aimed to link it, for example, to a genetic marker. Later, during greenhouse and field screening, the assessment of the level of susceptibility of different genotypes is relevant. Within this review, recent advances of digital phenotyping technologies for the detection of subtle resistance reactions and resistance breeding are highlighted and methodological requirements are critically discussed.
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Affiliation(s)
- Anne-Katrin Mahlein
- Institute for Sugar Beet Research, Germany; INRES Plant Disease, University Bonn, Germany.
| | | | | | | | - Jan Behmann
- INRES Plant Disease, University Bonn, Germany
| | | | - Kristian Kersting
- Department of Computer Science, Technical University Darmstadt, Germany
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49
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Dalal A, Bourstein R, Haish N, Shenhar I, Wallach R, Moshelion M. Dynamic Physiological Phenotyping of Drought-Stressed Pepper Plants Treated With "Productivity-Enhancing" and "Survivability-Enhancing" Biostimulants. FRONTIERS IN PLANT SCIENCE 2019; 10:905. [PMID: 31379898 PMCID: PMC6654182 DOI: 10.3389/fpls.2019.00905] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/20/2018] [Accepted: 06/26/2019] [Indexed: 05/02/2023]
Abstract
The improvement of crop productivity under abiotic stress is one of the biggest challenges faced by the agricultural scientific community. Despite extensive research, the research-to-commercial transfer rate of abiotic stress-resistant crops remains very low. This is mainly due to the complexity of genotype × environment interactions and in particular, the ability to quantify the dynamic plant physiological response profile to a dynamic environment. Most existing phenotyping facilities collect information using robotics and automated image acquisition and analysis. However, their ability to directly measure the physiological properties of the whole plant is limited. We demonstrate a high-throughput functional phenotyping system (HFPS) that enables comparing plants' dynamic responses to different ambient conditions in dynamic environments due to its direct and simultaneous measurement of yield-related physiological traits of plants under several treatments. The system is designed as one-to-one (1:1) plant-[sensors+controller] units, i.e., each individual plant has its own personalized sensor, controller and irrigation valves that enable (i) monitoring water-relation kinetics of each plant-environment response throughout the plant's life cycle with high spatiotemporal resolution, (ii) a truly randomized experimental design due to multiple independent treatment scenarios for every plant, and (iii) reduction of artificial ambient perturbations due to the immobility of the plants or other objects. In addition, we propose two new resilience-quantifying-related traits that can also be phenotyped using the HFPS: transpiration recovery rate and night water reabsorption. We use the HFPS to screen the effects of two commercial biostimulants (a seaweed extract -ICL-SW, and a metabolite formula - ICL-NewFo1) on Capsicum annuum under different irrigation regimes. Biostimulants are considered an alternative approach to improving crop productivity. However, their complex mode of action necessitates cost-effective pre-field phenotyping. The combination of two types of treatment (biostimulants and drought) enabled us to evaluate the precision and resolution of the system in investigating the effect of biostimulants on drought tolerance. We analyze and discuss plant behavior at different stages, and assess the penalty and trade-off between productivity and resilience. In this test case, we suggest a protocol for the screening of biostimulants' physiological mechanisms of action.
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Affiliation(s)
- Ahan Dalal
- The Robert H. Smith Faculty of Agriculture, Food and Environment, Institute of Plant Sciences and Genetics in Agriculture, The Hebrew University of Jerusalem, Rehovot, Israel
| | - Ronny Bourstein
- The Robert H. Smith Faculty of Agriculture, Food and Environment, Institute of Plant Sciences and Genetics in Agriculture, The Hebrew University of Jerusalem, Rehovot, Israel
- The Robert H. Smith Faculty of Agriculture, Food and Environment, The Department of Soil and Water Sciences, The Hebrew University of Jerusalem, Rehovot, Israel
| | - Nadav Haish
- The Robert H. Smith Faculty of Agriculture, Food and Environment, Institute of Plant Sciences and Genetics in Agriculture, The Hebrew University of Jerusalem, Rehovot, Israel
| | - Itamar Shenhar
- The Robert H. Smith Faculty of Agriculture, Food and Environment, Institute of Plant Sciences and Genetics in Agriculture, The Hebrew University of Jerusalem, Rehovot, Israel
| | - Rony Wallach
- The Robert H. Smith Faculty of Agriculture, Food and Environment, The Department of Soil and Water Sciences, The Hebrew University of Jerusalem, Rehovot, Israel
| | - Menachem Moshelion
- The Robert H. Smith Faculty of Agriculture, Food and Environment, Institute of Plant Sciences and Genetics in Agriculture, The Hebrew University of Jerusalem, Rehovot, Israel
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50
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Reynolds M, Schurr U. The 4th International Plant Phenotyping Symposium. PLANT SCIENCE : AN INTERNATIONAL JOURNAL OF EXPERIMENTAL PLANT BIOLOGY 2019; 282:1. [PMID: 31003605 DOI: 10.1016/j.plantsci.2019.01.025] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
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