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Garin V, Diallo C, Tékété ML, Théra K, Guitton B, Dagno K, Diallo AG, Kouressy M, Leiser W, Rattunde F, Sissoko I, Touré A, Nébié B, Samaké M, Kholovà J, Berger A, Frouin J, Pot D, Vaksmann M, Weltzien E, Témé N, Rami JF. Characterization of adaptation mechanisms in sorghum using a multireference back-cross nested association mapping design and envirotyping. Genetics 2024; 226:iyae003. [PMID: 38381593 PMCID: PMC10990433 DOI: 10.1093/genetics/iyae003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Accepted: 12/20/2023] [Indexed: 02/23/2024] Open
Abstract
Identifying the genetic factors impacting the adaptation of crops to environmental conditions is of key interest for conservation and selection purposes. It can be achieved using population genomics, and evolutionary or quantitative genetics. Here we present a sorghum multireference back-cross nested association mapping population composed of 3,901 lines produced by crossing 24 diverse parents to 3 elite parents from West and Central Africa-back-cross nested association mapping. The population was phenotyped in environments characterized by differences in photoperiod, rainfall pattern, temperature levels, and soil fertility. To integrate the multiparental and multi-environmental dimension of our data we proposed a new approach for quantitative trait loci (QTL) detection and parental effect estimation. We extended our model to estimate QTL effect sensitivity to environmental covariates, which facilitated the integration of envirotyping data. Our models allowed spatial projections of the QTL effects in agro-ecologies of interest. We utilized this strategy to analyze the genetic architecture of flowering time and plant height, which represents key adaptation mechanisms in environments like West Africa. Our results allowed a better characterization of well-known genomic regions influencing flowering time concerning their response to photoperiod with Ma6 and Ma1 being photoperiod-sensitive and the region of possible candidate gene Elf3 being photoperiod-insensitive. We also accessed a better understanding of plant height genetic determinism with the combined effects of phenology-dependent (Ma6) and independent (qHT7.1 and Dw3) genomic regions. Therefore, we argue that the West and Central Africa-back-cross nested association mapping and the presented analytical approach constitute unique resources to better understand adaptation in sorghum with direct application to develop climate-smart varieties.
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Affiliation(s)
- Vincent Garin
- Crop Physiology Laboratory, International Crops Research Institute for the Semi-Arid Tropics, Patancheru, 502 324, India
- CIRAD, UMR AGAP Institut, Montpellier, F-34398, France
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, F-34398, France
| | - Chiaka Diallo
- Sorghum Program, International Crops Research Institute for the Semi-Arid Tropics, Bamako, BP 320, Mali
- Département d’Enseignement et de Recherche des Sciences et Techniques Agricoles, Institut polytechnique rural de formation et de recherche appliquée de Katibougou, Koulikoro, BP 06, Mali
| | - Mohamed Lamine Tékété
- Institut d’Economie Rurale, Bamako, BP 262, Mali
- Faculté des Sciences et Techniques, Université des Sciences des Techniques et des Technologies de Bamako, Bamako, BP E 3206, Mali
| | | | - Baptiste Guitton
- CIRAD, UMR AGAP Institut, Montpellier, F-34398, France
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, F-34398, France
| | - Karim Dagno
- Institut d’Economie Rurale, Bamako, BP 262, Mali
| | | | | | - Willmar Leiser
- Sorghum Program, International Crops Research Institute for the Semi-Arid Tropics, Bamako, BP 320, Mali
| | - Fred Rattunde
- Agronomy Department, University of Wisconsin, Madison, WI 53705, WI, USA
| | - Ibrahima Sissoko
- Sorghum Program, International Crops Research Institute for the Semi-Arid Tropics, Bamako, BP 320, Mali
| | - Aboubacar Touré
- Sorghum Program, International Crops Research Institute for the Semi-Arid Tropics, Bamako, BP 320, Mali
| | - Baloua Nébié
- Dryland Crops Program, International Maize and Wheat Improvement Center (CIMMYT-Senegal) U/C CERAAS, Thiès, Po Box 3320, Senegal
| | - Moussa Samaké
- Faculté des Sciences et Techniques, Université des Sciences des Techniques et des Technologies de Bamako, Bamako, BP E 3206, Mali
| | - Jana Kholovà
- Crop Physiology Laboratory, International Crops Research Institute for the Semi-Arid Tropics, Patancheru, 502 324, India
- Department of Information Technologies, Faculty of Economics and Management, Czech University of Life Sciences, Prague, 165 00, Czech Republic
| | - Angélique Berger
- CIRAD, UMR AGAP Institut, Montpellier, F-34398, France
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, F-34398, France
| | - Julien Frouin
- CIRAD, UMR AGAP Institut, Montpellier, F-34398, France
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, F-34398, France
| | - David Pot
- CIRAD, UMR AGAP Institut, Montpellier, F-34398, France
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, F-34398, France
| | - Michel Vaksmann
- CIRAD, UMR AGAP Institut, Montpellier, F-34398, France
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, F-34398, France
| | - Eva Weltzien
- Sorghum Program, International Crops Research Institute for the Semi-Arid Tropics, Bamako, BP 320, Mali
- Agronomy Department, University of Wisconsin, Madison, WI 53705, WI, USA
| | - Niaba Témé
- Institut d’Economie Rurale, Bamako, BP 262, Mali
| | - Jean-François Rami
- CIRAD, UMR AGAP Institut, Montpellier, F-34398, France
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, F-34398, France
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Daryani P, Amirbakhtiar N, Soorni J, Loni F, Darzi Ramandi H, Shobbar ZS. Uncovering the Genomic Regions Associated with Yield Maintenance in Rice Under Drought Stress Using an Integrated Meta-Analysis Approach. RICE (NEW YORK, N.Y.) 2024; 17:7. [PMID: 38227151 DOI: 10.1186/s12284-024-00684-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/01/2023] [Accepted: 01/03/2024] [Indexed: 01/17/2024]
Abstract
The complex trait of yield is controlled by several quantitative trait loci (QTLs). Given the global water deficit issue, the development of rice varieties suitable for non-flooded cultivation holds significant importance in breeding programs. The powerful approach of Meta-QTL (MQTL) analysis can be used for the genetic dissection of complicated quantitative traits. In the current study, a comprehensive MQTL analysis was conducted to identify consistent QTL regions associated with drought tolerance and yield-related traits under water deficit conditions in rice. In total, 1087 QTLs from 134 rice populations, published between 2000 to 2021, were utilized in the analysis. Distinct MQTL analysis of the relevant traits resulted in the identification of 213 stable MQTLs. The confidence interval (CI) for the detected MQTLs was between 0.12 and 19.7 cM. The average CI of the identified MQTLs (4.68 cM) was 2.74 times narrower compared to the average CI of the initial QTLs. Interestingly, 63 MQTLs coincided with SNP peak positions detected by genome-wide association studies for yield and drought tolerance-associated traits under water deficit conditions in rice. Considering the genes located both in the QTL-overview peaks and the SNP peak positions, 19 novel candidate genes were introduced, which are associated with drought response index, plant height, panicle number, biomass, and grain yield. Moreover, an inclusive MQTL analysis was performed on all the traits to obtain "Breeding MQTLs". This analysis resulted in the identification of 96 MQTLs with a CI ranging from 0.01 to 9.0 cM. The mean CI of the obtained MQTLs (2.33 cM) was 4.66 times less than the mean CI of the original QTLs. Thirteen MQTLs fulfilling the criteria of having more than 10 initial QTLs, CI < 1 cM, and an average phenotypic variance explained greater than 10%, were designated as "Breeding MQTLs". These findings hold promise for assisting breeders in enhancing rice yield under drought stress conditions.
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Affiliation(s)
- Parisa Daryani
- Department of Systems Biology, Agricultural Biotechnology Research Institute of Iran (ABRII), Agricultural Research, Education and Extension Organization (AREEO), Karaj, Iran
| | - Nazanin Amirbakhtiar
- National Plant Gene Bank of Iran, Seed and Plant Improvement Institute (SPII), Agricultural Research, Education and Extension Organization (AREEO), Karaj, Iran
| | - Jahad Soorni
- Department of Systems Biology, Agricultural Biotechnology Research Institute of Iran (ABRII), Agricultural Research, Education and Extension Organization (AREEO), Karaj, Iran
| | - Fatemeh Loni
- Department of Systems Biology, Agricultural Biotechnology Research Institute of Iran (ABRII), Agricultural Research, Education and Extension Organization (AREEO), Karaj, Iran
| | - Hadi Darzi Ramandi
- Department of Plant Production and Genetics, Faculty of Agriculture, Bu-Ali Sina University, Hamedan, Iran.
| | - Zahra-Sadat Shobbar
- Department of Systems Biology, Agricultural Biotechnology Research Institute of Iran (ABRII), Agricultural Research, Education and Extension Organization (AREEO), Karaj, Iran.
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Sankarapillai LV, Vijayaraghavareddy P, Nanaiah K, Arpitha GD, Chaitanya PM, Sathishraj R, Shindhe D, Vemanna RS, Yin X, Struik PC, Sreeman S. Phenotyping and metabolome analysis reveal the role of AdoMetDC and Di19 genes in determining acquired tolerance to drought in rice. PHYSIOLOGIA PLANTARUM 2023; 175:e13992. [PMID: 37882292 DOI: 10.1111/ppl.13992] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Revised: 06/29/2023] [Accepted: 08/03/2023] [Indexed: 10/27/2023]
Abstract
Water-saving attempts for rice cultivation often reduce yields. Maintaining productivity under drought is possible when rice genotypes are bred with improved metabolism and spikelet fertility. Although attempts have been made to introgress water mining and water use efficiency traits, combining acquired tolerance traits (ATTs), that is, specific traits induced or upregulated to better tolerate severe stress, appears equally important. In our study, we screened 90 rice germplasm accessions that represented the molecular and phenotypic variations of 851 lines of the 3 K rice panel. Utilising phenomics, we identified markers linked to ATTs through association analysis of over 0.2 million SNPs derived from whole-genome sequences. Propensity to respond to 'induction' stress varied significantly among genotypes, reflecting differences in cellular protection against oxidative stress. Among the ATTs, the hydroxyl radical and proline contents exhibited the highest variability. Furthermore, these significant variations in ATTs were strongly correlated with spikelet fertility. The 43 significant markers associated with ATTs were further validated using a different subset of contrasting genotypes. Gene expression studies and metabolomic profiling of two well-known contrasting genotypes, APO (tolerant) and IR64 (sensitive), identified two ATT genes: AdoMetDC and Di19. Our study highlights the relevance of polyamine biosynthesis in modulating ATTs in rice. Genotypes with superior ATTs and the associated markers can be effectively employed in breeding rice varieties with sustained spikelet fertility and grain yield under drought.
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Affiliation(s)
| | - Preethi Vijayaraghavareddy
- Department of Crop Physiology, University of Agricultural Sciences, Bengaluru, India
- Centre for Crop Systems Analysis, Department of Plant Sciences, Wageningen University & Research, Wageningen, the Netherlands
| | - Karthik Nanaiah
- Department of Crop Physiology, University of Agricultural Sciences, Bengaluru, India
| | | | | | - Rajendran Sathishraj
- Wheat Genetics Resource Center and Department of Plant Pathology, Kansas State University, Manhattan, Kansas, USA
| | - Dhananjay Shindhe
- Department of Pathology and Microbiology, University of Nebraska Medical Centre, Omaha, Nebraska, USA
| | - Ramu S Vemanna
- Regional Centre for Biotechnology, Faridabad, Haryana, India
| | - Xinyou Yin
- Centre for Crop Systems Analysis, Department of Plant Sciences, Wageningen University & Research, Wageningen, the Netherlands
| | - Paul C Struik
- Centre for Crop Systems Analysis, Department of Plant Sciences, Wageningen University & Research, Wageningen, the Netherlands
| | - Sheshshayee Sreeman
- Department of Crop Physiology, University of Agricultural Sciences, Bengaluru, India
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4
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Jighly A, Weeks A, Christy B, O'Leary GJ, Kant S, Aggarwal R, Hessel D, Forrest KL, Technow F, Tibbits JFG, Totir R, Spangenberg GC, Hayden MJ, Munkvold J, Daetwyler HD. Integrating biophysical crop growth models and whole genome prediction for their mutual benefit: a case study in wheat phenology. JOURNAL OF EXPERIMENTAL BOTANY 2023; 74:4415-4426. [PMID: 37177829 DOI: 10.1093/jxb/erad162] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Accepted: 05/12/2023] [Indexed: 05/15/2023]
Abstract
Running crop growth models (CGM) coupled with whole genome prediction (WGP) as a CGM-WGP model introduces environmental information to WGP and genomic relatedness information to the genotype-specific parameters modelled through CGMs. Previous studies have primarily used CGM-WGP to infer prediction accuracy without exploring its potential to enhance CGM and WGP. Here, we implemented a heading and maturity date wheat phenology model within a CGM-WGP framework and compared it with CGM and WGP. The CGM-WGP resulted in more heritable genotype-specific parameters with more biologically realistic correlation structures between genotype-specific parameters and phenology traits compared with CGM-modelled genotype-specific parameters that reflected the correlation of measured phenotypes. Another advantage of CGM-WGP is the ability to infer accurate prediction with much smaller and less diverse reference data compared with that required for CGM. A genome-wide association analysis linked the genotype-specific parameters from the CGM-WGP model to nine significant phenology loci including Vrn-A1 and the three PPD1 genes, which were not detected for CGM-modelled genotype-specific parameters. Selection on genotype-specific parameters could be simpler than on observed phenotypes. For example, thermal time traits are theoretically more independent candidates, compared with the highly correlated heading and maturity dates, which could be used to achieve an environment-specific optimal flowering period. CGM-WGP combines the advantages of CGM and WGP to predict more accurate phenotypes for new genotypes under alternative or future environmental conditions.
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Affiliation(s)
- Abdulqader Jighly
- Agriculture Victoria, AgriBio, Centre for AgriBiosciences, Bundoora, VIC 3083, Australia
| | - Anna Weeks
- Agriculture Victoria, Rutherglen Centre, Rutherglen, VIC 3685, Australia
| | - Brendan Christy
- Agriculture Victoria, Rutherglen Centre, Rutherglen, VIC 3685, Australia
| | - Garry J O'Leary
- Agriculture Victoria, Grains Innovation Park, Horsham, VIC 3400, Australia
- Centre for Agricultural Innovation, The University of Melbourne, Parkville, VIC 3010Australia
| | - Surya Kant
- Agriculture Victoria, Grains Innovation Park, Horsham, VIC 3400, Australia
- School of Applied Systems Biology, La Trobe University, Bundoora, VIC 3083, Australia
| | | | | | - Kerrie L Forrest
- Agriculture Victoria, AgriBio, Centre for AgriBiosciences, Bundoora, VIC 3083, Australia
| | | | - Josquin F G Tibbits
- Agriculture Victoria, AgriBio, Centre for AgriBiosciences, Bundoora, VIC 3083, Australia
| | | | - German C Spangenberg
- Agriculture Victoria, AgriBio, Centre for AgriBiosciences, Bundoora, VIC 3083, Australia
- School of Applied Systems Biology, La Trobe University, Bundoora, VIC 3083, Australia
| | - Matthew J Hayden
- Agriculture Victoria, AgriBio, Centre for AgriBiosciences, Bundoora, VIC 3083, Australia
- School of Applied Systems Biology, La Trobe University, Bundoora, VIC 3083, Australia
| | | | - Hans D Daetwyler
- Agriculture Victoria, AgriBio, Centre for AgriBiosciences, Bundoora, VIC 3083, Australia
- School of Applied Systems Biology, La Trobe University, Bundoora, VIC 3083, Australia
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5
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Li Y, Tao F, Hao Y, Tong J, Xiao Y, Zhang H, He Z, Reynolds M. Linking genetic markers with an eco-physiological model to pyramid favourable alleles and design wheat ideotypes. PLANT, CELL & ENVIRONMENT 2023; 46:780-795. [PMID: 36517924 DOI: 10.1111/pce.14518] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/14/2022] [Accepted: 12/12/2022] [Indexed: 06/17/2023]
Abstract
Genetic markers can be linked with eco-physiological crop models to accurately predict genotype performance and individual markers' contributions in target environments, exploring interactions between genotype and environment. Here, wheat (Triticum aestivum L.) yield was dissected into seven traits corresponding to cultivar genetic coefficients in an eco-physiological model. Loci for these traits were discovered through the genome-wide association studies (GWAS). The cultivar genetic coefficients were derived from the loci using multiple linear regression or random forest, building a marker-based eco-physiological model. It is then applied to simulate wheat yields and design virtual ideotypes. The results indicated that the loci identified through GWAS explained 46%-75% variations in cultivar genetic coefficients. Using the marker-based model, the normalized root mean square error (nRMSE) between the simulated yield and observed yield was 13.95% by multiple linear regression and 13.62% by random forest. The nRMSE between the simulated and observed maturity dates was 1.24% by multiple linear regression and 1.11% by random forest, respectively. Structural equation modelling indicated that variations in grain yield could be well explained by cultivar genetic coefficients and phenological data. In addition, 24 pleiotropic loci in this study were detected on 15 chromosomes. More significant loci were detected by the model-based dissection method than considering yield per se. Ideotypes were identified by higher yield and more favourable alleles of cultivar genetic traits. This study proposes a genotype-to-phenotype approach and demonstrates novel ideas and tools to support the effective breeding of new cultivars with high yield through pyramiding favourable alleles and designing crop ideotypes.
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Affiliation(s)
- Yibo Li
- Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Fulu Tao
- Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Yuanfeng Hao
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Jingyang Tong
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Yonggui Xiao
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - He Zhang
- Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
| | - Zhonghu He
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Matthew Reynolds
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
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6
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Burgess AJ, Masclaux‐Daubresse C, Strittmatter G, Weber APM, Taylor SH, Harbinson J, Yin X, Long S, Paul MJ, Westhoff P, Loreto F, Ceriotti A, Saltenis VLR, Pribil M, Nacry P, Scharff LB, Jensen PE, Muller B, Cohan J, Foulkes J, Rogowsky P, Debaeke P, Meyer C, Nelissen H, Inzé D, Klein Lankhorst R, Parry MAJ, Murchie EH, Baekelandt A. Improving crop yield potential: Underlying biological processes and future prospects. Food Energy Secur 2022; 12:e435. [PMID: 37035025 PMCID: PMC10078444 DOI: 10.1002/fes3.435] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Revised: 10/07/2022] [Accepted: 11/10/2022] [Indexed: 12/05/2022] Open
Abstract
The growing world population and global increases in the standard of living both result in an increasing demand for food, feed and other plant-derived products. In the coming years, plant-based research will be among the major drivers ensuring food security and the expansion of the bio-based economy. Crop productivity is determined by several factors, including the available physical and agricultural resources, crop management, and the resource use efficiency, quality and intrinsic yield potential of the chosen crop. This review focuses on intrinsic yield potential, since understanding its determinants and their biological basis will allow to maximize the plant's potential in food and energy production. Yield potential is determined by a variety of complex traits that integrate strictly regulated processes and their underlying gene regulatory networks. Due to this inherent complexity, numerous potential targets have been identified that could be exploited to increase crop yield. These encompass diverse metabolic and physical processes at the cellular, organ and canopy level. We present an overview of some of the distinct biological processes considered to be crucial for yield determination that could further be exploited to improve future crop productivity.
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Affiliation(s)
- Alexandra J. Burgess
- School of Biosciences University of Nottingham, Sutton Bonington campus Loughborough UK
| | | | - Günter Strittmatter
- Institute of Plant Biochemistry, Cluster of Excellence on Plant Sciences (CEPLAS) Heinrich‐Heine‐Universität Düsseldorf Düsseldorf Germany
| | - Andreas P. M. Weber
- Institute of Plant Biochemistry, Cluster of Excellence on Plant Sciences (CEPLAS) Heinrich‐Heine‐Universität Düsseldorf Düsseldorf Germany
| | | | - Jeremy Harbinson
- Laboratory for Biophysics Wageningen University and Research Wageningen The Netherlands
| | - Xinyou Yin
- Centre for Crop Systems Analysis, Department of Plant Sciences Wageningen University & Research Wageningen The Netherlands
| | - Stephen Long
- Lancaster Environment Centre Lancaster University Lancaster UK
- Plant Biology and Crop Sciences University of Illinois at Urbana‐Champaign Urbana Illinois USA
| | | | - Peter Westhoff
- Institute of Plant Biochemistry, Cluster of Excellence on Plant Sciences (CEPLAS) Heinrich‐Heine‐Universität Düsseldorf Düsseldorf Germany
| | - Francesco Loreto
- Department of Biology, Agriculture and Food Sciences, National Research Council of Italy (CNR), Rome, Italy and University of Naples Federico II Napoli Italy
| | - Aldo Ceriotti
- Institute of Agricultural Biology and Biotechnology National Research Council (CNR) Milan Italy
| | - Vandasue L. R. Saltenis
- Copenhagen Plant Science Centre, Department of Plant and Environmental Sciences University of Copenhagen Copenhagen Denmark
| | - Mathias Pribil
- Copenhagen Plant Science Centre, Department of Plant and Environmental Sciences University of Copenhagen Copenhagen Denmark
| | - Philippe Nacry
- BPMP, Univ Montpellier, INRAE, CNRS Institut Agro Montpellier France
| | - Lars B. Scharff
- Copenhagen Plant Science Centre, Department of Plant and Environmental Sciences University of Copenhagen Copenhagen Denmark
| | - Poul Erik Jensen
- Department of Food Science University of Copenhagen Copenhagen Denmark
| | - Bertrand Muller
- Université de Montpellier ‐ LEPSE – INRAE Institut Agro Montpellier France
| | | | - John Foulkes
- School of Biosciences University of Nottingham, Sutton Bonington campus Loughborough UK
| | - Peter Rogowsky
- INRAE UMR Plant Reproduction and Development Lyon France
| | | | - Christian Meyer
- IJPB UMR1318 INRAE‐AgroParisTech‐Université Paris Saclay Versailles France
| | - Hilde Nelissen
- Department of Plant Biotechnology and Bioinformatics Ghent University Ghent Belgium
- VIB Center for Plant Systems Biology Ghent Belgium
| | - Dirk Inzé
- Department of Plant Biotechnology and Bioinformatics Ghent University Ghent Belgium
- VIB Center for Plant Systems Biology Ghent Belgium
| | - René Klein Lankhorst
- Wageningen Plant Research Wageningen University & Research Wageningen The Netherlands
| | | | - Erik H. Murchie
- School of Biosciences University of Nottingham, Sutton Bonington campus Loughborough UK
| | - Alexandra Baekelandt
- Department of Plant Biotechnology and Bioinformatics Ghent University Ghent Belgium
- VIB Center for Plant Systems Biology Ghent Belgium
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7
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Harbinson J, Yin X. Modelling the impact of improved photosynthetic properties on crop performance in Europe. Food Energy Secur 2022. [DOI: 10.1002/fes3.402] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Affiliation(s)
- Jeremy Harbinson
- Laboratory for Biophysics Wageningen University and Research Wageningen The Netherlands
| | - Xinyou Yin
- Centre for Crop Systems Analysis Department of Plant Sciences Wageningen University and Research Wageningen The Netherlands
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8
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Yin X, Gu J, Dingkuhn M, Struik PC. A model-guided holistic review of exploiting natural variation of photosynthesis traits in crop improvement. JOURNAL OF EXPERIMENTAL BOTANY 2022; 73:3173-3188. [PMID: 35323898 PMCID: PMC9126731 DOI: 10.1093/jxb/erac109] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Accepted: 03/22/2022] [Indexed: 05/18/2023]
Abstract
Breeding for improved leaf photosynthesis is considered as a viable approach to increase crop yield. Whether it should be improved in combination with other traits has not been assessed critically. Based on the quantitative crop model GECROS that interconnects various traits to crop productivity, we review natural variation in relevant traits, from biochemical aspects of leaf photosynthesis to morpho-physiological crop characteristics. While large phenotypic variations (sometimes >2-fold) for leaf photosynthesis and its underlying biochemical parameters were reported, few quantitative trait loci (QTL) were identified, accounting for a small percentage of phenotypic variation. More QTL were reported for sink size (that feeds back on photosynthesis) or morpho-physiological traits (that affect canopy productivity and duration), together explaining a much greater percentage of their phenotypic variation. Traits for both photosynthetic rate and sustaining it during grain filling were strongly related to nitrogen-related traits. Much of the molecular basis of known photosynthesis QTL thus resides in genes controlling photosynthesis indirectly. Simulation using GECROS demonstrated the overwhelming importance of electron transport parameters, compared with the maximum Rubisco activity that largely determines the commonly studied light-saturated photosynthetic rate. Exploiting photosynthetic natural variation might significantly improve crop yield if nitrogen uptake, sink capacity, and other morpho-physiological traits are co-selected synergistically.
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Affiliation(s)
- Xinyou Yin
- Centre for Crop Systems Analysis, Wageningen University & Research, PO Box 430, 6700 AK Wageningen, The Netherlands
- Correspondence:
| | - Junfei Gu
- College of Agriculture, Yangzhou University, 48 Wenhui East Road, Yangzhou, Jiangsu 225009, China
| | | | - Paul C Struik
- Centre for Crop Systems Analysis, Wageningen University & Research, PO Box 430, 6700 AK Wageningen, The Netherlands
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9
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Onogi A. Integration of Crop Growth Models and Genomic Prediction. Methods Mol Biol 2022; 2467:359-396. [PMID: 35451783 DOI: 10.1007/978-1-0716-2205-6_13] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Crop growth models (CGMs) consist of multiple equations that represent physiological processes of plants and simulate crop growth dynamically given environmental inputs. Because parameters of CGMs are often genotype-specific, gene effects can be related to environmental inputs through CGMs. Thus, CGMs are attractive tools for predicting genotype by environment (G×E) interactions. This chapter reviews CGMs, genetic analyses using these models, and the status of studies that integrate genomic prediction with CGMs. Examples of CGM analyses are also provided.
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Affiliation(s)
- Akio Onogi
- Department of Plant Life Science, Faculty of Agriculture, Ryukoku University, Otsu, Shiga, Japan.
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10
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Progressive Genomic Approaches to Explore Drought- and Salt-Induced Oxidative Stress Responses in Plants under Changing Climate. PLANTS 2021; 10:plants10091910. [PMID: 34579441 PMCID: PMC8471759 DOI: 10.3390/plants10091910] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Revised: 09/10/2021] [Accepted: 09/11/2021] [Indexed: 11/17/2022]
Abstract
Drought and salinity are the major environmental abiotic stresses that negatively impact crop development and yield. To improve yields under abiotic stress conditions, drought- and salinity-tolerant crops are key to support world crop production and mitigate the demand of the growing world population. Nevertheless, plant responses to abiotic stresses are highly complex and controlled by networks of genetic and ecological factors that are the main targets of crop breeding programs. Several genomics strategies are employed to improve crop productivity under abiotic stress conditions, but traditional techniques are not sufficient to prevent stress-related losses in productivity. Within the last decade, modern genomics studies have advanced our capabilities of improving crop genetics, especially those traits relevant to abiotic stress management. This review provided updated and comprehensive knowledge concerning all possible combinations of advanced genomics tools and the gene regulatory network of reactive oxygen species homeostasis for the appropriate planning of future breeding programs, which will assist sustainable crop production under salinity and drought conditions.
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11
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Dingkuhn M, Luquet D, Fabre D, Muller B, Yin X, Paul MJ. The case for improving crop carbon sink strength or plasticity for a CO 2-rich future. CURRENT OPINION IN PLANT BIOLOGY 2020; 56:259-272. [PMID: 32682621 DOI: 10.1016/j.pbi.2020.05.012] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/10/2020] [Revised: 05/13/2020] [Accepted: 05/29/2020] [Indexed: 06/11/2023]
Abstract
Atmospheric CO2 concentration [CO2] has increased from 260 to 280μmolmol-1 (level during crop domestication up to the industrial revolution) to currently 400 and will reach 550μmolmol-1 by 2050. C3 crops are expected to benefit from elevated [CO2] (e-CO2) thanks to photosynthesis responsiveness to [CO2] but this may require greater sink capacity. We review recent literature on crop e-CO2 responses, related source-sink interactions, how abiotic stresses potentially interact, and prospects to improve e-CO2 response via breeding or genetic engineering. Several lines of evidence suggest that e-CO2 responsiveness is related either to sink intrinsic capacity or adaptive plasticity, for example, involving enhanced branching. Wild relatives and old cultivars mostly showed lower photosynthetic rates, less downward acclimation of photosynthesis to e-CO2 and responded strongly to e-CO2 due to greater phenotypic plasticity. While reverting to such archaic traits would be an inappropriate strategy for breeding, we argue that substantial enhancement of vegetative sink vigor, inflorescence size and/or number and root sinks will be necessary to fully benefit from e-CO2. Potential ideotype features based on enhanced sinks are discussed. The generic 'feast-famine' sugar signaling pathway may be suited to engineer sink strength tissue-specifically and stage-specifically and help validate ideotype concepts. Finally, we argue that models better accounting for acclimation to e-CO2 are needed to predict which trait combinations should be targeted by breeders for a CO2-rich world.
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Affiliation(s)
| | | | - Denis Fabre
- CIRAD, UMR 108 AGAP, F-34398 Montpellier, France
| | - Bertrand Muller
- INRAE, UMR 759 LEPSE, Institut de Biologie Intégrative des Plantes, F-34060 Montpellier, France
| | - Xinyou Yin
- Centre for Crop Systems Analysis, Dept. Plant Sciences, Wageningen University & Research, Wageningen, The Netherlands
| | - Matthew J Paul
- Plant Science, Rothamsted Research, Harpenden, Hertfordshire, AL5 2JQ, United Kingdom
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12
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Peng B, Guan K, Tang J, Ainsworth EA, Asseng S, Bernacchi CJ, Cooper M, Delucia EH, Elliott JW, Ewert F, Grant RF, Gustafson DI, Hammer GL, Jin Z, Jones JW, Kimm H, Lawrence DM, Li Y, Lombardozzi DL, Marshall-Colon A, Messina CD, Ort DR, Schnable JC, Vallejos CE, Wu A, Yin X, Zhou W. Towards a multiscale crop modelling framework for climate change adaptation assessment. NATURE PLANTS 2020; 6:338-348. [PMID: 32296143 DOI: 10.1038/s41477-020-0625-3] [Citation(s) in RCA: 53] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/08/2019] [Accepted: 02/24/2020] [Indexed: 05/18/2023]
Abstract
Predicting the consequences of manipulating genotype (G) and agronomic management (M) on agricultural ecosystem performances under future environmental (E) conditions remains a challenge. Crop modelling has the potential to enable society to assess the efficacy of G × M technologies to mitigate and adapt crop production systems to climate change. Despite recent achievements, dedicated research to develop and improve modelling capabilities from gene to global scales is needed to provide guidance on designing G × M adaptation strategies with full consideration of their impacts on both crop productivity and ecosystem sustainability under varying climatic conditions. Opportunities to advance the multiscale crop modelling framework include representing crop genetic traits, interfacing crop models with large-scale models, improving the representation of physiological responses to climate change and management practices, closing data gaps and harnessing multisource data to improve model predictability and enable identification of emergent relationships. A fundamental challenge in multiscale prediction is the balance between process details required to assess the intervention and predictability of the system at the scales feasible to measure the impact. An advanced multiscale crop modelling framework will enable a gene-to-farm design of resilient and sustainable crop production systems under a changing climate at regional-to-global scales.
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Affiliation(s)
- Bin Peng
- Department of Natural Resources and Environmental Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, USA.
- National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, Urbana, IL, USA.
| | - Kaiyu Guan
- Department of Natural Resources and Environmental Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, USA.
- National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, Urbana, IL, USA.
- Institute for Sustainability, Energy, and Environment, University of Illinois at Urbana-Champaign, Urbana, IL, USA.
- Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, IL, USA.
| | - Jinyun Tang
- Climate Sciences Department, Climate and Ecosystem Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Elizabeth A Ainsworth
- Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Department of Plant Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- USDA ARS Global Change and Photosynthesis Research Unit, Urbana, IL, USA
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Senthold Asseng
- Agricultural and Biological Engineering Department, University of Florida, Gainesville, FL, USA
| | - Carl J Bernacchi
- Institute for Sustainability, Energy, and Environment, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Department of Plant Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- USDA ARS Global Change and Photosynthesis Research Unit, Urbana, IL, USA
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Mark Cooper
- Centre for Crop Science, Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, Queensland, Australia
| | - Evan H Delucia
- Department of Natural Resources and Environmental Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Institute for Sustainability, Energy, and Environment, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Department of Plant Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Joshua W Elliott
- Department of Computer Science, University of Chicago, Chicago, IL, USA
| | - Frank Ewert
- Crop Science Group, INRES, University of Bonn, Bonn, Germany
- Leibniz Centre for Agricultural Landscape Research (ZALF), Müncheberg, Germany
| | - Robert F Grant
- Department of Renewable Resources, University of Alberta, Edmonton, Alberta, Canada
| | | | - Graeme L Hammer
- Centre for Crop Science, Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, Queensland, Australia
- Australian Research Council Centre of Excellence for Translational Photosynthesis, The University of Queensland, Brisbane, Queensland, Australia
| | - Zhenong Jin
- Department of Bioproducts and Biosystems Engineering, University of Minnesota-Twin Cities, St. Paul, MN, USA
| | - James W Jones
- Agricultural and Biological Engineering Department, University of Florida, Gainesville, FL, USA
| | - Hyungsuk Kimm
- Department of Natural Resources and Environmental Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | | | - Yan Li
- State Key Laboratory of Earth Surface Processes and Resources Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing, China
| | | | - Amy Marshall-Colon
- National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Institute for Sustainability, Energy, and Environment, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Department of Plant Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | | | - Donald R Ort
- Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Department of Plant Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Department of Crop Science, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - James C Schnable
- Department of Agronomy & Horticulture, University of Nebraska-Lincoln, Lincoln, NE, USA
- Center for Plant Science Innovation, University of Nebraska-Lincoln, Lincoln, NE, USA
| | - C Eduardo Vallejos
- Horticultural Sciences Department, University of Florida, Gainesville, FL, USA
| | - Alex Wu
- Centre for Crop Science, Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, Queensland, Australia
- Australian Research Council Centre of Excellence for Translational Photosynthesis, The University of Queensland, Brisbane, Queensland, Australia
| | - Xinyou Yin
- Centre for Crop Systems Analysis, Department of Plant Sciences, Wageningen University & Research, Wageningen, The Netherlands
| | - Wang Zhou
- Department of Natural Resources and Environmental Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, USA
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13
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Kadam NN, Jagadish SVK, Struik PC, van der Linden CG, Yin X. Incorporating genome-wide association into eco-physiological simulation to identify markers for improving rice yields. JOURNAL OF EXPERIMENTAL BOTANY 2019; 70:2575-2586. [PMID: 30882149 PMCID: PMC6487590 DOI: 10.1093/jxb/erz120] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/14/2019] [Accepted: 03/11/2019] [Indexed: 05/22/2023]
Abstract
We explored the use of the eco-physiological crop model GECROS to identify markers for improved rice yield under well-watered (control) and water deficit conditions. Eight model parameters were measured from the control in one season for 267 indica genotypes. The model accounted for 58% of yield variation among genotypes under control and 40% under water deficit conditions. Using 213 randomly selected genotypes as the training set, 90 single nucleotide polymorphism (SNP) loci were identified using a genome-wide association study (GWAS), explaining 42-77% of crop model parameter variation. SNP-based parameter values estimated from the additive loci effects were fed into the model. For the training set, the SNP-based model accounted for 37% (control) and 29% (water deficit) of yield variation, less than the 78% explained by a statistical genomic prediction (GP) model for the control treatment. Both models failed in predicting yields of the 54 testing genotypes. However, compared with the GP model, the SNP-based crop model was advantageous when simulating yields under either control or water stress conditions in an independent season. Crop model sensitivity analysis ranked the SNP loci for their relative importance in accounting for yield variation, and the rank differed greatly between control and water deficit environments. Crop models have the potential to use single-environment information for predicting phenotypes under different environments.
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Affiliation(s)
- Niteen N Kadam
- Centre for Crop Systems Analysis, Department of Plant Sciences, Wageningen University & Research, AK Wageningen, The Netherlands
- International Rice Research Institute, Metro Manila, Philippines
| | - S V Krishna Jagadish
- International Rice Research Institute, Metro Manila, Philippines
- Department of Agronomy, Kansas State University, Manhattan, KS, USA
| | - Paul C Struik
- Centre for Crop Systems Analysis, Department of Plant Sciences, Wageningen University & Research, AK Wageningen, The Netherlands
| | - C Gerard van der Linden
- Plant Breeding, Department of Plant Sciences, Wageningen University & Research, AJ Wageningen, The Netherlands
| | - Xinyou Yin
- Centre for Crop Systems Analysis, Department of Plant Sciences, Wageningen University & Research, AK Wageningen, The Netherlands
- Correspondence:
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14
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Wang E, Brown HE, Rebetzke GJ, Zhao Z, Zheng B, Chapman SC. Improving process-based crop models to better capture genotype×environment×management interactions. JOURNAL OF EXPERIMENTAL BOTANY 2019; 70:2389-2401. [PMID: 30921457 DOI: 10.1093/jxb/erz092] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/23/2018] [Accepted: 03/05/2019] [Indexed: 05/07/2023]
Abstract
In spite of the increasing expectation for process-based crop modelling to capture genotype (G) by environment (E) by management (M) interactions to support breeding selections, it remains a challenge to use current crop models to accurately predict phenotypes from genotypes or from candidate genes. We use wheat as a target crop and the APSIM farming systems model (Holzworth et al., 2014) as an example to analyse the current status of process-based crop models with a major focus on need to improve simulation of specific eco-physiological processes and their linkage to underlying genetic controls. For challenging production environments in Australia, we examine the potential opportunities to capture physiological traits, and to integrate genetic and molecular approaches for future model development and applications. Model improvement will require both reducing the uncertainty in simulating key physiological processes and enhancing the capture of key observable traits and underlying genetic control of key physiological responses to environment. An approach consisting of three interactive stages is outlined to (i) improve modelling of crop physiology, (ii) develop linkage from model parameter to genotypes and further to loci or alleles, and (iii) further link to gene expression pathways. This helps to facilitate the integration of modelling, phenotyping, and functional gene detection and to effectively advance modelling of G×E×M interactions. While gene-based modelling is not always needed to simulate G×E×M, including well-understood gene effects can improve the estimation of genotype effects and prediction of phenotypes. Specific examples are given for enhanced modelling of wheat in the APSIM framework.
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Affiliation(s)
- Enli Wang
- CSIRO Agriculture & Food, Canberra, ACT, Australia
| | - Hamish E Brown
- The New Zealand Institute for Plant & Food Research Limited, Private Bag, Christchurch, New Zealand
| | | | - Zhigan Zhao
- CSIRO Agriculture & Food, Canberra, ACT, Australia
| | - Bangyou Zheng
- CSIRO Agriculture & Food, Queensland Biosciences Precinct, St Lucia, QLD, Australia
| | - Scott C Chapman
- CSIRO Agriculture & Food, Queensland Biosciences Precinct, St Lucia, QLD, Australia
- School of Agriculture and Food Sciences, The University of Queensland, Gatton, QLD, Australia
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15
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Hwang C, Correll M, Gezan S, Zhang L, Bhakta M, Vallejos C, Boote K, Clavijo-Michelangeli J, Jones J. Next generation crop models: A modular approach to model early vegetative and reproductive development of the common bean ( Phaseolus vulgaris L). AGRICULTURAL SYSTEMS 2017; 155:225-239. [PMID: 28701815 PMCID: PMC5485674 DOI: 10.1016/j.agsy.2016.10.010] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/06/2015] [Revised: 10/18/2016] [Accepted: 10/24/2016] [Indexed: 05/22/2023]
Abstract
The next generation of gene-based crop models offers the potential of predicting crop vegetative and reproductive development based on genotype and weather data as inputs. Here, we illustrate an approach for developing a dynamic modular gene-based model to simulate changes in main stem node numbers, time to first anthesis, and final node number on the main stem of common bean (Phaseolus vulgaris L.). In the modules, these crop characteristics are functions of relevant genes (quantitative trait loci (QTL)), the environment (E), and QTL × E interactions. The model was based on data from 187 recombinant inbred (RI) genotypes and the two parents grown at five sites (Citra, FL; Palmira, Colombia; Popayan, Colombia; Isabela Puerto Rico; and Prosper, North Dakota). The model consists of three dynamic QTL effect models for node addition rate (NAR, No. d- 1), daily rate of progress from emergence toward flowering (RF), and daily maximum main stem node number (MSNODmax), that were integrated to simulate main stem node number vs. time, and date of first flower using daily time steps. Model evaluation with genotypes not used in model development showed reliable predictions across all sites for time to first anthesis (R2 = 0.75) and main stem node numbers during the linear phase of node addition (R2 = 0.93), while prediction of the final main stem node number was less reliable (R2 = 0.27). The use of mixed-effects models to analyze multi-environment data from a wide range of genotypes holds considerable promise for assisting development of dynamic QTL effect models capable of simulating vegetative and reproductive development.
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Affiliation(s)
- C. Hwang
- Agricultural & Biological Engineering Dept., University of Florida, FL, USA
| | - M.J. Correll
- Agricultural & Biological Engineering Dept., University of Florida, FL, USA
- Corresponding author.
| | - S.A. Gezan
- School of Forest Resources & Conservation, University of Florida, FL, USA
| | - L. Zhang
- Agricultural & Biological Engineering Dept., University of Florida, FL, USA
| | - M.S. Bhakta
- Horticultural Sciences Dept., University of Florida, FL, USA
| | - C.E. Vallejos
- Horticultural Sciences Dept., University of Florida, FL, USA
| | - K.J. Boote
- Agricultural & Biological Engineering Dept., University of Florida, FL, USA
| | | | - J.W. Jones
- Agricultural & Biological Engineering Dept., University of Florida, FL, USA
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16
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Yin X, Struik PC. Can increased leaf photosynthesis be converted into higher crop mass production? A simulation study for rice using the crop model GECROS. JOURNAL OF EXPERIMENTAL BOTANY 2017; 68:2345-2360. [PMID: 28379522 PMCID: PMC5447886 DOI: 10.1093/jxb/erx085] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/06/2023]
Abstract
Various genetic engineering routes to enhance C3 leaf photosynthesis have been proposed to improve crop productivity. However, their potential contribution to crop productivity needs to be assessed under realistic field conditions. Using 31 year weather data, we ran the crop model GECROS for rice in tropical, subtropical, and temperate environments, to evaluate the following routes: (1) improving mesophyll conductance (gm); (2) improving Rubisco specificity (Sc/o); (3) improving both gm and Sc/o; (4) introducing C4 biochemistry; (5) introducing C4 Kranz anatomy that effectively minimizes CO2 leakage; (6) engineering the complete C4 mechanism; (7) engineering cyanobacterial bicarbonate transporters; (8) engineering a more elaborate cyanobacterial CO2-concentrating mechanism (CCM) with the carboxysome in the chloroplast; and (9) a mechanism that combines the low ATP cost of the cyanobacterial CCM and the high photosynthetic capacity per unit leaf nitrogen. All routes improved crop mass production, but benefits from Routes 1, 2, and 7 were ≤10%. Benefits were higher in the presence than in the absence of drought, and under the present climate than for the climate predicted for 2050. Simulated crop mass differences resulted not only from the increased canopy photosynthesis competence but also from changes in traits such as light interception and crop senescence. The route combinations gave larger effects than the sum of the effects of the single routes, but only Route 9 could bring an advantage of ≥50% under any environmental conditions. To supercharge crop productivity, exploring a combination of routes in improving the CCM, photosynthetic capacity, and quantum efficiency is required.
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Affiliation(s)
- Xinyou Yin
- Centre for Crop Systems Analysis, Department of Plant Sciences, Wageningen University & Research, PO Box 430, 6700 AK Wageningen, The Netherlands
| | - Paul C Struik
- Centre for Crop Systems Analysis, Department of Plant Sciences, Wageningen University & Research, PO Box 430, 6700 AK Wageningen, The Netherlands
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17
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Affiliation(s)
- Paul C Struik
- Centre for Crop Systems Analysis, Plant Sciences Group, Wageningen University and Research, Droevendaalsesteeg 1, 6708 PB Wageningen, the Netherlands
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18
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Perez RPA, Pallas B, Le Moguédec G, Rey H, Griffon S, Caliman JP, Costes E, Dauzat J. Integrating mixed-effect models into an architectural plant model to simulate inter- and intra-progeny variability: a case study on oil palm (Elaeis guineensis Jacq.). JOURNAL OF EXPERIMENTAL BOTANY 2016; 67:4507-21. [PMID: 27302128 DOI: 10.1093/jxb/erw203] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/13/2023]
Abstract
Three-dimensional (3D) reconstruction of plants is time-consuming and involves considerable levels of data acquisition. This is possibly one reason why the integration of genetic variability into 3D architectural models has so far been largely overlooked. In this study, an allometry-based approach was developed to account for architectural variability in 3D architectural models of oil palm (Elaeis guineensis Jacq.) as a case study. Allometric relationships were used to model architectural traits from individual leaflets to the entire crown while accounting for ontogenetic and morphogenetic gradients. Inter- and intra-progeny variabilities were evaluated for each trait and mixed-effect models were used to estimate the mean and variance parameters required for complete 3D virtual plants. Significant differences in leaf geometry (petiole length, density of leaflets, and rachis curvature) and leaflet morphology (gradients of leaflet length and width) were detected between and within progenies and were modelled in order to generate populations of plants that were consistent with the observed populations. The application of mixed-effect models on allometric relationships highlighted an interesting trade-off between model accuracy and ease of defining parameters for the 3D reconstruction of plants while at the same time integrating their observed variability. Future research will be dedicated to sensitivity analyses coupling the structural model presented here with a radiative balance model in order to identify the key architectural traits involved in light interception efficiency.
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Affiliation(s)
| | - Benoît Pallas
- INRA, UMR 1334 AGAP, 34398 Montpellier Cedex 5, France
| | | | - Hervé Rey
- CIRAD, UMR AMAP, Montpellier, F-34000 France
| | | | | | | | - Jean Dauzat
- CIRAD, UMR AMAP, Montpellier, F-34000 France
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19
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Onogi A, Watanabe M, Mochizuki T, Hayashi T, Nakagawa H, Hasegawa T, Iwata H. Toward integration of genomic selection with crop modelling: the development of an integrated approach to predicting rice heading dates. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2016; 129:805-817. [PMID: 26791836 DOI: 10.1007/s00122-016-2667-5] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/19/2015] [Accepted: 01/09/2016] [Indexed: 05/28/2023]
Abstract
It is suggested that accuracy in predicting plant phenotypes can be improved by integrating genomic prediction with crop modelling in a single hierarchical model. Accurate prediction of phenotypes is important for plant breeding and management. Although genomic prediction/selection aims to predict phenotypes on the basis of whole-genome marker information, it is often difficult to predict phenotypes of complex traits in diverse environments, because plant phenotypes are often influenced by genotype-environment interaction. A possible remedy is to integrate genomic prediction with crop/ecophysiological modelling, which enables us to predict plant phenotypes using environmental and management information. To this end, in the present study, we developed a novel method for integrating genomic prediction with phenological modelling of Asian rice (Oryza sativa, L.), allowing the heading date of untested genotypes in untested environments to be predicted. The method simultaneously infers the phenological model parameters and whole-genome marker effects on the parameters in a Bayesian framework. By cultivating backcross inbred lines of Koshihikari × Kasalath in nine environments, we evaluated the potential of the proposed method in comparison with conventional genomic prediction, phenological modelling, and two-step methods that applied genomic prediction to phenological model parameters inferred from Nelder-Mead or Markov chain Monte Carlo algorithms. In predicting heading dates of untested lines in untested environments, the proposed and two-step methods tended to provide more accurate predictions than the conventional genomic prediction methods, particularly in environments where phenotypes from environments similar to the target environment were unavailable for training genomic prediction. The proposed method showed greater accuracy in prediction than the two-step methods in all cross-validation schemes tested, suggesting the potential of the integrated approach in the prediction of phenotypes of plants.
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Affiliation(s)
- Akio Onogi
- Department of Agricultural and Environmental Biology, Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1 Yayoi, Bunkyo-ku, Tokyo, 113-8657, Japan
| | - Maya Watanabe
- Department of Agricultural and Environmental Biology, Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1 Yayoi, Bunkyo-ku, Tokyo, 113-8657, Japan
| | | | - Takeshi Hayashi
- National Agriculture and Food Research Organization Agricultural Research Center, Tsukuba, Ibaraki, 305-8666, Japan
| | - Hiroshi Nakagawa
- National Agriculture and Food Research Organization Agricultural Research Center, Tsukuba, Ibaraki, 305-8666, Japan
| | - Toshihiro Hasegawa
- National Institute for Agro-Environmental Sciences, Tsukuba, Ibaraki, 305-8604, Japan
| | - Hiroyoshi Iwata
- Department of Agricultural and Environmental Biology, Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1 Yayoi, Bunkyo-ku, Tokyo, 113-8657, Japan.
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20
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Anten NPR, Vermeulen PJ. Tragedies and Crops: Understanding Natural Selection To Improve Cropping Systems. Trends Ecol Evol 2016; 31:429-439. [PMID: 27012675 DOI: 10.1016/j.tree.2016.02.010] [Citation(s) in RCA: 56] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2015] [Revised: 02/10/2016] [Accepted: 02/11/2016] [Indexed: 11/16/2022]
Abstract
Plant communities with traits that would maximize community performance can be invaded by plants that invest extra in acquiring resources at the expense of others, lowering the overall community performance, a so-called tragedy of the commons (TOC). By contrast, maximum community performance is usually the objective in agriculture. We first give an overview of the occurrence of TOCs in plants, and explore the extent to which past crop breeding has led to trait values that go against an unwanted TOC. We then show how linking evolutionary game theory (EGT) with mechanistic knowledge of the physiological processes that drive trait expression and the ecological aspects of biotic interactions in agro-ecosystems might contribute to increasing crop yields and resource-use efficiency.
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Affiliation(s)
- Niels P R Anten
- Centre for Crop Systems Analysis, Wageningen University, PO Box 430, AK 6700 Wageningen, The Netherlands.
| | - Peter J Vermeulen
- Centre for Crop Systems Analysis, Wageningen University, PO Box 430, AK 6700 Wageningen, The Netherlands
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21
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Wuyts N, Dhondt S, Inzé D. Measurement of plant growth in view of an integrative analysis of regulatory networks. CURRENT OPINION IN PLANT BIOLOGY 2015; 25:90-97. [PMID: 26002069 DOI: 10.1016/j.pbi.2015.05.002] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/19/2014] [Revised: 04/17/2015] [Accepted: 05/01/2015] [Indexed: 05/29/2023]
Abstract
As the regulatory networks of growth at the cellular level are elucidated at a fast pace, their complexity is not reduced; on the contrary, the tissue, organ and even whole-plant level affect cell proliferation and expansion by means of development-induced and environment-induced signaling events in growth regulatory processes. Measurement of growth across different levels aids in gaining a mechanistic understanding of growth, and in defining the spatial and temporal resolution of sampling strategies for molecular analyses in the model Arabidopsis thaliana and increasingly also in crop species. The latter claim their place at the forefront of plant research, since global issues and future needs drive the translation from laboratory model-acquired knowledge of growth processes to improvements in crop productivity in field conditions.
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Affiliation(s)
- Nathalie Wuyts
- Department of Plant Systems Biology, VIB, Technologiepark 927, 9052 Gent, Belgium; Department of Plant Biotechnology and Bioinformatics, Ghent University, Technologiepark 927, 9052 Gent, Belgium
| | - Stijn Dhondt
- Department of Plant Systems Biology, VIB, Technologiepark 927, 9052 Gent, Belgium; Department of Plant Biotechnology and Bioinformatics, Ghent University, Technologiepark 927, 9052 Gent, Belgium
| | - Dirk Inzé
- Department of Plant Systems Biology, VIB, Technologiepark 927, 9052 Gent, Belgium; Department of Plant Biotechnology and Bioinformatics, Ghent University, Technologiepark 927, 9052 Gent, Belgium.
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