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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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Neupane S, Wright DM, Martinez RO, Butler J, Weller JL, Bett KE. Focusing the GWAS Lens on days to flower using latent variable phenotypes derived from global multienvironment trials. THE PLANT GENOME 2023; 16:e20269. [PMID: 36284473 DOI: 10.1002/tpg2.20269] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Accepted: 08/25/2022] [Indexed: 05/10/2023]
Abstract
Adaptation constraints within crop species have resulted in limited genetic diversity in some breeding programs and areas where new crops have been introduced, for example, for lentil (Lens culinaris Medik.) in North America. An improved understanding of the underlying genetics involved in phenology-related traits is valuable knowledge to aid breeders in overcoming limitations associated with unadapted germplasm and expanding their genetic diversity by introducing new, exotic material. We used a large, 18 site-year, multienvironment dataset phenotyped for phenology-related traits across nine locations and over 3 yr along with accompanying latent variable phenotypes derived from a photothermal model and principal component analysis (PCA) of days from sowing to flower (DTF) data for a lentil diversity panel (324 accessions), which has also been genotyped with an exome capture array. Genome-wide association studies (GWAS) on DTF across multiple environments helped confirm associations with known flowering-time genes and identify new quantitative trait loci (QTL), which may contain previously unknown flowering time genes. Additionally, the use of latent variable phenotypes, which can incorporate environmental data such as temperature and photoperiod as both GWAS traits and as covariates, strengthened associations, revealed additional hidden associations, and alluded to potential roles of the associated QTL. Our approach can be replicated with other crop species, and the results from our GWAS serve as a resource for further exploration into the complex nature of phenology-related traits across the major growing environments for cultivated lentil.
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Affiliation(s)
- Sandesh Neupane
- Dep. of Plant Sciences, Univ. of Saskatchewan, Saskatoon, SK, S7N 5A8, Canada
| | - Derek M Wright
- Dep. of Plant Sciences, Univ. of Saskatchewan, Saskatoon, SK, S7N 5A8, Canada
| | - Raul O Martinez
- School of Natural Sciences, Univ. of Tasmania, Hobart, TAS, 7001, Australia
| | - Jakob Butler
- School of Natural Sciences, Univ. of Tasmania, Hobart, TAS, 7001, Australia
| | - James L Weller
- School of Natural Sciences, Univ. of Tasmania, Hobart, TAS, 7001, Australia
| | - Kirstin E Bett
- Dep. of Plant Sciences, Univ. of Saskatchewan, Saskatoon, SK, S7N 5A8, Canada
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Li J, Zhang Z, Chong K, Xu Y. Chilling tolerance in rice: Past and present. JOURNAL OF PLANT PHYSIOLOGY 2022; 268:153576. [PMID: 34875419 DOI: 10.1016/j.jplph.2021.153576] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Revised: 11/21/2021] [Accepted: 11/21/2021] [Indexed: 06/13/2023]
Abstract
Rice is generally sensitive to chilling stress, which seriously affects growth and yield. Since early in the last century, considerable efforts have been made to understand the physiological and molecular mechanisms underlying the response to chilling stress and improve rice chilling tolerance. Here, we review the research trends and advances in this field. The phenotypic and biochemical changes caused by cold stress and the physiological explanations are briefly summarized. Using published data from the past 20 years, we reviewed the past progress and important techniques in the identification of quantitative trait loci (QTL), novel genes, and cellular pathways involved in rice chilling tolerance. The advent of novel technologies has significantly advanced studies of cold tolerance, and the characterization of QTLs, key genes, and molecular modules have sped up molecular design breeding for cold tolerance in rice varieties. In addition to gene function studies based on overexpression or artificially generated mutants, elucidating natural allelic variation in specific backgrounds is emerging as a novel approach for the study of cold tolerance in rice, and the superior alleles identified using this approach can directly facilitate breeding.
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Affiliation(s)
- Junhua Li
- College of Life Sciences, Henan Normal University, Xinxiang, 453007, China
| | - Zeyong Zhang
- Ministry of Education Key Laboratory of Cell Activities and Stress Adaptations, School of Life Sciences, Lanzhou University, Lanzhou, 730000, China
| | - Kang Chong
- Key Laboratory of Plant Molecular Physiology, Institute of Botany, Chinese Academy of Sciences, Beijing, 100093, China
| | - Yunyuan Xu
- Key Laboratory of Plant Molecular Physiology, Institute of Botany, Chinese Academy of Sciences, Beijing, 100093, China.
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Kumar U, Morel J, Bergkvist G, Palosuo T, Gustavsson AM, Peake A, Brown H, Ahmed M, Parsons D. Comparative Analysis of Phenology Algorithms of the Spring Barley Model in APSIM 7.9 and APSIM Next Generation: A Case Study for High Latitudes. PLANTS 2021; 10:plants10030443. [PMID: 33652737 PMCID: PMC7996762 DOI: 10.3390/plants10030443] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Revised: 02/18/2021] [Accepted: 02/22/2021] [Indexed: 11/16/2022]
Abstract
Phenology algorithms in crop growth models have inevitable systematic errors and uncertainties. In this study, the phenology simulation algorithms in APSIM classical (APSIM 7.9) and APSIM next generation (APSIM-NG) were compared for spring barley models at high latitudes. Phenological data of twelve spring barley varieties were used for the 2014-2018 cropping seasons from northern Sweden and Finland. A factorial-based calibration approach provided within APSIM-NG was performed to calibrate both models. The models have different mechanisms to simulate days to anthesis. The calibration was performed separately for days to anthesis and physiological maturity, and evaluations for the calibrations were done with independent datasets. The calibration performance for both growth stages of APSIM-NG was better compared to APSIM 7.9. However, in the evaluation, APSIM-NG showed an inclination to overestimate days to physiological maturity. The differences between the models are possibly due to slower thermal time accumulation mechanism, with higher cardinal temperatures in APSIM-NG. For a robust phenology prediction at high latitudes with APSIM-NG, more research on the conception of thermal time computation and implementation is suggested.
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Affiliation(s)
- Uttam Kumar
- Department of Agricultural Research for Northern Sweden, Swedish University of Agricultural Science, 90183 Umeå, Sweden; (J.M.); (A.-M.G.); (M.A.); (D.P.)
- Correspondence: or ; Tel.: +46-767614185
| | - Julien Morel
- Department of Agricultural Research for Northern Sweden, Swedish University of Agricultural Science, 90183 Umeå, Sweden; (J.M.); (A.-M.G.); (M.A.); (D.P.)
| | - Göran Bergkvist
- Department of Crop Production and Ecology, Swedish University of Agricultural Science, 705007 Uppsala, Sweden;
| | - Taru Palosuo
- Natural Resources Institute Finland (Luke), FI-00790 Helsinki, Finland;
| | - Anne-Maj Gustavsson
- Department of Agricultural Research for Northern Sweden, Swedish University of Agricultural Science, 90183 Umeå, Sweden; (J.M.); (A.-M.G.); (M.A.); (D.P.)
| | - Allan Peake
- CSIRO Agriculture and Food, Canberra 2601, Australia;
| | - Hamish Brown
- The New Zealand Institute for Plant and Food Research Limited, Private Bag 4704, Christchurch 8140, New Zealand;
| | - Mukhtar Ahmed
- Department of Agricultural Research for Northern Sweden, Swedish University of Agricultural Science, 90183 Umeå, Sweden; (J.M.); (A.-M.G.); (M.A.); (D.P.)
| | - David Parsons
- Department of Agricultural Research for Northern Sweden, Swedish University of Agricultural Science, 90183 Umeå, Sweden; (J.M.); (A.-M.G.); (M.A.); (D.P.)
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Larue F, Fumey D, Rouan L, Soulié JC, Roques S, Beurier G, Luquet D. Modelling tiller growth and mortality as a sink-driven process using Ecomeristem: implications for biomass sorghum ideotyping. ANNALS OF BOTANY 2019; 124:675-690. [PMID: 30953443 PMCID: PMC6821234 DOI: 10.1093/aob/mcz038] [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: 09/14/2018] [Accepted: 02/28/2019] [Indexed: 06/01/2023]
Abstract
BACKGROUND AND AIMS Plant modelling can efficiently support ideotype conception, particularly in multi-criteria selection contexts. This is the case for biomass sorghum, implying the need to consider traits related to biomass production and quality. This study evaluated three modelling approaches for their ability to predict tiller growth, mortality and their impact, together with other morphological and physiological traits, on biomass sorghum ideotype prediction. METHODS Three Ecomeristem model versions were compared to evaluate whether tillering cessation and mortality were source (access to light) or sink (age-based hierarchical access to C supply) driven. They were tested using a field data set considering two biomass sorghum genotypes at two planting densities. An additional data set comparing eight genotypes was used to validate the best approach for its ability to predict the genotypic and environmental control of biomass production. A sensitivity analysis was performed to explore the impact of key genotypic parameters and define optimal parameter combinations depending on planting density and targeted production (sugar and fibre). KEY RESULTS The sink-driven control of tillering cessation and mortality was the most accurate, and represented the phenotypic variability of studied sorghum genotypes in terms of biomass production and partitioning between structural and non-structural carbohydrates. Model sensitivity analysis revealed that light conversion efficiency and stem diameter are key traits to target for improving sorghum biomass within existing genetic diversity. Tillering contribution to biomass production appeared highly genotype and environment dependent, making it a challenging trait for designing ideotypes. CONCLUSIONS By modelling tiller growth and mortality as sink-driven processes, Ecomeristem could predict and explore the genotypic and environmental variability of biomass sorghum production. Its application to larger sorghum genetic diversity considering water deficit regulations and its coupling to a genetic model will make it a powerful tool to assist ideotyping for current and future climatic scenario.
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Affiliation(s)
- Florian Larue
- CIRAD, UMR AGAP, PAM, Montpellier, France
- UMR AGAP, Université Montpellier, CIRAD, INRA, Montpellier SupAgro, Montpellier, France
| | | | - Lauriane Rouan
- CIRAD, UMR AGAP, PAM, Montpellier, France
- UMR AGAP, Université Montpellier, CIRAD, INRA, Montpellier SupAgro, Montpellier, France
| | - Jean-Christophe Soulié
- CIRAD, UR Recycling & Risk, Montpellier, France
- Recycling & Risk Unit, University of Montpellier, CIRAD, Montpellier, France
| | - Sandrine Roques
- CIRAD, UMR AGAP, PAM, Montpellier, France
- UMR AGAP, Université Montpellier, CIRAD, INRA, Montpellier SupAgro, Montpellier, France
| | - Grégory Beurier
- CIRAD, UMR AGAP, PAM, Montpellier, France
- UMR AGAP, Université Montpellier, CIRAD, INRA, Montpellier SupAgro, Montpellier, France
| | - Delphine Luquet
- CIRAD, UMR AGAP, PAM, Montpellier, France
- UMR AGAP, Université Montpellier, CIRAD, INRA, Montpellier SupAgro, Montpellier, France
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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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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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Role of Modelling in International Crop Research: Overview and Some Case Studies. AGRONOMY-BASEL 2018. [DOI: 10.3390/agronomy8120291] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
Crop modelling has the potential to contribute to global food and nutrition security. This paper briefly examines the history of crop modelling by international crop research centres of the CGIAR (formerly Consultative Group on International Agricultural Research but now known simply as CGIAR), whose primary focus is on less developed countries. Basic principles of crop modelling building up to a Genotype × Environment × Management × Socioeconomic (G × E × M × S) paradigm, are explained. Modelling has contributed to better understanding of crop performance and yield gaps, better prediction of pest and insect outbreaks, and improving the efficiency of crop management including irrigation systems and optimization of planting dates. New developments include, for example, use of remote sensed data and mobile phone technology linked to crop management decision support models, data sharing in the new era of big data, and the use of genomic selection and crop simulation models linked to environmental data to help make crop breeding decisions. Socio-economic applications include foresight analysis of agricultural systems under global change scenarios, and the consequences of potential food system shocks are also described. These approaches are discussed in this paper which also calls for closer collaboration among disciplines in order to better serve the crop research and development communities by providing model based recommendations ranging from policy development at the level of governmental agencies to direct crop management support for resource poor farmers.
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