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Piepho HP, Williams E. Factor-Analytic Variance-Covariance Structures for Prediction Into a Target Population of Environments. Biom J 2024; 66:e202400008. [PMID: 39049627 DOI: 10.1002/bimj.202400008] [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: 01/11/2024] [Revised: 06/04/2024] [Accepted: 06/06/2024] [Indexed: 07/27/2024]
Abstract
Finlay-Wilkinson regression is a popular method for modeling genotype-environment interaction in plant breeding and crop variety testing. When environment is a random factor, this model may be cast as a factor-analytic variance-covariance structure, implying a regression on random latent environmental variables. This paper reviews such models with a focus on their use in the analysis of multi-environment trials for the purpose of making predictions in a target population of environments. We investigate the implication of random versus fixed effects assumptions, starting from basic analysis-of-variance models, then moving on to factor-analytic models and considering the transition to models involving observable environmental covariates, which promise to provide more accurate and targeted predictions than models with latent environmental variables.
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Affiliation(s)
- Hans-Peter Piepho
- Biostatistics Unit, Institute of Crop Science, University of Hohenheim, Stuttgart, Germany
| | - Emlyn Williams
- Statistical Support Network, Australian National University, Canberra, ACT, Australia
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2
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Chaves SFS, Damacena MB, Dias KOG, de Almada Oliveira CV, Bhering LL. Factor analytic selection tools and environmental feature-integration enable holistic decision-making in Eucalyptus breeding. Sci Rep 2024; 14:18429. [PMID: 39117704 PMCID: PMC11310510 DOI: 10.1038/s41598-024-69299-2] [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] [Received: 05/30/2024] [Accepted: 08/02/2024] [Indexed: 08/10/2024] Open
Abstract
Understanding the genotype-by-environment interaction (GEI) and considering it in the selection process is a sine qua non condition for the expansion of Brazilian eucalyptus silviculture. This study's objective is to select high-performance and stable eucalyptus clones based on a novel selection index that considers the Factor Analytic Selection Tools (FAST) and the clone's reliability. The investigation explores the nuances interplay of GEI and extends its insights by scrutinizing the relationship between latent factors and real environmental features. The analysis, conducted across seven trials in five Brazilian states involving 78 clones, employs FAST. The clonal selection was performed using an extended FAST index weighted by the clone's reliability. Further insights about GEI emerge from the integration of factor loadings with 25 environmental features through a principal component analysis. Ten clones, distinguished by high performance, stability, and reliability, have been selected across the target population of environments. The environmental features most closely associated with factor loadings, encompassing air temperature, radiation, and soil characteristics, emerge as pivotal drivers of GEI within this dataset. This study contributes insights to eucalyptus breeders, equipping them to enhance decision-making by harnessing a holistic understanding-from the genotypes under evaluation to the diverse environments anticipated in commercial plantations.
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Affiliation(s)
- Saulo F S Chaves
- Federal University of Viçosa, Department of Agronomy, Viçosa, MG, Brazil
| | | | - Kaio Olimpio G Dias
- Federal University of Viçosa, Department of General Biology, Viçosa, MG, Brazil
| | | | - Leonardo L Bhering
- Federal University of Viçosa, Department of General Biology, Viçosa, MG, Brazil.
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Resende RT, Xavier A, Silva PIT, Resende MPM, Jarquin D, Marcatti GE. GIS-based G × E modeling of maize hybrids through enviromic markers engineering. THE NEW PHYTOLOGIST 2024. [PMID: 39014516 DOI: 10.1111/nph.19951] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/22/2024] [Accepted: 06/22/2024] [Indexed: 07/18/2024]
Abstract
Through enviromics, precision breeding leverages innovative geotechnologies to customize crop varieties to specific environments, potentially improving both crop yield and genetic selection gains. In Brazil's four southernmost states, data from 183 distinct geographic field trials (also accounting for 2017-2021) covered information on 164 genotypes: 79 phenotyped maize hybrid genotypes for grain yield and their 85 nonphenotyped parents. Additionally, 1342 envirotypic covariates from weather, soil, sensor-based, and satellite sources were collected to engineer 10 K synthetic enviromic markers via machine learning. Soil, radiation light, and surface temperature variations remarkably affect differential genotype yield, hinting at ecophysiological adjustments including evapotranspiration and photosynthesis. The enviromic ensemble-based random regression model showcases superior predictive performance and efficiency compared to the baseline and kernel models, matching the best genotypes to specific geographic coordinates. Clustering analysis has identified regions that minimize genotype-environment (G × E) interactions. These findings underscore the potential of enviromics in crafting specific parental combinations to breed new, higher-yielding hybrid crops. The adequate use of envirotypic information can enhance the precision and efficiency of maize breeding by providing important inputs about the environmental factors that affect the average crop performance. Generating enviromic markers associated with grain yield can enable a better selection of hybrids for specific environments.
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Affiliation(s)
- Rafael T Resende
- Plant Breeding Sector, School of Agronomy (EA), Federal University of Goiás (UFG), Av. Esperança, s/n, Samambaia Campus, Goiânia, GO, 74690-900, Brazil
- TheCROP, A Precision Breeding Project, Av. Esperança, n° 1533, FUNAPE, Samambaia Technological Park, Samambaia Campus - UFG, Goiânia, GO, 74690-612, Brazil
| | - Alencar Xavier
- Corteva Agriscience, 8305 NW 62ndAve, Johnston, IA, 50131, USA
- Purdue University, 915 Mitch Daniels Blvd, West Lafayette, IN, 47907, USA
| | | | - Marcela P M Resende
- Plant Breeding Sector, School of Agronomy (EA), Federal University of Goiás (UFG), Av. Esperança, s/n, Samambaia Campus, Goiânia, GO, 74690-900, Brazil
| | - Diego Jarquin
- University of Florida, 1604 McCarty Drive G052B McCarty Hall D, Gainesville, FL, 32611, USA
| | - Gustavo E Marcatti
- TheCROP, A Precision Breeding Project, Av. Esperança, n° 1533, FUNAPE, Samambaia Technological Park, Samambaia Campus - UFG, Goiânia, GO, 74690-612, Brazil
- Forest Engineering Department, Federal University of São João del Rei (UFSJ), Sete Lagoas Campus, MG-424 Highway, Km 47, Sete Lagoas, MG, 35701-970, Brazil
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Groli EL, Frascaroli E, Maccaferri M, Ammar K, Tuberosa R. Dissecting the effect of heat stress on durum wheat under field conditions. FRONTIERS IN PLANT SCIENCE 2024; 15:1393349. [PMID: 39006958 PMCID: PMC11239346 DOI: 10.3389/fpls.2024.1393349] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/28/2024] [Accepted: 05/30/2024] [Indexed: 07/16/2024]
Abstract
Introduction Heat stress negatively affects wheat production in several ways, mainly by reducing growth rate, photosynthetic capacity and reducing spike fertility. Modeling stress response means analyzing simultaneous relationships among traits affecting the whole plant response and determinants of grain yield. The aim of this study was to dissect the diverse impacts of heat stress on key yield traits and to identify the most promising sources of alleles for heat tolerance. Methods We evaluated a diverse durum wheat panel of 183 cultivars and breeding lines from worldwide, for their response to long-term heat stress under field conditions (HS) with respect to non stress conditions (NS), considering phenological traits, grain yield (GY) and its components as a function of the timing of heat stress and climatic covariates. We investigated the relationships among plant and environmental variables by means of a structural equation model (SEM) and Genetic SEM (GSEM). Results Over two years of experiments at CENEB, CIMMYT, the effects of HS were particularly pronounced for the normalized difference vegetation index, NDVI (-51.3%), kernel weight per spike, KWS (-40.5%), grain filling period, GFP (-38.7%), and GY (-56.6%). Average temperatures around anthesis were negatively correlated with GY, thousand kernel weight TKW and test weight TWT, but also with spike density, a trait determined before heading/anthesis. Under HS, the correlation between the three major determinants of GY, i.e., fertile spike density, spike fertility and kernel size, were of noticeable magnitude. NDVI measured at medium milk-soft dough stage under HS was correlated with both spike fertility and grain weight while under NS it was less predictive of grain weight but still highly correlated with spike fertility. GSEM modeling suggested that the causal model of performance under HS directly involves genetic effects on GY, NDVI, KWS and HD. Discussion We identified consistently suitable sources of genetic resistance to heat stress to be used in different durum wheat pre-breeding programs. Among those, Desert Durums and CIMMYT'80 germplasm showed the highest degree of adaptation and capacity to yield under high temperatures and can be considered as a valuable source of alleles for adaptation to breed new HS resilient cultivars.
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Affiliation(s)
- Eder Licieri Groli
- Department of Agricultural and Food Sciences, DISTAL, University of Bologna, Bologna, Italy
| | - Elisabetta Frascaroli
- Department of Agricultural and Food Sciences, DISTAL, University of Bologna, Bologna, Italy
| | - Marco Maccaferri
- Department of Agricultural and Food Sciences, DISTAL, University of Bologna, Bologna, Italy
| | - Karim Ammar
- International Maize and Wheat Improvement Center, CIMMYT, El Batán, Mexico
| | - Roberto Tuberosa
- Department of Agricultural and Food Sciences, DISTAL, University of Bologna, Bologna, Italy
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Carvalho HF, Rio S, García-Abadillo J, Isidro Y Sánchez J. Revisiting superiority and stability metrics of cultivar performances using genomic data: derivations of new estimators. PLANT METHODS 2024; 20:85. [PMID: 38844940 PMCID: PMC11155189 DOI: 10.1186/s13007-024-01207-1] [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: 11/27/2023] [Accepted: 05/08/2024] [Indexed: 06/10/2024]
Abstract
The selection of highly productive genotypes with stable performance across environments is a major challenge of plant breeding programs due to genotype-by-environment (GE) interactions. Over the years, different metrics have been proposed that aim at characterizing the superiority and/or stability of genotype performance across environments. However, these metrics are traditionally estimated using phenotypic values only and are not well suited to an unbalanced design in which genotypes are not observed in all environments. The objective of this research was to propose and evaluate new estimators of the following GE metrics: Ecovalence, Environmental Variance, Finlay-Wilkinson regression coefficient, and Lin-Binns superiority measure. Drawing from a multi-environment genomic prediction model, we derived the best linear unbiased prediction for each GE metric. These derivations included both a squared expectation and a variance term. To assess the effectiveness of our new estimators, we conducted simulations that varied in traits and environment parameters. In our results, new estimators consistently outperformed traditional phenotype-based estimators in terms of accuracy. By incorporating a variance term into our new estimators, in addition to the squared expectation term, we were able to improve the precision of our estimates, particularly for Ecovalence in situations where heritability was low and/or sparseness was high. All methods are implemented in a new R-package: GEmetrics. These genomic-based estimators enable estimating GE metrics in unbalanced designs and predicting GE metrics for new genotypes, which should help improve the selection efficiency of high-performance and stable genotypes across environments.
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Affiliation(s)
- Humberto Fanelli Carvalho
- Centro de Biotecnología y Genómica de Plantas (CBGP, UPM-INIA)-Universidad Politécnica de Madrid (UPM)-Instituto Nacional de Investigación y Tecnologia Agraria y Alimentaria (INIA), Campus de Montegancedo-UPM, 28223, Pozuelo de Alarcón, Madrid, Spain
| | - Simon Rio
- CIRAD, UMR AGAP Institut, 34398, Montpellier, France
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, France
| | - Julian García-Abadillo
- Centro de Biotecnología y Genómica de Plantas (CBGP, UPM-INIA)-Universidad Politécnica de Madrid (UPM)-Instituto Nacional de Investigación y Tecnologia Agraria y Alimentaria (INIA), Campus de Montegancedo-UPM, 28223, Pozuelo de Alarcón, Madrid, Spain
| | - Julio Isidro Y Sánchez
- Centro de Biotecnología y Genómica de Plantas (CBGP, UPM-INIA)-Universidad Politécnica de Madrid (UPM)-Instituto Nacional de Investigación y Tecnologia Agraria y Alimentaria (INIA), Campus de Montegancedo-UPM, 28223, Pozuelo de Alarcón, Madrid, Spain.
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Resende RT, Hickey L, Amaral CH, Peixoto LL, Marcatti GE, Xu Y. Satellite-enabled enviromics to enhance crop improvement. MOLECULAR PLANT 2024; 17:848-866. [PMID: 38637991 DOI: 10.1016/j.molp.2024.04.005] [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: 11/15/2023] [Revised: 04/04/2024] [Accepted: 04/11/2024] [Indexed: 04/20/2024]
Abstract
Enviromics refers to the characterization of micro- and macroenvironments based on large-scale environmental datasets. By providing genotypic recommendations with predictive extrapolation at a site-specific level, enviromics could inform plant breeding decisions across varying conditions and anticipate productivity in a changing climate. Enviromics-based integration of statistics, envirotyping (i.e., determining environmental factors), and remote sensing could help unravel the complex interplay of genetics, environment, and management. To support this goal, exhaustive envirotyping to generate precise environmental profiles would significantly improve predictions of genotype performance and genetic gain in crops. Already, informatics management platforms aggregate diverse environmental datasets obtained using optical, thermal, radar, and light detection and ranging (LiDAR)sensors that capture detailed information about vegetation, surface structure, and terrain. This wealth of information, coupled with freely available climate data, fuels innovative enviromics research. While enviromics holds immense potential for breeding, a few obstacles remain, such as the need for (1) integrative methodologies to systematically collect field data to scale and expand observations across the landscape with satellite data; (2) state-of-the-art AI models for data integration, simulation, and prediction; (3) cyberinfrastructure for processing big data across scales and providing seamless interfaces to deliver forecasts to stakeholders; and (4) collaboration and data sharing among farmers, breeders, physiologists, geoinformatics experts, and programmers across research institutions. Overcoming these challenges is essential for leveraging the full potential of big data captured by satellites to transform 21st century agriculture and crop improvement through enviromics.
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Affiliation(s)
- Rafael T Resende
- Universidade Federal de Goiás (UFG), Agronomy Department, Plant Breeding Sector, Goiânia (GO) 74690-900, Brazil; TheCROP, a Precision-Breeding Startup: Enviromics, Phenomics, and Genomics, No Zip-code, Operating Virtually, Goiânia (GO) and Sete Lagoas (MG), Brazil.
| | - Lee Hickey
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, QLD, Australia
| | - Cibele H Amaral
- Earth Lab, Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, Boulder, CO 80303, USA; Environmental Data Science Innovation & Inclusion Lab, Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, Boulder, CO 80303, USA
| | - Lucas L Peixoto
- Universidade Federal de Goiás (UFG), Agronomy Department, Plant Breeding Sector, Goiânia (GO) 74690-900, Brazil
| | - Gustavo E Marcatti
- TheCROP, a Precision-Breeding Startup: Enviromics, Phenomics, and Genomics, No Zip-code, Operating Virtually, Goiânia (GO) and Sete Lagoas (MG), Brazil; Universidade Federal de São João del-Rei, Forest Engineering Department, Campus Sete Lagoas, Sete Lagoas (MG) 35701-970, Brazil
| | - Yunbi Xu
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China; Peking University Institute of Advanced Agricultural Sciences, Weifang, Shandong 261325, China; BGI Bioverse, Shenzhen 518083, China.
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Peixoto MA, Leach KA, Jarquin D, Flannery P, Zystro J, Tracy WF, Bhering L, Resende MFR. Utilizing genomic prediction to boost hybrid performance in a sweet corn breeding program. FRONTIERS IN PLANT SCIENCE 2024; 15:1293307. [PMID: 38726298 PMCID: PMC11080654 DOI: 10.3389/fpls.2024.1293307] [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: 09/12/2023] [Accepted: 03/26/2024] [Indexed: 05/12/2024]
Abstract
Sweet corn breeding programs, like field corn, focus on the development of elite inbred lines to produce commercial hybrids. For this reason, genomic selection models can help the in silico prediction of hybrid crosses from the elite lines, which is hypothesized to improve the test cross scheme, leading to higher genetic gain in a breeding program. This study aimed to explore the potential of implementing genomic selection in a sweet corn breeding program through hybrid prediction in a within-site across-year and across-site framework. A total of 506 hybrids were evaluated in six environments (California, Florida, and Wisconsin, in the years 2020 and 2021). A total of 20 traits from three different groups were measured (plant-, ear-, and flavor-related traits) across the six environments. Eight statistical models were considered for prediction, as the combination of two genomic prediction models (GBLUP and RKHS) with two different kernels (additive and additive + dominance), and in a single- and multi-trait framework. Also, three different cross-validation schemes were tested (CV1, CV0, and CV00). The different models were then compared based on the correlation between the estimated breeding values/total genetic values and phenotypic measurements. Overall, heritabilities and correlations varied among the traits. The models implemented showed good accuracies for trait prediction. The GBLUP implementation outperformed RKHS in all cross-validation schemes and models. Models with additive plus dominance kernels presented a slight improvement over the models with only additive kernels for some of the models examined. In addition, models for within-site across-year and across-site performed better in the CV0 than the CV00 scheme, on average. Hence, GBLUP should be considered as a standard model for sweet corn hybrid prediction. In addition, we found that the implementation of genomic prediction in a sweet corn breeding program presented reliable results, which can improve the testcross stage by identifying the top candidates that will reach advanced field-testing stages.
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Affiliation(s)
- Marco Antônio Peixoto
- Laboratório de Biometria, Universidade Federal de Viçosa, Viçosa, Minas Gerais, Brazil
- Department of Horticultural Sciences, University of Florida, Gainesville, FL, United States
| | - Kristen A. Leach
- Department of Horticultural Sciences, University of Florida, Gainesville, FL, United States
| | - Diego Jarquin
- Department of Agronomy, University of Florida, Gainesville, FL, United States
| | - Patrick Flannery
- Department of Plant and Agroecosystem Sciences, University of Wisconsin-Madison, Madison, WI, United States
| | - Jared Zystro
- Organic Seed Alliance, Port Townsend, WA, United States
| | - William F. Tracy
- Department of Plant and Agroecosystem Sciences, University of Wisconsin-Madison, Madison, WI, United States
| | - Leonardo Bhering
- Laboratório de Biometria, Universidade Federal de Viçosa, Viçosa, Minas Gerais, Brazil
| | - Márcio F. R. Resende
- Department of Horticultural Sciences, University of Florida, Gainesville, FL, United States
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Araújo MS, Chaves SFS, Dias LAS, Ferreira FM, Pereira GR, Bezerra ARG, Alves RS, Heinemann AB, Breseghello F, Carneiro PCS, Krause MD, Costa-Neto G, Dias KOG. GIS-FA: an approach to integrating thematic maps, factor-analytic, and envirotyping for cultivar targeting. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2024; 137:80. [PMID: 38472532 DOI: 10.1007/s00122-024-04579-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/15/2023] [Accepted: 02/06/2024] [Indexed: 03/14/2024]
Abstract
KEY MESSAGE We propose an "enviromics" prediction model for recommending cultivars based on thematic maps aimed at decision-makers. Parsimonious methods that capture genotype-by-environment interaction (GEI) in multi-environment trials (MET) are important in breeding programs. Understanding the causes and factors of GEI allows the utilization of genotype adaptations in the target population of environments through environmental features and factor-analytic (FA) models. Here, we present a novel predictive breeding approach called GIS-FA, which integrates geographic information systems (GIS) techniques, FA models, partial least squares (PLS) regression, and enviromics to predict phenotypic performance in untested environments. The GIS-FA approach enables: (i) the prediction of the phenotypic performance of tested genotypes in untested environments, (ii) the selection of the best-ranking genotypes based on their overall performance and stability using the FA selection tools, and (iii) the creation of thematic maps showing overall or pairwise performance and stability for decision-making. We exemplify the usage of the GIS-FA approach using two datasets of rice [Oryza sativa (L.)] and soybean [Glycine max (L.) Merr.] in MET spread over tropical areas. In summary, our novel predictive method allows the identification of new breeding scenarios by pinpointing groups of environments where genotypes demonstrate superior predicted performance. It also facilitates and optimizes cultivar recommendations by utilizing thematic maps.
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Affiliation(s)
- Maurício S Araújo
- Department of Agronomy, Federal University of Viçosa, Viçosa, Minas Gerais, Brazil
| | - Saulo F S Chaves
- Department of Agronomy, Federal University of Viçosa, Viçosa, Minas Gerais, Brazil
| | - Luiz A S Dias
- Department of Agronomy, Federal University of Viçosa, Viçosa, Minas Gerais, Brazil
| | - Filipe M Ferreira
- Department of Crop Science - College of Agricultural Sciences, São Paulo State University, Botucatu, São Paulo, Brazil
| | - Guilherme R Pereira
- Department of Agronomy, Federal University of Viçosa, Viçosa, Minas Gerais, Brazil
| | | | - Rodrigo S Alves
- Department of General Biology, Federal University of Viçosa, Viçosa, Minas Gerais, Brazil
| | - Alexandre B Heinemann
- Brazilian Agricultural Research Corporation (Embrapa Rice and Beans), Santo Antônio de Goiás, Goiás, Brazil
| | - Flávio Breseghello
- Brazilian Agricultural Research Corporation (Embrapa Rice and Beans), Santo Antônio de Goiás, Goiás, Brazil
| | - Pedro C S Carneiro
- Department of General Biology, Federal University of Viçosa, Viçosa, Minas Gerais, Brazil
| | | | | | - Kaio O G Dias
- Department of General Biology, Federal University of Viçosa, Viçosa, Minas Gerais, Brazil.
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Guo T, Wei J, Li X, Yu J. Environmental context of phenotypic plasticity in flowering time in sorghum and rice. JOURNAL OF EXPERIMENTAL BOTANY 2024; 75:1004-1015. [PMID: 37819624 PMCID: PMC10837014 DOI: 10.1093/jxb/erad398] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Accepted: 10/17/2023] [Indexed: 10/13/2023]
Abstract
Phenotypic plasticity is an important topic in biology and evolution. However, how to generate broadly applicable insights from individual studies remains a challenge. Here, with flowering time observed from a large geographical region for sorghum and rice genetic populations, we examine the consistency of parameter estimation for reaction norms of genotypes across different subsets of environments and searched for potential strategies to inform the study design. Both sample size and environmental mean range of the subset affected the consistency. The subset with either a large range of environmental mean or a large sample size resulted in genetic parameters consistent with the overall pattern. Furthermore, high accuracy through genomic prediction was obtained for reaction norm parameters of untested genotypes using models built from tested genotypes under the subsets of environments with either a large range or a large sample size. With 1428 and 1674 simulated settings, our analyses suggested that the distribution of environmental index values of a site should be considered in designing experiments. Overall, we showed that environmental context was critical, and considerations should be given to better cover the intended range of the environmental variable. Our findings have implications for the genetic architecture of complex traits, plant-environment interaction, and climate adaptation.
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Affiliation(s)
- Tingting Guo
- Hubei Hongshan Laboratory, Wuhan, Hubei, China
- College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, Hubei, China
| | - Jialu Wei
- Department of Agronomy, Iowa State University, Ames, IA, USA
| | - Xianran Li
- USDA, Agricultural Research Service, Wheat Health, Genetics, and Quality Research Unit, Pullman, WA, USA
| | - Jianming Yu
- Department of Agronomy, Iowa State University, Ames, IA, USA
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Pixley KV, Cairns JE, Lopez-Ridaura S, Ojiewo CO, Dawud MA, Drabo I, Mindaye T, Nebie B, Asea G, Das B, Daudi H, Desmae H, Batieno BJ, Boukar O, Mukankusi CTM, Nkalubo ST, Hearne SJ, Dhugga KS, Gandhi H, Snapp S, Zepeda-Villarreal EA. Redesigning crop varieties to win the race between climate change and food security. MOLECULAR PLANT 2023; 16:1590-1611. [PMID: 37674314 DOI: 10.1016/j.molp.2023.09.003] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/04/2023] [Revised: 08/17/2023] [Accepted: 09/03/2023] [Indexed: 09/08/2023]
Abstract
Climate change poses daunting challenges to agricultural production and food security. Rising temperatures, shifting weather patterns, and more frequent extreme events have already demonstrated their effects on local, regional, and global agricultural systems. Crop varieties that withstand climate-related stresses and are suitable for cultivation in innovative cropping systems will be crucial to maximize risk avoidance, productivity, and profitability under climate-changed environments. We surveyed 588 expert stakeholders to predict current and novel traits that may be essential for future pearl millet, sorghum, maize, groundnut, cowpea, and common bean varieties, particularly in sub-Saharan Africa. We then review the current progress and prospects for breeding three prioritized future-essential traits for each of these crops. Experts predict that most current breeding priorities will remain important, but that rates of genetic gain must increase to keep pace with climate challenges and consumer demands. Importantly, the predicted future-essential traits include innovative breeding targets that must also be prioritized; for example, (1) optimized rhizosphere microbiome, with benefits for P, N, and water use efficiency, (2) optimized performance across or in specific cropping systems, (3) lower nighttime respiration, (4) improved stover quality, and (5) increased early vigor. We further discuss cutting-edge tools and approaches to discover, validate, and incorporate novel genetic diversity from exotic germplasm into breeding populations with unprecedented precision, accuracy, and speed. We conclude that the greatest challenge to developing crop varieties to win the race between climate change and food security might be our innovativeness in defining and boldness to breed for the traits of tomorrow.
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Affiliation(s)
- Kevin V Pixley
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico.
| | - Jill E Cairns
- International Maize and Wheat Improvement Center (CIMMYT), Harare, Zimbabwe
| | | | - Chris O Ojiewo
- International Maize and Wheat Improvement Center (CIMMYT), Nairobi, Kenya
| | | | - Inoussa Drabo
- International Maize and Wheat Improvement Center (CIMMYT), Dakar, Senegal
| | - Taye Mindaye
- Ethiopian Institute of Agricultural Research (EIAR), Addis Ababa, Ethiopia
| | - Baloua Nebie
- International Maize and Wheat Improvement Center (CIMMYT), Dakar, Senegal
| | - Godfrey Asea
- National Agricultural Research Organization (NARO), Kampala, Uganda
| | - Biswanath Das
- International Maize and Wheat Improvement Center (CIMMYT), Nairobi, Kenya
| | - Happy Daudi
- Tanzania Agricultural Research Institute (TARI), Naliendele, Tanzania
| | - Haile Desmae
- International Maize and Wheat Improvement Center (CIMMYT), Dakar, Senegal
| | - Benoit Joseph Batieno
- Institut de l'Environnement et de Recherches Agricoles (INERA), Ouagadougou, Burkina Faso
| | - Ousmane Boukar
- International Institute of Tropicl Agriculture (IITA), Kano, Nigeria
| | | | | | - Sarah J Hearne
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Kanwarpal S Dhugga
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Harish Gandhi
- International Maize and Wheat Improvement Center (CIMMYT), Nairobi, Kenya
| | - Sieglinde Snapp
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
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Messina CD, Gho C, Hammer GL, Tang T, Cooper M. Two decades of harnessing standing genetic variation for physiological traits to improve drought tolerance in maize. JOURNAL OF EXPERIMENTAL BOTANY 2023; 74:4847-4861. [PMID: 37354091 PMCID: PMC10474595 DOI: 10.1093/jxb/erad231] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Accepted: 06/15/2023] [Indexed: 06/26/2023]
Abstract
We review approaches to maize breeding for improved drought tolerance during flowering and grain filling in the central and western US corn belt and place our findings in the context of results from public breeding. Here we show that after two decades of dedicated breeding efforts, the rate of crop improvement under drought increased from 6.2 g m-2 year-1 to 7.5 g m-2 year-1, closing the genetic gain gap with respect to the 8.6 g m-2 year-1 observed under water-sufficient conditions. The improvement relative to the long-term genetic gain was possible by harnessing favourable alleles for physiological traits available in the reference population of genotypes. Experimentation in managed stress environments that maximized the genetic correlation with target environments was key for breeders to identify and select for these alleles. We also show that the embedding of physiological understanding within genomic selection methods via crop growth models can hasten genetic gain under drought. We estimate a prediction accuracy differential (Δr) above current prediction approaches of ~30% (Δr=0.11, r=0.38), which increases with increasing complexity of the trait environment system as estimated by Shannon information theory. We propose this framework to inform breeding strategies for drought stress across geographies and crops.
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Affiliation(s)
- Carlos D Messina
- Horticultural Sciences Department, University of Florida, Gainesville, FL, USA
- ARC Centre of Excellence for Plant Success in Nature and Agriculture, The University of Queensland, Brisbane, Qld 4072, Australia
| | - Carla Gho
- School of Agriculture & Food Sciences, The University of Queensland, Brisbane, Qld 4072, Australia
| | - Graeme L Hammer
- ARC Centre of Excellence for Plant Success in Nature and Agriculture, The University of Queensland, Brisbane, Qld 4072, Australia
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, Qld 4072, Australia
| | - Tom Tang
- Corteva Agrisciences, Johnston, IA, USA
| | - Mark Cooper
- ARC Centre of Excellence for Plant Success in Nature and Agriculture, The University of Queensland, Brisbane, Qld 4072, Australia
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, Qld 4072, Australia
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12
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Liu J, Wang X, Lu T, Wang J, Shi W. Identification of the Efficacy of Ex Situ Conservation of Ammopiptanthus nanus Based on Its ETS-SSR Markers. PLANTS (BASEL, SWITZERLAND) 2023; 12:2670. [PMID: 37514284 PMCID: PMC10386304 DOI: 10.3390/plants12142670] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Revised: 07/07/2023] [Accepted: 07/12/2023] [Indexed: 07/30/2023]
Abstract
Ammopiptanthus possesses ancestral traits and, as a tertiary relict, is one of the surviving remnants of the ancient Mediterranean retreat and climate drought. It is also the only genus of super xerophytic, evergreen, broad-leaved shrubs. Ammopiptanthus nanus, one of the two species in this genus, is predominantly found in extremely arid and frigid environments, and is increasingly threatened with extinction. Study of the species' genetic diversity is thus beneficial for its survival and the efficacy of ex situ conservation efforts. Based on transcriptome data, 15 pairs of effective EST-SSR were screened to evaluate A. nanus genetic diversity. In all, 87 samples from three populations were evaluated, the results of which show that ex situ conservation in the Wuqia region needs to be supplemented. Conservation and breeding of individual A. nanus offspring should be strengthened in the future to ensure their progeny continue to exhibit high genetic diversity and variation.
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Affiliation(s)
- Jingdian Liu
- State Key Laboratory of Desert and Oasis Ecology, Key Laboratory of Ecological Safety and Sustainable, Development in Arid Lands, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Xinjiang 830011, China
- College of Forestry and Landscape Architecture, Xinjiang Agricultural University, Xinjiang 830011, China
| | - Xiyong Wang
- State Key Laboratory of Desert and Oasis Ecology, Key Laboratory of Ecological Safety and Sustainable, Development in Arid Lands, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Xinjiang 830011, China
- Turpan Eremophytes Botanic Garden, The Chinese Academy of Sciences, Xinjiang 838008, China
| | - Ting Lu
- College of Forestry and Landscape Architecture, Xinjiang Agricultural University, Xinjiang 830011, China
| | - Jiancheng Wang
- State Key Laboratory of Desert and Oasis Ecology, Key Laboratory of Ecological Safety and Sustainable, Development in Arid Lands, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Xinjiang 830011, China
- Turpan Eremophytes Botanic Garden, The Chinese Academy of Sciences, Xinjiang 838008, China
| | - Wei Shi
- State Key Laboratory of Desert and Oasis Ecology, Key Laboratory of Ecological Safety and Sustainable, Development in Arid Lands, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Xinjiang 830011, China
- Turpan Eremophytes Botanic Garden, The Chinese Academy of Sciences, Xinjiang 838008, China
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13
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Pinto F, Zaman-Allah M, Reynolds M, Schulthess U. Satellite imagery for high-throughput phenotyping in breeding plots. FRONTIERS IN PLANT SCIENCE 2023; 14:1114670. [PMID: 37260941 PMCID: PMC10227446 DOI: 10.3389/fpls.2023.1114670] [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: 12/02/2022] [Accepted: 04/12/2023] [Indexed: 06/02/2023]
Abstract
Advances in breeding efforts to increase the rate of genetic gains and enhance crop resilience to climate change have been limited by the procedure and costs of phenotyping methods. The recent rapid development of sensors, image-processing technology, and data-analysis has provided opportunities for multiple scales phenotyping methods and systems, including satellite imagery. Among these platforms, satellite imagery may represent one of the ultimate approaches to remotely monitor trials and nurseries planted in multiple locations while standardizing protocols and reducing costs. However, the deployment of satellite-based phenotyping in breeding trials has largely been limited by low spatial resolution of satellite images. The advent of a new generation of high-resolution satellites may finally overcome these limitations. The SkySat constellation started offering multispectral images at a 0.5 m resolution since 2020. In this communication we present a case study on the use of time series SkySat images to estimate NDVI from wheat and maize breeding plots encompassing different sizes and spacing. We evaluated the reliability of the calculated NDVI and tested its capacity to detect seasonal changes and genotypic differences. We discuss the advantages, limitations, and perspectives of this approach for high-throughput phenotyping in breeding programs.
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Affiliation(s)
- Francisco Pinto
- Global Wheat Program, International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Mainassara Zaman-Allah
- Global Maize Program, International Maize and Wheat Improvement Center (CIMMYT), Southern Africa Regional Office, Harare, Zimbabwe
| | - Matthew Reynolds
- Global Wheat Program, International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Urs Schulthess
- CIMMYT-China Wheat and Maize Joint Research Center, Agronomy College, Henan Agricultural University, Zhengzhou, China
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14
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Meng G, Rasmussen SK, Christensen CSL, Fan W, Torp AM. Molecular breeding of barley for quality traits and resilience to climate change. Front Genet 2023; 13:1039996. [PMID: 36685930 PMCID: PMC9851277 DOI: 10.3389/fgene.2022.1039996] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Accepted: 12/19/2022] [Indexed: 01/06/2023] Open
Abstract
Barley grains are a rich source of compounds, such as resistant starch, beta-glucans and anthocyanins, that can be explored in order to develop various products to support human health, while lignocellulose in straw can be optimised for feed in husbandry, bioconversion into bioethanol or as a starting material for new compounds. Existing natural variations of these compounds can be used to breed improved cultivars or integrated with a large number of mutant lines. The technical demands can be in opposition depending on barley's end use as feed or food or as a source of biofuel. For example beta-glucans are beneficial in human diets but can lead to issues in brewing and poultry feed. Barley breeders have taken action to integrate new technologies, such as induced mutations, transgenics, marker-assisted selection, genomic selection, site-directed mutagenesis and lastly machine learning, in order to improve quality traits. Although only a limited number of cultivars with new quality traits have so far reached the market, research has provided valuable knowledge and inspiration for future design and a combination of methodologies to achieve the desired traits. The changes in climate is expected to affect the quality of the harvested grain and it is already a challenge to mitigate the unpredictable seasonal and annual variations in temperature and precipitation under elevated [CO2] by breeding. This paper presents the mutants and encoded proteins, with a particular focus on anthocyanins and lignocellulose, that have been identified and characterised in detail and can provide inspiration for continued breeding to achieve desired grain and straw qualities.
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Affiliation(s)
- Geng Meng
- Department of Plant and Environmental Sciences, University of Copenhagen, Frederiksberg C, Denmark,College of Horticulture, Henan Agricultural University, Zhengzhou, China
| | - Søren K. Rasmussen
- Department of Plant and Environmental Sciences, University of Copenhagen, Frederiksberg C, Denmark,*Correspondence: Søren K. Rasmussen,
| | | | - Weiyao Fan
- Department of Plant and Environmental Sciences, University of Copenhagen, Frederiksberg C, Denmark
| | - Anna Maria Torp
- Department of Plant and Environmental Sciences, University of Copenhagen, Frederiksberg C, Denmark
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15
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Cooper M, Messina CD. Breeding crops for drought-affected environments and improved climate resilience. THE PLANT CELL 2023; 35:162-186. [PMID: 36370076 PMCID: PMC9806606 DOI: 10.1093/plcell/koac321] [Citation(s) in RCA: 19] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Accepted: 11/01/2022] [Indexed: 05/12/2023]
Abstract
Breeding climate-resilient crops with improved levels of abiotic and biotic stress resistance as a response to climate change presents both opportunities and challenges. Applying the framework of the "breeder's equation," which is used to predict the response to selection for a breeding program cycle, we review methodologies and strategies that have been used to successfully breed crops with improved levels of drought resistance, where the target population of environments (TPEs) is a spatially and temporally heterogeneous mixture of drought-affected and favorable (water-sufficient) environments. Long-term improvement of temperate maize for the US corn belt is used as a case study and compared with progress for other crops and geographies. Integration of trait information across scales, from genomes to ecosystems, is needed to accurately predict yield outcomes for genotypes within the current and future TPEs. This will require transdisciplinary teams to explore, identify, and exploit novel opportunities to accelerate breeding program outcomes; both improved germplasm resources and improved products (cultivars, hybrids, clones, and populations) that outperform and replace the products in use by farmers, in combination with modified agronomic management strategies suited to their local environments.
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Affiliation(s)
| | - Carlos D Messina
- Horticultural Sciences Department, University of Florida, Gainesville, Florida 32611, USA
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16
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Xu Y, Zhang X, Li H, Zheng H, Zhang J, Olsen MS, Varshney RK, Prasanna BM, Qian Q. Smart breeding driven by big data, artificial intelligence, and integrated genomic-enviromic prediction. MOLECULAR PLANT 2022; 15:1664-1695. [PMID: 36081348 DOI: 10.1016/j.molp.2022.09.001] [Citation(s) in RCA: 43] [Impact Index Per Article: 21.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Revised: 08/20/2022] [Accepted: 09/02/2022] [Indexed: 05/12/2023]
Abstract
The first paradigm of plant breeding involves direct selection-based phenotypic observation, followed by predictive breeding using statistical models for quantitative traits constructed based on genetic experimental design and, more recently, by incorporation of molecular marker genotypes. However, plant performance or phenotype (P) is determined by the combined effects of genotype (G), envirotype (E), and genotype by environment interaction (GEI). Phenotypes can be predicted more precisely by training a model using data collected from multiple sources, including spatiotemporal omics (genomics, phenomics, and enviromics across time and space). Integration of 3D information profiles (G-P-E), each with multidimensionality, provides predictive breeding with both tremendous opportunities and great challenges. Here, we first review innovative technologies for predictive breeding. We then evaluate multidimensional information profiles that can be integrated with a predictive breeding strategy, particularly envirotypic data, which have largely been neglected in data collection and are nearly untouched in model construction. We propose a smart breeding scheme, integrated genomic-enviromic prediction (iGEP), as an extension of genomic prediction, using integrated multiomics information, big data technology, and artificial intelligence (mainly focused on machine and deep learning). We discuss how to implement iGEP, including spatiotemporal models, environmental indices, factorial and spatiotemporal structure of plant breeding data, and cross-species prediction. A strategy is then proposed for prediction-based crop redesign at both the macro (individual, population, and species) and micro (gene, metabolism, and network) scales. Finally, we provide perspectives on translating smart breeding into genetic gain through integrative breeding platforms and open-source breeding initiatives. We call for coordinated efforts in smart breeding through iGEP, institutional partnerships, and innovative technological support.
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Affiliation(s)
- Yunbi Xu
- Institute of Crop Sciences, CIMMYT-China, Chinese Academy of Agricultural Sciences, Beijing 100081, China; CIMMYT-China Tropical Maize Research Center, School of Food Science and Engineering, Foshan University, Foshan, Guangdong 528231, China; Peking University Institute of Advanced Agricultural Sciences, Weifang, Shandong 261325, China.
| | - Xingping Zhang
- Peking University Institute of Advanced Agricultural Sciences, Weifang, Shandong 261325, China
| | - Huihui Li
- Institute of Crop Sciences, CIMMYT-China, Chinese Academy of Agricultural Sciences, Beijing 100081, China; National Nanfan Research Institute (Sanya), Chinese Academy of Agricultural Sciences, Sanya, Hainan 572024, China
| | - Hongjian Zheng
- CIMMYT-China Specialty Maize Research Center, Shanghai Academy of Agricultural Sciences, Shanghai 201400, China
| | - Jianan Zhang
- MolBreeding Biotechnology Co., Ltd., Shijiazhuang, Hebei 050035, China
| | - Michael S Olsen
- CIMMYT (International Maize and Wheat Improvement Center), ICRAF Campus, United Nations Avenue, Nairobi, Kenya
| | - Rajeev K Varshney
- State Agricultural Biotechnology Centre, Centre for Crop and Food Innovation, Food Futures Institute, Murdoch University, Murdoch, Australia
| | - Boddupalli M Prasanna
- CIMMYT (International Maize and Wheat Improvement Center), ICRAF Campus, United Nations Avenue, Nairobi, Kenya
| | - Qian Qian
- Institute of Crop Sciences, CIMMYT-China, Chinese Academy of Agricultural Sciences, Beijing 100081, China
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17
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Cortés AJ, López-Hernández F, Blair MW. Genome-Environment Associations, an Innovative Tool for Studying Heritable Evolutionary Adaptation in Orphan Crops and Wild Relatives. Front Genet 2022; 13:910386. [PMID: 35991553 PMCID: PMC9389289 DOI: 10.3389/fgene.2022.910386] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Accepted: 05/30/2022] [Indexed: 11/23/2022] Open
Abstract
Leveraging innovative tools to speed up prebreeding and discovery of genotypic sources of adaptation from landraces, crop wild relatives, and orphan crops is a key prerequisite to accelerate genetic gain of abiotic stress tolerance in annual crops such as legumes and cereals, many of which are still orphan species despite advances in major row crops. Here, we review a novel, interdisciplinary approach to combine ecological climate data with evolutionary genomics under the paradigm of a new field of study: genome-environment associations (GEAs). We first exemplify how GEA utilizes in situ georeferencing from genotypically characterized, gene bank accessions to pinpoint genomic signatures of natural selection. We later discuss the necessity to update the current GEA models to predict both regional- and local- or micro-habitat-based adaptation with mechanistic ecophysiological climate indices and cutting-edge GWAS-type genetic association models. Furthermore, to account for polygenic evolutionary adaptation, we encourage the community to start gathering genomic estimated adaptive values (GEAVs) for genomic prediction (GP) and multi-dimensional machine learning (ML) models. The latter two should ideally be weighted by de novo GWAS-based GEA estimates and optimized for a scalable marker subset. We end the review by envisioning avenues to make adaptation inferences more robust through the merging of high-resolution data sources, such as environmental remote sensing and summary statistics of the genomic site frequency spectrum, with the epigenetic molecular functionality responsible for plastic inheritance in the wild. Ultimately, we believe that coupling evolutionary adaptive predictions with innovations in ecological genomics such as GEA will help capture hidden genetic adaptations to abiotic stresses based on crop germplasm resources to assist responses to climate change. "I shall endeavor to find out how nature's forces act upon one another, and in what manner the geographic environment exerts its influence on animals and plants. In short, I must find out about the harmony in nature" Alexander von Humboldt-Letter to Karl Freiesleben, June 1799.
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Affiliation(s)
- Andrés J. Cortés
- Corporacion Colombiana de Investigacion Agropecuaria AGROSAVIA, C.I. La Selva, Rionegro, Colombia
| | - Felipe López-Hernández
- Corporacion Colombiana de Investigacion Agropecuaria AGROSAVIA, C.I. La Selva, Rionegro, Colombia
| | - Matthew W. Blair
- Department of Agricultural & Environmental Sciences, Tennessee State University, Nashville, TN, United States
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18
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Yan H, Harrison MT, Liu K, Wang B, Feng P, Fahad S, Meinke H, Yang R, Liu DL, Archontoulis S, Huber I, Tian X, Man J, Zhang Y, Zhou M. Crop traits enabling yield gains under more frequent extreme climatic events. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 808:152170. [PMID: 34875326 DOI: 10.1016/j.scitotenv.2021.152170] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Revised: 11/17/2021] [Accepted: 11/30/2021] [Indexed: 05/16/2023]
Abstract
Climate change (CC) in central China will change seasonal patterns of agricultural production through increasingly frequent extreme climatic events (ECEs). Breeding climate-resilient wheat (Triticum aestivum L.) genotypes may mitigate adverse effects of ECEs on crop productivity. To reveal crop traits conducive to long-term yield improvement in the target population of environments, we created 8,192 virtual genotypes with contrasting but realistic ranges of phenology, productivity and waterlogging tolerance. Using these virtual genotypes, we conducted a genotype (G) by environment (E) by management (M) factorial analysis (G×E×M) using locations distributed across the entire cereal cropping zone in mid-China. The G×E×M invoked locally-specific sowing dates under future climates that were premised on shared socioeconomic pathways SSP5-8.5, with a time horizon centred on 2080. Across the simulated adaptation landscape, productivity was primarily driven by yield components and phenology (average grain yield increase of 6-69% across sites with optimal combinations of these traits). When incident solar radiation was not limiting carbon assimilation, ideotypes with higher grain yields were characterised by earlier flowering, higher radiation-use efficiency and larger maximum kernel size. At sites with limited solar radiation, crops required longer growing periods to realise genetic yield potential, although higher radiation-use efficiency and larger maximum kernel size were again prospective traits enabling higher rates of yield gains. By 2080, extreme waterlogging stress in some regions of mid-China will impact substantially on productivity, with yield penalties of up to 1,010 kg ha-1. Ideotypes with optimal G×M could mitigate yield penalty caused by waterlogging by up to 15% under future climates. These results help distil promising crop trait by best management practice combinations that enable higher yields and robust adaptation to future climates and more frequent extreme climatic events, including flash flooding and soil waterlogging.
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Affiliation(s)
- Haoliang Yan
- Engineering Research Center of Ecology and Agricultural Use of Wetland, College of Agriculture, Yangtze University, Jingzhou 434025, Hubei, China
| | - Matthew Tom Harrison
- Tasmanian Institute of Agriculture, University of Tasmania, Burnie 7250, Tasmania, Australia
| | - Ke Liu
- Engineering Research Center of Ecology and Agricultural Use of Wetland, College of Agriculture, Yangtze University, Jingzhou 434025, Hubei, China; Tasmanian Institute of Agriculture, University of Tasmania, Burnie 7250, Tasmania, Australia.
| | - Bin Wang
- New South Wales Department of Primary Industries, Wagga Wagga Agriculture Institute, Wagga Wagga, New South Wales 2650, Australia
| | - Puyu Feng
- College of Land Science and Technology, China Agricultural University, Beijing 100193, China
| | - Shah Fahad
- Hainan Key Laboratory for Sustainable Utilization of Tropical Bioresource, College of Tropical Crops, Hainan University, Haikou 570228, Hainan, China; Department of Agronomy, The University of Haripur, Haripur, Khyber Pakhtunkhwa 22620, Pakistan
| | - Holger Meinke
- University of Tasmania, Hobart 7001, Tasmania, Australia
| | - Rui Yang
- Engineering Research Center of Ecology and Agricultural Use of Wetland, College of Agriculture, Yangtze University, Jingzhou 434025, Hubei, China
| | - De Li Liu
- New South Wales Department of Primary Industries, Wagga Wagga Agriculture Institute, Wagga Wagga, New South Wales 2650, Australia
| | | | - Isaiah Huber
- Department of Agronomy, Iowa State University, Ames, IA 50011, United States
| | - Xiaohai Tian
- Engineering Research Center of Ecology and Agricultural Use of Wetland, College of Agriculture, Yangtze University, Jingzhou 434025, Hubei, China
| | - Jianguo Man
- MARA Key Laboratory of Crop Ecophysiology and Farming System in the Middle Reaches of the Yangtze River, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Yunbo Zhang
- Engineering Research Center of Ecology and Agricultural Use of Wetland, College of Agriculture, Yangtze University, Jingzhou 434025, Hubei, China.
| | - Meixue Zhou
- Tasmanian Institute of Agriculture, University of Tasmania, Burnie 7250, Tasmania, Australia
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19
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Kohlhase DR, McCabe CE, Singh AK, O’Rourke JA, Graham MA. Comparing Early Transcriptomic Responses of 18 Soybean ( Glycine max) Genotypes to Iron Stress. Int J Mol Sci 2021; 22:11643. [PMID: 34769077 PMCID: PMC8583884 DOI: 10.3390/ijms222111643] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2021] [Revised: 10/22/2021] [Accepted: 10/25/2021] [Indexed: 11/21/2022] Open
Abstract
Iron deficiency chlorosis (IDC) is an abiotic stress that negatively affects soybean (Glycine max [L.] Merr.) production. Much of our knowledge of IDC stress responses is derived from model plant species. Gene expression, quantitative trait loci (QTL) mapping, and genome-wide association studies (GWAS) performed in soybean suggest that stress response differences exist between model and crop species. Our current understanding of the molecular response to IDC in soybeans is largely derived from gene expression studies using near-isogenic lines differing in iron efficiency. To improve iron efficiency in soybeans and other crops, we need to expand gene expression studies to include the diversity present in germplasm collections. Therefore, we collected 216 purified RNA samples (18 genotypes, two tissue types [leaves and roots], two iron treatments [sufficient and deficient], three replicates) and used RNA sequencing to examine the expression differences of 18 diverse soybean genotypes in response to iron deficiency. We found a rapid response to iron deficiency across genotypes, most responding within 60 min of stress. There was little evidence of an overlap of specific differentially expressed genes, and comparisons of gene ontology terms and transcription factor families suggest the utilization of different pathways in the stress response. These initial findings suggest an untapped genetic potential within the soybean germplasm collection that could be used for the continued improvement of iron efficiency in soybean.
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Affiliation(s)
- Daniel R. Kohlhase
- Department of Agronomy, Iowa State University, Ames, IA 50011, USA; (D.R.K.); (A.K.S.)
| | - Chantal E. McCabe
- U.S. Department of Agriculture (USDA)—Agricultural Research Service (ARS), Corn Insects and Crop Genetics Research Unit, Ames, IA 50011, USA;
| | - Asheesh K. Singh
- Department of Agronomy, Iowa State University, Ames, IA 50011, USA; (D.R.K.); (A.K.S.)
| | - Jamie A. O’Rourke
- U.S. Department of Agriculture (USDA)—Agricultural Research Service (ARS), Corn Insects and Crop Genetics Research Unit, Ames, IA 50011, USA;
| | - Michelle A. Graham
- U.S. Department of Agriculture (USDA)—Agricultural Research Service (ARS), Corn Insects and Crop Genetics Research Unit, Ames, IA 50011, USA;
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20
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Costa-Neto G, Crossa J, Fritsche-Neto R. Enviromic Assembly Increases Accuracy and Reduces Costs of the Genomic Prediction for Yield Plasticity in Maize. FRONTIERS IN PLANT SCIENCE 2021; 12:717552. [PMID: 34691099 PMCID: PMC8529011 DOI: 10.3389/fpls.2021.717552] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Accepted: 09/03/2021] [Indexed: 05/21/2023]
Abstract
Quantitative genetics states that phenotypic variation is a consequence of the interaction between genetic and environmental factors. Predictive breeding is based on this statement, and because of this, ways of modeling genetic effects are still evolving. At the same time, the same refinement must be used for processing environmental information. Here, we present an "enviromic assembly approach," which includes using ecophysiology knowledge in shaping environmental relatedness into whole-genome predictions (GP) for plant breeding (referred to as enviromic-aided genomic prediction, E-GP). We propose that the quality of an environment is defined by the core of environmental typologies and their frequencies, which describe different zones of plant adaptation. From this, we derived markers of environmental similarity cost-effectively. Combined with the traditional additive and non-additive effects, this approach may better represent the putative phenotypic variation observed across diverse growing conditions (i.e., phenotypic plasticity). Then, we designed optimized multi-environment trials coupling genetic algorithms, enviromic assembly, and genomic kinships capable of providing in-silico realization of the genotype-environment combinations that must be phenotyped in the field. As proof of concept, we highlighted two E-GP applications: (1) managing the lack of phenotypic information in training accurate GP models across diverse environments and (2) guiding an early screening for yield plasticity exerting optimized phenotyping efforts. Our approach was tested using two tropical maize sets, two types of enviromics assembly, six experimental network sizes, and two types of optimized training set across environments. We observed that E-GP outperforms benchmark GP in all scenarios, especially when considering smaller training sets. The representativeness of genotype-environment combinations is more critical than the size of multi-environment trials (METs). The conventional genomic best-unbiased prediction (GBLUP) is inefficient in predicting the quality of a yet-to-be-seen environment, while enviromic assembly enabled it by increasing the accuracy of yield plasticity predictions. Furthermore, we discussed theoretical backgrounds underlying how intrinsic envirotype-phenotype covariances within the phenotypic records can impact the accuracy of GP. The E-GP is an efficient approach to better use environmental databases to deliver climate-smart solutions, reduce field costs, and anticipate future scenarios.
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Affiliation(s)
- Germano Costa-Neto
- Department of Genetics, “Luiz de Queiroz” Agriculture College, University of São Paulo (ESALQ/USP), Piracicaba, Brazil
- Institute for Genomic Diversity, Cornell University, Ithaca, NY, United States
- *Correspondence: Germano Costa-Neto
| | - Jose Crossa
- Biometrics and Statistics Unit, International Maize and Wheat Improvement Center (CIMMYT), Mexico City, Mexico
- Colegio de Posgraduado, Mexico City, Mexico
| | - Roberto Fritsche-Neto
- Department of Genetics, “Luiz de Queiroz” Agriculture College, University of São Paulo (ESALQ/USP), Piracicaba, Brazil
- Breeding Analytics and Data Management Unit, International Rice Research Institute (IRRI), Los Baños, Philippines
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