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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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2
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Hemayati SS, Hamdi F, Saremirad A, Hamze H. Genotype by environment interaction and stability analysis for harvest date in sugar beet cultivars. Sci Rep 2024; 14:16015. [PMID: 38992210 PMCID: PMC11239863 DOI: 10.1038/s41598-024-67272-7] [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: 02/18/2024] [Accepted: 07/09/2024] [Indexed: 07/13/2024] Open
Abstract
This research assessed the quantitative and qualitative reactions of commercially grown sugar beets to four different harvest dates and their yield stability. The study followed a split-plot design within a randomized complete block design over 3 years. The main plot involved 10 sugar beet cultivars, while the subplot involved four harvest dates: August 13 (HD1), September 7 (HD2), October 3 (HD3), and November 12 (HD4). The study found that environmental conditions, genotypes, and harvest dates significantly affected various traits of sugar beet. Yearly environmental variations and their interactions with genotypes and harvest dates had substantial impacts on all measured traits at the 1% probability level. Additive main effect and multiplicative interaction analysis based on white sugar yield indicated that genotype and environment's additive effects, as well as the genotype-environment interaction, were significant at 1% probability level. Shokoufa and Arya, which exhibit high white sugar yield (WSY) and low first interaction principal component (IPC1) values, are identified as desirable due to their stability across different environments. Among the harvest dates in different years, the fourth and third dates showed a higher yield than the total average. Perfekta and Ekbatan exhibited high specific adaptability. According to the multi-trait stability index, Arta, Arya and Sina were recognized as stable and superior across all measured traits.
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Affiliation(s)
- Saeed Sadeghzadeh Hemayati
- Sugar Beet Seed Institute (SBSI), Agricultural Research, Education and Extension Organization (AREEO), Karaj, Iran.
| | - Farahnaz Hamdi
- Sugar Beet Seed Institute (SBSI), Agricultural Research, Education and Extension Organization (AREEO), Karaj, Iran
| | - Ali Saremirad
- Sugar Beet Seed Institute (SBSI), Agricultural Research, Education and Extension Organization (AREEO), Karaj, Iran
| | - Hamze Hamze
- Sugar Beet Research Department, Hamedan Agricultural and Natural Resources Research and Education Center, AREEO, Hamedan, Iran
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3
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Serrie M, Ribeyre F, Brun L, Audergon JM, Quilot B, Roth M. Dare to be resilient: the key to future pesticide-free orchards? JOURNAL OF EXPERIMENTAL BOTANY 2024; 75:3835-3848. [PMID: 38634690 PMCID: PMC11233412 DOI: 10.1093/jxb/erae150] [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: 10/03/2023] [Accepted: 04/15/2024] [Indexed: 04/19/2024]
Abstract
Considering the urgent need for more sustainable fruit tree production, it is high time to find durable alternatives to the systematic use of phytosanitary products in orchards. To this end, resilience can deliver a number of benefits. Relying on a combination of tolerance, resistance, and recovery traits, disease resilience appears as a cornerstone to cope with the multiple pest and disease challenges over an orchard's lifetime. Here, we describe resilience as the capacity of a tree to be minimally affected by external disturbances or to rapidly bounce back to normal functioning after being exposed to these disturbances. Based on a literature survey largely inspired from research on livestock, we highlight different approaches for dissecting phenotypic and genotypic components of resilience. In particular, multisite experimental designs and longitudinal measures of so-called 'resilience biomarkers' are required. We identified a list of promising biomarkers relying on ecophysiological and digital measurements. Recent advances in high-throughput phenotyping and genomics tools will likely facilitate fine scale temporal monitoring of tree health, allowing identification of resilient genotypes with the calculation of specific resilience indicators. Although resilience could be considered as a 'black box' trait, we demonstrate how it could become a realistic breeding goal.
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Affiliation(s)
| | | | - Laurent Brun
- INRAE, UERI Gotheron, Saint-Marcel-Lès-Valence, France
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Tadese D, Piepho HP, Hartung J. Accuracy of prediction from multi-environment trials for new locations using pedigree information and environmental covariates: the case of sorghum (Sorghum bicolor (L.) Moench) breeding. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2024; 137:181. [PMID: 38985188 PMCID: PMC11236881 DOI: 10.1007/s00122-024-04684-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/14/2023] [Accepted: 06/25/2024] [Indexed: 07/11/2024]
Abstract
KEY MESSAGES We investigate a method of extracting and fitting synthetic environmental covariates and pedigree information in multilocation trial data analysis to predict genotype performances in untested locations. Plant breeding trials are usually conducted across multiple testing locations to predict genotype performances in the targeted population of environments. The predictive accuracy can be increased by the use of adequate statistical models. We compared linear mixed models with and without synthetic covariates (SCs) and pedigree information under the identity, the diagonal and the factor-analytic variance-covariance structures of the genotype-by-location interactions. A comparison was made to evaluate the accuracy of different models in predicting genotype performances in untested locations using the mean squared error of predicted differences (MSEPD) and the Spearman rank correlation between predicted and adjusted means. A multi-environmental trial (MET) dataset evaluated for yield performance in the dry lowland sorghum (Sorghum bicolor (L.) Moench) breeding program of Ethiopia was used. For validating our models, we followed a leave-one-location-out cross-validation strategy. A total of 65 environmental covariates (ECs) obtained from the sorghum test locations were considered. The SCs were extracted from the ECs using multivariate partial least squares analysis and subsequently fitted in the linear mixed model. Then, the model was extended accounting for pedigree information. According to the MSEPD, models accounting for SC improve predictive accuracy of genotype performances in the three of the variance-covariance structures compared to others without SC. The rank correlation was also higher for the model with the SC. When the SC was fitted, the rank correlation was 0.58 for the factor analytic, 0.51 for the diagonal and 0.46 for the identity variance-covariance structures. Our approach indicates improvement in predictive accuracy with SC in the context of genotype-by-location interactions of a sorghum breeding in Ethiopia.
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Affiliation(s)
- Diriba Tadese
- Biostatistics Unit, Institute of Crop Science, University of Hohenheim, Fruwirthstraße 23, 70599, Stuttgart, Germany.
| | - Hans-Peter Piepho
- Biostatistics Unit, Institute of Crop Science, University of Hohenheim, Fruwirthstraße 23, 70599, Stuttgart, Germany
| | - Jens Hartung
- Biostatistics Unit, Institute of Crop Science, University of Hohenheim, Fruwirthstraße 23, 70599, Stuttgart, Germany
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Heckman RW, Pereira CG, Aspinwall MJ, Juenger TE. Physiological Responses of C 4 Perennial Bioenergy Grasses to Climate Change: Causes, Consequences, and Constraints. ANNUAL REVIEW OF PLANT BIOLOGY 2024; 75:737-769. [PMID: 38424068 DOI: 10.1146/annurev-arplant-070623-093952] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/02/2024]
Abstract
C4 perennial bioenergy grasses are an economically and ecologically important group whose responses to climate change will be important to the future bioeconomy. These grasses are highly productive and frequently possess large geographic ranges and broad environmental tolerances, which may contribute to the evolution of ecotypes that differ in physiological acclimation capacity and the evolution of distinct functional strategies. C4 perennial bioenergy grasses are predicted to thrive under climate change-C4 photosynthesis likely evolved to enhance photosynthetic efficiency under stressful conditions of low [CO2], high temperature, and drought-although few studies have examined how these species will respond to combined stresses or to extremes of temperature and precipitation. Important targets for C4 perennial bioenergy production in a changing world, such as sustainability and resilience, can benefit from combining knowledge of C4 physiology with recent advances in crop improvement, especially genomic selection.
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Affiliation(s)
- Robert W Heckman
- Rocky Mountain Research Station, US Department of Agriculture Forest Service, Cedar City, Utah, USA;
| | - Caio Guilherme Pereira
- Department of Integrative Biology, University of Texas at Austin, Austin, Texas, USA;
- Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | | | - Thomas E Juenger
- Department of Integrative Biology, University of Texas at Austin, Austin, Texas, USA;
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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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7
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Morales N, Anche MT, Kaczmar NS, Lepak N, Ni P, Romay MC, Santantonio N, Buckler ES, Gore MA, Mueller LA, Robbins KR. Spatio-temporal modeling of high-throughput multispectral aerial images improves agronomic trait genomic prediction in hybrid maize. Genetics 2024; 227:iyae037. [PMID: 38469622 PMCID: PMC11075545 DOI: 10.1093/genetics/iyae037] [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: 12/02/2023] [Revised: 12/02/2023] [Accepted: 02/18/2024] [Indexed: 03/13/2024] Open
Abstract
Design randomizations and spatial corrections have increased understanding of genotypic, spatial, and residual effects in field experiments, but precisely measuring spatial heterogeneity in the field remains a challenge. To this end, our study evaluated approaches to improve spatial modeling using high-throughput phenotypes (HTP) via unoccupied aerial vehicle (UAV) imagery. The normalized difference vegetation index was measured by a multispectral MicaSense camera and processed using ImageBreed. Contrasting to baseline agronomic trait spatial correction and a baseline multitrait model, a two-stage approach was proposed. Using longitudinal normalized difference vegetation index data, plot level permanent environment effects estimated spatial patterns in the field throughout the growing season. Normalized difference vegetation index permanent environment were separated from additive genetic effects using 2D spline, separable autoregressive models, or random regression models. The Permanent environment were leveraged within agronomic trait genomic best linear unbiased prediction either modeling an empirical covariance for random effects, or by modeling fixed effects as an average of permanent environment across time or split among three growth phases. Modeling approaches were tested using simulation data and Genomes-to-Fields hybrid maize (Zea mays L.) field experiments in 2015, 2017, 2019, and 2020 for grain yield, grain moisture, and ear height. The two-stage approach improved heritability, model fit, and genotypic effect estimation compared to baseline models. Electrical conductance and elevation from a 2019 soil survey significantly improved model fit, while 2D spline permanent environment were most strongly correlated with the soil parameters. Simulation of field effects demonstrated improved specificity for random regression models. In summary, the use of longitudinal normalized difference vegetation index measurements increased experimental accuracy and understanding of field spatio-temporal heterogeneity.
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Affiliation(s)
- Nicolas Morales
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
| | - Mahlet T Anche
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
| | - Nicholas S Kaczmar
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
| | - Nicholas Lepak
- United States Department of Agriculture-Agricultural Research Service, Robert W. Holley Center for Agriculture and Health, Ithaca, NY 14853, USA
| | - Pengzun Ni
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
- College of Bioscience and Biotechnology, Shenyang Agricultural University, Shenhe District, Shenyang, Liaoning Province, PR China
| | - Maria Cinta Romay
- Institute for Genomic Diversity, Cornell University, Ithaca, NY 14853, USA
| | - Nicholas Santantonio
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
- School of Plant and Environmental Sciences, Virginia Tech, Blacksburg, VA 24061, USA
| | - Edward S Buckler
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
- United States Department of Agriculture-Agricultural Research Service, Robert W. Holley Center for Agriculture and Health, Ithaca, NY 14853, USA
- Institute for Genomic Diversity, Cornell University, Ithaca, NY 14853, USA
| | - Michael A Gore
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
| | - Lukas A Mueller
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
- Boyce Thompson Institute, Ithaca, NY 14853, USA
| | - Kelly R Robbins
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
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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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Guadarrama-Escobar LM, Hunt J, Gurung A, Zarco-Tejada PJ, Shabala S, Camino C, Hernandez P, Pourkheirandish M. Back to the future for drought tolerance. THE NEW PHYTOLOGIST 2024; 242:372-383. [PMID: 38429882 DOI: 10.1111/nph.19619] [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: 08/14/2023] [Accepted: 01/22/2024] [Indexed: 03/03/2024]
Abstract
Global agriculture faces increasing pressure to produce more food with fewer resources. Drought, exacerbated by climate change, is a major agricultural constraint costing the industry an estimated US$80 billion per year in lost production. Wild relatives of domesticated crops, including wheat (Triticum spp.) and barley (Hordeum vulgare L.), are an underutilized source of drought tolerance genes. However, managing their undesirable characteristics, assessing drought responses, and selecting lines with heritable traits remains a significant challenge. Here, we propose a novel strategy of using multi-trait selection criteria based on high-throughput spectral images to facilitate the assessment and selection challenge. The importance of measuring plant capacity for sustained carbon fixation under drought stress is explored, and an image-based transpiration efficiency (iTE) index obtained via a combination of hyperspectral and thermal imaging, is proposed. Incorporating iTE along with other drought-related variables in selection criteria will allow the identification of accessions with diverse tolerance mechanisms. A comprehensive approach that merges high-throughput phenotyping and de novo domestication is proposed for developing drought-tolerant prebreeding material and providing breeders with access to gene pools containing unexplored drought tolerance mechanisms.
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Affiliation(s)
- Luis M Guadarrama-Escobar
- School of Agriculture, Food and Ecosystem Sciences (SAFES), University of Melbourne, Melbourne, Vic., 3010, Australia
| | - James Hunt
- School of Agriculture, Food and Ecosystem Sciences (SAFES), University of Melbourne, Melbourne, Vic., 3010, Australia
| | - Allison Gurung
- School of Agriculture, Food and Ecosystem Sciences (SAFES), University of Melbourne, Melbourne, Vic., 3010, Australia
| | - Pablo J Zarco-Tejada
- School of Agriculture, Food and Ecosystem Sciences (SAFES), University of Melbourne, Melbourne, Vic., 3010, Australia
- Department of Infrastructure Engineering (IE), Faculty of Engineering and Information Technology (FEIT), University of Melbourne, Melbourne, Vic., 3010, Australia
- Institute for Sustainable Agriculture (IAS), Spanish Council for Scientific Research (CSIC), Cordoba, 14004, Spain
| | - Sergey Shabala
- School of Biological Sciences, University of Western Australia, Perth, WA, 6009, Australia
- International Research Centre for Environmental Membrane Biology, Foshan University, Foshan, 528000, China
| | - Carlos Camino
- Joint Research Centre (JRC), European Commission (EC), Ispra, 21027, Italy
| | - Pilar Hernandez
- Institute for Sustainable Agriculture (IAS), Spanish Council for Scientific Research (CSIC), Cordoba, 14004, Spain
| | - Mohammad Pourkheirandish
- School of Agriculture, Food and Ecosystem Sciences (SAFES), University of Melbourne, Melbourne, Vic., 3010, Australia
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10
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Gilbert C, Martin N. Using agro-ecological zones to improve the representation of a multi-environment trial of soybean varieties. FRONTIERS IN PLANT SCIENCE 2024; 15:1310461. [PMID: 38590744 PMCID: PMC10999551 DOI: 10.3389/fpls.2024.1310461] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Accepted: 02/27/2024] [Indexed: 04/10/2024]
Abstract
This research introduces a novel framework for enhancing soybean cultivation in North America by categorizing growing environments into distinct ecological and maturity-based zones. Using an integrated analysis of long-term climatic data and records of soybean varietal trials, this research generates a zonal environmental characterization which captures major components of the growing environment which affect the range of adaptation of soybean varieties. These findings have immediate applications for optimizing multi-environment soybean trials. This characterization allows breeders to assess the environmental representation of a multi-environmental trial of soybean varieties, and to strategize the distribution of testing and the placement of test sites accordingly. This application is demonstrated with a historical scenario of a soybean multi-environment trial, using two resource allocation models: one targeted towards improving the general adaptation of soybean varieties, which focuses on widely cultivated areas, and one targeted towards specific adaptation, which captures diverse environmental conditions. Ultimately, the study aims to improve the efficiency and impact of soybean breeding programs, leading to the development of cultivars resilient to variable and changing climates.
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Affiliation(s)
- Catherine Gilbert
- University of Illinois at Urbana-Champaign, Department of Crop Sciences, Urbana, IL, United States
| | - Nicolas Martin
- University of Illinois at Urbana-Champaign, Department of Crop Sciences, Urbana, IL, United States
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11
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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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12
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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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13
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Swartz LG, Liu S, Dahlquist D, Kramer ST, Walter ES, McInturf SA, Bucksch A, Mendoza-Cózatl DG. OPEN leaf: an open-source cloud-based phenotyping system for tracking dynamic changes at leaf-specific resolution in Arabidopsis. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2023; 116:1600-1616. [PMID: 37733751 DOI: 10.1111/tpj.16449] [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: 12/24/2022] [Accepted: 08/16/2023] [Indexed: 09/23/2023]
Abstract
The first draft of the Arabidopsis genome was released more than 20 years ago and despite intensive molecular research, more than 30% of Arabidopsis genes remained uncharacterized or without an assigned function. This is in part due to gene redundancy within gene families or the essential nature of genes, where their deletion results in lethality (i.e., the dark genome). High-throughput plant phenotyping (HTPP) offers an automated and unbiased approach to characterize subtle or transient phenotypes resulting from gene redundancy or inducible gene silencing; however, access to commercial HTPP platforms remains limited. Here we describe the design and implementation of OPEN leaf, an open-source phenotyping system with cloud connectivity and remote bilateral communication to facilitate data collection, sharing and processing. OPEN leaf, coupled with our SMART imaging processing pipeline was able to consistently document and quantify dynamic changes at the whole rosette level and leaf-specific resolution when plants experienced changes in nutrient availability. Our data also demonstrate that VIS sensors remain underutilized and can be used in high-throughput screens to identify and characterize previously unidentified phenotypes in a leaf-specific time-dependent manner. Moreover, the modular and open-source design of OPEN leaf allows seamless integration of additional sensors based on users and experimental needs.
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Affiliation(s)
- Landon G Swartz
- Department of Electrical Engineering and Computer Science, University of Missouri, 411 S 6th St., Columbia, Missouri, 65201, USA
- Division of Plant Science and Technology, C.S. Bond Life Sciences Center, University of Missouri, 1201 Rollins St., Columbia, Missouri, 65211, USA
| | - Suxing Liu
- School of Plant Sciences, University of Arizona, 1140 E South Campus, Tucson, Arizona, 85721, USA
| | - Drew Dahlquist
- Department of Electrical Engineering and Computer Science, University of Missouri, 411 S 6th St., Columbia, Missouri, 65201, USA
| | - Skyler T Kramer
- MU Institute of Data Science and Informatics, C.S. Bond Life Sciences Center, University of Missouri, 1201 Rollinst St., Columbia, Missouri, 65211, USA
| | - Emily S Walter
- Division of Plant Science and Technology, C.S. Bond Life Sciences Center, University of Missouri, 1201 Rollins St., Columbia, Missouri, 65211, USA
| | - Samuel A McInturf
- Division of Plant Science and Technology, C.S. Bond Life Sciences Center, University of Missouri, 1201 Rollins St., Columbia, Missouri, 65211, USA
| | - Alexander Bucksch
- School of Plant Sciences, University of Arizona, 1140 E South Campus, Tucson, Arizona, 85721, USA
| | - David G Mendoza-Cózatl
- Department of Electrical Engineering and Computer Science, University of Missouri, 411 S 6th St., Columbia, Missouri, 65201, USA
- Division of Plant Science and Technology, C.S. Bond Life Sciences Center, University of Missouri, 1201 Rollins St., Columbia, Missouri, 65211, USA
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14
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Singh B, Kumar S, Elangovan A, Vasht D, Arya S, Duc NT, Swami P, Pawar GS, Raju D, Krishna H, Sathee L, Dalal M, Sahoo RN, Chinnusamy V. Phenomics based prediction of plant biomass and leaf area in wheat using machine learning approaches. FRONTIERS IN PLANT SCIENCE 2023; 14:1214801. [PMID: 37448870 PMCID: PMC10337996 DOI: 10.3389/fpls.2023.1214801] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/30/2023] [Accepted: 06/07/2023] [Indexed: 07/15/2023]
Abstract
Introduction Phenomics has emerged as important tool to bridge the genotype-phenotype gap. To dissect complex traits such as highly dynamic plant growth, and quantification of its component traits over a different growth phase of plant will immensely help dissect genetic basis of biomass production. Based on RGB images, models have been developed to predict biomass recently. However, it is very challenging to find a model performing stable across experiments. In this study, we recorded RGB and NIR images of wheat germplasm and Recombinant Inbred Lines (RILs) of Raj3765xHD2329, and examined the use of multimodal images from RGB, NIR sensors and machine learning models to predict biomass and leaf area non-invasively. Results The image-based traits (i-Traits) containing geometric features, RGB based indices, RGB colour classes and NIR features were categorized into architectural traits and physiological traits. Total 77 i-Traits were selected for prediction of biomass and leaf area consisting of 35 architectural and 42 physiological traits. We have shown that different biomass related traits such as fresh weight, dry weight and shoot area can be predicted accurately from RGB and NIR images using 16 machine learning models. We applied the models on two consecutive years of experiments and found that measurement accuracies were similar suggesting the generalized nature of models. Results showed that all biomass-related traits could be estimated with about 90% accuracy but the performance of model BLASSO was relatively stable and high in all the traits and experiments. The R2 of BLASSO for fresh weight prediction was 0.96 (both year experiments), for dry weight prediction was 0.90 (Experiment 1) and 0.93 (Experiment 2) and for shoot area prediction 0.96 (Experiment 1) and 0.93 (Experiment 2). Also, the RMSRE of BLASSO for fresh weight prediction was 0.53 (Experiment 1) and 0.24 (Experiment 2), for dry weight prediction was 0.85 (Experiment 1) and 0.25 (Experiment 2) and for shoot area prediction 0.59 (Experiment 1) and 0.53 (Experiment 2). Discussion Based on the quantification power analysis of i-Traits, the determinants of biomass accumulation were found which contains both architectural and physiological traits. The best predictor i-Trait for fresh weight and dry weight prediction was Area_SV and for shoot area prediction was projected shoot area. These results will be helpful for identification and genetic basis dissection of major determinants of biomass accumulation and also non-invasive high throughput estimation of plant growth during different phenological stages can identify hitherto uncovered genes for biomass production and its deployment in crop improvement for breaking the yield plateau.
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Affiliation(s)
- Biswabiplab Singh
- Division of Plant Physiology and Nanaji Deshmukh Plant Phenomics Centre (NDPPC), Indian Council of Agricultural Research (ICAR)-Indian Agricultural Research Institute, New Delhi, India
| | - Sudhir Kumar
- Division of Plant Physiology and Nanaji Deshmukh Plant Phenomics Centre (NDPPC), Indian Council of Agricultural Research (ICAR)-Indian Agricultural Research Institute, New Delhi, India
| | - Allimuthu Elangovan
- Division of Plant Physiology and Nanaji Deshmukh Plant Phenomics Centre (NDPPC), Indian Council of Agricultural Research (ICAR)-Indian Agricultural Research Institute, New Delhi, India
| | - Devendra Vasht
- Division of Plant Physiology and Nanaji Deshmukh Plant Phenomics Centre (NDPPC), Indian Council of Agricultural Research (ICAR)-Indian Agricultural Research Institute, New Delhi, India
| | - Sunny Arya
- Division of Plant Physiology and Nanaji Deshmukh Plant Phenomics Centre (NDPPC), Indian Council of Agricultural Research (ICAR)-Indian Agricultural Research Institute, New Delhi, India
| | - Nguyen Trung Duc
- Division of Plant Physiology and Nanaji Deshmukh Plant Phenomics Centre (NDPPC), Indian Council of Agricultural Research (ICAR)-Indian Agricultural Research Institute, New Delhi, India
- Vietnam National University of Agriculture, Hanoi, Vietnam
| | - Pooja Swami
- Division of Plant Physiology and Nanaji Deshmukh Plant Phenomics Centre (NDPPC), Indian Council of Agricultural Research (ICAR)-Indian Agricultural Research Institute, New Delhi, India
| | - Godawari Shivaji Pawar
- Division of Agricultural Botany, Vasantrao Naik Marathwada Krishi Vidyapeeth, Parbhani, India
| | - Dhandapani Raju
- Division of Plant Physiology and Nanaji Deshmukh Plant Phenomics Centre (NDPPC), Indian Council of Agricultural Research (ICAR)-Indian Agricultural Research Institute, New Delhi, India
| | - Hari Krishna
- Division of Genetics, ICAR-Indian Agricultural Research Institute, New Delhi, India
| | - Lekshmy Sathee
- Division of Plant Physiology and Nanaji Deshmukh Plant Phenomics Centre (NDPPC), Indian Council of Agricultural Research (ICAR)-Indian Agricultural Research Institute, New Delhi, India
| | - Monika Dalal
- ICAR-National Institute for Plant Biotechnology, New Delhi, India
| | - Rabi Narayan Sahoo
- Division of Agricultural Physics, ICAR-Indian Agricultural Research Institute, New Delhi, India
| | - Viswanathan Chinnusamy
- Division of Plant Physiology and Nanaji Deshmukh Plant Phenomics Centre (NDPPC), Indian Council of Agricultural Research (ICAR)-Indian Agricultural Research Institute, New Delhi, India
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15
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Parveen R, Kumar M, Singh D, Shahani M, Imam Z, Sahoo JP. Understanding the genomic selection for crop improvement: current progress and future prospects. Mol Genet Genomics 2023; 298:813-821. [PMID: 37162565 DOI: 10.1007/s00438-023-02026-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2023] [Accepted: 04/27/2023] [Indexed: 05/11/2023]
Abstract
Although increased use of modern breeding techniques and technology has resulted in long-term genetic gain, the pace of genetic gain must be sped up to satisfy global agricultural demand. However, marker-assisted selection has proven its potential for improving qualitative traits with large effects regulated by one to few genes. Its contribution to the improvement of the quantitative traits regulated by a number of small-effect genes is modest. In this context, genomic selection (GS) has been regarded as the most promising method for genetically enhancing complicated features that are regulated by several genes, each of which has minor effects. By examining a population's phenotypes and high-density marker scores, genomic selection can forecast the breeding potential of individual lines. The fact that GS uses all marker data in the prediction model prevents skewed marker effect estimations and maximizes the amount of variation caused by small-effect QTL. It has the ability to speed up the breeding cycle and as a consequence of which superior genotypes are selected rapidly. Developing the best GS models while taking into account non-additive effects, genotype-by-environment interaction, and cost-effectiveness will enable the widespread implementation of GS in plants. These steps will also increase heritability estimation and prediction accuracy. This review focuses on the shift from conventional selection methods to GS, underlying statistical tools and methodologies, the state of GS research in agricultural plants, and prospects for its effective use in the creation of climate-resilient crops.
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Affiliation(s)
- Rabiya Parveen
- Department of Genetics and Plant Breeding, Bihar Agricultural University, Sabour, Bhagalpur, 813210, India
| | - Mankesh Kumar
- Department of Genetics and Plant Breeding, Bihar Agricultural University, Sabour, Bhagalpur, 813210, India
| | - Digvijay Singh
- Department of Genetics and Plant Breeding, Narayan Institute of Agricultural Sciences, Gopal Narayan Singh University, Sasaram, 821305, India
| | - Monika Shahani
- Department of Genetics and Plant Breeding, Maharana Pratap University of Agriculture and Technology, Udaipur, 313001, India
| | - Zafar Imam
- Department of Genetics and Plant Breeding, Bihar Agricultural University, Sabour, Bhagalpur, 813210, India
| | - Jyoti Prakash Sahoo
- Department of Agriculture and Allied Sciences, C.V. Raman Global University, Bhubaneswar, 752054, India.
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16
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Kumar M, Patel M, Solanki S, Gami R. Evaluation of Indian ginseng [ Withania somnifera (L.) Dunal] breeding lines and genotype-by-environment interaction across production environments in western India. VEGETOS (BAREILLY, INDIA) 2023:1-13. [PMID: 37359123 PMCID: PMC10153036 DOI: 10.1007/s42535-023-00626-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Revised: 03/17/2023] [Accepted: 04/01/2023] [Indexed: 06/28/2023]
Abstract
In order to find location-specific and broadly adapted genotypes for total root alkaloid content and dry root yield along with additive main effects and multiplicative interactions (AMMI) and genotype (G) main effects plus genotype × environment (E) interaction in Indian ginseng (Withania somnifera (L.) Dunal), (GGE) biplot analyses were used in the current study. Trials were carried out in a randomized complete block design (RCBD) over three succeeding years viz., 2016-2017, 2017-2018 and 2018-2019 at three different locations (S. K. Nagar, Bhiloda and Jagudan). Analysis of variance (ANOVA) for AMMI for dry root yield revealed that the environment, genotype, and GE interaction, respectively, accounted for significant sums of squares of 35.31%, 24.89%, and 32.96%. For total root alkaloid content, a significance of 27.59% of total sum of squares was justified by environment, 17.72% by genotype and 43.13% by GEI. Nine experimental trials in total were taken into consideration as contexts for the GEI analysis in 16 genotypes, including one check. AMMI analysis showed that genotypes, SKA-11, SKA-27, SKA-23 and SKA-10 were superior for mean dry root yield and SKA-11, SKA-27 and SKA-21 had better performance for total root alkaloid content across environment. The GGE biplot analysis showed genotypes SKA-11, SKA-27, SKA-10 desirable for dry root yield and SKA-26, SKA-27, SKA-11 for total root alkaloid content. As a result of the GGE and AMMI biplot techniques, SKA-11 and SKA-27 were determined to be the most desired genotypes for both total root alkaloid content and dry root yield. Further, simultaneous stability index or SSI statistics identified SKA-6, SKA-10, SKA-27, SKA-11 and AWS-1 for higher dry root yield, whilst SKA-25, SKA-6, SKA-11, SKA-12 and AWS-1 for total alkaloid content from root. Based on trait variation, GGE biplot analysis identified two mega-environments for dry root yield and a total of four for total root alkaloid content. Additionally, two representative and discriminating environments-one for dry root production and the other for total root alkaloid content were found. Location-specific and breeding for broad adaptation could be advocated for improvement and release of varieties for Indian ginseng.
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Affiliation(s)
- Mithlesh Kumar
- Department of Genetics and Plant Breeding, C. P. College of Agriculture, S.D. Agricultural University, Sardarkrushinagar, Gujarat 385 506 India
- AICRN on Potential Crops, ARS Mandor, Agriculture University, Jodhpur, Rajasthan 342 304 India
| | - Manubhai Patel
- Department of Genetics and Plant Breeding, C. P. College of Agriculture, S.D. Agricultural University, Sardarkrushinagar, Gujarat 385 506 India
| | - Satyanarayan Solanki
- Department of Genetics and Plant Breeding, C. P. College of Agriculture, S.D. Agricultural University, Sardarkrushinagar, Gujarat 385 506 India
| | - Raman Gami
- Department of Genetics and Plant Breeding, C. P. College of Agriculture, S.D. Agricultural University, Sardarkrushinagar, Gujarat 385 506 India
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17
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Hu Y, Schmidhalter U. Opportunity and challenges of phenotyping plant salt tolerance. TRENDS IN PLANT SCIENCE 2023; 28:552-566. [PMID: 36628656 DOI: 10.1016/j.tplants.2022.12.010] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Revised: 12/03/2022] [Accepted: 12/15/2022] [Indexed: 05/22/2023]
Abstract
Salinity is a key factor limiting agricultural production worldwide. Recent advances in field phenotyping have enabled the recording of the environmental history and dynamic response of plants by considering both genotype × environment (G×E) interactions and envirotyping. However, only a few studies have focused on plant salt tolerance phenotyping. Therefore, we analyzed the potential opportunities and major challenges in improving plant salt tolerance using advanced field phenotyping technologies. RGB imaging and spectral and thermal sensors are the most useful and important sensing techniques for assessing key morphological and physiological traits of plant salt tolerance. However, field phenotyping faces challenges owing to its practical applications and high costs, limiting its use in early generation breeding and in developing countries.
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Affiliation(s)
- Yuncai Hu
- Chair of Plant Nutrition, School of Life Sciences, Technical University of Munich, D-85354 Freising, Germany.
| | - Urs Schmidhalter
- Chair of Plant Nutrition, School of Life Sciences, Technical University of Munich, D-85354 Freising, Germany
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18
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Genangeli A, Avola G, Bindi M, Cantini C, Cellini F, Grillo S, Petrozza A, Riggi E, Ruggiero A, Summerer S, Tedeschi A, Gioli B. Low-Cost Hyperspectral Imaging to Detect Drought Stress in High-Throughput Phenotyping. PLANTS (BASEL, SWITZERLAND) 2023; 12:1730. [PMID: 37111953 PMCID: PMC10143644 DOI: 10.3390/plants12081730] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Revised: 04/13/2023] [Accepted: 04/19/2023] [Indexed: 06/19/2023]
Abstract
Recent developments in low-cost imaging hyperspectral cameras have opened up new possibilities for high-throughput phenotyping (HTP), allowing for high-resolution spectral data to be obtained in the visible and near-infrared spectral range. This study presents, for the first time, the integration of a low-cost hyperspectral camera Senop HSC-2 into an HTP platform to evaluate the drought stress resistance and physiological response of four tomato genotypes (770P, 990P, Red Setter and Torremaggiore) during two cycles of well-watered and deficit irrigation. Over 120 gigabytes of hyperspectral data were collected, and an innovative segmentation method able to reduce the hyperspectral dataset by 85.5% was developed and applied. A hyperspectral index (H-index) based on the red-edge slope was selected, and its ability to discriminate stress conditions was compared with three optical indices (OIs) obtained by the HTP platform. The analysis of variance (ANOVA) applied to the OIs and H-index revealed the better capacity of the H-index to describe the dynamic of drought stress trend compared to OIs, especially in the first stress and recovery phases. Selected OIs were instead capable of describing structural changes during plant growth. Finally, the OIs and H-index results have revealed a higher susceptibility to drought stress in 770P and 990P than Red Setter and Torremaggiore genotypes.
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Affiliation(s)
- Andrea Genangeli
- Department of Agriculture, Food, Environment and Forestry (DAGRI), University of Florence, Piazzale delle Cascine 18, 50144 Florence, Italy; (A.G.); (M.B.)
| | - Giovanni Avola
- Institute of Bioeconomy (IBE), National Research Council (CNR), Via Caproni 8, 50145 Florence, Italy; (G.A.); (C.C.); (E.R.)
| | - Marco Bindi
- Department of Agriculture, Food, Environment and Forestry (DAGRI), University of Florence, Piazzale delle Cascine 18, 50144 Florence, Italy; (A.G.); (M.B.)
| | - Claudio Cantini
- Institute of Bioeconomy (IBE), National Research Council (CNR), Via Caproni 8, 50145 Florence, Italy; (G.A.); (C.C.); (E.R.)
| | - Francesco Cellini
- Centro Ricerche Metapontum Agrobios-Agenzia Lucana di Sviluppo e di Innovazione in Agricoltura (ALSIA), S.S. Jonica 106, km 448,2, 75010 Metaponto di Bernalda, Italy; (F.C.); (A.P.); (S.S.)
| | - Stefania Grillo
- D1 National Research Council of Italy, Institute of Biosciences and Bioresources, Via Università 133, 80055 Portici, Italy; (S.G.); (A.R.); (A.T.)
| | - Angelo Petrozza
- Centro Ricerche Metapontum Agrobios-Agenzia Lucana di Sviluppo e di Innovazione in Agricoltura (ALSIA), S.S. Jonica 106, km 448,2, 75010 Metaponto di Bernalda, Italy; (F.C.); (A.P.); (S.S.)
| | - Ezio Riggi
- Institute of Bioeconomy (IBE), National Research Council (CNR), Via Caproni 8, 50145 Florence, Italy; (G.A.); (C.C.); (E.R.)
| | - Alessandra Ruggiero
- D1 National Research Council of Italy, Institute of Biosciences and Bioresources, Via Università 133, 80055 Portici, Italy; (S.G.); (A.R.); (A.T.)
| | - Stephan Summerer
- Centro Ricerche Metapontum Agrobios-Agenzia Lucana di Sviluppo e di Innovazione in Agricoltura (ALSIA), S.S. Jonica 106, km 448,2, 75010 Metaponto di Bernalda, Italy; (F.C.); (A.P.); (S.S.)
| | - Anna Tedeschi
- D1 National Research Council of Italy, Institute of Biosciences and Bioresources, Via Università 133, 80055 Portici, Italy; (S.G.); (A.R.); (A.T.)
| | - Beniamino Gioli
- Institute of Bioeconomy (IBE), National Research Council (CNR), Via Caproni 8, 50145 Florence, Italy; (G.A.); (C.C.); (E.R.)
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19
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Thuraga V, Martinsson UD, Vetukuri RR, Chawade A. Delineation of Genotype X Environment Interaction for Grain Yield in Spring Barley under Untreated and Fungicide-Treated Environments. PLANTS (BASEL, SWITZERLAND) 2023; 12:715. [PMID: 36840063 PMCID: PMC9961658 DOI: 10.3390/plants12040715] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Revised: 01/25/2023] [Accepted: 01/30/2023] [Indexed: 06/18/2023]
Abstract
Barley (Hordeul vulgare L.) is the fourth most important cereal crop based on production and cultivated area. Biotic stresses, especially fungal diseases in barley, are devastating, incurring high possibilities of absolute yield loss. Identifying superior and stable yielding genotypes is crucial for accompanying the increasing barley demand. However, the identification and recommendation of superior genotypes is challenging due to the interaction between genotype and environment. Hence, the present investigation was aimed at evaluating the grain yield of different sets of spring barley genotypes when undergoing one of two treatments (no treatment and fungicide treatment) laid out in an alpha lattice design in six to seven locations for five years, through additive main effects and multiplicative interaction (AMMI), GGE biplot (genotype + genotype X environment), and stability analysis. The combined analysis of variance indicated that the environment was the main factor that contributed to the variation in grain yield, followed by genotype X environment interaction (GEI) effects and genotypic effects. Ten mega environments (MEs) with five MEs from each of the treatments harboured well-adapted, stable yielding genotypes. Exploiting the stable yielding genotypes with discreet use of the representative and discriminative environments identified in the present study could aid in breeding for the improvement of grain yield in spring barley genotypes.
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Affiliation(s)
- Vishnukiran Thuraga
- Department of Plant Breeding, Swedish University of Agricultural Sciences, 23422 Lomma, Sweden
| | | | - Ramesh R. Vetukuri
- Department of Plant Breeding, Swedish University of Agricultural Sciences, 23422 Lomma, Sweden
| | - Aakash Chawade
- Department of Plant Breeding, Swedish University of Agricultural Sciences, 23422 Lomma, Sweden
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20
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Guo T, Li X. Machine learning for predicting phenotype from genotype and environment. Curr Opin Biotechnol 2023; 79:102853. [PMID: 36463837 DOI: 10.1016/j.copbio.2022.102853] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Revised: 11/01/2022] [Accepted: 11/07/2022] [Indexed: 12/03/2022]
Abstract
Predicting phenotype with genomic and environmental information is critically needed and challenging. Machine learning methods have emerged as powerful tools to make accurate predictions from large and complex biological data. Here, we review the progress of phenotype prediction models enabled or improved by machine learning methods. We categorized the applications into three scenarios: prediction with genotypic information, with environmental information, and with both. In each scenario, we illustrate the practicality of prediction models, the advantages of machine learning, and the challenges of modeling complex relationships. We discuss the promising potential of leveraging machine learning and genetics theories to develop models that can predict phenotype and also interpret the biological consequences of changes in genotype and environment.
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Affiliation(s)
- Tingting Guo
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China; Hubei Hongshan Laboratory, Wuhan 430070, China.
| | - Xianran Li
- USDA, Agricultural Research Service, Wheat Health, Genetics, and Quality Research Unit, Pullman, WA 99164, USA; Department of Crop and Soil Sciences, Washington State University, Pullman, WA 99164, USA.
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21
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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: 10] [Impact Index Per Article: 10.0] [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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22
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Xiong W, Reynolds M, Xu Y. Climate change challenges plant breeding. CURRENT OPINION IN PLANT BIOLOGY 2022; 70:102308. [PMID: 36279790 DOI: 10.1016/j.pbi.2022.102308] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Revised: 09/12/2022] [Accepted: 09/17/2022] [Indexed: 06/16/2023]
Abstract
Plant breeding is important to cope with climate change impacts, complementing crop management and policy interventions to ensure global food production. However, changes in environmental factors also affect the objectives, efficiency, and genetic gains of the current plant breeding system. In this review, we summarize the challenges prompted by climate change to breeding climate-resilient crops and the limitations of the next-generation breeding approach in addressing climate change. It is anticipated that the integration of multi-disciplines and technologies into three schemes of genotyping, phenotyping, and envirotyping will result in the delivery of climate change-ready crops in less time.
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Affiliation(s)
- Wei Xiong
- CIMMYT-Henan Joint Center for Wheat and Maize Improvement, Henan Agricultural University, Zhengzhou, China; International Maize and Wheat Improvement Center (CIMMYT), El Batan, Texcoco, Mexico.
| | - Matthew Reynolds
- International Maize and Wheat Improvement Center (CIMMYT), El Batan, Texcoco, Mexico
| | - Yunbi Xu
- International Maize and Wheat Improvement Center (CIMMYT), El Batan, Texcoco, Mexico; Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing, China
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23
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Tao H, Xu S, Tian Y, Li Z, Ge Y, Zhang J, Wang Y, Zhou G, Deng X, Zhang Z, Ding Y, Jiang D, Guo Q, Jin S. Proximal and remote sensing in plant phenomics: 20 years of progress, challenges, and perspectives. PLANT COMMUNICATIONS 2022; 3:100344. [PMID: 35655429 PMCID: PMC9700174 DOI: 10.1016/j.xplc.2022.100344] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Revised: 05/08/2022] [Accepted: 05/27/2022] [Indexed: 06/01/2023]
Abstract
Plant phenomics (PP) has been recognized as a bottleneck in studying the interactions of genomics and environment on plants, limiting the progress of smart breeding and precise cultivation. High-throughput plant phenotyping is challenging owing to the spatio-temporal dynamics of traits. Proximal and remote sensing (PRS) techniques are increasingly used for plant phenotyping because of their advantages in multi-dimensional data acquisition and analysis. Substantial progress of PRS applications in PP has been observed over the last two decades and is analyzed here from an interdisciplinary perspective based on 2972 publications. This progress covers most aspects of PRS application in PP, including patterns of global spatial distribution and temporal dynamics, specific PRS technologies, phenotypic research fields, working environments, species, and traits. Subsequently, we demonstrate how to link PRS to multi-omics studies, including how to achieve multi-dimensional PRS data acquisition and processing, how to systematically integrate all kinds of phenotypic information and derive phenotypic knowledge with biological significance, and how to link PP to multi-omics association analysis. Finally, we identify three future perspectives for PRS-based PP: (1) strengthening the spatial and temporal consistency of PRS data, (2) exploring novel phenotypic traits, and (3) facilitating multi-omics communication.
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Affiliation(s)
- Haiyu Tao
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, National Engineering and Technology Center for Information Agriculture, Collaborative Innovation Centre for Modern Crop Production co-sponsored by Province and Ministry, Nanjing Agricultural University, Address: No. 1 Weigang, Xuanwu District, Nanjing 210095, China
| | - Shan Xu
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, National Engineering and Technology Center for Information Agriculture, Collaborative Innovation Centre for Modern Crop Production co-sponsored by Province and Ministry, Nanjing Agricultural University, Address: No. 1 Weigang, Xuanwu District, Nanjing 210095, China
| | - Yongchao Tian
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, National Engineering and Technology Center for Information Agriculture, Collaborative Innovation Centre for Modern Crop Production co-sponsored by Province and Ministry, Nanjing Agricultural University, Address: No. 1 Weigang, Xuanwu District, Nanjing 210095, China
| | - Zhaofeng Li
- The Key Laboratory of Oasis Eco-agriculture, Xinjiang Production and Construction Corps, Agriculture College, Shihezi University, Shihezi 832003, China
| | - Yan Ge
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, National Engineering and Technology Center for Information Agriculture, Collaborative Innovation Centre for Modern Crop Production co-sponsored by Province and Ministry, Nanjing Agricultural University, Address: No. 1 Weigang, Xuanwu District, Nanjing 210095, China
| | - Jiaoping Zhang
- State Key Laboratory of Crop Genetics and Germplasm Enhancement, National Center for Soybean Improvement, Key Laboratory for Biology and Genetic Improvement of Soybean (General, Ministry of Agriculture), Nanjing Agricultural University, Nanjing 210095, China
| | - Yu Wang
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, National Engineering and Technology Center for Information Agriculture, Collaborative Innovation Centre for Modern Crop Production co-sponsored by Province and Ministry, Nanjing Agricultural University, Address: No. 1 Weigang, Xuanwu District, Nanjing 210095, China
| | - Guodong Zhou
- Sanya Research Institute of Nanjing Agriculture University, Sanya 572024, China
| | - Xiong Deng
- Key Laboratory of Plant Molecular Physiology, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China
| | - Ze Zhang
- The Key Laboratory of Oasis Eco-agriculture, Xinjiang Production and Construction Corps, Agriculture College, Shihezi University, Shihezi 832003, China
| | - Yanfeng Ding
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, National Engineering and Technology Center for Information Agriculture, Collaborative Innovation Centre for Modern Crop Production co-sponsored by Province and Ministry, Nanjing Agricultural University, Address: No. 1 Weigang, Xuanwu District, Nanjing 210095, China; Hainan Yazhou Bay Seed Laboratory, Sanya 572025, China; Sanya Research Institute of Nanjing Agriculture University, Sanya 572024, China
| | - Dong Jiang
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, National Engineering and Technology Center for Information Agriculture, Collaborative Innovation Centre for Modern Crop Production co-sponsored by Province and Ministry, Nanjing Agricultural University, Address: No. 1 Weigang, Xuanwu District, Nanjing 210095, China; Hainan Yazhou Bay Seed Laboratory, Sanya 572025, China; Sanya Research Institute of Nanjing Agriculture University, Sanya 572024, China
| | - Qinghua Guo
- Institute of Ecology, College of Urban and Environmental Science, Peking University, Beijing 100871, China
| | - Shichao Jin
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, National Engineering and Technology Center for Information Agriculture, Collaborative Innovation Centre for Modern Crop Production co-sponsored by Province and Ministry, Nanjing Agricultural University, Address: No. 1 Weigang, Xuanwu District, Nanjing 210095, China; Hainan Yazhou Bay Seed Laboratory, Sanya 572025, China; Sanya Research Institute of Nanjing Agriculture University, Sanya 572024, China; Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, International Institute for Earth System Sciences, Nanjing University, Nanjing, Jiangsu 210023, China.
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24
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Yue H, Olivoto T, Bu J, Li J, Wei J, Xie J, Chen S, Peng H, Nardino M, Jiang X. Multi-trait selection for mean performance and stability of maize hybrids in mega-environments delineated using envirotyping techniques. FRONTIERS IN PLANT SCIENCE 2022; 13:1030521. [PMID: 36452111 PMCID: PMC9702090 DOI: 10.3389/fpls.2022.1030521] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Accepted: 10/26/2022] [Indexed: 06/17/2023]
Abstract
Under global climate changes, understanding climate variables that are most associated with environmental kinships can contribute to improving the success of hybrid selection, mainly in environments with high climate variations. The main goal of this study is to integrate envirotyping techniques and multi-trait selection for mean performance and the stability of maize genotypes growing in the Huanghuaihai plain in China. A panel of 26 maize hybrids growing in 10 locations in two crop seasons was evaluated for 9 traits. Considering 20 years of climate information and 19 environmental covariables, we identified four mega-environments (ME) in the Huanghuaihai plain which grouped locations that share similar long-term weather patterns. All the studied traits were significantly affected by the genotype × mega-environment × year interaction, suggesting that evaluating maize stability using single-year, multi-environment trials may provide misleading recommendations. Counterintuitively, the highest yields were not observed in the locations with higher accumulated rainfall, leading to the hypothesis that lower vapor pressure deficit, minimum temperatures, and high relative humidity are climate variables that -under no water restriction- reduce plant transpiration and consequently the yield. Utilizing the multi-trait mean performance and stability index (MTMPS) prominent hybrids with satisfactory mean performance and stability across cultivation years were identified. G23 and G25 were selected within three out of the four mega-environments, being considered the most stable and widely adapted hybrids from the panel. The G5 showed satisfactory yield and stability across contrasting years in the drier, warmer, and with higher vapor pressure deficit mega-environment, which included locations in the Hubei province. Overall, this study opens the door to a more systematic and dynamic characterization of the environment to better understand the genotype-by-environment interaction in multi-environment trials.
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Affiliation(s)
- Haiwang Yue
- Hebei Provincial Key Laboratory of Crops Drought Resistance Research, Dryland Farming Institute, Hebei Academy of Agriculture and Forestry Sciences, Hengshui, China
| | - Tiago Olivoto
- Department of Plant Science, Center of Agrarian Sciences, Federal University of Santa Catarina, Florianópolis, SC, Brazil
| | - Junzhou Bu
- Hebei Provincial Key Laboratory of Crops Drought Resistance Research, Dryland Farming Institute, Hebei Academy of Agriculture and Forestry Sciences, Hengshui, China
| | - Jie Li
- Hebei Provincial Key Laboratory of Crops Drought Resistance Research, Dryland Farming Institute, Hebei Academy of Agriculture and Forestry Sciences, Hengshui, China
| | - Jianwei Wei
- Hebei Provincial Key Laboratory of Crops Drought Resistance Research, Dryland Farming Institute, Hebei Academy of Agriculture and Forestry Sciences, Hengshui, China
| | - Junliang Xie
- Hebei Provincial Key Laboratory of Crops Drought Resistance Research, Dryland Farming Institute, Hebei Academy of Agriculture and Forestry Sciences, Hengshui, China
| | - Shuping Chen
- Hebei Provincial Key Laboratory of Crops Drought Resistance Research, Dryland Farming Institute, Hebei Academy of Agriculture and Forestry Sciences, Hengshui, China
| | - Haicheng Peng
- Hebei Provincial Key Laboratory of Crops Drought Resistance Research, Dryland Farming Institute, Hebei Academy of Agriculture and Forestry Sciences, Hengshui, China
| | - Maicon Nardino
- Department of Agronomy, Federal University of Viçosa, Viçosa, MG, Brazil
| | - Xuwen Jiang
- Maize Research Institute, Qingdao Agricultural University, Qingdao, China
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25
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Genetic Dissection of Phosphorus Use Efficiency and Genotype-by-Environment Interaction in Maize. Int J Mol Sci 2022; 23:ijms232213943. [PMID: 36430424 PMCID: PMC9697416 DOI: 10.3390/ijms232213943] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Revised: 11/07/2022] [Accepted: 11/08/2022] [Indexed: 11/16/2022] Open
Abstract
Genotype-by-environment interaction (G-by-E) is a common but potentially problematic phenomenon in plant breeding. In this study, we investigated the genotypic performance and two measures of plasticity on a phenotypic and genetic level by assessing 234 maize doubled haploid lines from six populations for 15 traits in seven macro-environments with a focus on varying soil phosphorus levels. It was found intergenic regions contributed the most to the variation of phenotypic linear plasticity. For 15 traits, 124 and 31 quantitative trait loci (QTL) were identified for genotypic performance and phenotypic plasticity, respectively. Further, some genes associated with phosphorus use efficiency, such as Zm00001eb117170, Zm00001eb258520, and Zm00001eb265410, encode small ubiquitin-like modifier E3 ligase were identified. By significantly testing the main effect and G-by-E effect, 38 main QTL and 17 interaction QTL were identified, respectively, in which MQTL38 contained the gene Zm00001eb374120, and its effect was related to phosphorus concentration in the soil, the lower the concentration, the greater the effect. Differences in the size and sign of the QTL effect in multiple environments could account for G-by-E. At last, the superiority of G-by-E in genomic selection was observed. In summary, our findings will provide theoretical guidance for breeding P-efficient and broadly adaptable varieties.
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26
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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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27
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Casadebaig P, Gauffreteau A, Landré A, Langlade NB, Mestries E, Sarron J, Trépos R, Vincourt P, Debaeke P. Optimized cultivar deployment improves the efficiency and stability of sunflower crop production at national scale. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2022; 135:4049-4063. [PMID: 35294575 DOI: 10.1007/s00122-022-04072-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/06/2022] [Accepted: 02/26/2022] [Indexed: 06/14/2023]
Abstract
Crop simulation helps to analyze environmental impacts on crops and provides year-independent context information. This information is of major importance when deciding which cultivar to choose at sowing time. Plant breeding programs design new crop cultivars which, while developed for distinct populations of environments, are nevertheless grown over large areas during their time in the market. Over its cultivation area, the crop is exposed to highly diverse stress patterns caused by climatic uncertainty and multiple management options, which often leads to decreased expected crop performance. In this study, we aim to assess how finer spatial management of genetic resources could reduce the yield variance explained by genotype × environment interactions in a set of cropping environments and ultimately improve the efficiency and stability of crop production. We used modeling and simulation to predict the crop performance resulting from the interaction between cultivar growth and development, climate and soil conditions, and management practices. We designed a computational experiment that evaluated the performance of a collection of commercial sunflower cultivars in a realistic population of cropping conditions in France, built from extensive agricultural surveys. Distinct farming locations sharing similar simulated abiotic stress patterns were clustered together to specify environment types. We then used optimization methods to search for cultivars × environments combinations leading to increased yield expectations. Results showed that a single cultivar choice adapted to the most frequent environment-type in the population is a robust strategy. However, the relevance of cultivar recommendations to specific locations was gradually increasing with the knowledge of pedo-climatic conditions. We argue that this approach while being operational on current genetic material could act synergistically with plant breeding as more diverse material could enable access to cultivars with distinctive traits, more adapted to specific conditions.
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Affiliation(s)
| | - Arnaud Gauffreteau
- Univ. Paris-Saclay, INRAE, AgroParisTech, UMR Agronomie, 78850, Thiverval-Grignon, France
| | - Amélia Landré
- Univ. Toulouse, INRAE, UMR AGIR, 31320, Castanet-Tolosan, France
| | | | | | - Julien Sarron
- Univ. Toulouse, INRAE, UMR AGIR, 31320, Castanet-Tolosan, France
- Univ. Montpellier, CIRAD, UPR HortSys, 34398, Montpellier, France
| | - Ronan Trépos
- Univ. Toulouse, INRAE, UR MIAT, 31320, Castanet-Tolosan, France
| | - Patrick Vincourt
- Univ. Toulouse, INRAE, UMR LIPM, 31320, Castanet-Tolosan, France
| | - Philippe Debaeke
- Univ. Toulouse, INRAE, UMR AGIR, 31320, Castanet-Tolosan, France
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28
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Bhardwaj V, Rawat S, Tiwari J, Sood S, Dua VK, Singh B, Lal M, Mangal V, Govindakrishnan PM. Characterizing the Potato Growing Regions in India Using Meteorological Parameters. Life (Basel) 2022; 12:life12101619. [PMID: 36295054 PMCID: PMC9605082 DOI: 10.3390/life12101619] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Revised: 09/30/2022] [Accepted: 10/08/2022] [Indexed: 11/16/2022] Open
Abstract
Currently, the multi-location testing of advanced hybrids in India is carried out at 25 centers under the All India Co-ordinated Research Project on Potato (AICRP-P), which is spread across the country. These centres have been chosen to represent different potato growing regions based on soil and agronomic features. However, the reliable deployment of the newly bred varieties in different regions requires a scientific delineation of potato growing zones with homogenous climates. The present study was undertaken to develop homogenous zones in the Indian sub-continent based on the environmental parameters of the potato growing season. A total of 1253 locations were identified across the country as having a plausible potato growing season of at least 70 days with suitable thermal limits. Six variables including five meteorological parameters including Physiological days (P days), Growing degree days (GDD), Mean daily temperature, Mean night temperature and Mean daily incident solar radiation, together with altitude as the sixth variable, were used for Agglomerative Hierarchical Clustering (AHC) and the Principal Component Analysis by Multidimensional Scaling (MDS) technique to derive identical classes. The thematic map of the classes was overlaid on potato growing districts of India using ArcGIS 9.1 software. The study clearly depicted that the clustering technique can effectively delineate the target population of environments (TPE) for potato genotypes performing well at different testing environments in India. The study also identifies target locations for future focus on breeding strategies, especially the high night temperature class having a large expanse in India. This is also vital in view of the impending climate change situation.
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Affiliation(s)
- Vinay Bhardwaj
- ICAR-Central Potato Research Institute, Shimla 171001, India
- Correspondence:
| | - Shashi Rawat
- ICAR-Central Potato Research Institute, Shimla 171001, India
| | - Jagesh Tiwari
- ICAR-Central Potato Research Institute, Shimla 171001, India
| | - Salej Sood
- ICAR-Central Potato Research Institute, Shimla 171001, India
| | - Vijay Kumar Dua
- ICAR-Central Potato Research Institute, Shimla 171001, India
| | - Baljeet Singh
- ICAR-Central Potato Research Institute, Shimla 171001, India
| | - Mehi Lal
- ICAR-Central Potato Research Institute, Modipuram 250110, India
| | - Vikas Mangal
- ICAR-Central Potato Research Institute, Shimla 171001, India
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29
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Li Y, Tao F, Hao Y, Tong J, Xiao Y, He Z, Reynolds M. Wheat traits and the associated loci conferring radiation use efficiency. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2022; 112:565-582. [PMID: 36004546 DOI: 10.1111/tpj.15954] [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: 05/02/2022] [Revised: 08/16/2022] [Accepted: 08/20/2022] [Indexed: 06/15/2023]
Abstract
Wheat (Triticum aestivum L.) radiation use efficiency (RUE) must be raised through crop breeding to further increase the yield potential, as the harvest index is now close to its theoretical limit. Field experiments including 209 wheat cultivars which have been widely cultivated in China since the 1940s were conducted in two growing seasons (2018-2019 and 2019-2020) to evaluate the variations of phenological, physiological, plant architectural, and yield-related traits and their contributions to RUE and to identify limiting factors for wheat yield potential. The average annual genetic gain in grain yield was 0.60% (or 45.32 kg ha-1 year-1 ; R2 = 0.44, P < 0.01), mainly attributed to the gain in RUE (r = 0.85, P < 0.01). The net photosynthetic rates were positively and closely correlated with grain RUE and grain yield, suggesting source as a limiting factor to future yield gains. Thirty-four cultivars were identified, exhibiting not only high RUE, but also traits contributing to high RUE and 11 other critical traits - of known genetic basis - as potential parents for breeding to improve yield and RUE. Our findings reveal wheat traits and the associated loci conferring RUE, which are valuable for facilitating marker-assisted breeding to improve wheat RUE and yield potential.
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Affiliation(s)
- Yibo Li
- Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing, 100101, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Fulu Tao
- Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing, 100101, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
- Natural Resources Institute Finland (Luke), Helsinki, Finland
| | - Yuanfeng Hao
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Jingyang Tong
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Yonggui Xiao
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Zhonghu He
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Matthew Reynolds
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
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Seyum EG, Bille NH, Abtew WG, Munyengwa N, Bell JM, Cros D. Genomic selection in tropical perennial crops and plantation trees: a review. MOLECULAR BREEDING : NEW STRATEGIES IN PLANT IMPROVEMENT 2022; 42:58. [PMID: 37313015 PMCID: PMC10248687 DOI: 10.1007/s11032-022-01326-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Accepted: 09/06/2022] [Indexed: 06/15/2023]
Abstract
To overcome the multiple challenges currently faced by agriculture, such as climate change and soil deterioration, more efficient plant breeding strategies are required. Genomic selection (GS) is crucial for the genetic improvement of quantitative traits, as it can increase selection intensity, shorten the generation interval, and improve selection accuracy for traits that are difficult to phenotype. Tropical perennial crops and plantation trees are of major economic importance and have consequently been the subject of many GS articles. In this review, we discuss the factors that affect GS accuracy (statistical models, linkage disequilibrium, information concerning markers, relatedness between training and target populations, the size of the training population, and trait heritability) and the genetic gain expected in these species. The impact of GS will be particularly strong in tropical perennial crops and plantation trees as they have long breeding cycles and constrained selection intensity. Future GS prospects are also discussed. High-throughput phenotyping will allow constructing of large training populations and implementing of phenomic selection. Optimized modeling is needed for longitudinal traits and multi-environment trials. The use of multi-omics, haploblocks, and structural variants will enable going beyond single-locus genotype data. Innovative statistical approaches, like artificial neural networks, are expected to efficiently handle the increasing amounts of heterogeneous multi-scale data. Targeted recombinations on sites identified from profiles of marker effects have the potential to further increase genetic gain. GS can also aid re-domestication and introgression breeding. Finally, GS consortia will play an important role in making the best of these opportunities. Supplementary Information The online version contains supplementary material available at 10.1007/s11032-022-01326-4.
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Affiliation(s)
- Essubalew Getachew Seyum
- Department of Plant Biology and Physiology, Faculty of Sciences, University of Yaoundé I, Yaoundé, Cameroon
- Department of Horticulture and Plant Sciences, College of Agriculture and Veterinary Medicine, Jimma University, P.O. Box 307, Jimma, Ethiopia
| | - Ngalle Hermine Bille
- Department of Plant Biology and Physiology, Faculty of Sciences, University of Yaoundé I, Yaoundé, Cameroon
| | - Wosene Gebreselassie Abtew
- Department of Horticulture and Plant Sciences, College of Agriculture and Veterinary Medicine, Jimma University, P.O. Box 307, Jimma, Ethiopia
| | - Norman Munyengwa
- Queensland Alliance for Agriculture and Food Innovation, University of Queensland, Brisbane, QLD 4072 Australia
| | - Joseph Martin Bell
- Department of Plant Biology and Physiology, Faculty of Sciences, University of Yaoundé I, Yaoundé, Cameroon
| | - David Cros
- CIRAD, UMR AGAP Institut, 34398 Montpellier, France
- UMR AGAP Institut, CIRAD, INRAE, Univ. Montpellier, Institut Agro, 34398 Montpellier, France
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31
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Applications of Artificial Intelligence in Climate-Resilient Smart-Crop Breeding. Int J Mol Sci 2022; 23:ijms231911156. [PMID: 36232455 PMCID: PMC9570104 DOI: 10.3390/ijms231911156] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2022] [Revised: 09/18/2022] [Accepted: 09/19/2022] [Indexed: 11/21/2022] Open
Abstract
Recently, Artificial intelligence (AI) has emerged as a revolutionary field, providing a great opportunity in shaping modern crop breeding, and is extensively used indoors for plant science. Advances in crop phenomics, enviromics, together with the other “omics” approaches are paving ways for elucidating the detailed complex biological mechanisms that motivate crop functions in response to environmental trepidations. These “omics” approaches have provided plant researchers with precise tools to evaluate the important agronomic traits for larger-sized germplasm at a reduced time interval in the early growth stages. However, the big data and the complex relationships within impede the understanding of the complex mechanisms behind genes driving the agronomic-trait formations. AI brings huge computational power and many new tools and strategies for future breeding. The present review will encompass how applications of AI technology, utilized for current breeding practice, assist to solve the problem in high-throughput phenotyping and gene functional analysis, and how advances in AI technologies bring new opportunities for future breeding, to make envirotyping data widely utilized in breeding. Furthermore, in the current breeding methods, linking genotype to phenotype remains a massive challenge and impedes the optimal application of high-throughput field phenotyping, genomics, and enviromics. In this review, we elaborate on how AI will be the preferred tool to increase the accuracy in high-throughput crop phenotyping, genotyping, and envirotyping data; moreover, we explore the developing approaches and challenges for multiomics big computing data integration. Therefore, the integration of AI with “omics” tools can allow rapid gene identification and eventually accelerate crop-improvement programs.
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32
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Resende RT, Chenu K, Rasmussen SK, Heinemann AB, Fritsche-Neto R. Editorial: Enviromics in Plant Breeding. FRONTIERS IN PLANT SCIENCE 2022; 13:935380. [PMID: 35845710 PMCID: PMC9280691 DOI: 10.3389/fpls.2022.935380] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Accepted: 05/19/2022] [Indexed: 05/26/2023]
Affiliation(s)
| | - Karine Chenu
- Queensland Alliance for Agriculture and Food Innovation, University of Queensland, St Lucia, QLD, Australia
| | - Soren K. Rasmussen
- Section for Plant Biochemistry, Department of Plant and Environmental Sciences, Faculty of Natural and Life Sciences, University of Copenhagen, Frederiksberg, Denmark
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Gill T, Gill SK, Saini DK, Chopra Y, de Koff JP, Sandhu KS. A Comprehensive Review of High Throughput Phenotyping and Machine Learning for Plant Stress Phenotyping. PHENOMICS (CHAM, SWITZERLAND) 2022; 2:156-183. [PMID: 36939773 PMCID: PMC9590503 DOI: 10.1007/s43657-022-00048-z] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Revised: 01/29/2022] [Accepted: 02/11/2022] [Indexed: 02/04/2023]
Abstract
During the last decade, there has been rapid adoption of ground and aerial platforms with multiple sensors for phenotyping various biotic and abiotic stresses throughout the developmental stages of the crop plant. High throughput phenotyping (HTP) involves the application of these tools to phenotype the plants and can vary from ground-based imaging to aerial phenotyping to remote sensing. Adoption of these HTP tools has tried to reduce the phenotyping bottleneck in breeding programs and help to increase the pace of genetic gain. More specifically, several root phenotyping tools are discussed to study the plant's hidden half and an area long neglected. However, the use of these HTP technologies produces big data sets that impede the inference from those datasets. Machine learning and deep learning provide an alternative opportunity for the extraction of useful information for making conclusions. These are interdisciplinary approaches for data analysis using probability, statistics, classification, regression, decision theory, data visualization, and neural networks to relate information extracted with the phenotypes obtained. These techniques use feature extraction, identification, classification, and prediction criteria to identify pertinent data for use in plant breeding and pathology activities. This review focuses on the recent findings where machine learning and deep learning approaches have been used for plant stress phenotyping with data being collected using various HTP platforms. We have provided a comprehensive overview of different machine learning and deep learning tools available with their potential advantages and pitfalls. Overall, this review provides an avenue for studying various HTP platforms with particular emphasis on using the machine learning and deep learning tools for drawing legitimate conclusions. Finally, we propose the conceptual challenges being faced and provide insights on future perspectives for managing those issues.
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Affiliation(s)
- Taqdeer Gill
- grid.280741.80000 0001 2284 9820Department of Agricultural and Environmental Sciences, Tennessee State University, Nashville, TN 37209 USA
| | - Simranveer K. Gill
- grid.412577.20000 0001 2176 2352College of Agriculture, Punjab Agricultural University, Ludhiana, Punjab 141004 India
| | - Dinesh K. Saini
- grid.412577.20000 0001 2176 2352Department of Plant Breeding and Genetics, Punjab Agricultural University, Ludhiana, Punjab 141004 India
| | - Yuvraj Chopra
- grid.412577.20000 0001 2176 2352College of Agriculture, Punjab Agricultural University, Ludhiana, Punjab 141004 India
| | - Jason P. de Koff
- grid.280741.80000 0001 2284 9820Department of Agricultural and Environmental Sciences, Tennessee State University, Nashville, TN 37209 USA
| | - Karansher S. Sandhu
- grid.30064.310000 0001 2157 6568Department of Crop and Soil Sciences, Washington State University, Pullman, WA 99163 USA
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Mohd Saad NS, Neik TX, Thomas WJW, Amas JC, Cantila AY, Craig RJ, Edwards D, Batley J. Advancing designer crops for climate resilience through an integrated genomics approach. CURRENT OPINION IN PLANT BIOLOGY 2022; 67:102220. [PMID: 35489163 DOI: 10.1016/j.pbi.2022.102220] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Revised: 03/15/2022] [Accepted: 03/25/2022] [Indexed: 06/14/2023]
Abstract
Climate change and exponential population growth are exposing an immediate need for developing future crops that are highly resilient and adaptable to changing environments to maintain global food security in the next decade. Rigorous selection from long domestication history has rendered cultivated crops genetically disadvantaged, raising concerns in their ability to adapt to these new challenges and limiting their usefulness in breeding programmes. As a result, future crop improvement efforts must rely on integrating various genomic strategies ranging from high-throughput sequencing to machine learning, in order to exploit germplasm diversity and overcome bottlenecks created by domestication, expansive multi-dimensional phenotypes, arduous breeding processes, complex traits and big data.
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Affiliation(s)
- Nur Shuhadah Mohd Saad
- UWA School of Biological Sciences and the UWA Institute of Agriculture, University of Western Australia, Crawley, WA, Australia
| | - Ting Xiang Neik
- Sunway College Kuala Lumpur, Bandar Sunway, 47500, Selangor, Malaysia
| | - William J W Thomas
- UWA School of Biological Sciences and the UWA Institute of Agriculture, University of Western Australia, Crawley, WA, Australia
| | - Junrey C Amas
- UWA School of Biological Sciences and the UWA Institute of Agriculture, University of Western Australia, Crawley, WA, Australia
| | - Aldrin Y Cantila
- UWA School of Biological Sciences and the UWA Institute of Agriculture, University of Western Australia, Crawley, WA, Australia
| | - Ryan J Craig
- UWA School of Biological Sciences and the UWA Institute of Agriculture, University of Western Australia, Crawley, WA, Australia
| | - David Edwards
- UWA School of Biological Sciences and the UWA Institute of Agriculture, University of Western Australia, Crawley, WA, Australia
| | - Jacqueline Batley
- UWA School of Biological Sciences and the UWA Institute of Agriculture, University of Western Australia, Crawley, WA, Australia.
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Alberdi A, Andersen SB, Limborg MT, Dunn RR, Gilbert MTP. Disentangling host-microbiota complexity through hologenomics. Nat Rev Genet 2022; 23:281-297. [PMID: 34675394 DOI: 10.1038/s41576-021-00421-0] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/14/2021] [Indexed: 02/07/2023]
Abstract
Research on animal-microbiota interactions has become a central topic in biological sciences because of its relevance to basic eco-evolutionary processes and applied questions in agriculture and health. However, animal hosts and their associated microbial communities are still seldom studied in a systemic fashion. Hologenomics, the integrated study of the genetic features of a eukaryotic host alongside that of its associated microbes, is becoming a feasible - yet still underexploited - approach that overcomes this limitation. Acknowledging the biological and genetic properties of both hosts and microbes, along with the advantages and disadvantages of implemented techniques, is essential for designing optimal studies that enable some of the major questions in biology to be addressed.
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Affiliation(s)
- Antton Alberdi
- Center for Evolutionary Hologenomics, The GLOBE Institute, University of Copenhagen, Copenhagen, Denmark.
| | - Sandra B Andersen
- Center for Evolutionary Hologenomics, The GLOBE Institute, University of Copenhagen, Copenhagen, Denmark
| | - Morten T Limborg
- Center for Evolutionary Hologenomics, The GLOBE Institute, University of Copenhagen, Copenhagen, Denmark
| | - Robert R Dunn
- Center for Evolutionary Hologenomics, The GLOBE Institute, University of Copenhagen, Copenhagen, Denmark.,Department of Applied Ecology, North Carolina State University, Raleigh, NC, USA
| | - M Thomas P Gilbert
- Center for Evolutionary Hologenomics, The GLOBE Institute, University of Copenhagen, Copenhagen, Denmark.,University Museum, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
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Li X, Guo T, Bai G, Zhang Z, See D, Marshall J, Garland-Campbell KA, Yu J. Genetics-inspired data-driven approaches explain and predict crop performance fluctuations attributed to changing climatic conditions. MOLECULAR PLANT 2022; 15:203-206. [PMID: 34999020 DOI: 10.1016/j.molp.2022.01.001] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/11/2021] [Revised: 12/17/2021] [Accepted: 01/04/2022] [Indexed: 06/14/2023]
Affiliation(s)
- Xianran Li
- USDA, Agricultural Research Service, Wheat Health, Genetics, and Quality Research Unit, Pullman, WA 99164, USA.
| | - Tingting Guo
- Department of Agronomy, Iowa State University, Ames, IA 50011, USA
| | - Guihua Bai
- USDA, Agricultural Research Service, Hard Winter Wheat Genetics Research Unit, Manhattan, KS 66506, USA
| | - Zhiwu Zhang
- Department of Crop and Soil Sciences, Washington State University, Pullman, WA 99164, USA
| | - Deven See
- USDA, Agricultural Research Service, Wheat Health, Genetics, and Quality Research Unit, Pullman, WA 99164, USA
| | - Juliet Marshall
- Department of Plant Sciences, University of Idaho Research and Extension, Idaho Falls, ID 83402, USA
| | - Kimberly A Garland-Campbell
- USDA, Agricultural Research Service, Wheat Health, Genetics, and Quality Research Unit, Pullman, WA 99164, USA
| | - Jianming Yu
- Department of Agronomy, Iowa State University, Ames, IA 50011, USA
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37
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Price EJ, Vitale CM, Miller GW, David A, Barouki R, Audouze K, Walker DI, Antignac JP, Coumoul X, Bessonneau V, Klánová J. Merging the exposome into an integrated framework for “omics” sciences. iScience 2022; 25:103976. [PMID: 35310334 PMCID: PMC8924626 DOI: 10.1016/j.isci.2022.103976] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/05/2022] Open
Abstract
The exposome concept encourages holistic consideration of the non-genetic factors (environmental exposures including lifestyle) that influence an individual’s health over their life course. However, disconnect between the concept and practical application has promoted divergent interpretations of the exposome across disciplines and reinforced separation of the environmental (emphasizing exposures) and biological (emphasizing responses) research communities. In particular, while knowledge of biological responses can help to distinguish actual (i.e. experienced) from potential exposures, the inclusion of endogenous processes has generated confusion about the position of the exposome in a multi-omics systems biology context. We propose a reattribution of “exposome” to exclusively represent the totality of contact with external factors that a biological entity experiences, and introduce the term “functional exposomics” to denote the systematic study of exposure-phenotype interaction. This reoriented definition of the exposome allows a more readily integrable dataset for multi-omics and systems biology research. Reattribution of exposome concept to exclusively represent environmental exposures Generalized the exposome concept for all levels of biological organization Functional exposome presented as the totality of exposure-phenotype interaction
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38
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Mi J, Vallarino JG, Petřík I, Novák O, Correa SM, Chodasiewicz M, Havaux M, Rodriguez-Concepcion M, Al-Babili S, Fernie AR, Skirycz A, Moreno JC. A manipulation of carotenoid metabolism influence biomass partitioning and fitness in tomato. Metab Eng 2022; 70:166-180. [PMID: 35031492 DOI: 10.1016/j.ymben.2022.01.004] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Revised: 12/29/2021] [Accepted: 01/09/2022] [Indexed: 12/25/2022]
Abstract
Improving yield, nutritional value and tolerance to abiotic stress are major targets of current breeding and biotechnological approaches that aim at increasing crop production and ensuring food security. Metabolic engineering of carotenoids, the precursor of vitamin-A and plant hormones that regulate plant growth and response to adverse growth conditions, has been mainly focusing on provitamin A biofortification or the production of high-value carotenoids. Here, we show that the introduction of a single gene of the carotenoid biosynthetic pathway in different tomato cultivars induced profound metabolic alterations in carotenoid, apocarotenoid and phytohormones pathways. Alterations in isoprenoid- (abscisic acid, gibberellins, cytokinins) and non-isoprenoid (auxin and jasmonic acid) derived hormones together with enhanced xanthophyll content influenced biomass partitioning and abiotic stress tolerance (high light, salt, and drought), and it caused an up to 77% fruit yield increase and enhanced fruit's provitamin A content. In addition, metabolic and hormonal changes led to accumulation of key primary metabolites (e.g. osmoprotectants and antiaging agents) contributing with enhanced abiotic stress tolerance and fruit shelf life. Our findings pave the way for developing a new generation of crops that combine high productivity and increased nutritional value with the capability to cope with climate change-related environmental challenges.
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Affiliation(s)
- Jianing Mi
- Center for Desert Agriculture, Biological and Environmental Science and Engineering Division (BESE), King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
| | - Jose G Vallarino
- Max Planck Institut für Molekulare Pflanzenphysiologie, Am Mühlenberg1 D-14476, Potsdam-Golm, Germany
| | - Ivan Petřík
- Laboratory of Growth Regulators, Faculty of Science, Palacký University and Institute of Experimental Botany, The Czech Academy of Sciences, Šlechtitelů 27, CZ-78371, Olomouc, Czech Republic
| | - Ondřej Novák
- Laboratory of Growth Regulators, Faculty of Science, Palacký University and Institute of Experimental Botany, The Czech Academy of Sciences, Šlechtitelů 27, CZ-78371, Olomouc, Czech Republic
| | - Sandra M Correa
- Max Planck Institut für Molekulare Pflanzenphysiologie, Am Mühlenberg1 D-14476, Potsdam-Golm, Germany
| | - Monika Chodasiewicz
- Center for Desert Agriculture, Biological and Environmental Science and Engineering Division (BESE), King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia; Max Planck Institut für Molekulare Pflanzenphysiologie, Am Mühlenberg1 D-14476, Potsdam-Golm, Germany
| | - Michel Havaux
- Aix-Marseille University, CEA, CNRS UMR7265, BIAM, CEA/Cadarache, F-13108 Saint-Paul-lez-Durance, France
| | | | - Salim Al-Babili
- Center for Desert Agriculture, Biological and Environmental Science and Engineering Division (BESE), King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
| | - Alisdair R Fernie
- Max Planck Institut für Molekulare Pflanzenphysiologie, Am Mühlenberg1 D-14476, Potsdam-Golm, Germany
| | - Aleksandra Skirycz
- Max Planck Institut für Molekulare Pflanzenphysiologie, Am Mühlenberg1 D-14476, Potsdam-Golm, Germany; Boyce Thompson Institute, Cornell University, Ithaca, NY, United States
| | - Juan C Moreno
- Center for Desert Agriculture, Biological and Environmental Science and Engineering Division (BESE), King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia; Max Planck Institut für Molekulare Pflanzenphysiologie, Am Mühlenberg1 D-14476, Potsdam-Golm, Germany.
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Fumia N, Pironon S, Rubinoff D, Khoury CK, Gore MA, Kantar MB. Wild relatives of potato may bolster its adaptation to new niches under future climate scenarios. Food Energy Secur 2022. [DOI: 10.1002/fes3.360] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Affiliation(s)
- Nathan Fumia
- Department of Tropical Plant and Soil Science University of Hawaii at Manoa Honolulu Hawaii USA
| | | | - Daniel Rubinoff
- Department of Plant and Environmental Protection Sciences University of Hawaii at Manoa Honolulu Hawaii USA
| | - Colin K. Khoury
- International Center for Tropical Agriculture (CIAT) Cali Colombia
- San Diego Botanic Garden Encinitas California USA
| | - Michael A. Gore
- Plant Breeding and Genetics Section School of Integrative Plant Science Cornell University Ithaca New York USA
| | - Michael B. Kantar
- Department of Tropical Plant and Soil Science University of Hawaii at Manoa Honolulu Hawaii USA
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40
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Bartholomé J, Prakash PT, Cobb JN. Genomic Prediction: Progress and Perspectives for Rice Improvement. Methods Mol Biol 2022; 2467:569-617. [PMID: 35451791 DOI: 10.1007/978-1-0716-2205-6_21] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Genomic prediction can be a powerful tool to achieve greater rates of genetic gain for quantitative traits if thoroughly integrated into a breeding strategy. In rice as in other crops, the interest in genomic prediction is very strong with a number of studies addressing multiple aspects of its use, ranging from the more conceptual to the more practical. In this chapter, we review the literature on rice (Oryza sativa) and summarize important considerations for the integration of genomic prediction in breeding programs. The irrigated breeding program at the International Rice Research Institute is used as a concrete example on which we provide data and R scripts to reproduce the analysis but also to highlight practical challenges regarding the use of predictions. The adage "To someone with a hammer, everything looks like a nail" describes a common psychological pitfall that sometimes plagues the integration and application of new technologies to a discipline. We have designed this chapter to help rice breeders avoid that pitfall and appreciate the benefits and limitations of applying genomic prediction, as it is not always the best approach nor the first step to increasing the rate of genetic gain in every context.
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Affiliation(s)
- Jérôme Bartholomé
- CIRAD, UMR AGAP Institut, Montpellier, France.
- AGAP Institut, Univ Montpellier, CIRAD, INRAE, Montpellier SupAgro, Montpellier, France.
- Rice Breeding Platform, International Rice Research Institute, Manila, Philippines.
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Martini JWR, Gao N, Crossa J. Incorporating Omics Data in Genomic Prediction. Methods Mol Biol 2022; 2467:341-357. [PMID: 35451782 DOI: 10.1007/978-1-0716-2205-6_12] [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
In this chapter, we discuss the motivation for integrating other types of omics data into genomic prediction methods. We give an overview of literature investigating the performance of omics-enhanced predictions, and highlight potential pitfalls when applying these methods in breeding. We emphasize that the statistical methods available for genomic data can be transferred to the general omics case. However, when using a framework of omic relationship matrices, the standardization of the variables may be more relevant than it is for a genomic relationship matrix based on single-nucleotide polymorphisms.
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Affiliation(s)
- Johannes W R Martini
- International Maize and Wheat Improvement Center (CIMMYT), Veracruz, CP, Mexico.
| | - Ning Gao
- School of Life Sciences, Sun Yat-Sen University, Guangzhou, China
| | - José Crossa
- International Maize and Wheat Improvement Center (CIMMYT), Veracruz, CP, Mexico
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42
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Martins Oliveira IC, Bernardeli A, Soler Guilhen JH, Pastina MM. Genomic Prediction of Complex Traits in an Allogamous Annual Crop: The Case of Maize Single-Cross Hybrids. Methods Mol Biol 2022; 2467:543-567. [PMID: 35451790 DOI: 10.1007/978-1-0716-2205-6_20] [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
For many plant and animal species, commercial products are hybrids between individuals from different genetic groups. For allogamous plant species such as maize, the breeding objective is to produce single-cross hybrid varieties from two inbred lines each selected in complementary groups. Efficient hybrid breeding requires methods that (1) quickly generate homozygous and homogeneous parental lines with high combining abilities, (2) efficiently choose among the large number of available parental lines the most promising ones, and (3) predict the performances of sets of non-phenotyped single-cross hybrids, or hybrids phenotyped in a limited number of environments, based on their relationship with another set of hybrids with known performances. The maize breeding community has been developing model-based prediction of hybrid performances well before the genomic era. This chapter (1) provides a reminder of the maize breeding scheme before the genomic era; (2) describes how genomic data were incorporated in the prediction models involved in different steps of genomic-based single-cross maize hybrid breeding; and (3) reviews factors affecting the accuracy of genomic prediction, approaches for optimizing GP-based single-cross maize hybrid breeding schemes, and ensuring the long-term sustainability of genomic selection.
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Affiliation(s)
| | - Arthur Bernardeli
- Department of Agronomy, Universidade Federal de Viçosa, Viçosa-MG, Brazil
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Impacts of environmental conditions, and allelic variation of cytosolic glutamine synthetase on maize hybrid kernel production. Commun Biol 2021; 4:1095. [PMID: 34535763 PMCID: PMC8448750 DOI: 10.1038/s42003-021-02598-w] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2020] [Accepted: 08/24/2021] [Indexed: 11/19/2022] Open
Abstract
Cytosolic glutamine synthetase (GS1) is the enzyme mainly responsible of ammonium assimilation and reassimilation in maize leaves. The agronomic potential of GS1 in maize kernel production was investigated by examining the impact of an overexpression of the enzyme in the leaf cells. Transgenic hybrids exhibiting a three-fold increase in leaf GS activity were produced and characterized using plants grown in the field. Several independent hybrids overexpressing Gln1-3, a gene encoding cytosolic (GS1), in the leaf and bundle sheath mesophyll cells were grown over five years in different locations. On average, a 3.8% increase in kernel yield was obtained in the transgenic hybrids compared to controls. However, we observed that such an increase was simultaneously dependent upon both the environmental conditions and the transgenic event for a given field trial. Although variable from one environment to another, significant associations were also found between two GS1 genes (Gln1-3 and Gln1-4) polymorphic regions and kernel yield in different locations. We propose that the GS1 enzyme is a potential lead for producing high yielding maize hybrids using either genetic engineering or marker-assisted selection. However, for these hybrids, yield increases will be largely dependent upon the environmental conditions used to grow the plants. Amiour et al. use a multi-year field trial evaluation and association mapping to determine if increased enzyme activity and native allelic variations at the GS1 loci in maize contribute to differences in grain yield. Overexpression of GS1 and polymorphisms in the corresponding loci were associated with kernel yield, indicating that GS1 expression can directly control kernel production and that GS1 has a potential lead in the production of high yielding maize hybrids depending on environmental conditions.
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Engels JMM, Ebert AW. A Critical Review of the Current Global Ex Situ Conservation System for Plant Agrobiodiversity. II. Strengths and Weaknesses of the Current System and Recommendations for Its Improvement. PLANTS (BASEL, SWITZERLAND) 2021; 10:1904. [PMID: 34579439 PMCID: PMC8472064 DOI: 10.3390/plants10091904] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/08/2021] [Revised: 09/05/2021] [Accepted: 09/09/2021] [Indexed: 02/08/2023]
Abstract
In this paper, we review gene bank operations that have an influence on the global conservation system, with the intention to identify critical aspects that should be improved for optimum performance. We describe the role of active and base collections and the importance of linking germplasm conservation and use, also in view of new developments in genomics and phenomics that facilitate more effective and efficient conservation and use of plant agrobiodiversity. Strengths, limitations, and opportunities of the existing global ex situ conservation system are discussed, and measures are proposed to achieve a rational, more effective, and efficient global system for germplasm conservation and sustainable use. The proposed measures include filling genetic and geographic gaps in current ex situ collections; determining unique accessions at the global level for long-term conservation in virtual base collections; intensifying existing international collaborations among gene banks and forging collaborations with the botanic gardens community; increasing investment in conservation research and user-oriented supportive research; improved accession-level description of the genetic diversity of crop collections; improvements of the legal and policy framework; and oversight of the proposed network of global base collections.
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Stanschewski CS, Rey E, Fiene G, Craine EB, Wellman G, Melino VJ, S. R. Patiranage D, Johansen K, Schmöckel SM, Bertero D, Oakey H, Colque-Little C, Afzal I, Raubach S, Miller N, Streich J, Amby DB, Emrani N, Warmington M, Mousa MAA, Wu D, Jacobson D, Andreasen C, Jung C, Murphy K, Bazile D, Tester M. Quinoa Phenotyping Methodologies: An International Consensus. PLANTS (BASEL, SWITZERLAND) 2021; 10:1759. [PMID: 34579292 PMCID: PMC8472428 DOI: 10.3390/plants10091759] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/05/2021] [Revised: 08/09/2021] [Accepted: 08/12/2021] [Indexed: 11/30/2022]
Abstract
Quinoa is a crop originating in the Andes but grown more widely and with the genetic potential for significant further expansion. Due to the phenotypic plasticity of quinoa, varieties need to be assessed across years and multiple locations. To improve comparability among field trials across the globe and to facilitate collaborations, components of the trials need to be kept consistent, including the type and methods of data collected. Here, an internationally open-access framework for phenotyping a wide range of quinoa features is proposed to facilitate the systematic agronomic, physiological and genetic characterization of quinoa for crop adaptation and improvement. Mature plant phenotyping is a central aspect of this paper, including detailed descriptions and the provision of phenotyping cards to facilitate consistency in data collection. High-throughput methods for multi-temporal phenotyping based on remote sensing technologies are described. Tools for higher-throughput post-harvest phenotyping of seeds are presented. A guideline for approaching quinoa field trials including the collection of environmental data and designing layouts with statistical robustness is suggested. To move towards developing resources for quinoa in line with major cereal crops, a database was created. The Quinoa Germinate Platform will serve as a central repository of data for quinoa researchers globally.
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Affiliation(s)
- Clara S. Stanschewski
- Center for Desert Agriculture, Biological and Environmental Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955, Saudi Arabia; (C.S.S.); (E.R.); (G.F.); (G.W.); (V.J.M.); (D.S.R.P.)
| | - Elodie Rey
- Center for Desert Agriculture, Biological and Environmental Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955, Saudi Arabia; (C.S.S.); (E.R.); (G.F.); (G.W.); (V.J.M.); (D.S.R.P.)
| | - Gabriele Fiene
- Center for Desert Agriculture, Biological and Environmental Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955, Saudi Arabia; (C.S.S.); (E.R.); (G.F.); (G.W.); (V.J.M.); (D.S.R.P.)
| | - Evan B. Craine
- Department of Crop and Soil Sciences, Washington State University, Pullman, WA 99164, USA; (E.B.C.); (K.M.)
| | - Gordon Wellman
- Center for Desert Agriculture, Biological and Environmental Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955, Saudi Arabia; (C.S.S.); (E.R.); (G.F.); (G.W.); (V.J.M.); (D.S.R.P.)
| | - Vanessa J. Melino
- Center for Desert Agriculture, Biological and Environmental Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955, Saudi Arabia; (C.S.S.); (E.R.); (G.F.); (G.W.); (V.J.M.); (D.S.R.P.)
| | - Dilan S. R. Patiranage
- Center for Desert Agriculture, Biological and Environmental Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955, Saudi Arabia; (C.S.S.); (E.R.); (G.F.); (G.W.); (V.J.M.); (D.S.R.P.)
- Plant Breeding Institute, Christian-Albrechts-University of Kiel, 24118 Kiel, Germany; (N.E.); (C.J.)
| | - Kasper Johansen
- Water Desalination and Reuse Center, King Abdullah University of Science and Technology (KAUST), Thuwal 23955, Saudi Arabia;
| | - Sandra M. Schmöckel
- Department Physiology of Yield Stability, Institute of Crop Science, University of Hohenheim, 70599 Stuttgart, Germany;
| | - Daniel Bertero
- Department of Plant Production, School of Agriculture, University of Buenos Aires, Buenos Aires C1417DSE, Argentina;
| | - Helena Oakey
- Robinson Research Institute, Adelaide Medical School, University of Adelaide, Adelaide, SA 5005, Australia;
| | - Carla Colque-Little
- Department of Plant and Environmental Sciences, University of Copenhagen, DK-2630 Taastrup, Denmark; (C.C.-L.); (D.B.A.); (C.A.)
| | - Irfan Afzal
- Department of Agronomy, University of Agriculture, Faisalabad 38000, Pakistan;
| | - Sebastian Raubach
- Department of Information and Computational Sciences, The James Hutton Institute, Invergowrie, Dundee AB15 8QH, UK;
| | - Nathan Miller
- Department of Botany, University of Wisconsin, 430 Lincoln Dr, Madison, WI 53706, USA;
| | - Jared Streich
- Biosciences, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA; (J.S.); (D.J.)
| | - Daniel Buchvaldt Amby
- Department of Plant and Environmental Sciences, University of Copenhagen, DK-2630 Taastrup, Denmark; (C.C.-L.); (D.B.A.); (C.A.)
| | - Nazgol Emrani
- Plant Breeding Institute, Christian-Albrechts-University of Kiel, 24118 Kiel, Germany; (N.E.); (C.J.)
| | - Mark Warmington
- Department of Primary Industries and Regional Development, Agriculture and Food, Kununurra, WA 6743, Australia;
| | - Magdi A. A. Mousa
- Department of Arid Land Agriculture, Faculty of Meteorology, Environment and Arid Land Agriculture, King Abdulaziz University, Jeddah 21589, Saudi Arabia;
- Department of Vegetables, Faculty of Agriculture, Assiut University, Assiut 71526, Egypt
| | - David Wu
- Shanxi Jiaqi Agri-Tech Co., Ltd., Taiyuan 030006, China;
| | - Daniel Jacobson
- Biosciences, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA; (J.S.); (D.J.)
| | - Christian Andreasen
- Department of Plant and Environmental Sciences, University of Copenhagen, DK-2630 Taastrup, Denmark; (C.C.-L.); (D.B.A.); (C.A.)
| | - Christian Jung
- Plant Breeding Institute, Christian-Albrechts-University of Kiel, 24118 Kiel, Germany; (N.E.); (C.J.)
| | - Kevin Murphy
- Department of Crop and Soil Sciences, Washington State University, Pullman, WA 99164, USA; (E.B.C.); (K.M.)
| | - Didier Bazile
- CIRAD, UMR SENS, 34398 Montpellier, France;
- SENS, CIRAD, IRD, University Paul Valery Montpellier 3, 34090 Montpellier, France
| | - Mark Tester
- Center for Desert Agriculture, Biological and Environmental Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955, Saudi Arabia; (C.S.S.); (E.R.); (G.F.); (G.W.); (V.J.M.); (D.S.R.P.)
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Kholová J, Urban MO, Cock J, Arcos J, Arnaud E, Aytekin D, Azevedo V, Barnes AP, Ceccarelli S, Chavarriaga P, Cobb JN, Connor D, Cooper M, Craufurd P, Debouck D, Fungo R, Grando S, Hammer GL, Jara CE, Messina C, Mosquera G, Nchanji E, Ng EH, Prager S, Sankaran S, Selvaraj M, Tardieu F, Thornton P, Valdes-Gutierrez SP, van Etten J, Wenzl P, Xu Y. In pursuit of a better world: crop improvement and the CGIAR. JOURNAL OF EXPERIMENTAL BOTANY 2021; 72:5158-5179. [PMID: 34021317 PMCID: PMC8272562 DOI: 10.1093/jxb/erab226] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Accepted: 05/20/2021] [Indexed: 05/10/2023]
Abstract
The CGIAR crop improvement (CI) programs, unlike commercial CI programs, which are mainly geared to profit though meeting farmers' needs, are charged with meeting multiple objectives with target populations that include both farmers and the community at large. We compiled the opinions from >30 experts in the private and public sector on key strategies, methodologies, and activities that could the help CGIAR meet the challenges of providing farmers with improved varieties while simultaneously meeting the goals of: (i) nutrition, health, and food security; (ii) poverty reduction, livelihoods, and jobs; (iii) gender equality, youth, and inclusion; (iv) climate adaptation and mitigation; and (v) environmental health and biodiversity. We review the crop improvement processes starting with crop choice, moving through to breeding objectives, production of potential new varieties, selection, and finally adoption by farmers. The importance of multidisciplinary teams working towards common objectives is stressed as a key factor to success. The role of the distinct disciplines, actors, and their interactions throughout the process from crop choice through to adoption by farmers is discussed and illustrated.
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Affiliation(s)
- Jana Kholová
- International Crops Research Institute for the Semi-Arid Tropics, Hyderabad-502324, India
| | - Milan Oldřich Urban
- International Center for Tropical Agriculture, Km 17 Recta Cali-Palmira, CP 763537, A.A. 12 6713, Cali, Colombia
| | - James Cock
- International Center for Tropical Agriculture, Km 17 Recta Cali-Palmira, CP 763537, A.A. 12 6713, Cali, Colombia
| | - Jairo Arcos
- HarvestPlus, Km 17 Recta Cali-Palmira, CP 763537, A.A. 12 6713, Cali, Colombia
| | - Elizabeth Arnaud
- Bioversity International, Parc scientifique Agropolis II, 1990 Boulevard de la Lironde, 34397 Montpellier, France
| | | | - Vania Azevedo
- International Crops Research Institute for the Semi-Arid Tropics, Hyderabad-502324, India
| | | | | | - Paul Chavarriaga
- International Center for Tropical Agriculture, Km 17 Recta Cali-Palmira, CP 763537, A.A. 12 6713, Cali, Colombia
| | | | - David Connor
- Department of Agriculture and Food, The University of Melbourne, Australia
| | - Mark Cooper
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, Qld 4072, Australia
| | - Peter Craufurd
- CIMMYT, 1st floor, National Plant Breeding and Genetics Centre, NARC Research Station, Khumaltor, Lalitpur, PO Box 5186, Kathmandu, Nepal
| | - Daniel Debouck
- International Center for Tropical Agriculture, Km 17 Recta Cali-Palmira, CP 763537, A.A. 12 6713, Cali, Colombia
| | - Robert Fungo
- International Center for Tropical Agriculture, PO Box 6247, Kampala, Uganda
- School of Food Technology, Nutrition & Bio-Engineering, Makerere University, PO Box, 7062, Kampala, Uganda
| | - Stefania Grando
- Independent Consultant, Corso Mazzini 256, 63100 Ascoli Piceno, Italy
| | - Graeme L Hammer
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, Qld 4072, Australia
| | - Carlos E Jara
- Independent Consultant, Hacienda Real, Torre 2, CP 760033, Cali, Colombia
| | - Charlie Messina
- Corteva Agriscience, 7200 62nd Avenue, Johnston, IA 50131, USA
| | - Gloria Mosquera
- International Center for Tropical Agriculture, Km 17 Recta Cali-Palmira, CP 763537, A.A. 12 6713, Cali, Colombia
| | - Eileen Nchanji
- International Center for Tropical Agriculture, African hub, Box 823-00621, Nairobi, Kenya
| | - Eng Hwa Ng
- International Maize and Wheat Improvement Center (CIMMYT); México-Veracruz, El Batán Km. 45, 56237, Mexico
| | - Steven Prager
- International Center for Tropical Agriculture, Km 17 Recta Cali-Palmira, CP 763537, A.A. 12 6713, Cali, Colombia
| | - Sindhujan Sankaran
- Department of Biological Systems Engineering, Washington State University, 1935 E. Grimes Way, PO Box 646120, Pullman, WA 99164, USA
| | - Michael Selvaraj
- International Center for Tropical Agriculture, Km 17 Recta Cali-Palmira, CP 763537, A.A. 12 6713, Cali, Colombia
| | - François Tardieu
- INRA Centre de Montpellier, Montpellier, Languedoc-Roussillon, France
| | - Philip Thornton
- CGIAR Research Program on Climate Change, Agriculture 37 and Food Security (CCAFS), International Livestock Research Institute (ILRI), Nairobi, Kenya
| | - Sandra P Valdes-Gutierrez
- International Center for Tropical Agriculture, Km 17 Recta Cali-Palmira, CP 763537, A.A. 12 6713, Cali, Colombia
| | - Jacob van Etten
- Bioversity International, Parc scientifique Agropolis II, 1990 Boulevard de la Lironde, 34397 Montpellier, France
| | - Peter Wenzl
- International Center for Tropical Agriculture, Km 17 Recta Cali-Palmira, CP 763537, A.A. 12 6713, Cali, Colombia
| | - Yunbi Xu
- Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing 100081, China
- International Maize and Wheat Improvement Center (CIMMYT), El Batan Texcoco 56130, Mexico
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47
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Kholová J, Urban MO, Cock J, Arcos J, Arnaud E, Aytekin D, Azevedo V, Barnes AP, Ceccarelli S, Chavarriaga P, Cobb JN, Connor D, Cooper M, Craufurd P, Debouck D, Fungo R, Grando S, Hammer GL, Jara CE, Messina C, Mosquera G, Nchanji E, Ng EH, Prager S, Sankaran S, Selvaraj M, Tardieu F, Thornton P, Valdes-Gutierrez SP, van Etten J, Wenzl P, Xu Y. In pursuit of a better world: crop improvement and the CGIAR. JOURNAL OF EXPERIMENTAL BOTANY 2021. [PMID: 34021317 DOI: 10.5281/zenodo.4638248] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
The CGIAR crop improvement (CI) programs, unlike commercial CI programs, which are mainly geared to profit though meeting farmers' needs, are charged with meeting multiple objectives with target populations that include both farmers and the community at large. We compiled the opinions from >30 experts in the private and public sector on key strategies, methodologies, and activities that could the help CGIAR meet the challenges of providing farmers with improved varieties while simultaneously meeting the goals of: (i) nutrition, health, and food security; (ii) poverty reduction, livelihoods, and jobs; (iii) gender equality, youth, and inclusion; (iv) climate adaptation and mitigation; and (v) environmental health and biodiversity. We review the crop improvement processes starting with crop choice, moving through to breeding objectives, production of potential new varieties, selection, and finally adoption by farmers. The importance of multidisciplinary teams working towards common objectives is stressed as a key factor to success. The role of the distinct disciplines, actors, and their interactions throughout the process from crop choice through to adoption by farmers is discussed and illustrated.
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Affiliation(s)
- Jana Kholová
- International Crops Research Institute for the Semi-Arid Tropics, Hyderabad-502324, India
| | - Milan Oldřich Urban
- International Center for Tropical Agriculture, Km 17 Recta Cali-Palmira, CP 763537, A.A. 12 6713, Cali, Colombia
| | - James Cock
- International Center for Tropical Agriculture, Km 17 Recta Cali-Palmira, CP 763537, A.A. 12 6713, Cali, Colombia
| | - Jairo Arcos
- HarvestPlus, Km 17 Recta Cali-Palmira, CP 763537, A.A. 12 6713, Cali, Colombia
| | - Elizabeth Arnaud
- Bioversity International, Parc scientifique Agropolis II, 1990 Boulevard de la Lironde, 34397 Montpellier, France
| | | | - Vania Azevedo
- International Crops Research Institute for the Semi-Arid Tropics, Hyderabad-502324, India
| | | | | | - Paul Chavarriaga
- International Center for Tropical Agriculture, Km 17 Recta Cali-Palmira, CP 763537, A.A. 12 6713, Cali, Colombia
| | | | - David Connor
- Department of Agriculture and Food, The University of Melbourne, Australia
| | - Mark Cooper
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, Qld 4072, Australia
| | - Peter Craufurd
- CIMMYT, 1st floor, National Plant Breeding and Genetics Centre, NARC Research Station, Khumaltor, Lalitpur, PO Box 5186, Kathmandu, Nepal
| | - Daniel Debouck
- International Center for Tropical Agriculture, Km 17 Recta Cali-Palmira, CP 763537, A.A. 12 6713, Cali, Colombia
| | - Robert Fungo
- International Center for Tropical Agriculture, PO Box 6247, Kampala, Uganda
- School of Food Technology, Nutrition & Bio-Engineering, Makerere University, PO Box, 7062, Kampala, Uganda
| | - Stefania Grando
- Independent Consultant, Corso Mazzini 256, 63100 Ascoli Piceno, Italy
| | - Graeme L Hammer
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, Qld 4072, Australia
| | - Carlos E Jara
- Independent Consultant, Hacienda Real, Torre 2, CP 760033, Cali, Colombia
| | - Charlie Messina
- Corteva Agriscience, 7200 62nd Avenue, Johnston, IA 50131, USA
| | - Gloria Mosquera
- International Center for Tropical Agriculture, Km 17 Recta Cali-Palmira, CP 763537, A.A. 12 6713, Cali, Colombia
| | - Eileen Nchanji
- International Center for Tropical Agriculture, African hub, Box 823-00621, Nairobi, Kenya
| | - Eng Hwa Ng
- International Maize and Wheat Improvement Center (CIMMYT); México-Veracruz, El Batán Km. 45, 56237, Mexico
| | - Steven Prager
- International Center for Tropical Agriculture, Km 17 Recta Cali-Palmira, CP 763537, A.A. 12 6713, Cali, Colombia
| | - Sindhujan Sankaran
- Department of Biological Systems Engineering, Washington State University, 1935 E. Grimes Way, PO Box 646120, Pullman, WA 99164, USA
| | - Michael Selvaraj
- International Center for Tropical Agriculture, Km 17 Recta Cali-Palmira, CP 763537, A.A. 12 6713, Cali, Colombia
| | - François Tardieu
- INRA Centre de Montpellier, Montpellier, Languedoc-Roussillon, France
| | - Philip Thornton
- CGIAR Research Program on Climate Change, Agriculture 37 and Food Security (CCAFS), International Livestock Research Institute (ILRI), Nairobi, Kenya
| | - Sandra P Valdes-Gutierrez
- International Center for Tropical Agriculture, Km 17 Recta Cali-Palmira, CP 763537, A.A. 12 6713, Cali, Colombia
| | - Jacob van Etten
- Bioversity International, Parc scientifique Agropolis II, 1990 Boulevard de la Lironde, 34397 Montpellier, France
| | - Peter Wenzl
- International Center for Tropical Agriculture, Km 17 Recta Cali-Palmira, CP 763537, A.A. 12 6713, Cali, Colombia
| | - Yunbi Xu
- Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing 100081, China
- International Maize and Wheat Improvement Center (CIMMYT), El Batan Texcoco 56130, Mexico
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Costa-Neto G, Galli G, Carvalho HF, Crossa J, Fritsche-Neto R. EnvRtype: a software to interplay enviromics and quantitative genomics in agriculture. G3-GENES GENOMES GENETICS 2021; 11:6129777. [PMID: 33835165 PMCID: PMC8049414 DOI: 10.1093/g3journal/jkab040] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Accepted: 01/21/2021] [Indexed: 11/13/2022]
Abstract
Envirotyping is an essential technique used to unfold the nongenetic drivers associated with the phenotypic adaptation of living organisms. Here, we introduce the EnvRtype R package, a novel toolkit developed to interplay large-scale envirotyping data (enviromics) into quantitative genomics. To start a user-friendly envirotyping pipeline, this package offers: (1) remote sensing tools for collecting (get_weather and extract_GIS functions) and processing ecophysiological variables (processWTH function) from raw environmental data at single locations or worldwide; (2) environmental characterization by typing environments and profiling descriptors of environmental quality (env_typing function), in addition to gathering environmental covariables as quantitative descriptors for predictive purposes (W_matrix function); and (3) identification of environmental similarity that can be used as an enviromic-based kernel (env_typing function) in whole-genome prediction (GP), aimed at increasing ecophysiological knowledge in genomic best-unbiased predictions (GBLUP) and emulating reaction norm effects (get_kernel and kernel_model functions). We highlight literature mining concepts in fine-tuning envirotyping parameters for each plant species and target growing environments. We show that envirotyping for predictive breeding collects raw data and processes it in an eco-physiologically smart way. Examples of its use for creating global-scale envirotyping networks and integrating reaction-norm modeling in GP are also outlined. We conclude that EnvRtype provides a cost-effective envirotyping pipeline capable of providing high quality enviromic data for a diverse set of genomic-based studies, especially for increasing accuracy in GP across untested growing environments.
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Affiliation(s)
- Germano Costa-Neto
- Department of Genetics, 'Luiz de Queiroz' Agriculture College, University of São Paulo, São Paulo, Brazil
| | - Giovanni Galli
- Department of Genetics, 'Luiz de Queiroz' Agriculture College, University of São Paulo, São Paulo, Brazil
| | - Humberto Fanelli Carvalho
- Department of Genetics, 'Luiz de Queiroz' Agriculture College, University of São Paulo, São Paulo, Brazil
| | - José Crossa
- Biometrics and Statistics Unit, International Maize and Wheat Improvement Center (CIMMYT), Km 45 Carretera Mexico-Veracruz, El Batan Km. 45, CP 56237 Mexico; Colegio de Postgraduados, Montecillos, Edo. de Mexico, CP 56264, Mexico
| | - Roberto Fritsche-Neto
- Department of Genetics, 'Luiz de Queiroz' Agriculture College, University of São Paulo, São Paulo, Brazil.,Quantitative Genetics and Biometrics Cluster, International Rice Research Institute (IRRI), Los Baños, Philippines
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49
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Li X, Guo T, Wang J, Bekele WA, Sukumaran S, Vanous AE, McNellie JP, Tibbs-Cortes LE, Lopes MS, Lamkey KR, Westgate ME, McKay JK, Archontoulis SV, Reynolds MP, Tinker NA, Schnable PS, Yu J. An integrated framework reinstating the environmental dimension for GWAS and genomic selection in crops. MOLECULAR PLANT 2021; 14:874-887. [PMID: 33713844 DOI: 10.1016/j.molp.2021.03.010] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Revised: 02/03/2021] [Accepted: 03/09/2021] [Indexed: 05/08/2023]
Abstract
Identifying mechanisms and pathways involved in gene-environment interplay and phenotypic plasticity is a long-standing challenge. It is highly desirable to establish an integrated framework with an environmental dimension for complex trait dissection and prediction. A critical step is to identify an environmental index that is both biologically relevant and estimable for new environments. With extensive field-observed complex traits, environmental profiles, and genome-wide single nucleotide polymorphisms for three major crops (maize, wheat, and oat), we demonstrated that identifying such an environmental index (i.e., a combination of environmental parameter and growth window) enables genome-wide association studies and genomic selection of complex traits to be conducted with an explicit environmental dimension. Interestingly, genes identified for two reaction-norm parameters (i.e., intercept and slope) derived from flowering time values along the environmental index were less colocalized for a diverse maize panel than for wheat and oat breeding panels, agreeing with the different diversity levels and genetic constitutions of the panels. In addition, we showcased the usefulness of this framework for systematically forecasting the performance of diverse germplasm panels in new environments. This general framework and the companion CERIS-JGRA analytical package should facilitate biologically informed dissection of complex traits, enhanced performance prediction in breeding for future climates, and coordinated efforts to enrich our understanding of mechanisms underlying phenotypic variation.
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Affiliation(s)
- Xianran Li
- Department of Agronomy, Iowa State University, Ames, IA 50011, USA
| | - Tingting Guo
- Department of Agronomy, Iowa State University, Ames, IA 50011, USA
| | - Jinyu Wang
- Department of Agronomy, Iowa State University, Ames, IA 50011, USA
| | - Wubishet A Bekele
- Ottawa Research and Development Centre, Agriculture and Agri-Food Canada, Ottawa, ON, Canada
| | - Sivakumar Sukumaran
- International Maize and Wheat Improvement Center (CIMMYT), Mexico City, Mexico
| | - Adam E Vanous
- Department of Agronomy, Iowa State University, Ames, IA 50011, USA
| | - James P McNellie
- Department of Agronomy, Iowa State University, Ames, IA 50011, USA
| | | | - Marta S Lopes
- International Maize and Wheat Improvement Center (CIMMYT), Mexico City, Mexico
| | - Kendall R Lamkey
- Department of Agronomy, Iowa State University, Ames, IA 50011, USA
| | - Mark E Westgate
- Department of Agronomy, Iowa State University, Ames, IA 50011, USA
| | - John K McKay
- Department of Bioagricultural Sciences and Pest Management, Colorado State University, Fort Collins, CO 80523, USA
| | | | - Matthew P Reynolds
- International Maize and Wheat Improvement Center (CIMMYT), Mexico City, Mexico
| | - Nicholas A Tinker
- Ottawa Research and Development Centre, Agriculture and Agri-Food Canada, Ottawa, ON, Canada
| | | | - Jianming Yu
- Department of Agronomy, Iowa State University, Ames, IA 50011, USA.
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50
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Profiling, isolation and characterisation of beneficial microbes from the seed microbiomes of drought tolerant wheat. Sci Rep 2021; 11:11916. [PMID: 34099781 PMCID: PMC8184954 DOI: 10.1038/s41598-021-91351-8] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2020] [Accepted: 05/18/2021] [Indexed: 11/09/2022] Open
Abstract
Climate change is predicted to increase the incidence and severity of drought conditions, posing a significant challenge for agriculture globally. Plant microbiomes have been demonstrated to aid crop species in the mitigation of drought stress. The study investigated the differences between the seed microbiomes of drought tolerant and drought susceptible wheat lines. Furthermore, it highlighted and quantified the degree of drought tolerance conferred by specific microbes isolated from drought tolerant wheat seed microbiomes. Metagenomic and culture-based methods were used to profile and characterise the seed microbiome composition of drought tolerant and drought susceptible wheat lines under rainfed and drought conditions. Isolates from certain genera were enriched by drought tolerant wheat lines when placed under drought stress. Wheat inoculated with isolates from these targeted genera, such as Curtobacterium flaccumfaciens (Cf D3-25) and Arthrobacter sp. (Ar sp. D4-14) demonstrated the ability to promote growth under drought conditions. This study indicates seed microbiomes from genetically distinct wheat lines enrich for beneficial bacteria in ways that are both line-specific and responsive to environmental stress. As such, seed from stress-phenotyped lines represent an invaluable resource for the identification of beneficial microbes with plant growth promoting activity that could improve commercial crop production.
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