1
|
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.
Collapse
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.
| |
Collapse
|
2
|
Gerard G, Mondal S, Piñera-Chávez F, Rivera-Amado C, Molero G, Crossa J, Huerta-Espino J, Velu G, Braun H, Singh R, Crespo-Herrera L. Enhanced radiation use efficiency and grain filling rate as the main drivers of grain yield genetic gains in the CIMMYT elite spring wheat yield trial. Sci Rep 2024; 14:10975. [PMID: 38744876 PMCID: PMC11094180 DOI: 10.1038/s41598-024-60853-6] [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: 10/08/2023] [Accepted: 04/29/2024] [Indexed: 05/16/2024] Open
Abstract
Common wheat (Triticum aestivum L.) is a major staple food crop, providing a fifth of food calories and proteins to the world's human population. Despite the impressive growth in global wheat production in recent decades, further increases in grain yield are required to meet future demands. Here we estimated genetic gain and genotype stability for grain yield (GY) and determined the trait associations that contributed uniquely or in combination to increased GY, through a retrospective analysis of top-performing genotypes selected from the elite spring wheat yield trial (ESWYT) evaluated internationally during a 14-year period (2003 to 2016). Fifty-six ESWYT genotypes and four checks were sown under optimally irrigated conditions in three phenotyping trials during three consecutive growing seasons (2018-2019 to 2020-2021) at Norman E. Borlaug Research Station, Ciudad Obregon, Mexico. The mean GY rose from 6.75 (24th ESWYT) to 7.87 t ha-1 (37th ESWYT), representing a cumulative increase of 1.12 t ha-1. The annual genetic gain for GY was estimated at 0.96% (65 kg ha-1 year-1) accompanied by a positive trend in genotype stability over time. The GY progress was mainly associated with increases in biomass (BM), grain filling rate (GFR), total radiation use efficiency (RUE_total), grain weight per spike (GWS), and reduction in days to heading (DTH), which together explained 95.5% of the GY variation. Regression lines over the years showed significant increases of 0.015 kg m-2 year-1 (p < 0.01), 0.074 g m-2 year-1 (p < 0.05), and 0.017 g MJ-1 year-1 (p < 0.001) for BM, GFR, and RUE_total, respectively. Grain weight per spike exhibited a positive but no significant trend (0.014 g year-1, p = 0.07), whereas a negative tendency for DTH was observed (- 0.43 days year-1, p < 0.001). Analysis of the top ten highest-yielding genotypes revealed differential GY-associated trait contributions, demonstrating that improved GY can be attained through different mechanisms and indicating that no single trait criterion is adopted by CIMMYT breeders for developing new superior lines. We conclude that CIMMYT's Bread Wheat Breeding Program has continued to deliver adapted and more productive wheat genotypes to National partners worldwide, mainly driven by enhancing RUE_total and GFR and that future yield increases could be achieved by intercrossing genetically diverse top performer genotypes.
Collapse
Affiliation(s)
- Guillermo Gerard
- International Maize and Wheat Improvement Center (CIMMYT), Carretera México- Veracruz Km. 45, El Batán, CP 56237, Texcoco, Mexico, Mexico.
| | - Suchismita Mondal
- Plant Sciences and Plant Pathology Department, Montana State University, Bozeman, MT, 59717, USA
| | - Francisco Piñera-Chávez
- International Maize and Wheat Improvement Center (CIMMYT), Carretera México- Veracruz Km. 45, El Batán, CP 56237, Texcoco, Mexico, Mexico
| | - Carolina Rivera-Amado
- International Maize and Wheat Improvement Center (CIMMYT), Carretera México- Veracruz Km. 45, El Batán, CP 56237, Texcoco, Mexico, Mexico
| | - Gemma Molero
- KWS Momont Recherche, 59246, Mons-en-Pévèle, Hauts-de-France, France
| | - Jose Crossa
- International Maize and Wheat Improvement Center (CIMMYT), Carretera México- Veracruz Km. 45, El Batán, CP 56237, Texcoco, Mexico, Mexico
- Colegio de Postgraduados, CP 56230, Montecillos, Mexico, Mexico
| | | | - Govidan Velu
- International Maize and Wheat Improvement Center (CIMMYT), Carretera México- Veracruz Km. 45, El Batán, CP 56237, Texcoco, Mexico, Mexico
| | - Hans Braun
- International Maize and Wheat Improvement Center (CIMMYT), Carretera México- Veracruz Km. 45, El Batán, CP 56237, Texcoco, Mexico, Mexico
| | - Ravi Singh
- International Maize and Wheat Improvement Center (CIMMYT), Carretera México- Veracruz Km. 45, El Batán, CP 56237, Texcoco, Mexico, Mexico
| | - Leonardo Crespo-Herrera
- International Maize and Wheat Improvement Center (CIMMYT), Carretera México- Veracruz Km. 45, El Batán, CP 56237, Texcoco, Mexico, Mexico.
| |
Collapse
|
3
|
García-Barrios G, Crespo-Herrera L, Cruz-Izquierdo S, Vitale P, Sandoval-Islas JS, Gerard GS, Aguilar-Rincón VH, Corona-Torres T, Crossa J, Pacheco-Gil RA. Genomic Prediction from Multi-Environment Trials of Wheat Breeding. Genes (Basel) 2024; 15:417. [PMID: 38674352 PMCID: PMC11049976 DOI: 10.3390/genes15040417] [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: 02/24/2024] [Revised: 03/24/2024] [Accepted: 03/26/2024] [Indexed: 04/28/2024] Open
Abstract
Genomic prediction relates a set of markers to variability in observed phenotypes of cultivars and allows for the prediction of phenotypes or breeding values of genotypes on unobserved individuals. Most genomic prediction approaches predict breeding values based solely on additive effects. However, the economic value of wheat lines is not only influenced by their additive component but also encompasses a non-additive part (e.g., additive × additive epistasis interaction). In this study, genomic prediction models were implemented in three target populations of environments (TPE) in South Asia. Four models that incorporate genotype × environment interaction (G × E) and genotype × genotype (GG) were tested: Factor Analytic (FA), FA with genomic relationship matrix (FA + G), FA with epistatic relationship matrix (FA + GG), and FA with both genomic and epistatic relationship matrices (FA + G + GG). Results show that the FA + G and FA + G + GG models displayed the best and a similar performance across all tests, leading us to infer that the FA + G model effectively captures certain epistatic effects. The wheat lines tested in sites in different TPE were predicted with different precisions depending on the cross-validation employed. In general, the best prediction accuracy was obtained when some lines were observed in some sites of particular TPEs and the worse genomic prediction was observed when wheat lines were never observed in any site of one TPE.
Collapse
Affiliation(s)
- Guillermo García-Barrios
- Postgrado en Recursos Genéticos y Productividad-Genética, Colegio de Postgraduados, Texcoco 56264, Estado de México, Mexico; (G.G.-B.); (S.C.-I.); (V.H.A.-R.); (T.C.-T.)
| | - Leonardo Crespo-Herrera
- International Maize and Wheat Improvement Center (CIMMYT), Km 35 Carretera México-Veracruz, Texcoco 56237, Estado de México, Mexico; (L.C.-H.); (P.V.); (G.S.G.)
| | - Serafín Cruz-Izquierdo
- Postgrado en Recursos Genéticos y Productividad-Genética, Colegio de Postgraduados, Texcoco 56264, Estado de México, Mexico; (G.G.-B.); (S.C.-I.); (V.H.A.-R.); (T.C.-T.)
| | - Paolo Vitale
- International Maize and Wheat Improvement Center (CIMMYT), Km 35 Carretera México-Veracruz, Texcoco 56237, Estado de México, Mexico; (L.C.-H.); (P.V.); (G.S.G.)
| | | | - Guillermo Sebastián Gerard
- International Maize and Wheat Improvement Center (CIMMYT), Km 35 Carretera México-Veracruz, Texcoco 56237, Estado de México, Mexico; (L.C.-H.); (P.V.); (G.S.G.)
| | - Víctor Heber Aguilar-Rincón
- Postgrado en Recursos Genéticos y Productividad-Genética, Colegio de Postgraduados, Texcoco 56264, Estado de México, Mexico; (G.G.-B.); (S.C.-I.); (V.H.A.-R.); (T.C.-T.)
| | - Tarsicio Corona-Torres
- Postgrado en Recursos Genéticos y Productividad-Genética, Colegio de Postgraduados, Texcoco 56264, Estado de México, Mexico; (G.G.-B.); (S.C.-I.); (V.H.A.-R.); (T.C.-T.)
| | - José Crossa
- International Maize and Wheat Improvement Center (CIMMYT), Km 35 Carretera México-Veracruz, Texcoco 56237, Estado de México, Mexico; (L.C.-H.); (P.V.); (G.S.G.)
- Posgrado en Socioeconomía Estadística e Informática, Colegio de Postgraduados, Texcoco 56264, Estado de México, Mexico
| | - Rosa Angela Pacheco-Gil
- International Maize and Wheat Improvement Center (CIMMYT), Km 35 Carretera México-Veracruz, Texcoco 56237, Estado de México, Mexico; (L.C.-H.); (P.V.); (G.S.G.)
| |
Collapse
|
4
|
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.
Collapse
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
| |
Collapse
|
5
|
Winn ZJ, Lyerly JH, Brown-Guedira G, Murphy JP, Mason RE. Utilization of a publicly available diversity panel in genomic prediction of Fusarium head blight resistance traits in wheat. THE PLANT GENOME 2023; 16:e20353. [PMID: 37194437 DOI: 10.1002/tpg2.20353] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Revised: 04/25/2023] [Accepted: 04/27/2023] [Indexed: 05/18/2023]
Abstract
Fusarium head blight (FHB) is an economically and environmentally concerning disease of wheat (Triticum aestivum L). A two-pronged approach of marker-assisted selection coupled with genomic selection has been suggested when breeding for FHB resistance. A historical dataset comprised of entries in the Southern Uniform Winter Wheat Scab Nursery (SUWWSN) from 2011 to 2021 was partitioned and used in genomic prediction. Two traits were curated from 2011 to 2021 in the SUWWSN: percent Fusarium damaged kernels (FDK) and deoxynivalenol (DON) content. Heritability was estimated for each trait-by-environment combination. A consistent set of check lines was drawn from each year in the SUWWSN, and k-means clustering was performed across environments to assign environments into clusters. Two clusters were identified as FDK and three for DON. Cross-validation on SUWWSN data from 2011 to 2019 indicated no outperforming training population in comparison to the combined dataset. Forward validation for FDK on the SUWWSN 2020 and 2021 data indicated a predictive accuracyr ≈ 0.58 $r \approx 0.58$ andr ≈ 0.53 $r \approx 0.53$ , respectively. Forward validation for DON indicated a predictive accuracy ofr ≈ 0.57 $r \approx 0.57$ andr ≈ 0.45 $r \approx 0.45$ , respectively. Forward validation using environments in cluster one for FDK indicated a predictive accuracy ofr ≈ 0.65 $r \approx 0.65$ andr ≈ 0.60 $r \approx 0.60$ , respectively. Forward validation using environments in cluster one for DON indicated a predictive accuracy ofr ≈ 0.67 $r \approx 0.67$ andr ≈ 0.60 $r \approx 0.60$ , respectively. These results indicated that selecting environments based on check performance may produce higher forward prediction accuracies. This work may be used as a model for utilizing public resources for genomic prediction of FHB resistance traits across public wheat breeding programs.
Collapse
Affiliation(s)
- Zachary J Winn
- Department of Crop and Soil Sciences, North Carolina State University, Raleigh, North Carolina, USA
- Department of Crop and Soil Sciences, Colorado State University, Fort Collins, Colorado, USA
| | - Jeanette H Lyerly
- Department of Crop and Soil Sciences, North Carolina State University, Raleigh, North Carolina, USA
| | - Gina Brown-Guedira
- Department of Crop and Soil Sciences, North Carolina State University, Raleigh, North Carolina, USA
- USDA-ARS, Raleigh, North Carolina, USA
| | - Joseph P Murphy
- Department of Crop and Soil Sciences, North Carolina State University, Raleigh, North Carolina, USA
| | - Richard Esten Mason
- Department of Crop and Soil Sciences, Colorado State University, Fort Collins, Colorado, USA
| |
Collapse
|
6
|
Fichtl L, Hofmann M, Kahlen K, Voss-Fels KP, Cast CS, Ollat N, Vivin P, Loose S, Nsibi M, Schmid J, Strack T, Schultz HR, Smith J, Friedel M. Towards grapevine root architectural models to adapt viticulture to drought. FRONTIERS IN PLANT SCIENCE 2023; 14:1162506. [PMID: 36998680 PMCID: PMC10043487 DOI: 10.3389/fpls.2023.1162506] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Accepted: 02/27/2023] [Indexed: 05/31/2023]
Abstract
To sustainably adapt viticultural production to drought, the planting of rootstock genotypes adapted to a changing climate is a promising means. Rootstocks contribute to the regulation of scion vigor and water consumption, modulate scion phenological development and determine resource availability by root system architecture development. There is, however, a lack of knowledge on spatio-temporal root system development of rootstock genotypes and its interactions with environment and management that prevents efficient knowledge transfer into practice. Hence, winegrowers take only limited advantage of the large variability of existing rootstock genotypes. Models of vineyard water balance combined with root architectural models, using both static and dynamic representations of the root system, seem promising tools to match rootstock genotypes to frequently occurring future drought stress scenarios and address scientific knowledge gaps. In this perspective, we discuss how current developments in vineyard water balance modeling may provide the background for a better understanding of the interplay of rootstock genotypes, environment and management. We argue that root architecture traits are key drivers of this interplay, but our knowledge on rootstock architectures in the field remains limited both qualitatively and quantitatively. We propose phenotyping methods to help close current knowledge gaps and discuss approaches to integrate phenotyping data into different models to advance our understanding of rootstock x environment x management interactions and predict rootstock genotype performance in a changing climate. This could also provide a valuable basis for optimizing breeding efforts to develop new grapevine rootstock cultivars with optimal trait configurations for future growing conditions.
Collapse
Affiliation(s)
- Lukas Fichtl
- Department of General and Organic Viticulture, Hochschule Geisenheim University, Geisenheim, Germany
| | - Marco Hofmann
- Department of General and Organic Viticulture, Hochschule Geisenheim University, Geisenheim, Germany
| | - Katrin Kahlen
- Department of Modeling and Systems Analysis, Hochschule Geisenheim University, Geisenheim, Germany
| | - Kai P. Voss-Fels
- Department of Grapevine Breeding, Hochschule Geisenheim University, Geisenheim, Germany
| | - Clément Saint Cast
- EGFV, University of Bordeaux, Bordeaux Sciences Agro, INRAE, ISVV, Villenave d’Ornon, France
| | - Nathalie Ollat
- EGFV, University of Bordeaux, Bordeaux Sciences Agro, INRAE, ISVV, Villenave d’Ornon, France
| | - Philippe Vivin
- EGFV, University of Bordeaux, Bordeaux Sciences Agro, INRAE, ISVV, Villenave d’Ornon, France
| | - Simone Loose
- Department of Wine and Beverage Business, Hochschule Geisenheim University, Geisenheim, Germany
| | - Mariem Nsibi
- Department of Grapevine Breeding, Hochschule Geisenheim University, Geisenheim, Germany
| | - Joachim Schmid
- Department of Grapevine Breeding, Hochschule Geisenheim University, Geisenheim, Germany
| | - Timo Strack
- Department of Grapevine Breeding, Hochschule Geisenheim University, Geisenheim, Germany
| | - Hans Reiner Schultz
- Department of General and Organic Viticulture, Hochschule Geisenheim University, Geisenheim, Germany
| | - Jason Smith
- Gulbali Institute for Agriculture, Water and Environment, Charles Sturt University, Orange, NSW, Australia
| | - Matthias Friedel
- Department of General and Organic Viticulture, Hochschule Geisenheim University, Geisenheim, Germany
| |
Collapse
|
7
|
Nguyen VH, Morantte RIZ, Lopena V, Verdeprado H, Murori R, Ndayiragije A, Katiyar SK, Islam MR, Juma RU, Flandez-Galvez H, Glaszmann JC, Cobb JN, Bartholomé J. Multi-environment Genomic Selection in Rice Elite Breeding Lines. RICE (NEW YORK, N.Y.) 2023; 16:7. [PMID: 36752880 PMCID: PMC9908796 DOI: 10.1186/s12284-023-00623-6] [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: 10/04/2022] [Accepted: 01/31/2023] [Indexed: 06/18/2023]
Abstract
BACKGROUND Assessing the performance of elite lines in target environments is essential for breeding programs to select the most relevant genotypes. One of the main complexities in this task resides in accounting for the genotype by environment interactions. Genomic prediction models that integrate information from multi-environment trials and environmental covariates can be efficient tools in this context. The objective of this study was to assess the predictive ability of different genomic prediction models to optimize the use of multi-environment information. We used 111 elite breeding lines representing the diversity of the international rice research institute breeding program for irrigated ecosystems. The lines were evaluated for three traits (days to flowering, plant height, and grain yield) in 15 environments in Asia and Africa and genotyped with 882 SNP markers. We evaluated the efficiency of genomic prediction to predict untested environments using seven multi-environment models and three cross-validation scenarios. RESULTS The elite lines were found to belong to the indica group and more specifically the indica-1B subgroup which gathered improved material originating from the Green Revolution. Phenotypic correlations between environments were high for days to flowering and plant height (33% and 54% of pairwise correlation greater than 0.5) but low for grain yield (lower than 0.2 in most cases). Clustering analyses based on environmental covariates separated Asia's and Africa's environments into different clusters or subclusters. The predictive abilities ranged from 0.06 to 0.79 for days to flowering, 0.25-0.88 for plant height, and - 0.29-0.62 for grain yield. We found that models integrating genotype-by-environment interaction effects did not perform significantly better than models integrating only main effects (genotypes and environment or environmental covariates). The different cross-validation scenarios showed that, in most cases, the use of all available environments gave better results than a subset. CONCLUSION Multi-environment genomic prediction models with main effects were sufficient for accurate phenotypic prediction of elite lines in targeted environments. These results will help refine the testing strategy to update the genomic prediction models to improve predictive ability.
Collapse
Affiliation(s)
- Van Hieu Nguyen
- CIRAD, UMR AGAP Institut, 34398, Montpellier, France
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, France
- Rice Breeding Innovation Platform, International Rice Research Institute, DAPO, Box7777, Metro Manila, Philippines
- Institute of Crop Science, College of Agriculture and Food Science, University of the Philippines, Los Baños, Laguna, Philippines
| | - Rose Imee Zhella Morantte
- Rice Breeding Innovation Platform, International Rice Research Institute, DAPO, Box7777, Metro Manila, Philippines
| | - Vitaliano Lopena
- Rice Breeding Innovation Platform, International Rice Research Institute, DAPO, Box7777, Metro Manila, Philippines
| | - Holden Verdeprado
- Rice Breeding Innovation Platform, International Rice Research Institute, DAPO, Box7777, Metro Manila, Philippines
| | - Rosemary Murori
- Rice Breeding Innovation Platform, International Rice Research Institute, DAPO, Box7777, Metro Manila, Philippines
| | - Alexis Ndayiragije
- Rice Breeding Innovation Platform, International Rice Research Institute, DAPO, Box7777, Metro Manila, Philippines
| | - Sanjay Kumar Katiyar
- Rice Breeding Innovation Platform, International Rice Research Institute, DAPO, Box7777, Metro Manila, Philippines
| | - Md Rafiqul Islam
- Rice Breeding Innovation Platform, International Rice Research Institute, DAPO, Box7777, Metro Manila, Philippines
| | - Roselyne Uside Juma
- Rice Breeding Innovation Platform, International Rice Research Institute, DAPO, Box7777, Metro Manila, Philippines
| | - Hayde Flandez-Galvez
- Institute of Crop Science, College of Agriculture and Food Science, University of the Philippines, Los Baños, Laguna, Philippines
| | - Jean-Christophe Glaszmann
- CIRAD, UMR AGAP Institut, 34398, Montpellier, France
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, France
| | - Joshua N Cobb
- Rice Breeding Innovation Platform, International Rice Research Institute, DAPO, Box7777, Metro Manila, Philippines
- RiceTec. Inc, PO Box 1305, Alvin, TX, 77512, USA
| | - Jérôme Bartholomé
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, France.
- CIRAD, UMR AGAP Institut, Cali, Colombia.
- Alliance Bioversity-CIAT, Cali, Colombia.
| |
Collapse
|
8
|
Bowerman AF, Byrt CS, Roy SJ, Whitney SM, Mortimer JC, Ankeny RA, Gilliham M, Zhang D, Millar AA, Rebetzke GJ, Pogson BJ. Potential abiotic stress targets for modern genetic manipulation. THE PLANT CELL 2023; 35:139-161. [PMID: 36377770 PMCID: PMC9806601 DOI: 10.1093/plcell/koac327] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Accepted: 11/03/2022] [Indexed: 05/06/2023]
Abstract
Research into crop yield and resilience has underpinned global food security, evident in yields tripling in the past 5 decades. The challenges that global agriculture now faces are not just to feed 10+ billion people within a generation, but to do so under a harsher, more variable, and less predictable climate, and in many cases with less water, more expensive inputs, and declining soil quality. The challenges of climate change are not simply to breed for a "hotter drier climate," but to enable resilience to floods and droughts and frosts and heat waves, possibly even within a single growing season. How well we prepare for the coming decades of climate variability will depend on our ability to modify current practices, innovate with novel breeding methods, and communicate and work with farming communities to ensure viability and profitability. Here we define how future climates will impact farming systems and growing seasons, thereby identifying the traits and practices needed and including exemplars being implemented and developed. Critically, this review will also consider societal perspectives and public engagement about emerging technologies for climate resilience, with participatory approaches presented as the best approach.
Collapse
Affiliation(s)
- Andrew F Bowerman
- ARC Training Centre for Accelerated Future Crops Development, The Australian National University, Canberra, Australian Capital Territory, Australia
| | - Caitlin S Byrt
- ARC Training Centre for Accelerated Future Crops Development, The Australian National University, Canberra, Australian Capital Territory, Australia
| | - Stuart John Roy
- ARC Training Centre for Accelerated Future Crops Development, University of Adelaide, South Australia, Australia
- School of Agriculture, Food and Wine & Waite Research Institute, University of Adelaide, Glen Osmond, South Australia, Australia
| | - Spencer M Whitney
- ARC Training Centre for Accelerated Future Crops Development, The Australian National University, Canberra, Australian Capital Territory, Australia
| | - Jenny C Mortimer
- ARC Training Centre for Accelerated Future Crops Development, University of Adelaide, South Australia, Australia
- School of Agriculture, Food and Wine & Waite Research Institute, University of Adelaide, Glen Osmond, South Australia, Australia
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, USA
| | - Rachel A Ankeny
- ARC Training Centre for Accelerated Future Crops Development, University of Adelaide, South Australia, Australia
- School of Humanities, University of Adelaide, North Terrace, South Australia, Australia
| | - Matthew Gilliham
- ARC Training Centre for Accelerated Future Crops Development, University of Adelaide, South Australia, Australia
- School of Agriculture, Food and Wine & Waite Research Institute, University of Adelaide, Glen Osmond, South Australia, Australia
| | - Dabing Zhang
- ARC Training Centre for Accelerated Future Crops Development, University of Adelaide, South Australia, Australia
- School of Agriculture, Food and Wine & Waite Research Institute, University of Adelaide, Glen Osmond, South Australia, Australia
| | - Anthony A Millar
- ARC Training Centre for Accelerated Future Crops Development, The Australian National University, Canberra, Australian Capital Territory, Australia
| | - Greg J Rebetzke
- CSIRO Agriculture & Food, Canberra, Australian Capital Territory, Australia
| | - Barry J Pogson
- ARC Training Centre for Accelerated Future Crops Development, The Australian National University, Canberra, Australian Capital Territory, Australia
| |
Collapse
|
9
|
Costa-Neto G, Crespo-Herrera L, Fradgley N, Gardner K, Bentley AR, Dreisigacker S, Fritsche-Neto R, Montesinos-López OA, Crossa J. Envirome-wide associations enhance multi-year genome-based prediction of historical wheat breeding data. G3 (BETHESDA, MD.) 2022; 13:6861853. [PMID: 36454213 PMCID: PMC9911085 DOI: 10.1093/g3journal/jkac313] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Revised: 11/02/2022] [Accepted: 11/03/2022] [Indexed: 12/03/2022]
Abstract
Linking high-throughput environmental data (enviromics) to genomic prediction (GP) is a cost-effective strategy for increasing selection intensity under genotype-by-environment interactions (G × E). This study developed a data-driven approach based on Environment-Phenotype Association (EPA) aimed at recycling important G × E information from historical breeding data. EPA was developed in two applications: (1) scanning a secondary source of genetic variation, weighted from the shared reaction-norms of past-evaluated genotypes and (2) pinpointing weights of the similarity among trial-sites (locations), given the historical impact of each envirotyping data variable for a given site. These results were then used as a dimensionality reduction strategy, integrating historical data to feed multi-environment GP models, which led to the development of four new G × E kernels considering genomics, enviromics, and EPA outcomes. The wheat trial data used included 36 locations, 8 years, and three target populations of environments (TPEs) in India. Four prediction scenarios and six kernel models within/across TPEs were tested. Our results suggest that the conventional GBLUP, without enviromic data or when omitting EPA, is inefficient in predicting the performance of wheat lines in future years. Nevertheless, when EPA was introduced as an intermediary learning step to reduce the dimensionality of the G × E kernels while connecting phenotypic and environmental-wide variation, a significant enhancement of G × E prediction accuracy was evident. EPA revealed that the effect of seasonality makes strategies such as "covariable selection" unfeasible because G × E is year-germplasm specific. We propose that the EPA effectively serves as a "reinforcement learner" algorithm capable of uncovering the effect of seasonality over the reaction-norms, with the benefits of better forecasting the similarities between past and future trialing sites. EPA combines the benefits of dimensionality reduction while reducing the uncertainty of genotype-by-year predictions and increasing the resolution of GP for the genotype-specific level.
Collapse
Affiliation(s)
- Germano Costa-Neto
- Institute for Genomics Diversity, Cornell University, Ithaca, NY 14853, USA
| | - Leonardo Crespo-Herrera
- International Maize and Wheat Improvement Center (CIMMYT), Km 45 Carretera México-Veracruz, El Batan, Edo. de México 5623, Mexico
| | - Nick Fradgley
- NIAB, 93 Lawrence Weaver Road, Cambridge CB3 0LE, UK
| | - Keith Gardner
- International Maize and Wheat Improvement Center (CIMMYT), Km 45 Carretera México-Veracruz, El Batan, Edo. de México 5623, Mexico
| | - Alison R Bentley
- International Maize and Wheat Improvement Center (CIMMYT), Km 45 Carretera México-Veracruz, El Batan, Edo. de México 5623, Mexico
| | - Susanne Dreisigacker
- International Maize and Wheat Improvement Center (CIMMYT), Km 45 Carretera México-Veracruz, El Batan, Edo. de México 5623, Mexico
| | | | - Osval A Montesinos-López
- Corresponding authors: Facultad de Telematica, Universidad de Colima, Mexico. ; and International Maize and Wheat Improvement Center (CIMMYT) and Colegio de Post-Graduados, Mexico.
| | - Jose Crossa
- Corresponding authors: Facultad de Telematica, Universidad de Colima, Mexico. ; and International Maize and Wheat Improvement Center (CIMMYT) and Colegio de Post-Graduados, Mexico.
| |
Collapse
|
10
|
Lopez-Cruz M, Dreisigacker S, Crespo-Herrera L, Bentley AR, Singh R, Poland J, Shrestha S, Huerta-Espino J, Govindan V, Juliana P, Mondal S, Pérez-Rodríguez P, Crossa J. Sparse kernel models provide optimization of training set design for genomic prediction in multiyear wheat breeding data. THE PLANT GENOME 2022; 15:e20254. [PMID: 36043341 DOI: 10.1002/tpg2.20254] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/19/2021] [Accepted: 07/17/2022] [Indexed: 06/15/2023]
Abstract
The success of genomic selection (GS) in breeding schemes relies on its ability to provide accurate predictions of unobserved lines at early stages. Multigeneration data provides opportunities to increase the training data size and thus, the likelihood of extracting useful information from ancestors to improve prediction accuracy. The genomic best linear unbiased predictions (GBLUPs) are performed by borrowing information through kinship relationships between individuals. Multigeneration data usually becomes heterogeneous with complex family relationship patterns that are increasingly entangled with each generation. Under these conditions, historical data may not be optimal for model training as the accuracy could be compromised. The sparse selection index (SSI) is a method for training set (TRN) optimization, in which training individuals provide predictions to some but not all predicted subjects. We added an additional trimming process to the original SSI (trimmed SSI) to remove less important training individuals for prediction. Using a large multigeneration (8 yr) wheat (Triticum aestivum L.) grain yield dataset (n = 68,836), we found increases in accuracy as more years are included in the TRN, with improvements of ∼0.05 in the GBLUP accuracy when using 5 yr of historical data relative to when using only 1 yr. The SSI method showed a small gain over the GBLUP accuracy but with an important reduction on the TRN size. These reduced TRNs were formed with a similar number of subjects from each training generation. Our results suggest that the SSI provides a more stable ranking of genotypes than the GBLUP as the TRN becomes larger.
Collapse
Affiliation(s)
- Marco Lopez-Cruz
- Dep. of Epidemiology and Biostatistics, Michigan State Univ., East Lansing, MI, USA
| | - Susanne Dreisigacker
- Global Wheat Program, International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Leonardo Crespo-Herrera
- Global Wheat Program, International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Alison R Bentley
- Global Wheat Program, International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Ravi Singh
- Global Wheat Program, International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Jesse Poland
- Dep. of Agronomy, Kansas State Univ., Manhattan, KS, USA
| | | | - Julio Huerta-Espino
- Campo Experimental Valle de Mexico, Instituto Nacional de Investigaciones Forestales, Agricolas y Pecuarias (INIFAP), Chapingo, Mexico
| | - Velu Govindan
- Global Wheat Program, International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Philomin Juliana
- Global Wheat Program, International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Suchismita Mondal
- Global Wheat Program, International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | | | - Jose Crossa
- Global Wheat Program, International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
- Colegio de Postgraduados, Montecillos, Mexico
| |
Collapse
|
11
|
Navathe S, Pandey AK, Sharma S, Chand R, Mishra VK, Kumar D, Jaiswal S, Iquebal MA, Govindan V, Joshi AK, Singh PK. New Genomic Regions Identified for Resistance to Spot Blotch and Terminal Heat Stress in an Interspecific Population of Triticum aestivum and T. spelta. PLANTS (BASEL, SWITZERLAND) 2022; 11:2987. [PMID: 36365440 PMCID: PMC9657703 DOI: 10.3390/plants11212987] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Revised: 10/28/2022] [Accepted: 11/03/2022] [Indexed: 06/16/2023]
Abstract
Wheat is one of the most widely grown and consumed food crops in the world. Spot blotch and terminal heat stress are the two significant constraints mainly in the Indo-Gangetic plains of South Asia. The study was undertaken using 185 recombinant lines (RILs) derived from the interspecific hybridization of 'Triticum aestivum (HUW234) × T. spelta (H+26)' to reveal genomic regions associated with tolerance to combined stress to spot blotch and terminal heat. Different physiological (NDVI, canopy temperature, leaf chlorophyll) and grain traits (TGW, grain size) were observed under stressed (spot blotch, terminal heat) and non-stressed environments. The mean maturity duration of RILs under combined stress was reduced by 12 days, whereas the normalized difference vegetation index (NDVI) was 46.03%. Similarly, the grain size was depleted under combined stress by 32.23% and thousand kernel weight (TKW) by 27.56% due to spot blotch and terminal heat stress, respectively. The genetic analysis using 6734 SNP markers identified 37 significant loci for the area under the disease progress curve (AUDPC) and NDVI. The genome-wide functional annotation of the SNP markers revealed gene functions such as plant chitinases, NB-ARC and NBS-LRR, and the peroxidase superfamily Cytochrome P450 have a positive role in the resistance through a hypersensitive response. Zinc finger domains, cysteine protease coding gene, F-box protein, ubiquitin, and associated proteins, play a substantial role in the combined stress of spot blotch and terminal heat in bread wheat, according to genomic domains ascribed to them. The study also highlights T. speltoides as a source of resistance to spot blotch and terminal heat tolerance.
Collapse
Affiliation(s)
- Sudhir Navathe
- Institute of Agricultural Sciences, Banaras Hindu University, Varanasi 221005, India
- Agharkar Research Institute, G.G. Agharkar Road, Pune 411004, India
| | - Ajeet Kumar Pandey
- Institute of Agricultural Sciences, Banaras Hindu University, Varanasi 221005, India
| | - Sandeep Sharma
- Institute of Agricultural Sciences, Banaras Hindu University, Varanasi 221005, India
| | - Ramesh Chand
- Institute of Agricultural Sciences, Banaras Hindu University, Varanasi 221005, India
| | - Vinod Kumar Mishra
- Institute of Agricultural Sciences, Banaras Hindu University, Varanasi 221005, India
| | - Dinesh Kumar
- Centre for Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, Library Avenue, PUSA, New Delhi 110012, India
- Department of Biotechnology, School of Interdisciplinary and Applied Sciences, Central University of Haryana, Mahendergarh 123031, India
| | - Sarika Jaiswal
- Centre for Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, Library Avenue, PUSA, New Delhi 110012, India
| | - Mir Asif Iquebal
- Centre for Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, Library Avenue, PUSA, New Delhi 110012, India
| | - Velu Govindan
- International Maize and Wheat Improvement Center (CIMMYT), Veracruz 56237, Mexico
| | - Arun Kumar Joshi
- Borlaug Institute for South Asia, NASC Complex, DPS Marg, New Delhi 110012, India
- International Maize and Wheat Improvement Center (CIMMYT), G-2, B-Block, NASC Complex, DPS Marg, New Delhi 110012, India
| | - Pawan Kumar Singh
- International Maize and Wheat Improvement Center (CIMMYT), Veracruz 56237, Mexico
| |
Collapse
|
12
|
Atanda SA, Govindan V, Singh R, Robbins KR, Crossa J, Bentley AR. Sparse testing using genomic prediction improves selection for breeding targets in elite spring wheat. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2022; 135:1939-1950. [PMID: 35348821 PMCID: PMC9205816 DOI: 10.1007/s00122-022-04085-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Accepted: 03/16/2022] [Indexed: 06/08/2023]
Abstract
Sparse testing using genomic prediction can be efficiently used to increase the number of testing environments while maintaining selection intensity in the early yield testing stage without increasing the breeding budget. Sparse testing using genomic prediction enables expanded use of selection environments in early-stage yield testing without increasing phenotyping cost. We evaluated different sparse testing strategies in the yield testing stage of a CIMMYT spring wheat breeding pipeline characterized by multiple populations each with small family sizes of 1-9 individuals. Our results indicated that a substantial overlap between lines across environments should be used to achieve optimal prediction accuracy. As sparse testing leverages information generated within and across environments, the genetic correlations between environments and genomic relationships of lines across environments were the main drivers of prediction accuracy in multi-environment yield trials. Including information from previous evaluation years did not consistently improve the prediction performance. Genomic best linear unbiased prediction was found to be the best predictor of true breeding value, and therefore, we propose that it should be used as a selection decision metric in the early yield testing stages. We also propose it as a proxy for assessing prediction performance to mirror breeder's advancement decisions in a breeding program so that it can be readily applied for advancement decisions by breeding programs.
Collapse
Affiliation(s)
| | - Velu Govindan
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Ravi Singh
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Kelly R Robbins
- Section of Plant Breeding and Genetics, School of Integrative Plant Sciences, Cornell University, Ithaca, NY, USA
| | - Jose Crossa
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Alison R Bentley
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico.
| |
Collapse
|
13
|
Kromdijk J, McCormick AJ. Genetic variation in photosynthesis: many variants make light work. JOURNAL OF EXPERIMENTAL BOTANY 2022; 73:3053-3056. [PMID: 35606158 PMCID: PMC9126730 DOI: 10.1093/jxb/erac129] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Affiliation(s)
- Johannes Kromdijk
- Department of Plant Sciences, University of Cambridge, Downing Street, Cambridge CB2 3EA, UK
- Carl R Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, 1206 W Gregory drive, Urbana, IL 61801, USA
| | - Alistair J McCormick
- SynthSys & Institute of Molecular Plant Sciences, School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3BF, UK
| |
Collapse
|
14
|
Carcedo AJP, Mayor L, Demarco P, Morris GP, Lingenfelser J, Messina CD, Ciampitti IA. Environment Characterization in Sorghum ( Sorghum bicolor L.) by Modeling Water-Deficit and Heat Patterns in the Great Plains Region, United States. FRONTIERS IN PLANT SCIENCE 2022; 13:768610. [PMID: 35310654 PMCID: PMC8929132 DOI: 10.3389/fpls.2022.768610] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Accepted: 01/31/2022] [Indexed: 05/26/2023]
Abstract
Environmental characterization for defining the target population of environments (TPE) is critical to improve the efficiency of breeding programs in crops, such as sorghum (Sorghum bicolor L.). The aim of this study was to characterize the spatial and temporal variation for a TPE for sorghum within the United States. APSIM-sorghum, included in the Agricultural Production Systems sIMulator software platform, was used to quantify water-deficit and heat patterns for 15 sites in the sorghum belt. Historical weather data (∼35 years) was used to identify water (WSP) and heat (HSP) stress patterns to develop water-heat clusters. Four WSPs were identified with large differences in the timing of onset, intensity, and duration of the stress. In the western region of Kansas, Oklahoma, and Texas, the most frequent WSP (∼35%) was stress during grain filling with late recovery. For northeast Kansas, WSP frequencies were more evenly distributed, suggesting large temporal variation. Three HSPs were defined, with the low HSP being most frequent (∼68%). Field data from Kansas State University sorghum hybrid yield performance trials (2006-2013 period, 6 hybrids, 10 sites, 46 site × year combinations) were classified into the previously defined WSP and HSP clusters. As the intensity of the environmental stress increased, there was a clear reduction on grain yield. Both simulated and observed yield data showed similar yield trends when the level of heat or water stressed increased. Field yield data clearly separated contrasting clusters for both water and heat patterns (with vs. without stress). Thus, the patterns were regrouped into four categories, which account for the observed genotype by environment interaction (GxE) and can be applied in a breeding program. A better definition of TPE to improve predictability of GxE could accelerate genetic gains and help bridge the gap between breeders, agronomists, and farmers.
Collapse
Affiliation(s)
- Ana J. P. Carcedo
- Department of Agronomy, Kansas State University, Manhattan, KS, United States
| | - Laura Mayor
- Corteva Agriscience, Johnston, IA, United States
| | - Paula Demarco
- Department of Agronomy, Kansas State University, Manhattan, KS, United States
| | - Geoffrey P. Morris
- Department of Soil and Crop Science, Colorado State University, Fort Collins, CO, United States
| | - Jane Lingenfelser
- Department of Agronomy, Kansas State University, Manhattan, KS, United States
| | - Carlos D. Messina
- Horticultural Sciences Department, University of Florida, Gainesville, FL, United States
| | | |
Collapse
|
15
|
Effect of Flowering Time-Related Genes on Biomass, Harvest Index, and Grain Yield in CIMMYT Elite Spring Bread Wheat. BIOLOGY 2021; 10:biology10090855. [PMID: 34571732 PMCID: PMC8471161 DOI: 10.3390/biology10090855] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/31/2021] [Revised: 08/16/2021] [Accepted: 08/19/2021] [Indexed: 11/23/2022]
Abstract
Simple Summary Allelic variants of vernalization (Vrn), photoperiod (Ppd), and earliness per se (Eps) genes in two panels of elite spring wheat were used to estimate their effects on the phenological stages, biomass (BM), harvest index (HI), and grain yield (YLD). Major spring alleles of Vrn-1 had the largest effect on shortening the time to anthesis, while the Ppd-insensitive allele Ppd-D1a had the most significant positive effect on YLD. Furthermore, alleles at recently identified loci TaTOE-B1 and TaFT3-B1 promoted between 3.8% and 7.6% higher YLD and 4.2% and 10.2% higher HI in the two panels. Further, when the possible effects of the TaTOE-B1 and TaFT3-B1 alleles on the sink and source traits were explored, the favorable allele at TaTOE-B1 showed positive effects on several sink traits related mainly to the grain number. Favorable alleles at TaFT3-B1 followed a different pattern, with positive effects on the traits related to grain weight. The results of this study expanded the wheat breeders’ toolbox in the quest to breed better-adapted and higher-yielding wheat cultivars. Abstract Grain yield (YLD) is a function of the total biomass (BM) and of partitioning the biomass by grains, i.e., the harvest index (HI). The most critical developmental stage for their determination is the flowering time, which mainly depends on the vernalization requirement (Vrn) and photoperiod sensitivity genes (Ppd) loci. Allelic variants at the Vrn, Ppd, and earliness per se (Eps) genes of elite spring wheat genotypes included in High Biomass Association Panel (HiBAP) I and II were used to estimate their effects on the phenological stages BM, HI, and YLD. Each panel was grown for two consecutive years in Northwest Mexico. Spring alleles at Vrn-1 had the largest effect on shortening the time to anthesis, and the Ppd-insensitive allele Ppd-D1a had the most significant positive effect on YLD in both panels. In addition, alleles at TaTOE-B1 and TaFT3-B1 promoted between 3.8% and 7.6% higher YLD and 4.2% and 10.2% higher HI in HiBAP I and II, respectively. When the possible effects of the TaTOE-B1 and TaFT3-B1 alleles on the sink and source traits were explored, the favorable allele at TaTOE-B1 showed positive effects on several sink traits mainly related to grain number. The favorable alleles at TaFT3-B1 followed a different pattern, with positive effects on the traits related to grain weight. The results of this study expanded the wheat breeders’ toolbox in the quest to breed better-adapted and higher-yielding wheat cultivars.
Collapse
|
16
|
Cooper M, Messina CD. Can We Harness "Enviromics" to Accelerate Crop Improvement by Integrating Breeding and Agronomy? FRONTIERS IN PLANT SCIENCE 2021; 12:735143. [PMID: 34567047 PMCID: PMC8461239 DOI: 10.3389/fpls.2021.735143] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Accepted: 08/16/2021] [Indexed: 05/02/2023]
Abstract
The diverse consequences of genotype-by-environment (GxE) interactions determine trait phenotypes across levels of biological organization for crops, challenging our ambition to predict trait phenotypes from genomic information alone. GxE interactions have many implications for optimizing both genetic gain through plant breeding and crop productivity through on-farm agronomic management. Advances in genomics technologies have provided many suitable predictors for the genotype dimension of GxE interactions. Emerging advances in high-throughput proximal and remote sensor technologies have stimulated the development of "enviromics" as a community of practice, which has the potential to provide suitable predictors for the environment dimension of GxE interactions. Recently, several bespoke examples have emerged demonstrating the nascent potential for enhancing the prediction of yield and other complex trait phenotypes of crop plants through including effects of GxE interactions within prediction models. These encouraging results motivate the development of new prediction methods to accelerate crop improvement. If we can automate methods to identify and harness suitable sets of coordinated genotypic and environmental predictors, this will open new opportunities to upscale and operationalize prediction of the consequences of GxE interactions. This would provide a foundation for accelerating crop improvement through integrating the contributions of both breeding and agronomy. Here we draw on our experience from improvement of maize productivity for the range of water-driven environments across the US corn-belt. We provide perspectives from the maize case study to prioritize promising opportunities to further develop and automate "enviromics" methodologies to accelerate crop improvement through integrated breeding and agronomic approaches for a wider range of crops and environmental targets.
Collapse
Affiliation(s)
- Mark Cooper
- Queensland Alliance for Agriculture and Food Innovation (QAAFI), The University of Queensland, Brisbane, QLD, Australia
- *Correspondence: Mark Cooper,
| | | |
Collapse
|
17
|
Costa-Neto G, Crossa J, Fritsche-Neto R. Enviromic Assembly Increases Accuracy and Reduces Costs of the Genomic Prediction for Yield Plasticity in Maize. FRONTIERS IN PLANT SCIENCE 2021; 12:717552. [PMID: 34691099 PMCID: PMC8529011 DOI: 10.3389/fpls.2021.717552] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Accepted: 09/03/2021] [Indexed: 05/21/2023]
Abstract
Quantitative genetics states that phenotypic variation is a consequence of the interaction between genetic and environmental factors. Predictive breeding is based on this statement, and because of this, ways of modeling genetic effects are still evolving. At the same time, the same refinement must be used for processing environmental information. Here, we present an "enviromic assembly approach," which includes using ecophysiology knowledge in shaping environmental relatedness into whole-genome predictions (GP) for plant breeding (referred to as enviromic-aided genomic prediction, E-GP). We propose that the quality of an environment is defined by the core of environmental typologies and their frequencies, which describe different zones of plant adaptation. From this, we derived markers of environmental similarity cost-effectively. Combined with the traditional additive and non-additive effects, this approach may better represent the putative phenotypic variation observed across diverse growing conditions (i.e., phenotypic plasticity). Then, we designed optimized multi-environment trials coupling genetic algorithms, enviromic assembly, and genomic kinships capable of providing in-silico realization of the genotype-environment combinations that must be phenotyped in the field. As proof of concept, we highlighted two E-GP applications: (1) managing the lack of phenotypic information in training accurate GP models across diverse environments and (2) guiding an early screening for yield plasticity exerting optimized phenotyping efforts. Our approach was tested using two tropical maize sets, two types of enviromics assembly, six experimental network sizes, and two types of optimized training set across environments. We observed that E-GP outperforms benchmark GP in all scenarios, especially when considering smaller training sets. The representativeness of genotype-environment combinations is more critical than the size of multi-environment trials (METs). The conventional genomic best-unbiased prediction (GBLUP) is inefficient in predicting the quality of a yet-to-be-seen environment, while enviromic assembly enabled it by increasing the accuracy of yield plasticity predictions. Furthermore, we discussed theoretical backgrounds underlying how intrinsic envirotype-phenotype covariances within the phenotypic records can impact the accuracy of GP. The E-GP is an efficient approach to better use environmental databases to deliver climate-smart solutions, reduce field costs, and anticipate future scenarios.
Collapse
Affiliation(s)
- Germano Costa-Neto
- Department of Genetics, “Luiz de Queiroz” Agriculture College, University of São Paulo (ESALQ/USP), Piracicaba, Brazil
- Institute for Genomic Diversity, Cornell University, Ithaca, NY, United States
- *Correspondence: Germano Costa-Neto
| | - Jose Crossa
- Biometrics and Statistics Unit, International Maize and Wheat Improvement Center (CIMMYT), Mexico City, Mexico
- Colegio de Posgraduado, Mexico City, Mexico
| | - Roberto Fritsche-Neto
- Department of Genetics, “Luiz de Queiroz” Agriculture College, University of São Paulo (ESALQ/USP), Piracicaba, Brazil
- Breeding Analytics and Data Management Unit, International Rice Research Institute (IRRI), Los Baños, Philippines
| |
Collapse
|