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Messina CD, Gho C, Hammer GL, Tang T, Cooper M. Two decades of harnessing standing genetic variation for physiological traits to improve drought tolerance in maize. JOURNAL OF EXPERIMENTAL BOTANY 2023; 74:4847-4861. [PMID: 37354091 PMCID: PMC10474595 DOI: 10.1093/jxb/erad231] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Accepted: 06/15/2023] [Indexed: 06/26/2023]
Abstract
We review approaches to maize breeding for improved drought tolerance during flowering and grain filling in the central and western US corn belt and place our findings in the context of results from public breeding. Here we show that after two decades of dedicated breeding efforts, the rate of crop improvement under drought increased from 6.2 g m-2 year-1 to 7.5 g m-2 year-1, closing the genetic gain gap with respect to the 8.6 g m-2 year-1 observed under water-sufficient conditions. The improvement relative to the long-term genetic gain was possible by harnessing favourable alleles for physiological traits available in the reference population of genotypes. Experimentation in managed stress environments that maximized the genetic correlation with target environments was key for breeders to identify and select for these alleles. We also show that the embedding of physiological understanding within genomic selection methods via crop growth models can hasten genetic gain under drought. We estimate a prediction accuracy differential (Δr) above current prediction approaches of ~30% (Δr=0.11, r=0.38), which increases with increasing complexity of the trait environment system as estimated by Shannon information theory. We propose this framework to inform breeding strategies for drought stress across geographies and crops.
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Affiliation(s)
- Carlos D Messina
- Horticultural Sciences Department, University of Florida, Gainesville, FL, USA
- ARC Centre of Excellence for Plant Success in Nature and Agriculture, The University of Queensland, Brisbane, Qld 4072, Australia
| | - Carla Gho
- School of Agriculture & Food Sciences, The University of Queensland, Brisbane, Qld 4072, Australia
| | - Graeme L Hammer
- ARC Centre of Excellence for Plant Success in Nature and Agriculture, The University of Queensland, Brisbane, Qld 4072, Australia
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, Qld 4072, Australia
| | - Tom Tang
- Corteva Agrisciences, Johnston, IA, USA
| | - Mark Cooper
- ARC Centre of Excellence for Plant Success in Nature and Agriculture, The University of Queensland, Brisbane, Qld 4072, Australia
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, Qld 4072, Australia
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2
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Vadez V, Messina CD, Carminati A. Combatting drought: a multi-dimensional challenge. JOURNAL OF EXPERIMENTAL BOTANY 2023; 74:4765-4769. [PMID: 37658757 DOI: 10.1093/jxb/erad301] [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: 09/05/2023]
Abstract
Water will be a major limitation to food production in the 21st century, and drought issues already prevail in many parts of the world. Finding solutions to ensure that farmers harvest profitable crops, and secure food supplies for families and feed for animals that will provide for them through to the next season are urgent necessities. The Interdrought community has been addressing this issue for almost 30 years in a series of international conferences, characterized by a multi-disciplinary approach across the domains of molecular biology, physiology, genetics, agronomy, breeding, environmental and social sciences, policy, and systems modeling. This special issue presents papers from the 7th edition of the conference, the first to be held in Africa, that paid special attention to drought in a smallholder context, adding a 'system' dimension to the crop focus from the previous Interdrought events (Varshney et al., 2018; Hammer et al., 2021).
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Affiliation(s)
- Vincent Vadez
- Institute for Research and Development (IRD), DIADE Research Unit, University of Montpellier, 34394 Montpellier cedex 5, France
- International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru, Telangana, India
- Centre d'Etudes Régional pour l'Amélioration de l'Adaptation à la Sècheresse (CERAAS), Campus ENSA, Thiès, Sénégal
| | - Carlos D Messina
- Department of Horticulture, University of Florida, Gainesville FL, USA
| | - Andrea Carminati
- Physics of Soils and Terrestrial Ecosystems, Department of Environmental Systems Science, ETH Zurich, Zurich, Switzerland
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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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McMillen MS, Mahama AA, Sibiya J, Lübberstedt T, Suza WP. Improving drought tolerance in maize: Tools and techniques. Front Genet 2022; 13:1001001. [PMID: 36386797 PMCID: PMC9651916 DOI: 10.3389/fgene.2022.1001001] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Accepted: 10/14/2022] [Indexed: 05/01/2024] Open
Abstract
Drought is an important constraint to agricultural productivity worldwide and is expected to worsen with climate change. To assist farmers, especially in sub-Saharan Africa (SSA), to adapt to climate change, continuous generation of stress-tolerant and farmer-preferred crop varieties, and their adoption by farmers, is critical to curb food insecurity. Maize is the most widely grown staple crop in SSA and plays a significant role in food security. The aim of this review is to present an overview of a broad range of tools and techniques used to improve drought tolerance in maize. We also present a summary of progress in breeding for maize drought tolerance, while incorporating research findings from disciplines such as physiology, molecular biology, and systems modeling. The review is expected to complement existing knowledge about breeding maize for climate resilience. Collaborative maize drought tolerance breeding projects in SSA emphasize the value of public-private partnerships in increasing access to genomic techniques and useful transgenes. To sustain the impact of maize drought tolerance projects in SSA, there must be complementary efforts to train the next generation of plant breeders and crop scientists.
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Affiliation(s)
| | - Anthony A. Mahama
- Department of Agronomy, Iowa State University, Ames, IA, United States
| | - Julia Sibiya
- School of Agricultural, Earth and Environmental Sciences, University of KwaZulu-Natal, Pietermaritzburg, South Africa
| | | | - Walter P. Suza
- Department of Agronomy, Iowa State University, Ames, IA, United States
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5
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Messina CD, Rotundo J, Hammer GL, Gho C, Reyes A, Fang Y, van Oosterom E, Borras L, Cooper M. Radiation use efficiency increased over a century of maize (Zea mays L.) breeding in the US corn belt. JOURNAL OF EXPERIMENTAL BOTANY 2022; 73:5503-5513. [PMID: 35640591 DOI: 10.1093/jxb/erac212] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Accepted: 05/23/2022] [Indexed: 05/26/2023]
Abstract
In the absence of stress, crop growth depends on the amount of light intercepted by the canopy and the conversion efficiency [radiation use efficiency (RUE)]. This study tested the hypothesis that long-term genetic gain for grain yield was partly due to improved RUE. The hypothesis was tested using 30 elite maize hybrids commercialized in the US corn belt between 1930 and 2017. Crops grown under irrigation showed that pre-flowering crop growth increased at a rate of 0.11 g m-2 year-1, while light interception remained constant. Therefore, RUE increased at a rate of 0.0049 g MJ-1 year-1, translating into an average of 3 g m-2 year-1 of grain yield over 100 years of maize breeding. Considering that the harvest index has not changed for crops grown at optimal density for the hybrid, the cumulative RUE increase over the history of commercial maize breeding in the USA can account for ~32% of the documented yield trend for maize grown in the central US corn belt. The remaining RUE gap between this study and theoretical maximum values suggests that a yield improvement of a similar magnitude could be achieved by further increasing RUE.
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Affiliation(s)
- Carlos D Messina
- Horticultural Sciences Department, University of Florida, Gainesville, FL 32611, USA
| | - Jose Rotundo
- Corteva Agriscience, 8305 62nd Avenue, Johnston, IA 50131, USA
| | - Graeme L Hammer
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, Queensland 4072, Australia
| | - Carla Gho
- Corteva Agriscience, 8305 62nd Avenue, Johnston, IA 50131, USA
| | - Andres Reyes
- Corteva Agriscience, 18369 County Rd 96, Woodland, CA, USA
| | - Yinan Fang
- Corteva Agriscience, 8305 62nd Avenue, Johnston, IA 50131, USA
| | - Erik van Oosterom
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, Queensland 4072, Australia
| | - Lucas Borras
- Corteva Agriscience, 8305 62nd Avenue, Johnston, IA 50131, USA
| | - Mark Cooper
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, Queensland 4072, Australia
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Gleason SM, Barnard DM, Green TR, Mackay S, Wang DR, Ainsworth EA, Altenhofen J, Brodribb TJ, Cochard H, Comas LH, Cooper M, Creek D, DeJonge KC, Delzon S, Fritschi FB, Hammer G, Hunter C, Lombardozzi D, Messina CD, Ocheltree T, Stevens BM, Stewart JJ, Vadez V, Wenz J, Wright IJ, Yemoto K, Zhang H. Physiological trait networks enhance understanding of crop growth and water use in contrasting environments. PLANT, CELL & ENVIRONMENT 2022; 45:2554-2572. [PMID: 35735161 DOI: 10.1111/pce.14382] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Accepted: 06/20/2022] [Indexed: 06/15/2023]
Abstract
Plant function arises from a complex network of structural and physiological traits. Explicit representation of these traits, as well as their connections with other biophysical processes, is required to advance our understanding of plant-soil-climate interactions. We used the Terrestrial Regional Ecosystem Exchange Simulator (TREES) to evaluate physiological trait networks in maize. Net primary productivity (NPP) and grain yield were simulated across five contrasting climate scenarios. Simulations achieving high NPP and grain yield in high precipitation environments featured trait networks conferring high water use strategies: deep roots, high stomatal conductance at low water potential ("risky" stomatal regulation), high xylem hydraulic conductivity and high maximal leaf area index. In contrast, high NPP and grain yield was achieved in dry environments with low late-season precipitation via water conserving trait networks: deep roots, high embolism resistance and low stomatal conductance at low leaf water potential ("conservative" stomatal regulation). We suggest that our approach, which allows for the simultaneous evaluation of physiological traits, soil characteristics and their interactions (i.e., networks), has potential to improve our understanding of crop performance in different environments. In contrast, evaluating single traits in isolation of other coordinated traits does not appear to be an effective strategy for predicting plant performance.
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Affiliation(s)
- Sean M Gleason
- United States Department of Agriculture, Water Management and Systems Research Unit, Agricultural Research Service, Fort Collins, Colorado, USA
| | - Dave M Barnard
- United States Department of Agriculture, Water Management and Systems Research Unit, Agricultural Research Service, Fort Collins, Colorado, USA
| | - Timothy R Green
- United States Department of Agriculture, Water Management and Systems Research Unit, Agricultural Research Service, Fort Collins, Colorado, USA
| | - Scott Mackay
- Department of Geography & Department of Environment and Sustainability, University at Buffalo, Buffalo, New York, USA
| | - Diane R Wang
- Department of Agronomy, Purdue University, West Lafayette, Indiana, USA
| | - Elizabeth A Ainsworth
- United States Department of Agriculture, Global Change and Photosynthesis Research Unit, Agricultural Research Service, Urbana, Illinois, USA
| | - Jon Altenhofen
- Northern Colorado Water Conservancy District, Berthoud, Colorado, USA
| | - Timothy J Brodribb
- School of Natural Sciences, University of Tasmania, Hobart, Tasmania, Australia
- Australian Research Council Centre of Excellence for Plant Success in Nature and Agriculture, The University of Tasmania Node, Hobart, Tasmania, Australia
| | - Hervé Cochard
- Université Clermont Auvergne, INRAE, PIAF, Clermont-Ferrand, France
| | - Louise H Comas
- United States Department of Agriculture, Water Management and Systems Research Unit, Agricultural Research Service, Fort Collins, Colorado, USA
| | - Mark Cooper
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, Queensland, Australia
- Australian Research Council Centre of Excellence for Plant Success in Nature and Agriculture, The University of Queensland Node, St. Lucia, Queensland, Australia
| | - Danielle Creek
- Université Clermont Auvergne, INRAE, PIAF, Clermont-Ferrand, France
- Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences (NMBU), Ås, Norway
| | - Kendall C DeJonge
- United States Department of Agriculture, Water Management and Systems Research Unit, Agricultural Research Service, Fort Collins, Colorado, USA
| | - Sylvain Delzon
- Université Bordeaux, INRAE, BIOGECO, Pessac, cedex, France
| | - Felix B Fritschi
- Division of Plant Science and Technology, University of Missouri, Columbia, Missouri, USA
| | - Graeme Hammer
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, Queensland, Australia
- Australian Research Council Centre of Excellence for Plant Success in Nature and Agriculture, The University of Queensland Node, St. Lucia, Queensland, Australia
| | - Cameron Hunter
- Department of Biology, Colorado State University, Fort Collins, Colorado, USA
| | - Danica Lombardozzi
- National Center for Atmospheric Research (NCAR), Climate & Global Dynamics Lab, Boulder, Colorado, USA
| | - Carlos D Messina
- Department of Horticultural Sciences, University of Florida, Gainesville, Florida, USA
| | - Troy Ocheltree
- Department of Forest and Rangeland Stewardship, Colorado State University, Fort Collins, Colorado, USA
| | - Bo Maxwell Stevens
- United States Department of Agriculture, Water Management and Systems Research Unit, Agricultural Research Service, Fort Collins, Colorado, USA
| | - Jared J Stewart
- United States Department of Agriculture, Water Management and Systems Research Unit, Agricultural Research Service, Fort Collins, Colorado, USA
- Department of Ecology & Evolutionary Biology, University of Colorado, Boulder, Colorado, USA
| | | | - Joshua Wenz
- United States Department of Agriculture, Water Management and Systems Research Unit, Agricultural Research Service, Fort Collins, Colorado, USA
| | - Ian J Wright
- Hawkesbury Institute for the Environment, Western Sydney University, Penrith, New South Wales, Australia
- Department of Biological Sciences, Macquarie University, North Ryde, New South Wales, Australia
- Australian Research Council Centre of Excellence for Plant Success in Nature and Agriculture, Western Sydney University Node, Penrith, New South Wales, Australia
| | - Kevin Yemoto
- United States Department of Agriculture, Water Management and Systems Research Unit, Agricultural Research Service, Fort Collins, Colorado, USA
| | - Huihui Zhang
- United States Department of Agriculture, Water Management and Systems Research Unit, Agricultural Research Service, Fort Collins, Colorado, USA
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Diepenbrock CH, Tang T, Jines M, Technow F, Lira S, Podlich D, Cooper M, Messina C. Can we harness digital technologies and physiology to hasten genetic gain in US maize breeding? PLANT PHYSIOLOGY 2022; 188:1141-1157. [PMID: 34791474 PMCID: PMC8825268 DOI: 10.1093/plphys/kiab527] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Accepted: 10/01/2021] [Indexed: 05/26/2023]
Abstract
Plant physiology can offer invaluable insights to accelerate genetic gain. However, translating physiological understanding into breeding decisions has been an ongoing and complex endeavor. Here we demonstrate an approach to leverage physiology and genomics to hasten crop improvement. A half-diallel maize (Zea mays) experiment resulting from crossing 9 elite inbreds was conducted at 17 locations in the USA corn belt and 6 locations at managed stress environments between 2017 and 2019 covering a range of water environments from 377 to 760 mm of evapotranspiration and family mean yields from 542 to 1,874 g m-2. Results from analyses of 35 families and 2,367 hybrids using crop growth models linked to whole-genome prediction (CGM-WGP) demonstrated that CGM-WGP offered a predictive accuracy advantage compared to BayesA for untested genotypes evaluated in untested environments (r = 0.43 versus r = 0.27). In contrast to WGP, CGMs can deal effectively with time-dependent interactions between a physiological process and the environment. To facilitate the selection/identification of traits for modeling yield, an algorithmic approach was introduced. The method was able to identify 4 out of 12 candidate traits known to explain yield variation in maize. The estimation of allelic and physiological values for each genotype using the CGM created in silico phenotypes (e.g. root elongation) and physiological hypotheses that could be tested within the breeding program in an iterative manner. Overall, the approach and results suggest a promising future to fully harness digital technologies, gap analysis, and physiological knowledge to hasten genetic gain by improving predictive skill and definition of breeding goals.
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Affiliation(s)
| | - Tom Tang
- Research & Development, Corteva Agriscience, Johnston, Iowa 50131, USA
| | - Michael Jines
- Research & Development, Corteva Agriscience, Windfall, Indiana 46076, USA
| | - Frank Technow
- Research & Development, Corteva Agriscience, Tavistock, ON N4S 7W1, Canada
| | - Sara Lira
- Research & Development, Corteva Agriscience, Johnston, Iowa 50131, USA
| | - Dean Podlich
- Research & Development, Corteva Agriscience, Johnston, Iowa 50131, USA
| | - Mark Cooper
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, Queensland 4072, Australia
| | - Carlos Messina
- Research & Development, Corteva Agriscience, Johnston, Iowa 50131, USA
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Moore VM, Schlautman B, Fei SZ, Roberts LM, Wolfe M, Ryan MR, Wells S, Lorenz AJ. Plant Breeding for Intercropping in Temperate Field Crop Systems: A Review. FRONTIERS IN PLANT SCIENCE 2022; 13:843065. [PMID: 35432391 PMCID: PMC9009171 DOI: 10.3389/fpls.2022.843065] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/24/2021] [Accepted: 03/07/2022] [Indexed: 05/14/2023]
Abstract
Monoculture cropping systems currently dominate temperate agroecosystems. However, intercropping can provide valuable benefits, including greater yield stability, increased total productivity, and resilience in the face of pest and disease outbreaks. Plant breeding efforts in temperate field crops are largely focused on monoculture production, but as intercropping becomes more widespread, there is a need for cultivars adapted to these cropping systems. Cultivar development for intercropping systems requires a systems approach, from the decision to breed for intercropping systems through the final stages of variety testing and release. Design of a breeding scheme should include information about species variation for performance in intercropping, presence of genotype × management interaction, observation of key traits conferring success in intercropping systems, and the specificity of intercropping performance. Together this information can help to identify an optimal selection scheme. Agronomic and ecological knowledge are critical in the design of selection schemes in cropping systems with greater complexity, and interaction with other researchers and key stakeholders inform breeding decisions throughout the process. This review explores the above considerations through three case studies: (1) forage mixtures, (2) perennial groundcover systems (PGC), and (3) soybean-pennycress intercropping. We provide an overview of each cropping system, identify relevant considerations for plant breeding efforts, describe previous breeding focused on the cropping system, examine the extent to which proposed theoretical approaches have been implemented in breeding programs, and identify areas for future development.
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Affiliation(s)
- Virginia M. Moore
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, United States
- *Correspondence: Virginia M. Moore,
| | | | - Shui-zhang Fei
- Department of Horticulture, Iowa State University, Ames, IA, United States
| | - Lucas M. Roberts
- Department of Agronomy and Plant Genetics, University of Minnesota, Saint Paul, MN, United States
| | - Marnin Wolfe
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, United States
- Department of Crop, Soil and Environmental Sciences, College of Agriculture, Auburn University, Auburn, AL, United States
| | - Matthew R. Ryan
- Soil and Crop Sciences Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, United States
| | - Samantha Wells
- Department of Agronomy and Plant Genetics, University of Minnesota, Saint Paul, MN, United States
| | - Aaron J. Lorenz
- Department of Agronomy and Plant Genetics, University of Minnesota, Saint Paul, MN, United States
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Fernandez JA, Nippert JB, Prasad PVV, Messina CD, Ciampitti IA. Post-silking 15N labelling reveals an enhanced nitrogen allocation to leaves in modern maize (Zea mays) genotypes. JOURNAL OF PLANT PHYSIOLOGY 2022; 268:153577. [PMID: 34871987 DOI: 10.1016/j.jplph.2021.153577] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/10/2021] [Revised: 11/20/2021] [Accepted: 11/21/2021] [Indexed: 06/13/2023]
Abstract
Nitrogen (N) metabolism is a major research target for increasing productivity in crop plants. In maize (Zea mays L.), yield gain over the last few decades has been associated with increased N absorption and utilization efficiency (i.e. grain biomass per unit of N absorbed). However, a dynamical framework is still needed to unravel the role of internal processes such as uptake, allocation, and translocation of N in these adaptations. This study aimed to 1) characterize how genetic enhancement in N efficiency conceals changes in allocation and translocation of N, and 2) quantify internal fluxes behind grain N sources in two historical genotypes under high and low N supply. The genotypes 3394 and P1197, landmark hybrids representing key eras of genetic improvement (1990s and 2010s), were grown under high and low N supply in a two-year field study. Using stable isotope 15N labelling, post-silking nitrogen fluxes were modeled through Bayesian estimation by considering the external N (exogenous-N) and the pre-existing N (endogenous-N) supply across plant organs. Regardless of N availability, P1197 exhibited greater exogenous-N accumulated in leaves and cob-husks. This response was translated to a larger amount of N mobilized to grains (as endogenous-N) during grain-filling in this genotype. Furthermore, the enhanced N supply to leaves in P1197 was associated with increased post-silking carbon accumulation. The overall findings suggest that increased N utilization efficiency over time in maize genotypes was associated with an increased allocation of N to leaves and subsequent translocation to the grains.
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Affiliation(s)
- Javier A Fernandez
- Department of Agronomy, Kansas State University, Manhattan, KS, 66506, United States.
| | - Jesse B Nippert
- Division of Biology, Kansas State University, Manhattan, KS, 66506, United States
| | - P V Vara Prasad
- Department of Agronomy, Kansas State University, Manhattan, KS, 66506, United States
| | | | - Ignacio A Ciampitti
- Department of Agronomy, Kansas State University, Manhattan, KS, 66506, United States.
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10
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Hammer GL, Cooper M, Reynolds MP. Plant production in water-limited environments. JOURNAL OF EXPERIMENTAL BOTANY 2021; 72:5097-5101. [PMID: 34245562 DOI: 10.1093/jxb/erab273] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/15/2023]
Affiliation(s)
- Graeme L Hammer
- The University of Queensland, Queensland Alliance for Agriculture and Food Innovation, Centre for Crop Science, Brisbane, QLD 4072, Australia
| | - Mark Cooper
- The University of Queensland, Queensland Alliance for Agriculture and Food Innovation, Centre for Crop Science, Brisbane, QLD 4072, Australia
| | - Matthew P Reynolds
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
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11
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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: 16] [Impact Index Per Article: 5.3] [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.
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Affiliation(s)
- Mark Cooper
- Queensland Alliance for Agriculture and Food Innovation (QAAFI), The University of Queensland, Brisbane, QLD, Australia
- *Correspondence: Mark Cooper,
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