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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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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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3
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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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Physiological adaptive traits are a potential allele reservoir for maize genetic progress under challenging conditions. Nat Commun 2022; 13:3225. [PMID: 35680899 PMCID: PMC9184527 DOI: 10.1038/s41467-022-30872-w] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Accepted: 05/23/2022] [Indexed: 11/08/2022] Open
Abstract
Combined phenomic and genomic approaches are required to evaluate the margin of progress of breeding strategies. Here, we analyze 65 years of genetic progress in maize yield, which was similar (101 kg ha-1 year-1) across most frequent environmental scenarios in the European growing area. Yield gains were linked to physiologically simple traits (plant phenology and architecture) which indirectly affected reproductive development and light interception in all studied environments, marked by significant genomic signatures of selection. Conversely, studied physiological processes involved in stress adaptation remained phenotypically unchanged (e.g. stomatal conductance and growth sensitivity to drought) and showed no signatures of selection. By selecting for yield, breeders indirectly selected traits with stable effects on yield, but not physiological traits whose effects on yield can be positive or negative depending on environmental conditions. Because yield stability under climate change is desirable, novel breeding strategies may be needed for exploiting alleles governing physiological adaptive traits.
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Fernández JA, Messina CD, Salinas A, Prasad PVV, Nippert JB, Ciampitti IA. Kernel weight contribution to yield genetic gain of maize: a global review and US case studies. JOURNAL OF EXPERIMENTAL BOTANY 2022; 73:3597-3609. [PMID: 35279716 DOI: 10.1093/jxb/erac103] [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: 12/04/2021] [Accepted: 03/09/2022] [Indexed: 06/14/2023]
Abstract
Over the past century of maize (Zea mays L.) breeding, grain yield progress has been the result of improvements in several other intrinsic physiological and morphological traits. In this study, we describe (i) the contribution of kernel weight (KW) to yield genetic gain across multiple agronomic settings and breeding programs, and (ii) the physiological bases for improvements in KW for US hybrids. A global-scale literature review concludes that rates of KW improvement in US hybrids were similar to those of other commercial breeding programs but extended over a longer period of time. There is room for a continued increase of kernel size in maize for most of the genetic materials analysed, but the trade-off between kernel number and KW poses a challenge for future yield progress. Through phenotypic characterization of Pioneer Hi-Bred ERA hybrids in the USA, we determine that improvements in KW have been predominantly related to an extended kernel-filling duration. Likewise, crop improvement has conferred on modern hybrids greater KW plasticity, expressed as a better ability to respond to changes in assimilate availability. Our analysis of past trends and current state of development helps to identify candidate targets for future improvements in maize.
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Affiliation(s)
- Javier A Fernández
- Department of Agronomy, Kansas State University, 2004 Throckmorton Plant Science Center, Manhattan, KS 66506, USA
| | - Carlos D Messina
- Horticultural Sciences Department, University of Florida, Gainesville, FL 32611, USA
| | - Andrea Salinas
- Corteva Agriscience, 7250 NW 62nd Ave., Johnston, IA 50310, USA
| | - P V Vara Prasad
- Department of Agronomy, Kansas State University, 2004 Throckmorton Plant Science Center, Manhattan, KS 66506, USA
| | - Jesse B Nippert
- Division of Biology, Kansas State University, Manhattan, KS 66506, USA
| | - Ignacio A Ciampitti
- Department of Agronomy, Kansas State University, 2004 Throckmorton Plant Science Center, Manhattan, KS 66506, USA
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6
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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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7
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Abbaraju HKR, Gupta R, Appenzeller LM, Fallis LP, Hazebroek J, Zhu G, Bourett TM, Howard RJ, Weers B, Lafitte RH, Hakimi SM, Schussler JR, Loussaert DF, Habben JE, Dhugga KS. A vegetative storage protein improves drought tolerance in maize. PLANT BIOTECHNOLOGY JOURNAL 2022; 20:374-389. [PMID: 34614273 PMCID: PMC8753363 DOI: 10.1111/pbi.13720] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Revised: 09/26/2021] [Accepted: 09/28/2021] [Indexed: 06/13/2023]
Abstract
Vegetative storage proteins (VSPs) are known to serve as nitrogen reserves in many dicot plants but remain undiscovered in grasses, most widely grown group of crops globally. We identified and characterized a VSP in maize and demonstrated that its overexpression improved drought tolerance. Nitrogen supplementation selectively induced a mesophyll lipoxygenase (ZmLOX6), which was targeted to chloroplasts by a novel N-terminal transit peptide of 62 amino acids. When ectopically expressed under the control of various tissue-specific promoters, it accumulated to a fivefold higher level upon expression in the mesophyll cells than the wild-type plants. Constitutive expression or targeted expression specifically to the bundle sheath cells increased its accumulation by less than twofold. The overexpressed ZmLOX6 was remobilized from the leaves like other major proteins during grain development. Evaluated in the field over locations and years, transgenic hybrids overexpressing ZmLOX6 in the mesophyll cells significantly outyielded nontransgenic sibs under managed drought stress imposed at flowering. Additional storage of nitrogen as a VSP in maize leaves ameliorated the effect of drought on grain yield.
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Affiliation(s)
| | - Rajeev Gupta
- Corteva AgriscienceJohnstonIAUSA
- USDA‐ARSFargoNDUSA
| | | | | | | | | | | | | | | | - Renee H. Lafitte
- Corteva AgriscienceWoodlandCAUSA
- Bill & Melinda Gates FoundationSeattleWAUSA
| | | | | | | | | | - Kanwarpal S. Dhugga
- Corteva AgriscienceJohnstonIAUSA
- International Maize and Wheat Improvement Center (CIMMYT)El BatanMexico
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8
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Messina C, McDonald D, Poffenbarger H, Clark R, Salinas A, Fang Y, Gho C, Tang T, Graham G, Hammer GL, Cooper M. Reproductive resilience but not root architecture underpins yield improvement under drought in maize. JOURNAL OF EXPERIMENTAL BOTANY 2021; 72:5235-5245. [PMID: 34037765 PMCID: PMC8272564 DOI: 10.1093/jxb/erab231] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Accepted: 05/21/2021] [Indexed: 05/08/2023]
Abstract
Because plants capture water and nutrients through roots, it was proposed that changes in root systems architecture (RSA) might underpin the 3-fold increase in maize (Zea mays L.) grain yield over the last century. Here we show that both RSA and yield have changed with decades of maize breeding, but not the crop water uptake. Results from X-ray phenotyping in controlled environments showed that single cross (SX) hybrids have smaller root systems than double cross (DX) hybrids for root diameters between 2465 µm and 181µm (P<0.05). Soil water extraction measured under field conditions ranged between 2.6 mm d-1 and 2.9 mm d-1 but were not significantly different between SX and DX hybrids. Yield and yield components were higher for SX than DX hybrids across densities and irrigation (P<0.001). Taken together, the results suggest that changes in RSA were not the cause of increased water uptake but an adaptation to high-density stands used in modern agriculture. This adaptation may have contributed to shift in resource allocation to the ear and indirectly improved reproductive resilience. Advances in root physiology and phenotyping can create opportunities to maintain long-term genetic gain in maize, but a shift from ideotype to crop and production system thinking will be required.
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Affiliation(s)
- Carlos Messina
- Corteva Agriscience, 7250 NW 62nd Ave, Johnston, IA 50310, USA
| | - Dan McDonald
- Phenotype Screening Corporation, 4028 Papermill Road, Knoxville, TN 37909, USA
| | | | - Randy Clark
- Corteva Agriscience, 7250 NW 62nd Ave, Johnston, IA 50310, USA
| | - Andrea Salinas
- Corteva Agriscience, 7250 NW 62nd Ave, Johnston, IA 50310, USA
| | - Yinan Fang
- Corteva Agriscience, 7250 NW 62nd Ave, Johnston, IA 50310, USA
| | - Carla Gho
- Corteva Agriscience, 7250 NW 62nd Ave, Johnston, IA 50310, USA
| | - Tom Tang
- Corteva Agriscience, 7250 NW 62nd Ave, Johnston, IA 50310, USA
| | - Geoff Graham
- Corteva Agriscience, 7250 NW 62nd Ave, Johnston, IA 50310, USA
| | - 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
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9
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Simmons CR, Lafitte HR, Reimann KS, Brugière N, Roesler K, Albertsen MC, Greene TW, Habben JE. Successes and insights of an industry biotech program to enhance maize agronomic traits. PLANT SCIENCE : AN INTERNATIONAL JOURNAL OF EXPERIMENTAL PLANT BIOLOGY 2021; 307:110899. [PMID: 33902858 DOI: 10.1016/j.plantsci.2021.110899] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/28/2021] [Revised: 03/22/2021] [Accepted: 03/26/2021] [Indexed: 05/18/2023]
Abstract
Corteva Agriscience™ ran a discovery research program to identify biotech leads for improving maize Agronomic Traits such as yield, drought tolerance, and nitrogen use efficiency. Arising from many discovery sources involving thousands of genes, this program generated over 3331 DNA cassette constructs involving a diverse set of circa 1671 genes, whose transformed maize events were field tested from 2000 to 2018 under managed environments designed to evaluate their potential for commercialization. We demonstrate that a subgroup of these transgenic events improved yield in field-grown elite maize breeding germplasm. A set of at least 22 validated gene leads are identified and described which represent diverse molecular and physiological functions. These leads illuminate sectors of biology that could guide crop improvement in maize and perhaps other crops. In this review and interpretation, we share some of our approaches and results, and key lessons learned in discovering and developing these maize Agronomic Traits leads.
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Affiliation(s)
- Carl R Simmons
- Corteva Agriscience, 7300 NW 62nd Avenue, Johnston, IA, 50131, USA.
| | - H Renee Lafitte
- Corteva Agriscience, 7300 NW 62nd Avenue, Johnston, IA, 50131, USA
| | - Kellie S Reimann
- Corteva Agriscience, 7300 NW 62nd Avenue, Johnston, IA, 50131, USA
| | - Norbert Brugière
- Corteva Agriscience, 7300 NW 62nd Avenue, Johnston, IA, 50131, USA
| | - Keith Roesler
- Corteva Agriscience, 7300 NW 62nd Avenue, Johnston, IA, 50131, USA
| | - Marc C Albertsen
- Corteva Agriscience, 7300 NW 62nd Avenue, Johnston, IA, 50131, USA
| | - Thomas W Greene
- Corteva Agriscience, 7300 NW 62nd Avenue, Johnston, IA, 50131, USA
| | - Jeffrey E Habben
- Corteva Agriscience, 7300 NW 62nd Avenue, Johnston, IA, 50131, USA
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10
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Akhavizadegan F, Ansarifar J, Wang L, Huber I, Archontoulis SV. A time-dependent parameter estimation framework for crop modeling. Sci Rep 2021; 11:11437. [PMID: 34075079 PMCID: PMC8169860 DOI: 10.1038/s41598-021-90835-x] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2020] [Accepted: 05/17/2021] [Indexed: 02/06/2023] Open
Abstract
The performance of crop models in simulating various aspects of the cropping system is sensitive to parameter calibration. Parameter estimation is challenging, especially for time-dependent parameters such as cultivar parameters with 2-3 years of lifespan. Manual calibration of the parameters is time-consuming, requires expertise, and is prone to error. This research develops a new automated framework to estimate time-dependent parameters for crop models using a parallel Bayesian optimization algorithm. This approach integrates the power of optimization and machine learning with prior agronomic knowledge. To test the proposed time-dependent parameter estimation method, we simulated historical yield increase (from 1985 to 2018) in 25 environments in the US Corn Belt with APSIM. Then we compared yield simulation results and nine parameter estimates from our proposed parallel Bayesian framework, with Bayesian optimization and manual calibration. Results indicated that parameters calibrated using the proposed framework achieved an 11.6% reduction in the prediction error over Bayesian optimization and a 52.1% reduction over manual calibration. We also trained nine machine learning models for yield prediction and found that none of them was able to outperform the proposed method in terms of root mean square error and R2. The most significant contribution of the new automated framework for time-dependent parameter estimation is its capability to find close-to-optimal parameters for the crop model. The proposed approach also produced explainable insight into cultivar traits' trends over 34 years (1985-2018).
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Affiliation(s)
- Faezeh Akhavizadegan
- Department of Industrial and Manufacturing Systems Engineering, Iowa State University, Ames, IA, 50011, USA.
| | - Javad Ansarifar
- Department of Industrial and Manufacturing Systems Engineering, Iowa State University, Ames, IA, 50011, USA
| | - Lizhi Wang
- Department of Industrial and Manufacturing Systems Engineering, Iowa State University, Ames, IA, 50011, USA
| | - Isaiah Huber
- Department of Agronomy, Iowa State University, Ames, IA, 50011, USA
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11
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Worku M, De Groote H, Munyua B, Makumbi D, Owino F, Crossa J, Beyene Y, Mugo S, Jumbo M, Asea G, Mutinda C, Kwemoi DB, Woyengo V, Olsen M, Prasanna BM. On-farm performance and farmers' participatory assessment of new stress-tolerant maize hybrids in Eastern Africa. FIELD CROPS RESEARCH 2020. [PMID: 32015590 DOI: 10.1016/j.fcr.2019.107683] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/13/2023]
Abstract
The development and deployment of high-yielding stress tolerant maize hybrids are important components of the efforts to increase maize productivity in eastern Africa. This study was conducted to: i) evaluate selected, stress-tolerant maize hybrids under farmers' conditions; ii) identify farmers' selection criteria in selecting maize hybrids; and iii) have farmers evaluate the new varieties according to those criteria. Two sets of trials, one with 12 early-to-intermediate maturing and the other with 13 intermediate-to-late maturing hybrids, improved for tolerance to multiple stresses common in farmers' fields in eastern Africa (drought, northern corn leaf blight, gray leaf spot, common rust, maize streak virus), were evaluated on-farm under smallholder farmers' conditions in a total of 42 and 40 environments (site-year-management combinations), respectively, across Kenya, Uganda, Tanzania and Rwanda in 2016 and 2017. Farmer-participatory variety evaluation was conducted at 27 sites in Kenya and Rwanda, with a total of 2025 participating farmers. Differential performance of the hybrids was observed under low-yielding (<3 t ha-1) and high-yielding (>3 t ha-1) environments. The new stress-tolerant maize hybrids had a much better grain-yield performance than the best commercial checks under smallholder farmer growing environments but had a comparable grain-yield performance under optimal conditions. These hybrids also showed better grain-yield stability across the testing environments, providing an evidence for the success of the maize-breeding approach. In addition, the new stress- tolerant varieties outperformed the internal genetic checks, indicating genetic gain under farmers' conditions. Farmers gave high importance to grain yield in both farmer-stated preferences (through scores) and farmer-revealed preferences of criteria (revealed by regressing the overall scores on the scores for the individual criteria). The top-yielding hybrids in both maturity groups also received the farmers' highest overall scores. Farmers ranked yield, early maturity, cob size and number of cobs as the most important traits for variety preference. The criteria for the different hybrids did not differ between men and women farmers. Farmers gave priority to many different traits in addition to grain yield, but this may not be applicable across all maize-growing regions. Farmer-stated importance of the different criteria, however, were quite different from farmer- revealed importance. Further, there were significant differences between men and women in the revealed-importance of the criteria. We conclude that incorporating farmers' selection criteria in the stage-gate advancement process of new hybrids by the breeders is useful under the changing maize-growing environments in sub-Saharan Africa, and recommended to increase the turnover of new maize hybrids.
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Affiliation(s)
- Mosisa Worku
- International Maize and Wheat Improvement Center (CIMMYT), P. O. Box 1041, Village Market, 00621, Nairobi, Kenya
| | - Hugo De Groote
- International Maize and Wheat Improvement Center (CIMMYT), P. O. Box 1041, Village Market, 00621, Nairobi, Kenya
| | - Bernard Munyua
- International Maize and Wheat Improvement Center (CIMMYT), P. O. Box 1041, Village Market, 00621, Nairobi, Kenya
| | - Dan Makumbi
- International Maize and Wheat Improvement Center (CIMMYT), P. O. Box 1041, Village Market, 00621, Nairobi, Kenya
| | - Fidelis Owino
- International Maize and Wheat Improvement Center (CIMMYT), P. O. Box 1041, Village Market, 00621, Nairobi, Kenya
| | - Jose Crossa
- CIMMYT, Apdo. Postal 041, C.A.P. Plaza Galerías, Col. Verónica Anzures, 11305 Ciudad de México, Mexico
| | - Yoseph Beyene
- International Maize and Wheat Improvement Center (CIMMYT), P. O. Box 1041, Village Market, 00621, Nairobi, Kenya
| | - Stephen Mugo
- International Maize and Wheat Improvement Center (CIMMYT), P. O. Box 1041, Village Market, 00621, Nairobi, Kenya
| | - McDonald Jumbo
- International Maize and Wheat Improvement Center (CIMMYT), P. O. Box 1041, Village Market, 00621, Nairobi, Kenya
| | - Godfrey Asea
- National Crops Resources Research Institute (NaCRRI), P.O. Box 7084, Namulonge, Uganda
| | - Charles Mutinda
- Embu Research Center, Kenya Agricultural and Livestock Research Organization (KALRO), P.O. Box 27, 60100, Embu, Kenya
| | - Daniel Bomet Kwemoi
- National Crops Resources Research Institute (NaCRRI), P.O. Box 7084, Namulonge, Uganda
| | - Vincent Woyengo
- Kakamega Research Center, Kenya Agricultural and Livestock Research Organization (KALRO), Kakamega, P.O. Box 169, 50100, Kenya
| | - Michael Olsen
- International Maize and Wheat Improvement Center (CIMMYT), P. O. Box 1041, Village Market, 00621, Nairobi, Kenya
| | - Boddupalli M Prasanna
- International Maize and Wheat Improvement Center (CIMMYT), P. O. Box 1041, Village Market, 00621, Nairobi, Kenya
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12
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Gaffney J, Bing J, Byrne PF, Cassman KG, Ciampitti I, Delmer D, Habben J, Lafitte HR, Lidstrom UE, Porter DO, Sawyer JE, Schussler J, Setter T, Sharp RE, Vyn TJ, Warner D. Science-based intensive agriculture: Sustainability, food security, and the role of technology. GLOBAL FOOD SECURITY-AGRICULTURE POLICY ECONOMICS AND ENVIRONMENT 2019. [DOI: 10.1016/j.gfs.2019.08.003] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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13
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Combining Evapotranspiration and Soil Apparent Electrical Conductivity Mapping to Identify Potential Precision Irrigation Benefits. REMOTE SENSING 2019. [DOI: 10.3390/rs11212460] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Precision irrigation optimizes the spatiotemporal application of water using evapotranspiration (ET) maps to assess water stress or soil apparent electrical conductivity (ECa) maps as a proxy for plant available water content. However, ET and ECa maps are rarely used together. We developed high-resolution ET and ECa maps for six irrigated fields in the Midwest United States between 2014–2016. Our research goals were to (1) validate ET maps developed using the High-Resolution Mapping of EvapoTranspiration (HRMET) model and aerial imagery via comparison with ground observations in potato, sweet corn, and pea agroecosystems; (2) characterize relationships between ET and ECa; and (3) identify potential precision irrigation benefits across rotations. We demonstrated the synergy of combined ET and ECa mapping for evaluating whether intrafield differences in ECa correspond to actual water use for different crop rotations. We found that ET and ECa have stronger relationships in sweet corn and potato rotations than field corn. Thus, sweet corn and potato crops may benefit more from precision irrigation than field corn, even when grown rotationally on the same field. We recommend that future research consider crop rotation, intrafield soil variability, and existing irrigation practices together when determining potential water use, savings, and yield gains from precision irrigation.
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Simultaneous gains in grain yield and nitrogen efficiency over 70 years of maize genetic improvement. Sci Rep 2019; 9:9095. [PMID: 31235885 PMCID: PMC6591295 DOI: 10.1038/s41598-019-45485-5] [Citation(s) in RCA: 44] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2018] [Accepted: 06/06/2019] [Indexed: 11/29/2022] Open
Abstract
The competing demands of increasing grain yields to feed a growing population and decreasing nitrogen (N) fertilizer use and loss to the environment poses a grand challenge to farmers and society, and necessitates achieving improved N use efficiency (NUE) in cereal crops. Although selection for increased yield in maize has improved NUE over time, the present understanding of the physiological determinants of NUE and its key components hampers the design of more effective breeding strategies conducive to accelerating genetic gain for this trait. We show that maize NUE gains have been supported by more efficient allocation of N among plant organs during the grain filling period. Comparing seven maize hybrids commercialized between 1946 and 2015 from a single seed company in multiple N fertilizer treatments, we demonstrate that modern hybrids produced more grain per unit of accumulated N by more efficiently remobilizing N stored in stems than in leaves to support kernel growth. Increases in N fertilizer recovery and N harvest index at maturity were mirrored by a steady decrease in stem N allocation in this era study. These insights can inform future breeding strategies for continued NUE gains through improved conversion efficiency of accumulated plant N into grain yield.
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15
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DeLucia EH, Chen S, Guan K, Peng B, Li Y, Gomez‐Casanovas N, Kantola IB, Bernacchi CJ, Huang Y, Long SP, Ort DR. Are we approaching a water ceiling to maize yields in the United States? Ecosphere 2019. [DOI: 10.1002/ecs2.2773] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Affiliation(s)
- Evan H. DeLucia
- Department of Plant Biology University of Illinois Urbana Illinois 61801 USA
- Institute for Sustainability, Energy, and Environment University of Illinois Urbana Illinois 61801 USA
- Carl R. Woese Institute for Genomic Biology University of Illinois Urbana Illinois 61801 USA
- Center for Advanced Bioenergy and Bioproducts Innovation University of Illinois Urbana Illinois 61801 USA
- Department of Natural Resources and Environmental Science University of Illinois Urbana Illinois 61801 USA
| | - Shiliu Chen
- Department of Natural Resources and Environmental Science University of Illinois Urbana Illinois 61801 USA
- State Key Laboratory of Plateau Ecology and Agriculture Qinghai University Xining Qinghai 810016 China
| | - Kaiyu Guan
- Institute for Sustainability, Energy, and Environment University of Illinois Urbana Illinois 61801 USA
- Center for Advanced Bioenergy and Bioproducts Innovation University of Illinois Urbana Illinois 61801 USA
- Department of Natural Resources and Environmental Science University of Illinois Urbana Illinois 61801 USA
- National Center for Supercomputing Applications University of Illinois Urbana Illinois 61801 USA
- State Key Laboratory of Plateau Ecology and Agriculture Qinghai University Xining Qinghai 810016 China
| | - Bin Peng
- Department of Natural Resources and Environmental Science University of Illinois Urbana Illinois 61801 USA
- National Center for Supercomputing Applications University of Illinois Urbana Illinois 61801 USA
| | - Yan Li
- Department of Natural Resources and Environmental Science University of Illinois Urbana Illinois 61801 USA
| | - Nuria Gomez‐Casanovas
- Department of Plant Biology University of Illinois Urbana Illinois 61801 USA
- Institute for Sustainability, Energy, and Environment University of Illinois Urbana Illinois 61801 USA
- Carl R. Woese Institute for Genomic Biology University of Illinois Urbana Illinois 61801 USA
| | - Ilsa B. Kantola
- Institute for Sustainability, Energy, and Environment University of Illinois Urbana Illinois 61801 USA
- Carl R. Woese Institute for Genomic Biology University of Illinois Urbana Illinois 61801 USA
| | - Carl J. Bernacchi
- Department of Plant Biology University of Illinois Urbana Illinois 61801 USA
- Carl R. Woese Institute for Genomic Biology University of Illinois Urbana Illinois 61801 USA
- Center for Advanced Bioenergy and Bioproducts Innovation University of Illinois Urbana Illinois 61801 USA
- USDA‐ARS Global Change and Photosynthesis Research Unit Urbana Illinois 61801 USA
| | - Yuefei Huang
- Department of Hydraulic Engineering State Key Laboratory of Hydroscience and Engineering Tsinghua University Beijing 100084 China
- State Key Laboratory of Plateau Ecology and Agriculture Qinghai University Xining Qinghai 810016 China
| | - Stephen P. Long
- Department of Plant Biology University of Illinois Urbana Illinois 61801 USA
- Institute for Sustainability, Energy, and Environment University of Illinois Urbana Illinois 61801 USA
- Carl R. Woese Institute for Genomic Biology University of Illinois Urbana Illinois 61801 USA
- Center for Advanced Bioenergy and Bioproducts Innovation University of Illinois Urbana Illinois 61801 USA
- National Center for Supercomputing Applications University of Illinois Urbana Illinois 61801 USA
| | - Donald R. Ort
- Department of Plant Biology University of Illinois Urbana Illinois 61801 USA
- Carl R. Woese Institute for Genomic Biology University of Illinois Urbana Illinois 61801 USA
- Center for Advanced Bioenergy and Bioproducts Innovation University of Illinois Urbana Illinois 61801 USA
- Department of Crop Science University of Illinois Urbana Illinois 61801 USA
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16
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Leakey ADB, Ferguson JN, Pignon CP, Wu A, Jin Z, Hammer GL, Lobell DB. Water Use Efficiency as a Constraint and Target for Improving the Resilience and Productivity of C 3 and C 4 Crops. ANNUAL REVIEW OF PLANT BIOLOGY 2019; 70:781-808. [PMID: 31035829 DOI: 10.1146/annurev-arplant-042817-040305] [Citation(s) in RCA: 108] [Impact Index Per Article: 21.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/20/2023]
Abstract
The ratio of plant carbon gain to water use, known as water use efficiency (WUE), has long been recognized as a key constraint on crop production and an important target for crop improvement. WUE is a physiologically and genetically complex trait that can be defined at a range of scales. Many component traits directly influence WUE, including photosynthesis, stomatal and mesophyll conductances, and canopy structure. Interactions of carbon and water relations with diverse aspects of the environment and crop development also modulate WUE. As a consequence, enhancing WUE by breeding or biotechnology has proven challenging but not impossible. This review aims to synthesize new knowledge of WUE arising from advances in phenotyping, modeling, physiology, genetics, and molecular biology in the context of classical theoretical principles. In addition, we discuss how rising atmospheric CO2 concentration has created and will continue to create opportunities for enhancing WUE by modifying the trade-off between photosynthesis and transpiration.
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Affiliation(s)
- Andrew D B Leakey
- Department of Plant Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA;
- Department of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA
- Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA
| | - John N Ferguson
- Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA
| | - Charles P Pignon
- Department of Plant Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA;
| | - Alex Wu
- Centre for Crop Science and Centre of Excellence for Translational Photosynthesis, University of Queensland, St. Lucia, Queensland 4069, Australia
| | - Zhenong Jin
- Department of Earth System Science and Center for Food Security and Environment, Stanford University, Stanford, California 94305, USA
| | - Graeme L Hammer
- Centre for Crop Science and Centre of Excellence for Translational Photosynthesis, University of Queensland, St. Lucia, Queensland 4069, Australia
| | - David B Lobell
- Department of Earth System Science and Center for Food Security and Environment, Stanford University, Stanford, California 94305, USA
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17
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Voss-Fels KP, Cooper M, Hayes BJ. Accelerating crop genetic gains with genomic selection. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2019; 132:669-686. [PMID: 30569365 DOI: 10.1007/s00122-018-3270-8] [Citation(s) in RCA: 119] [Impact Index Per Article: 23.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/09/2018] [Accepted: 12/12/2018] [Indexed: 05/05/2023]
Abstract
Genomic prediction based on additive genetic effects can accelerate genetic gain. There are opportunities for further improvement by including non-additive effects that access untapped sources of genetic diversity. Several studies have reported a worrying gap between the projected global future demand for plant-based products and the current annual rates of production increase, indicating that enhancing the rate of genetic gain might be critical for future food security. Therefore, new breeding technologies and strategies are required to significantly boost genetic improvement of future crop cultivars. Genomic selection (GS) has delivered considerable genetic gain in animal breeding and is becoming an essential component of many modern plant breeding programmes as well. In this paper, we review the lessons learned from implementing GS in livestock and the impact of GS on crop breeding, and discuss important features for the success of GS under different breeding scenarios. We highlight major challenges associated with GS including rapid genotyping, phenotyping, genotype-by-environment interaction and non-additivity and give examples for opportunities to overcome these issues. Finally, the potential of combining GS with other modern technologies in order to maximise the rate of crop genetic improvement is discussed, including the potential of increasing prediction accuracy by integration of crop growth models in GS frameworks.
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Affiliation(s)
- Kai Peter Voss-Fels
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, QLD, 4072, Australia
| | - Mark Cooper
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, QLD, 4072, Australia
| | - Ben John Hayes
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, QLD, 4072, Australia.
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18
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Borrás L, Vitantonio-Mazzini LN. Maize reproductive development and kernel set under limited plant growth environments. JOURNAL OF EXPERIMENTAL BOTANY 2018; 69:3235-3243. [PMID: 29304259 DOI: 10.1093/jxb/erx452] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/01/2017] [Accepted: 11/23/2017] [Indexed: 05/26/2023]
Abstract
Maize grain yield is highly related to the number of kernels that are established during the flowering period. Kernel number depends on the accumulation of ear biomass and the efficiency of using this biomass for kernel set. Ear biomass depends on the rate of plant biomass accumulation and the proportion of this biomass that is allocated to the ear. In contrast to other major crops, the proportion of plant biomass that is allocated to the ear is not constant in maize, being almost zero under stress conditions. Fortunately, there is wide native genetic variability for this trait, with major practical implications for crop management and plant breeding. Conditions that inhibit plant growth commonly delay silk appearance relative to male anthesis. Time to silking and silk extrusion, which is a tissue expansion process, is dependent on water turgor and ear biomass accumulation, and the magnitude of this delay is used as a marker to phenotype for stress susceptibility. Ear biomass accumulation can also be used for predicting the number of silks that have been extruded if genotype-specific parameters are known. Here, several mechanistic plant and canopy traits are described, together with their implications for better understanding maize yield determination under limited plant growth environments. An ideal genotype sustains growth in environments with limited water or nutrients, has uniform canopies, has increased biomass partitioning to the ear at reduced plant growth, reaches silking with minimum ear biomass, and has rapid silk extrusion for minimizing developmental delays between competing structures within the ear. All these traits help maximize kernel set and yield at limited plant growth, and most have been indirectly selected by breeders when increasing yield.
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Affiliation(s)
- Lucas Borrás
- Universidad Nacional de Rosario, Campo Experimental Villarino S/N, Zavalla, Prov. de Santa Fe, Argentina
| | - Lucas N Vitantonio-Mazzini
- Universidad Nacional de Rosario, Campo Experimental Villarino S/N, Zavalla, Prov. de Santa Fe, Argentina
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19
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Tardieu F, Varshney RK, Tuberosa R. Improving crop performance under drought - cross-fertilization of disciplines. JOURNAL OF EXPERIMENTAL BOTANY 2017; 68:1393-1398. [PMID: 28338855 PMCID: PMC5444440 DOI: 10.1093/jxb/erx042] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/09/2023]
Affiliation(s)
| | - Rajeev K Varshney
- Research Programme - Genetic Gains, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru-502 324, India
| | - Roberto Tuberosa
- Department of Agricultural Sciences, Viale Fanin 44, 40127 Bologna, Italy
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20
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Raines C, Traynor M, Ingram J. Experimental botany in 2017. JOURNAL OF EXPERIMENTAL BOTANY 2017; 68:347-349. [PMID: 28201652 PMCID: PMC5444439 DOI: 10.1093/jxb/erw504] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Affiliation(s)
- Christine Raines
- Editor in Chief, Journal of Experimental BotanyDepartment of Biological Sciences, University of Essex, Colchester, UK
| | - Mary Traynor
- Executive Editor, Journal of Experimental BotanyBailrigg House, Lancaster University, Lancaster, UK
| | - Jonathan Ingram
- Senior Commissioning Editor/ Science Writer, Journal of Experimental BotanyBailrigg House, Lancaster University, Lancaster, UK
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21
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From inspiration to impact: delivering value from global root research. JOURNAL OF EXPERIMENTAL BOTANY 2016; 67:3601-3603. [PMCID: PMC4896363 DOI: 10.1093/jxb/erw215] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
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22
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Gaffney J, Anderson J, Franks C, Collinson S, MacRobert J, Woldemariam W, Albertsen M. Robust seed systems, emerging technologies, and hybrid crops for Africa. GLOBAL FOOD SECURITY-AGRICULTURE POLICY ECONOMICS AND ENVIRONMENT 2016. [DOI: 10.1016/j.gfs.2016.06.001] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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23
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Xu Y. Envirotyping for deciphering environmental impacts on crop plants. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2016; 129:653-673. [PMID: 26932121 PMCID: PMC4799247 DOI: 10.1007/s00122-016-2691-5] [Citation(s) in RCA: 101] [Impact Index Per Article: 12.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/04/2015] [Accepted: 02/08/2016] [Indexed: 05/17/2023]
Abstract
Global climate change imposes increasing impacts on our environments and crop production. To decipher environmental impacts on crop plants, the concept "envirotyping" is proposed, as a third "typing" technology, complementing with genotyping and phenotyping. Environmental factors can be collected through multiple environmental trials, geographic and soil information systems, measurement of soil and canopy properties, and evaluation of companion organisms. Envirotyping contributes to crop modeling and phenotype prediction through its functional components, including genotype-by-environment interaction (GEI), genes responsive to environmental signals, biotic and abiotic stresses, and integrative phenotyping. Envirotyping, driven by information and support systems, has a wide range of applications, including environmental characterization, GEI analysis, phenotype prediction, near-iso-environment construction, agronomic genomics, precision agriculture and breeding, and development of a four-dimensional profile of crop science involving genotype (G), phenotype (P), envirotype (E) and time (T) (developmental stage). In the future, envirotyping needs to zoom into specific experimental plots and individual plants, along with the development of high-throughput and precision envirotyping platforms, to integrate genotypic, phenotypic and envirotypic information for establishing a high-efficient precision breeding and sustainable crop production system based on deciphered environmental impacts.
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Affiliation(s)
- Yunbi Xu
- Institute of Crop Science, Chinese Academy of Agricultural Sciences, 100081, Beijing, China.
- International Maize and Wheat Improvement Center (CIMMYT), El Batan, Texcoco, CP 56130, Mexico.
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24
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Schmidt JE, Bowles TM, Gaudin ACM. Using Ancient Traits to Convert Soil Health into Crop Yield: Impact of Selection on Maize Root and Rhizosphere Function. FRONTIERS IN PLANT SCIENCE 2016; 7:373. [PMID: 27066028 PMCID: PMC4811947 DOI: 10.3389/fpls.2016.00373] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/07/2016] [Accepted: 03/11/2016] [Indexed: 05/21/2023]
Abstract
The effect of domestication and modern breeding on aboveground traits in maize (Zea mays) has been well-characterized, but the impact on root systems and the rhizosphere remain unclear. The transition from wild ecosystems to modern agriculture has focused on selecting traits that yielded the largest aboveground production with increasing levels of crop management and nutrient inputs. Root morphology, anatomy, and ecophysiological processes may have been affected by the substantial environmental and genetic shifts associated with this transition. As a result, root and rhizosphere traits that allow more efficient foraging and uptake in lower synthetic input environments might have been lost. The development of modern maize has led to a shift in microbiome community composition, but questions remain as to the dynamics and drivers of this change during maize evolution and its implications for resource acquisition and agroecosystem functioning under different management practices. Better understanding of how domestication and breeding affected root and rhizosphere microbial traits could inform breeding strategies, facilitate the sourcing of favorable alleles, and open new frontiers to improve resource use efficiency through greater integration of root development and ecophysiology with agroecosystem functioning.
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Affiliation(s)
- Jennifer E. Schmidt
- Department of Plant Sciences, University of California at DavisDavis, CA, USA
| | - Timothy M. Bowles
- Department of Natural Resources and the Environment, University of New HampshireDurham, NH, USA
| | - Amélie C. M. Gaudin
- Department of Plant Sciences, University of California at DavisDavis, CA, USA
- *Correspondence: Amélie C. M. Gaudin
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