ArticlePlant physiology2022
Can we harness digital technologies and physiology to hasten genetic gain in US maize breeding?
Article in Plant physiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed, 58 citations in OpenAlex.
- The hidden geometry of breeding constraints.Nature plants · 2026Review
- Ensemble-based genomic prediction for maize flowering time improves prediction accuracy and reveals novel insights into trait genetic variation.G3 (Bethesda, Md.) · 2026Article
- Multi-environment evaluation and genomic prediction of agronomic traits in the southern US rice genepool.The plant genome · 2026Article
- Back to the future 2: the implications of germplasm structure on the balance between short- and long-term genetic gain in a changing target population of environments.G3 (Bethesda, Md.) · 2026Article
- Understanding rates of genetic gain in sorghum [Sorghum bicolor (L.) Moench] in the United States.The plant genome · 2025Article
- Integration of crop modeling and sensing into molecular breeding for nutritional quality and stress tolerance.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Review
- Breeding perspectives on tackling trait genome-to-phenome (G2P) dimensionality using ensemble-based genomic prediction.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Review
- Toward a general framework for AI-enabled prediction in crop improvement.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Article
- Genomic selection: Essence, applications, and prospects.The plant genome · 2025Review
- Sparse testing designs for optimizing resource allocation in multi-environment cassava breeding trials.The plant genome · 2025Article
- Article
- Redefining the accumulated temperature index for accurate prediction of rice flowering time in diverse environments.Plant biotechnology journal · 2025Article
- Global Genotype by Environment Prediction Competition Reveals That Diverse Modeling Strategies Can Deliver Satisfactory Maize Yield Estimates.bioRxiv : the preprint server for biology · 2024Article
- Modeling soybean growth: A mixed model approach.PLoS computational biology · 2024Article
- Mapping and quantifying unique branching structures in lentil (Lens culinaris Medik.).Plant methods · 2024Article
- GIS-FA: an approach to integrating thematic maps, factor-analytic, and envirotyping for cultivar targeting.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2024Article
- Biocultural diversity and crop improvement.Emerging topics in life sciences · 2023Review
- Leveraging data from the Genomes-to-Fields Initiative to investigate genotype-by-environment interactions in maize in North America.Nature communications · 2023Article
- Two decades of harnessing standing genetic variation for physiological traits to improve drought tolerance in maize.Journal of experimental botany · 2023Review
- Image-based phenomic prediction can provide valuable decision support in wheat breeding.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2023Article
Corrections and comments
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Authors and funding
8 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
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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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.