Evidence map›Paper›PMID 40781147›Full record

ReviewTAG. Theoretical and applied genetics. Theoretische und angewandte Genetik2025

Integration of crop modeling and sensing into molecular breeding for nutritional quality and stress tolerance.

Jonathan Berlingeri, Abelina Fuentes, Earl Ranario, Heesup Yun, Ellen Y Rim, Oscar Garrett, Alexander Howard, Mary-Francis LaPorte, Sassoum Lo, Duke Pauli and 11 more

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

  1. Article
  2. Review
  3. Review
  4. Article
  5. Spinach (Foods (Basel, Switzerland) · 2025
    Article
  6. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

21 authors.

Jonathan BerlingeriDepartment of Plant Sciences, University of California, Davis, USA.
Abelina FuentesDepartment of Plant Sciences, University of California, Davis, USA.
Earl RanarioDepartment of Biological & Agricultural Engineering, University of California, Davis, USA.
Heesup YunDepartment of Biological & Agricultural Engineering, University of California, Davis, USA.
Ellen Y RimDepartment of Plant Pathology, University of California, Davis, USA.
Oscar GarrettDepartment of Plant Pathology, University of California, Davis, USA.
Alexander HowardDepartment of Plant Pathology, University of California, Davis, USA.
Mary-Francis LaPorteDepartment of Plant Sciences, University of California, Davis, USA.
Sassoum LoDepartment of Plant Sciences, University of California, Davis, USA.
Duke PauliSchool of Plant Sciences, University of Arizona, Tucson, USA.
Jenna HershbergerDepartment of Plant and Environmental Sciences, Pee Dee Research and Education Center, Clemson University, Florence, SC, USA.
Mason EarlesDepartment of Biological & Agricultural Engineering, University of California, Davis, USA.
Allen Van DeynzeDepartment of Plant Sciences, University of California, Davis, USA.
Edward Charles BrummerDepartment of Plant Sciences, University of California, Davis, USA.
Richard MichelmoreDepartment of Plant Sciences, University of California, Davis, USA.
Christopher Y S WongDepartment of Plant Sciences, University of California, Davis, USA.
Troy S MagneyDepartment of Plant Sciences, University of California, Davis, USA.
Pamela C RonaldDepartment of Plant Pathology, University of California, Davis, USA.
Daniel E RuncieDepartment of Plant Sciences, University of California, Davis, USA.
Brian N BaileyDepartment of Plant Sciences, University of California, Davis, USA.
Christine H DiepenbrockDepartment of Plant Sciences, University of California, Davis, USA. chdiepenbrock@ucdavis.edu.ORCID http://orcid.org/0000-0001-8411-0343

Funding

Sulfotyrosine, an essential determinant for diverse protein-protein interactionsR35GM148173 · NIGMS · UNIVERSITY OF CALIFORNIA AT DAVIS · PI PAMELA C RONALD · 2023 to 2026
$1.8M
Biological and Environmental Research DE-SC0023305Department of Plant Sciences, University of California, Davis administered by University of California AgricultureDepartment of Plant Sciences, University of California, Davis Graduate Student Research assistantship scholarship funded by endowmentsDepartment of Plant Sciences, University of California, Davis Natural ResourcesDepartment of Plant Sciences, University of California, Davis particularly the James Monroe McDonald EndowmentDivision of Biological Infrastructure 2019674Division of Biological Infrastructure 2417511Division of Integrative Organismal Systems 2023310Division of Integrative Organismal Systems 2102120Life Sciences Research Foundation Postdoctoral FellowshipNational Institute of Food and Agriculture 2021-38420-34061National Institute of Food and Agriculture 2021-51181-35903National Institute of Food and Agriculture AgricultureNational Institute of Food and Agriculture Food Research Initiative Competitive Grant no. 2020-67021-32855/project accession no. 1024262NIGMS NIH HHS R35 GM148173NIH HHS 1R35GM148173Specialty Crop Multi-State Program 21-0730-001-SF
6 · The paper itself

Abstract

Integrating innovative technologies into plant breeding is critical to bolster food and nutritional security under biotic and abiotic stresses in changing climates. While breeding efforts have focused primarily on yield and stress tolerance, emerging evidence highlights the need to also prioritize nutritional quality. Advanced molecular breeding approaches have enhanced our ability to develop improved crop varieties and could be substantially informed by the routine integration of crop modeling and remote sensing technologies. This review article discusses the potential of combining crop modeling and sensing with molecular breeding to address the dual challenge of nutritional quality and stress tolerance. We provide overviews of stress response strategies, challenges in breeding for quality traits, and the use of environmental data in genomic prediction. We also describe the status of crop modeling and sensing technologies in grain legumes, rice, and leafy greens, alongside the status of -omics tools in these crops and the use of AI with directed evolution to identify novel resistance genes. We describe the pairwise and three-way integration of AI-enabled sensing and biophysically and empirically constrained crop modeling into breeding to enable prediction of phenotypic and breeding values and dissection of genotype-by-environment-by-management interactions with increasing fidelity, efficiency, and temporal/spatial resolution to inform selection decisions. This article highlights current initiatives and future trends that focus on leveraging these advancements to develop more climate-resilient and nutritionally dense crops, ultimately enhancing the effectiveness of molecular breeding.

Indexed as

Crops, AgriculturalNutritive ValuePlant BreedingStress, PhysiologicalPhenotype

Identifiers

PMID40781147
PMCPMC12334538

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.