Evidence map›Paper›PMID 42038784›Full record

ArticlePlant phenomics (Washington, D.C.)2026

Predictions of wheat phenotypic variability by integrating high-throughput phenotyping observations into a crop growth model.

Dong Cai, Chen Zhu, Xu Wang, Loic Manceau, Stéphane Jezequel, Mathieu Marguerie, Benoit de Solan, Frédéric Baret, Samuel Buis, Shouyang Liu and 1 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Plant phenomics (Washington, D.C.), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. Article
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

11 authors.

Dong CaiEngineering Research Center of Plant Phenotyping, Ministry of Education, State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Jiangsu Collaborative Innovation Center for Modern Crop Production, Academy for Advanced Interdisciplinary Studies, Nanjing Agricultural University, 210095, Nanjing, China.
Chen ZhuEngineering Research Center of Plant Phenotyping, Ministry of Education, State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Jiangsu Collaborative Innovation Center for Modern Crop Production, Academy for Advanced Interdisciplinary Studies, Nanjing Agricultural University, 210095, Nanjing, China.
Xu WangEngineering Research Center of Plant Phenotyping, Ministry of Education, State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Jiangsu Collaborative Innovation Center for Modern Crop Production, Academy for Advanced Interdisciplinary Studies, Nanjing Agricultural University, 210095, Nanjing, China.
Loic ManceauLEPSE, Univ Montpellier, INRAE, Institut Agro Montpellier, Montpellier, France.
Stéphane JezequelARVALIS, Gréoux-les-Bains, France.
Mathieu MarguerieARVALIS, Gréoux-les-Bains, France.
Benoit de SolanARVALIS, Avignon, France.
Frédéric BaretEMMAH, INRAE, Avignon Université, Avignon, France.
Samuel BuisEMMAH, INRAE, Avignon Université, Avignon, France.
Shouyang LiuEngineering Research Center of Plant Phenotyping, Ministry of Education, State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Jiangsu Collaborative Innovation Center for Modern Crop Production, Academy for Advanced Interdisciplinary Studies, Nanjing Agricultural University, 210095, Nanjing, China.
Pierre MartreLEPSE, Univ Montpellier, INRAE, Institut Agro Montpellier, Montpellier, France.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Accurate prediction of phenotypes across genotypes and environments is crucial for accelerating crop improvement. Process-based crop growth models (CGMs) can capture complex genotype-by-environment interactions, but their use is limited by labor-intensive genotypic parameter measurements. Here, we developed a faster data assimilation pipeline integrating high-throughput phenotyping (HTP) observations with the

Indexed as

Bayesian inferenceCrop growth modelData assimilationGenetic variabilityGenotypic parameterHigh-throughput phenotypingLookup tableWheat

Identifiers

PMID42038784
PMCPMC13109301

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.