Evidence map›Paper›PMID 37118190›Full record

ArticleNature food2022

Data-driven projections suggest large opportunities to improve Europe's soybean self-sufficiency under climate change.

Nicolas Guilpart, Toshichika Iizumi, David Makowski

Abstract read
PubMed Publisher
In one paragraph

Article in Nature food, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing 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

15 citing papers in PubMed.

  1. Review
  2. IndigenousFrontiers in plant science · 2026
    Article
  3. Article
  4. Article
  5. Article
  6. Genome editing, an opportunity to revive soybean cultivation in Europe.The Plant journal : for cell and molecular biology · 2025
    Review
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. 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

3 authors.

Nicolas GuilpartUniversité Paris-Saclay, AgroParisTech, INRAE, UMR Agronomie, Thiverval-Grignon, France. nicolas.guilpart@agroparistech.fr.ORCID 0000-0003-3804-0211
Toshichika IizumiInstitute for Agro-Environmental Sciences, National Agriculture and Food Research Organization (NARO), Tsukuba, Ibaraki, Japan.ORCID 0000-0002-0611-4637
David MakowskiUniversité Paris-Saclay, AgroParisTech, INRAE, UMR MIA-Paris, Paris, France.ORCID 0000-0001-6385-3703

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The rapid expansion of soybean-growing areas across Europe raises questions about the suitability of agroclimatic conditions for soybean production. Here, using data-driven relationships between climate and soybean yield derived from machine-learning, we made yield projections under current and future climate with moderate (Representative Concentration Pathway (RCP) 4.5) to intense (RCP 8.5) warming, up to the 2050s and 2090s time horizons. The selected model showed high R

Identifiers

What OpenQuestion holds

Textmetadata
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.