Evidence map›Paper›PMID 42010794›Full record

ReviewJournal of integrative plant biology2026

Turbocharging crop breeding with integrated biotechnology for a climate-resilient future.

Zhao Wang, Dandan Yang, Cao Xu

Abstract readReview
In one paragraph

Review in Journal of integrative plant biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Review
  3. 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.

Zhao WangKey Laboratory of Seed Innovation, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, 100101, China.ORCID https://orcid.org/0009-0005-3510-3098
Dandan YangKey Laboratory of Seed Innovation, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, 100101, China.ORCID https://orcid.org/0000-0002-2306-0626
Cao XuKey Laboratory of Seed Innovation, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, 100101, China.ORCID https://orcid.org/0000-0002-9055-7691

Funding

CAS Project for Young Scientists in Basic Research YSBR-078National Natural Science Foundation of China 32388101
6 · The paper itself

Abstract

Global agriculture faces unprecedented challenges from climate change and population growth, creating an urgent demand for the rapid development of resilient and high-yielding crop varieties. Although conventional breeding has achieved substantial progress in crop improvement, it is increasingly constrained by bottlenecks in genetic diversity, efficiency, and the uncertainty of trait inheritance under complex environments. Recent advances in integrative biotechnology offer transformative opportunities to reconfigure crop improvement into a predictive and design-driven process. This review synthesizes these advances into an integrated, multidisciplinary framework for precise breeding of climate-resilient crops, emphasizing the need to move beyond descriptive data accumulation toward mechanistic integration and beyond single-trait modification toward systems-level design. By integrating genome-phenome-environment insights with artificial intelligence-powered predictive modeling, we envision the rise of precise breeding frameworks capable of rapidly delivering climate-resilient, high-yielding crops. Such approaches are critical to fortifying agricultural systems, mitigating climate vulnerability, and securing a sustainable food future.

Indexed as

BiotechnologyClimate ChangeCrops, AgriculturalPlant Breedingartificial intelligenceclimate‐resilient cropsprecision breedingsustainable agriculture

Identifiers

PMID42010794
PMCPMC13446588

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.