ReviewPlant communications2026
Leveraging AI and integrated genomic-enviromic prediction for intelligent sugarcane breeding.
Review in Plant communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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.
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.
Who cites it
3 citing papers in PubMed.
- Decoding the Mystery of Sugarcane Genome-Based Breeding: Current Advancements, Strategies, and Future Challenges.Plant, cell & environment · 2026Review
- The Rise of Plant Pan-Genomes: From Genome Variation to Predictive Breeding.Advanced genetics (Hoboken, N.J.) · 2026Article
- Sugarcane under siege: integrated physiological, molecular responses and mitigation strategies to salinity and drought stress.Frontiers in plant science · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
Traditional sugarcane breeding, reliant on phenotypic selection, is being transformed by genomic tools. However, the crop's highly polyploid genome and significant genotype-by-environment interactions pose challenges that conventional models cannot adequately address. Although the integrated genomic-enviromic prediction (iGEP) framework offers a promising path forward, its application to a complex clonal crop such as sugarcane requires significant extension. This review provides the first comprehensive road map for implementing iGEP in sugarcane, systematically addressing its unique biological constraints, and synthesizes a tailored "three-model" computational framework (genetic, environmental, and phenotypic) to decode polyploid allelic dosage, quantify high-resolution environmental drivers through an "isoenvironment" design, and predict clonal performance. In addition, we describe extensions of artificial intelligence (AI) and iGEP models to leverage clonal propagation, optimize multi-trait selection, and overcome perennial ratoon dynamics. Finally, we present a phased road map for construction of an AI model, outlining a transformative path from digitization to synthetic design. By combining cutting-edge predictive analytics with the distinctive biology of sugarcane, this work establishes a new paradigm for accelerating genetic gain in this vital crop and offers a transferable strategy for other species with complex genomes.
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What OpenQuestion holds
Registered trials
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.