Integrating Envirotyping and Phenomics for AI-Enabled Multi-Environment Genomic Prediction in Crop Breeding
- 1Cold Region Wetland Ecology and Environment Research Key Laboratory of Heilongjiang Province , Harbin University , Harbin 150086 , China
- 2State Key Laboratory of Urban Water Resource and Environment , Harbin Institute of Technology , Harbin 150086 , China
- 3College of Agricultural Science , Heilongjiang Bayi Agricultural University , Daqing 163319 , China
Abstract
Genomic prediction is now routine in crop improvement, but its main bottleneck has shifted from marker density to environmental complexity. Breeders rarely need predictions for one fixed environment; they need to rank genotypes across target populations of environments that differ in weather, soils, management, and stress timing. This makes genotype-by-environment interaction a primary breeding problem rather than a secondary statistical nuisance. This review examines how genomic, environmental, and phenomic information can be integrated to improve multi-environment prediction in crop breeding pipelines. The review is narrative rather than PRISMA-style, but the literature search and selection logic were structured and explicitly defined. Peer-reviewed English-language studies were identified through structured searches of Web of Science Core Collection and Scopus, supplemented by backward citation screening, with emphasis on literature published from January 2023 to March 2026. Four conclusions emerge. First, environmental information is most useful when it is developmentally aligned, biologically interpretable, and matched to the target population of environments. Second, strong structured statistical baselines remain highly competitive, especially in moderate-sized or highly unbalanced datasets, whereas gains from more flexible machine-learning and deep-learning approaches are most evident in large, sparse, heterogeneous, and multimodal settings. Third, phenomic markers often improve prediction for complex traits, especially yield, because they capture realized crop responses not fully represented by markers alone. Fourth, practical value depends less on isolated gains in predictive accuracy than on evaluation under realistic deployment scenarios, including untested genotype and untested environment settings. Progress therefore requires transparent reporting, benchmark design, stage-aware envirotyping, multimodal integration, uncertainty reporting, and cost-aware deployment.
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BibTeX
@article{liang2026integrating,
title = {{Integrating Envirotyping and Phenomics for AI-Enabled Multi-Environment Genomic Prediction in Crop Breeding}},
author = {Xiongwei Liang and S Y Yu and Yongfu Ju and Yue Wang and Dawei Yin},
journal = {Agronomy},
year = {2026},
month = may,
doi = {10.3390/agronomy16101019},
url = {https://doi.org/10.3390/agronomy16101019},
note = {CC BY},
}
RIS
TY - JOUR
TI - Integrating Envirotyping and Phenomics for AI-Enabled Multi-Environment Genomic Prediction in Crop Breeding
AU - Xiongwei Liang
AU - S Y Yu
AU - Yongfu Ju
AU - Yue Wang
AU - Dawei Yin
PY - 2026
DA - 2026/05/21
JO - Agronomy
DO - 10.3390/agronomy16101019
UR - https://doi.org/10.3390/agronomy16101019
AB - Genomic prediction is now routine in crop improvement, but its main bottleneck has shifted from marker density to environmental complexity. Breeders rarely need predictions for one fixed environment; they need to rank genotypes across target populations of environments that differ in weather, soils, management, and stress timing. This makes genotype-by-environment interaction a primary breeding problem rather than a secondary statistical nuisance. This review examines how genomic, environmental, and phenomic information can be integrated to improve multi-environment prediction in crop breeding pipelines. The review is narrative rather than PRISMA-style, but the literature search and selection logic were structured and explicitly defined. Peer-reviewed English-language studies were identified through structured searches of Web of Science Core Collection and Scopus, supplemented by backward citation screening, with emphasis on literature published from January 2023 to March 2026. Four conclusions emerge. First, environmental information is most useful when it is developmentally aligned, biologically interpretable, and matched to the target population of environments. Second, strong structured statistical baselines remain highly competitive, especially in moderate-sized or highly unbalanced datasets, whereas gains from more flexible machine-learning and deep-learning approaches are most evident in large, sparse, heterogeneous, and multimodal settings. Third, phenomic markers often improve prediction for complex traits, especially yield, because they capture realized crop responses not fully represented by markers alone. Fourth, practical value depends less on isolated gains in predictive accuracy than on evaluation under realistic deployment scenarios, including untested genotype and untested environment settings. Progress therefore requires transparent reporting, benchmark design, stage-aware envirotyping, multimodal integration, uncertainty reporting, and cost-aware deployment.
SN - 2073-4395
ER -