Genomic prediction of maize yield across European environmental conditions

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Modelling and Using Spatial Effects in Nationwide Historical Data Improve Genomic Prediction of Rice Heading Date in Japan

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Multi-country pine species allocation under different climate scenarios

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Exploring Data Augmentation Algorithm to Improve Genomic Prediction of Top-Ranking Cultivars

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Improvement in genomic prediction of maize with prior gene ontology information depends on traits and environmental conditions

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Digital phenotyping and genotype-to-phenotype (G2P) models to predict complex traits in cereal crops

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Data-Driven Identification of Environmental Variables Influencing Phenotypic Plasticity to Facilitate Breeding for Future Climates: A Case Study Involving Grain Yield of Hybrid Maize

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Harnessing genomic prediction in Brassica napus through a nested association mapping population

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Reassessing data management in increasingly complex phenotypic datasets

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Explainable Artificial Intelligence for Genomic Prediction in Plants: A Critical Review of Methods, Biological Insights, and Practical Challenges

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Genotype‐environment interaction in maize hybrids: a multivariate relationship between yield components, morphological variables and disease tolerance

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The relevance of dominance and functional annotations to predict agronomic traits in hybrid maizePreprint

Ramstein, G. P., Larsson, S. J., Cook, J. P., Edwards, J. W., Ersoz, E. S., Flint-Garcia, S. A., Gardner, C. A. C., Holland, J. B., Lorenz, A. J., McMullen, M. D., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/745208 · Cited by 2

A new hypothesis-testing model for phyllochron based on a stochastic process - application to analysis of genetic and environment effects in maizePreprint

Plancade, S., Marchadier, E., Huet, S., Ressayre, A., Noûs, C., & Dillmann, C. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.01.11.426247 · Cited by 2

Paving the way for FAIR data in plant phenotyping

Papoutsoglou, E. (2021) · doi:10.18174/546089 · Cited by 2

REML implementations of kernel-based genomic prediction models for genotype x environment x management interactions

Melsen, K. A. C., Gezan, S., Tolhurst, D. J., van Eeuwijk, F. A., & Peeters, C. F. W. (2025) · arXiv · doi:10.48550/arxiv.2501.06844 · Cited by 3

Hyper-seq Technology and Genome-Wide Selection Breeding of Soybeans

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VegeDiff: Latent Diffusion Model for Geospatial Vegetation Forecasting

Zhao, S., Chen, H., Zhang, X., Xiao, P., & Bai, L. (2025) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2025.3564317 · Cited by 4

Prediction of biomass sorghum hybrids using environmental feature-enriched genomic combining ability models in tropical environments

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Optimization of maize hybrids in the Brazilian Northeast: a multi-trait selection and envirotyping techniques in multi-environment trials

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Integrating genetic and ecophysiology frameworks for crop adaptation

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A case for breeding heat-tolerant broccoli

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Does Breeding for Abiotic Stress Tolerance Mitigate G × E Interactions Under a Changing Climate?

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Characterizing yield through wheat’s perception of chronological progression: a multi-omics plant time warping approach

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Environment-specific genomic prediction ability in maize using environmental covariates depends on environmental similarity to training data

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Genomic prediction and association mapping of maize grain yield in multi-environment trials based on reaction norm models

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Utility of climatic information via combining ability models to improve genomic prediction for yield within the genomes to fields maize project

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Accurate genomic prediction for grain yield and grain moisture content of maize hybrids using multi‐environment data

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Using machine learning to combine genetic and environmental data for maize grain yield predictions across multi-environment trials

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