GEWorld: A Genotype-Environment World Model for Crop Phenotype Prediction and Rollout

Phenotype prediction aims to predict traits from genotype and environmental information. Most existing methods learn static mappings from aggregated data, making it difficult to dynamically adapt to new observations or changing environments. Here, we propose $GEWorld$, a genotype–environment world model that casts phenotype prediction as the evolution of a latent physiological state: it fuses genotypes with an environmental embedding through a gated, multiplicative genotype-by-environment interaction to initialize this state, assimilates observed phenotype prefixes through a GRU-based observation module to correct the state as new measurements arrive, and evolves the state open-loop through a Mamba-2-inspired state-space transition under future environments and elapsed time to project how the trajectory unfolds. GEWorld decodes static terminal traits from the initial state and residual dynamic trajectories from the rolled states; environmental counterfactuals are obtained by rolling the same assimilated state under alternative future weather. Across static and dynamic benchmarks spanning five crops, GEWorld achieves state-of-the-art predictive accuracy, while its forecasted phenotype trajectories closely track observed development. The code is publicly available at https://anonymous.4open.science/r/GEWorld-E070/.