This study aimed to predict dry matter partitioning in cucumber fruit (Cucumis sativus L.) by developing a simulation model that integrates photosynthetic characteristics based on leaf age and cropping type. Leaf gas exchange, growth, and environmental data from semi-forcing and forcing cropping types were used to calibrate models including the Farquhar-von Caemmerer-Berry (FvCB) model and other growth-related models. The FvCB model revealed reduced V_cmax and J_max values in older leaves across all cropping types, with semi-forcing crops showing higher photosynthetic capacities than forcing crops. Simulation results showed that, in predicting dry matter partitioning to fruit, the leaf-position-specific simulation model exhibited higher average R2 and lower RMSE (g m−2) compared to the leaf position-independent model, which applied the middle leaf FvCB model across all leaf ranks. Additionally, bias comparisons indicated greater consistency in the leaf-position-specific model. This approach allows growers to optimize environmental strategies by utilizing photosynthetic data form each leaf position. However, to further improve canopy-level predictions, future models should incorporate the temperature dependence of mesophyll conductance and the effects of photoperiodicity. This study underscores the value of integrating physiological and environmental complexities into crop simulation models, providing a foundation for enhanced predictions and the development of improved crop management strategies across various cultivation scenarios.

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