This paper proposes a Systems Modeling Language (SysML)-based simulation model aggregation framework to develop aggregated simulation models with high accuracy. The framework consists of three major steps: 1) system conceptual modeling, 2) simulation modeling, and 3) additive regression model-based parameter estimation. SysML is first used to construct the system conceptual model for a generic seedling propagation system in terms of system structure and activities in a hierarchical manner (i.e. low, medium and high levels). Simulation models conforming to the conceptual model are then constructed in Arena. An additive regression model-based approach is proposed to estimate parameters for the aggregated simulation model. The proposed framework is demonstrated via one of the largest grafted seedling propagation systems in North America. The results reveal that 1) the proposed framework allows us to construct accurate but computationally affordable simulation models for seedling propagation system, and 2) model aggregation increases the randomness of simulation outputs.