Abstract
Leaf growth is a dynamic process that critically determines canopy architecture and assimilate allocation in rapeseed. Quantifying the distribution of leaf biomass along the main stem across developmental stages is essential for advancing functional-structural plant models of rapeseed. To address the lack of a leaf biomass partitioning model in existing rapeseed growth models, this study developed a rank-specific leaf biomass partitioning coefficient model for the main stem in rapeseed. Field experiments were conducted over three growing seasons (2012-2015) using three cultivars: Ningyou 18 (V1, conventional), Ningyou 16 (V2, conventional), and Ningza 19 (V3, hybrid). The experiments were conducted under factorial combinations of cultivar, nitrogen fertilizer, and transplanting density. The leaf biomass partitioning coefficient was calculated as the ratio of leaf biomass at a given leaf rank to the total main-stem leaf biomass, with leaf ranks normalized to the (0-1] interval. Model parameters were estimated to elucidate how cultivar and environmental factors influence partitioning patterns across leaf positions. Validation using independent experimental data showed strong agreement between the simulated and observed values, with a correlation coefficient (r) more than 0.9 (p < 0.001). The mean absolute difference (da) ranged from -0.080 to 0.011 g g-1, and the ratio of da to the average observation (dap) varied between 3.077% (anthesis stage) and 13.083% (normalized leaf rank). The root mean square error (RMSE) values were all below 0.193 g g-1 across all stages, with the most stage-specific RMSE values under 0.032 g g-1. The results demonstrate that the model performs reliably in simulating the main-stem leaf biomass partitioning coefficient across hierarchical leaf ranks in rapeseed. By integrating leaf-level biomass allocation with whole-plant growth processes, this work provides a key component for developing a functional-structural rapeseed model and supports further research on source-sink regulation and canopy optimization.