Estimating the transpiration of kiwifruit using an optimized canopy resistance model based on the synthesis of sunlit and shaded leaves

Abstract

Accurate estimation of transpiration (T) in kiwifruit trees is essential for effective irrigation and water management. Canopy resistance (r_c) is crucial for estimating T, but existing models do not fully consider the unique canopy structure and microclimate variations in kiwifruit trees. This study established a r_c estimation model based on a synthesis of sunlit and shaded leaves (SSL) and optimized it using Ant Colony Optimization (ACO), Grey Wolf Optimizer (GWO), and Whale Optimization Algorithm (WOA). Using the r_c value inverted by the Penman-Monteith model as a standard, we compared the simulation accuracy of the SSL and Jarvis models to identify the optimal model for accurate T estimation under various data availability conditions. The results indicated significant physiological differences between sunlit and shaded leaves, with shaded leaves showing lower net photosynthetic rates and higher stomatal resistance. The optimization SSL model demonstrated improved accuracy over the Jarvis model. The simulation accuracy of the SSL model optimized by the WOA algorithm was the highest, yielding R², RRMSE, and MAE of r_c and T are 0.83, 0.12, 82.55 s m⁻¹, and 0.81, 0.09, 0.23 mm d⁻¹, respectively. In the Jarvis model with different restriction functions the highest accuracy for r_c and T, achieved after optimizing by ACO algorithm, yielded R², RRMSE, and MAE of 0.71, 0.33, 305.94 s m⁻¹, and 0.72, 0.23, 0.65 mm d⁻¹, respectively. Therefore, the SSL model can more accurately estimate the r_c and T, and it provides a valuable way for scientific water use and precise irrigation in kiwifruit orchards.