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

Zongyang Li, Lu Zhao, Zhengxin Zhao, Huanjie Cai, Liwen Xing and Ningbo Cui

Agricultural Water Management, 2024, vol. 306, issue C

Abstract: Accurate estimation of transpiration (T) in kiwifruit trees is essential for effective irrigation and water management. Canopy resistance (rc) 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 rc 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 rc 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 rc 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 rc 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 rc and T, and it provides a valuable way for scientific water use and precise irrigation in kiwifruit orchards.

Keywords: Sunlit and shaded leaves; Jarvis model; Intelligence optimization algorithms; Simulation accuracy (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:agiwat:v:306:y:2024:i:c:s0378377424005298

DOI: 10.1016/j.agwat.2024.109193

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