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Accurate evapotranspiration (ET) estimation is paramount for effective agricultural water management. As the key parameter of surface resistance (r s)-based ET models, the direct acquisition of canopy resistance (r c) remains challenge. Moreover, the applicability of r c models and r s -based ET models has not been fully validated on kiwifruit. To address above problems, this study investigates tree growth patterns, water consumption change, and soil water content (SWC) dynamics under different irrigation strategies based on a two-year experiment in Northwest China. Subsequently, the Whale Algorithm (WOA), Quantum Whale Algorithm (QWOA), and Differential Whale Algorithm (DWOA) were employed to optimize Jarvis-type r c models (JA) constructed by different constraint functions of SWC. After internal comparison within Jarvis-type models and external comparison with Kelliher-Perrier (KP), Faria (FA), and Stannard (ST) model, the recommended r c model was integrated into Penman-Monteith (PM), Shuttleworth-Wallace (SW), and Clumping (CL) to select most suitable kiwifruit ET model. The results indicated that the water stress coefficient is suitable for JA model as a constraint function of SWC, achieving R2, NSE, RMSE, and MAPE of 0.782–0.805, 0.672–0.737, 654.191–3396.594 s m−1, and 0.102–0.183 under sufficient irrigation strategy, respectively. Under deficit irrigation strategies, the corresponding R2, NSE, RMSE, and MAPE ranged 0.886–0.917, 0.828–0.885, 325.736–3251.434 s m−1, and 0.111–0.166, respectively. Besides, DWOA is the best algorithm during whole growth period, which improved original JA by 13.2–32.9% for R2, 23.0–60.2% for NSE, 26.8–36.8% for RMSE, and 17.5–36.2%, respectively. Regarding r s -based ET models, the CL performed best under various irrigation conditions, with values of R2, NSE, RMSE, and MAPE ranging 0.908–0.961, 0.842–0.947, 0.311–0.808 mm, and 0.088–0.197, respectively. Overall, the CL integrated with DWOA-JA3 is most recommended for estimating kiwifruit ET under different irrigation strategies, which is helpful for orchardists to enhance agricultural water utilization efficiency and promote sustainable kiwifruit production. • Soil water deficit coefficient was accepted as constraint function to develop canopy resistance model. • Jarvis-type models were optimized using Quantum and Differential Whale Optimization Algorithm. • The accuracy of canopy resistance model was evaluated under different irrigation strategies. • Surface resistance-based evapotranspiration models were compared for kiwifruit in Northwest China. [ABSTRACT FROM AUTHOR]

Deficit drip irrigation is an advanced agricultural technology widely promoted and applied around the world to maintain/increase yield and quality while simultaneously minimizing water consumption. However, there is still a lack of comprehensive research on the effects of deficit drip irrigation at different stages on apple yield, water productivity, and quality. The aim was to investigate the effects of deficit drip irrigation treatments (DDITs) on apple yield, quality, and water productivity (WP), and optimize the deficit drip irrigation strategy for apples in the Loess Plateau of China. a two-year field experiment was conducted with 17 irrigation treatments, including a control treatment (CK, full irrigation (FI)) and four DDITs (D15%, D30%, D45%, D60%) during the bud burst to leafing stage (I), flowering to fruit set stage (II), fruit expansion stage (III), and fruit maturation stage (IV). The optimal irrigation management was determined using the fuzzy Borda combined model, which was based on four different single evaluation models of the TOPSIS method, principal component analysis (PCA), grey correlation analysis (GCA), and membership function analysis (MFA). The DDITs at stage IV decreased midday leaf water potential (Ψs) by 2.57–20.00% compared with CK, and the difference of Ψs between IV-D30%, IV-D45%, IV-D60%, and CK were significant in two years (P<0.05). DDITs at stages I, II, and IV improved fruit weight (FW), fruit shape index (FSI), and fruit firmness (FN), but did not significantly affect fruit water content (FWC). The DDITs at stages III and IV significantly enhanced total soluble solids (TSS), soluble sugars (SS), and vitamin C (V c) in 2021 and 2022, but a notable decrease in titratable acidity (TA) was observed. DDITs at stage IV exhibited the highest comprehensive benefit of apple, followed by DDITs at stages I and II, and DDITs at stage III had the lowest values. The DDITs of D15%, D30%, CK, and D15% at stages I, II, III, and IV were identified as the optimal irrigation strategy to improve apple yield, quality, and WP. Our findings can guide the development of appropriate irrigation strategies for apple cultivation in Loess Plateau of China, promoting efficient and sustainable apple production. • Deficit drip irrigation treatments (DDITs) greatly decreased midday leaf water potential. • Proper DDITs at fruit maturation stage improved apples physical/ chemical quality. • Proper DDITs during the early growth stage greatly improved WP without yield loss. • The optimal irrigation strategy was determined using the fuzzy Borda combined model. [ABSTRACT FROM AUTHOR]