Understanding crop water requirements is important for establishing irrigation schedules, and improving water use efficiency (WUE), crop yield and crop quality. In order to reveal the optimal water requirement of tomatoes in various growth stages, the responses of the water requirement, crop coefficient, fruit yield and quality of tomato to different irrigation levels were studied in a solar greenhouse in Hetian, Southwestern Taklimakan Desert, China from August 2019 to June 2020. The medium irrigation quota (Ia) was calculated in different tomato growth stages based on the root distribution range, suitable soil moisture content of high yield, and the planned wetted percentage of drip irrigation. Five irrigation levels (60%, 80%, 100%, 120% and 140% I_a) were used. The technique for order preference by similarity to ideal solution (TOPSIS) results showed that 120% I_a was the optimal irrigation quota for the yield, water use efficiency (WUE), and fruit quality of tomato. The daily water requirement of 120% I_a were 2.26, 4.28, and 2.35 mm·d⁻¹ in three growth stages in the autumn–winter season, while it was 1.96, 3.99, and 3.80 mm·d⁻¹ in the winter–spring season. The crop coefficients of the three stages in each growth season were 0.49, 1.10, and 0.76, and 0.61, 1.09, and 0.78, respectively. The results could provide guideliens for improving the productivity of protected agriculture in the Southwestern Taklimakan Desert or other similar regions.

In arid and semi-arid agricultural regions, the increasing frequency of extreme climatic events—particularly high temperatures and drought—has severely disrupted crop growth dynamics, leading to significant yield uncertainty and potential threats to the growing global food demand. Optimizing irrigation strategies by integrating dynamic crop growth monitoring and accurate yield estimation is essential for mitigating the adverse effects of extreme weather and promoting sustainable agricultural development. Therefore, this study conducted two consecutive years of field experiments in cotton fields to evaluate the effects of irrigation interval and drip irrigation frequency on cotton growth dynamics and yield, and to develop an optimized irrigation schedule based on the AquaCrop model assimilated with Particle Swarm Optimization (AquaCrop-PSO). The sensitivity analysis identified the canopy growth coefficient (CGC), maximum canopy cover (CCX), and canopy cover at 90% emergence (CCS) as the most influential parameters for canopy cover (CC) simulation, while the crop coefficient at full canopy (KCTRX), water productivity (WP), and CGC were most sensitive for aboveground biomass (AGB) simulation. Ridge regression models integrating multiple vegetation indices outperformed single-index models in estimating CC and AGB across different growth stages, achieving R² values of 0.73 and 0.87, respectively. Assimilating both CC and AGB as dual-state variables significantly improved the model’s predictive accuracy for cotton yield, with R² values of 0.96 and 0.95 in 2023 and 2024, respectively. Scenario simulations revealed that the optimal irrigation quotas for dry, normal, and wet years were 520 mm, 420 mm, and 420 mm, respectively, with a consistent irrigation interval of five days. This study provides theoretical insights and practical guidance for irrigation scheduling, yield prediction, and smart irrigation management in drip-irrigated cotton fields in Xinjiang, China.

Addressing the challenges of low resource-use efficiency and supply–demand mismatch in Hami melon production, this study investigated the interactive effects of irrigation and fertilization to identify an optimal regime that balances yield, water conservation, and resource-use efficiency (i.e., water use efficiency and fertilizer partial factor productivity). A greenhouse experiment was conducted in Hami, Xinjiang, employing a two-factor design with five irrigation levels (W1–W5: 60–100% of full irrigation) and three fertilization levels (F1–F3: 80–100% of standard rate), replicated three times. Growth parameters, yield, water use efficiency (WUE), and partial factor productivity of fertilizer (PFP) were evaluated and comprehensively analyzed using the entropy-weighted TOPSIS method, regression analysis, and the NSGA-II multi-objective genetic algorithm. Results demonstrated that irrigation volume was the dominant factor influencing growth and yield. The W4F3 treatment (90% irrigation with 100% fertilization) achieved the optimal outcome, yielding 75.74 t ha⁻¹—a 9.71% increase over the control—while simultaneously enhancing WUE and PFP. Both the entropy-weighted TOPSIS evaluation (C = 0.998) and regression analysis (optimal irrigation level at w = 0.79, ~90% of full irrigation) identified W4F3 as superior. NSGA-II optimization further validated this, generating Pareto-optimal solutions highly consistent with the experimental optimum. The model-predicted optimal regime for greenhouse Hami melon in Xinjiang is an irrigation amount of 3276 m³ ha⁻¹ and a fertilizer application rate of 814.8 kg ha⁻¹. This regime facilitates a 10% reduction in irrigation water and a 5% reduction in fertilizer input without compromising yield, alongside significantly improved resource-use efficiencies.

A field experiment was conducted on 16-year-old ‘Wen 185’ walnut trees in Aksu, Southern Xinjiang, to identify optimal water and fertilizer management under subsurface drip irrigation. Four irrigation levels were established: 75% ET_c (W1), 100% ET_c (W2), 125% ET_c (W3), and 150% ET_c (W4). These were combined with three fertilizer levels: N 270, P 240, K 300 kg ha⁻¹ (F1), N 360, P 320, K 400 kg ha⁻¹ (F2), and N 450, P 400, K 500 kg ha⁻¹ (F3). This resulted in a total of 12 treatments. This study assessed the impact of different water and fertilizer treatments on walnut growth dynamics, yield, fruit quality, water and fertilizer use efficiency, and soil nitrate residue. Principal component analysis (PCA) was used to construct comprehensive growth and photosynthesis indices (CGI and CPI). Parameters significantly correlated with yield and quality were then screened via Pearson analysis, and a game theory-based combination weighting method was adopted to determine weights for integrating six categories of indicators: growth, photosynthesis, yield, quality, resource use efficiency, and environmental impact. A coupled TOPSIS-GRA model was developed for comprehensive evaluation. Furthermore, binary quadratic regression was employed to optimize the application ranges of water and fertilizer. The results showed that the W2F2 treatment achieved the highest rank by synergistically enhancing growth, photosynthetic performance, yield, and quality. This treatment also maintained high water use efficiency (WUE) and partial factor productivity of fertilizer (PFP) and effectively reduced nitrate accumulation in deep soil layers. The CGI and CPI, derived from PCA, effectively quantified phenological growth and photosynthetic characteristics. Correlation analysis identified seven core parameters, among which IV-CPI correlated most strongly with yield. In contrast, II-CPI was more closely associated with increased single-fruit weight and reduced tannin content. Within the comprehensive evaluation system that used game theory-based combination weighting, yield received the highest weight (0.215), while IV-CPI was assigned the lowest (0.011). The TOPSIS-GRA coupled model identified the W2F2 treatment as the highest-ranked. Furthermore, regression optimization determined the optimal total seasonal application ranges to be 5869.94–6519.81 m³ ha⁻¹ for irrigation and 975.54–1107.49 kg ha⁻¹ for fertilization. The coupled TOPSIS-GRA model enabled a balanced assessment of the objectives: high yield, superior quality, resource use efficiency, and environmental sustainability. Thus, it provides a theoretical foundation and practical guidance for enhancing the productivity and sustainability of subsurface drip-irrigated walnut orchards in Southern Xinjiang.

To address the issue of existing automatic irrigation systems’ excessive reliance on electrical power and communication networks, a one-inlet, four-outlet Hydraulically Actuated Irrigation Control Valve (HAICV) was designed that operates based on water pressure variations. Its hydraulic characteristics and flow field features were investigated through experimental and numerical simulation methods. The results indicated that power–function relationships exist between pressure and flow rate, as well as between flow rate and head loss. The flow coefficient and resistance coefficient were found to range within [77.46, 81.06] and [15.94, 17.46], respectively. Dynamic simulations based on User-Defined Functions (UDF) revealed that during the opening process, the internal pressure of the valve spool remains high, with the primary pressure drop concentrated in the outlet region, and the low-pressure zone shrinks as the opening degree increases. A high-velocity band forms at the outlet, with jet flow and turbulence observed at small to medium openings, while the flow field stabilizes after full opening. The unique spool shape and non-straight flow passage structure of the HAICV result in relatively high energy loss, making it suitable for self-pressure irrigation systems. This study provides a theoretical foundation for evaluating its performance and broader applications.

In arid regions, water scarcity and soil potassium destruction are major constraints on the sustainable development of the jujube industry. In this regard, the use of crop models can compensate for time-consuming and costly field trials to screen for better irrigation regimes, but their predictive accuracy is often compromised by parameter uncertainty. To address this issue, we utilized data from a three-year (2022–2024) field trial (with irrigation at 50%, 75%, and 100% of evapotranspiration and potassium applications of 120, 180, and 240 kg/ha) to simulate the growth process of jujube trees in arid regions using the WOFOST model. In this study, parameter sensitivity analyses were conducted to determine that photosynthetic capacity maximization (A_max), the potassium nutrition index (K_status), the water stress factor (SWF), the water–potassium photosynthetic coefficient of synergy (α), and potassium partitioning weight coefficients (β_i) were the important parameters affecting the simulated growth process of the crop. Path analysis using segmented structural equations also showed that water stress factor (SWF) and potassium nutrition index (K_status) indirectly controlled yield by significantly affecting photosynthesis (path coefficients: 0.72 and 0.75, respectively). The ability of the crop model to simulate the growth process and yield of jujube trees was improved by the introduction of water and potassium parameters (R² = 0.94–0.96, NRMSE = 4.1–12.2%). The subsequent multi-objective optimization of yield and crop water productivity of dates under different combinations of water and potassium treatments under a bi-objective optimization model based on the NSGA-II algorithm showed that the optimal strategy was irrigation at 80% ET_c combined with 300 kg/ha of potassium application. This management model ensures yield and maximizes crop water use efficiency (CWP), thus providing a scientific and efficient irrigation and fertilization regime for jujube trees in arid zones.