Yanqun Zhang

Selecting suitable maize hybrids and appropriately increasing planting density is a crucial strategy for improving maize yield under subsurface drip irrigation. A two-year field experiment was conducted to assess the impacts of two maize hybrids (Zhengdan 958, ZD958; Jingke 968, JK968) and four planting densities of 60,000 (PD1), 75,000 (PD2), 90,000 (PD3), and 105,000 plants ha⁻¹ (PD4), on maize growth indices, ear leaf photosynthetic parameters, nitrogen content, grain yield, and yield components. The results indicated that with increasing planting density, the plant height of ZD958 initially increased and then decreased, whereas that of JK968 continued to increase. The leaf area index of both hybrids consistently increased, while stem diameter, rind puncture strength, and stalk breaking strength gradually decreased. Dry matter accumulation initially increased and then decreased, peaking at PD3. Ear leaf nitrogen content, relative chlorophyll content, net photosynthetic rate, and stomatal conductance all decreased with increasing planting density, while intrinsic water use efficiency (iWUE) first increased and then declined, reaching its maximum at PD3. Notably, iWUE of JK968 was significantly higher than that of ZD958 during the dough stage (p < 0.01). Ear traits, including ear length, ear diameter, kernels per ear, and grain weight per ear, all decreased continuously with increasing planting density. Grain yield followed a unimodal curve, peaking at the PD3 treatment, with two-year average yields of 12.7 and 13.5 t ha⁻¹ for ZD958 and JK968, respectively. JK968 exhibited significantly higher leaf area index, stem diameter, rind puncture strength, stalk breaking strength, ear length, kernels per ear, grain weight per ear, and grain yield compared to ZD958 (p < 0.01), demonstrating superior tolerance to high planting density and enhanced source–sink coordination. In conclusion, in the North China Plain, JK968 planted at a density of 90,000 plants ha⁻¹ can synergistically optimize population structure, improve stalk mechanical strength, and enhance photosynthetic efficiency, under subsurface drip irrigation.

Filtration systems are essential for drip irrigation using sediment-laden water sources such as the Yellow River. This study focused on a sand filter (filtration accuracy: 150 μm), a disc filter (filtration accuracy: 125 μm), and their combined multi-stage filtration system (flow rate: 30–50 m³/h). In situ tests were conducted under Yellow River water conditions in the Hetao Irrigation District, Inner Mongolia, China, to evaluate the response of filtration performance to sediment characteristics, flow rate, and operating time. On this basis, Differential Evolution-optimized Random Forest Regression (DE/RFR) was further established to predict filtration performance. The results showed that: (1) Under sediment concentrations of 0.62–3.6 g/L and median particle sizes of 4.70–16.03 μm, the head loss of the sand filter (ΔH_si) remained stable over the operating time. Conversely, the head loss of the disc filter (ΔH_di) increased with the operating time; the magnitude of this increase grew with higher flow rates, sediment concentrations, and median particle sizes, reaching 0.07 MPa after 16–235 min of operation. The head loss of the multi-stage filtration system (ΔH_i) was primarily generated by the disc filter. (2) The filtration efficiency of the filters and the filtration system was 2.5–6.4%. The outlet sediment concentration and particle size distribution were linearly correlated with the inlet values, and the outlet sediment particle size distribution remained below the clogging risk threshold for emitters. (3) Prediction models for ΔH_si, ΔH_di, and ΔH_i were developed based on MLR, RFR, and DE/RFR. Among these, DE/RFR exhibited the highest accuracy in predicting these variables, with R² values ranging from 0.71 to 0.93 and RMSE values from 0.0017 to 0.0104 MPa. (4) Results from Pearson correlation and feature importance analysis indicated that ΔH_si, ΔH_di, and ΔH_i were primarily influenced by flow rate, sediment concentration and operating time, and flow rate and operating time, respectively. (5) Building upon the DE/RFR model, a Filtration Cycle Prediction Model (FCPM) was developed to determine the operational duration required for the head loss across both the filters and the filtration system to reach 0.07 MPa. The two models developed in this study provide technical support for the configuration and operation of drip irrigation filtration systems using sediment-laden water.

Understanding the patterns and regulatory mechanisms of wheat stomatal responses to elevated temperature and reduced water availability is fundamental for adapting to changing global climate. This knowledge forms the basis for simulating and quantifying the impact of dry hot winds during the late growth stage on yield losses of wheat in northern China. Using potted wheat plants, we established four treatments at the flag leaf stage: ambient temperature with well‐watered conditions (control, CK), elevated temperature (H), mild drought (D), and combined elevated temperature and drought (HD). After 25 days of treatment, gas exchange parameters, light response curves, and stomatal morphology of the flag leaves were measured. Stomatal conductance models were fitted, and differences in the parameters among treatments were analysed, with an attempt to identify the sources of variability in each parameter. The results showed that (1) the H treatment induced moderate stomatal closure (stomatal opening reduced by 20.1%), maintaining stable net photosynthetic rate ( A _n ) while improving CO ₂ assimilation efficiency (16.4% decrease in intercellular CO ₂ concentration), thereby forming an efficient water use strategy; (2) under D and HD treatments, the maximum net photosynthetic rate ( P _max ) decreased by 19.9%–25.5%. Additionally, the photosystem adapted to low‐light conditions by downregulating the light saturation point ( I _sat : 41.9%–42.5%) and light compensation point ( I _c : 38.0%–51.6%); (3) Morphological parameters such as stomatal density and stomatal area remained stable, while stomatal aperture significantly decreased by 20.1%–37.8% under H, D, and HD treatments, and the theoretical maximum stomatal conductance ( g _smax ) significantly decreased by 17.6%–18.4%, revealing the rapid response characteristics of stomatal dynamic regulation; (4) The slope parameters ( m and g ₁ ) of the Ball‐Berry and Medlyn models showed a significant positive correlation with stomatal aperture, with a significant decrease in parameters under the H treatment (41.1% and 32.7%), confirming that stomatal movement is the core regulatory factor for model parameters. The relevant results provide key parameterization schemes and theoretical support for modelling the mechanisms of crop responses to combined elevated temperature and lower water supply conditions.

Canopy conductance (G_c) is an important biological constant for quantifying the water vapor flux at the canopy-atmosphere interface, reflecting the coupling strength between crop transpiration and microclimate. To elucidate how mulching modulates G_c dynamics under varying environments, we measured the transpiration of maize based on thermal equilibrium method from 2020 and 2021, synchronously recording solar radiation (R_s), temperature (T), relative humidity (RH), and vapor pressure deficit (VPD) under no-mulching (NM), transparent film (TFM), black film (BM), and straw mulching (SM) treatments in the North China Plain. The results showed that in the near-surface microenvironment, at early stages (seedling-jointing), compared to the NM treatment, TFM and BM treatments unexpectedly reduced temperature by 0.1–1.1% while increasing humidity by 0.2–4.0%, lowering VPD by 0.7–15.5%, contradicting presumed warming effects. During tasseling-filling stages, both plastic films elevated temperature by 3.5–5.2%, decreased humidity by 5.2–6.9%, and sharply increased VPD by 23.4–27.6%, inducing heat-VPD coupling stress. Throughout the entire growth period, SM treatment resulted in an initial increase followed by a decrease in temperature, but the decrease in humidity and increase in VPD occurred earlier and smoothly compared to film mulching treatment in the near-surface microenvironment. All treatments increased average temperature but decreased average humidity in the near-ground microenvironment throughout growth stages, ultimately leading to an increase in average VPD. In addition, all treatments increased G_c at noon by 10.3–81.2%. Under different solar radiation conditions, TFM, BM, and SM treatments increased the reference conductance (G_cR) but did not always increase G_c sensitivity to VPD (m). We propose a specific mulching strategy: Using black or transparent plastic film mulching in arid/cold regions and straw mulching in high-temperature and drought-prone/rain-fed agricultural areas can reconcile the trade-off between microclimate optimization and physiological adaptation, advancing precision water management in arid-prone croplands.

Drought stress severely limits agricultural productivity, with atmospheric and soil water deficits often occurring simultaneously in field conditions. While plant responses to individual drought factors are well-documented, recovery mechanisms following combined atmospheric–soil drought remain poorly understood, hindering drought resistance strategies and irrigation optimization. We set up two VPD treatments (low and high vapor pressure deficit) and two soil moisture treatments (CK: control soil moisture with sufficient irrigation, 85–95% field capacity; drought: soil moisture with deficit irrigation, 50–60% field capacity) in the pot experiment. We investigated wheat’s hydraulic transport (leaf hydraulic conductance, K_leaf) and gas exchange (stomatal conductance, g_s; photosynthetic rate, A_n) responses to combined drought stress from atmospheric and soil conditions at the heading stage, as well as rewatering 55 days after treatment initiation. The results revealed that: (1) high VPD and soil drought significantly reduced leaf hydraulic conductance (K_leaf), with a high VPD decreasing K_leaf by 31.6% and soil drought reducing K_leaf by 33.2%; The high VPD decreased stomatal conductance (g_s) by 43.6% but the photosynthetic rate (A_n) by only 12.3%; (2) After rewatering, g_s and A_n of atmospheric and soil drought recovered relatively rapidly, while K_leaf did not; (3) Atmospheric and soil drought stress led to adaptive changes in wheat’s stomatal regulation strategies, with an increasing severity of drought stress characterized by a shift from non-conservative to conservative water regulation behavior. These findings elucidate wheat’s hydraulic–stomatal coordination mechanisms under drought stress and their differential recovery patterns, providing theoretical foundation for improved irrigation management practices.

Estimating leaf water potential (Ψ_leaf) is essential for understanding plant physiological processes’ response to drought. The estimation of Ψ_leaf based on different regression analysis methods with hyperspectral vegetation indices (VIs) has been proven to be a simple and efficient technique. However, models constructed by existing methods and VIs still face challenges regarding the generalizability and limited ranges of field experiment datasets. In this study, leaf dehydration experiments of three maize cultivars were applied to provide a dataset covering a wide range of Ψ_leaf variations, which is often challenging to obtain in field trials. The analysis screened published VIs highly correlated with Ψ_leaf and constructed a model for Ψ_leaf estimation based on three algorithms—partial least squares regression (PLSR), random forest (RF), and multiple linear stepwise regression (MLR)—for each cultivar and all three cultivars. Models were constructed using PLSR and MLR for each cultivar and PLSR, MLR, and RF for the samples from all three cultivars. The performance of the models developed for each cultivar was compared with the performance of the cross-cultivar model. Simultaneously, the normalized ratio (ND) and double-difference (DDn) were applied to determine the VIs and models. Finally, the relationship between the optimal VIs and Ψ_leaf was analyzed using discontinuous linear segmental fitting. The results showed that leaf spectral reflectance variations in the 350~700 nm bands and 1450~2500 nm bands were significantly sensitive to Ψ_leaf. The RF method achieved the highest prediction accuracy when all three cultivars’ data were used, with a normalized root mean square error (NRMSE) of 9.02%. In contrast, there was little difference in the predictive effectiveness of the models constructed for each cultivar and all three cultivars. Moreover, the simple linear regression model built based on the DDn(2030,45) outperformed the RF method regarding prediction accuracy, with an NRMSE of 7.94%. Ψ_leaf at the breakpoint obtained by discontinuous linear segment fitting was about −1.20 MPa, consistent with the published range of the turgor loss point (Ψ_TLP). This study provides an effective methodology for Ψ_leaf monitoring with significant practical value, particularly in irrigation decision-making and drought prediction.

Reducing the flow rate (q) of the emitter can increase the dripline laying length and reduce the engineering investment of the drip irrigation system; however, reducing q increases the risk of emitter clogging. In this study, based on the OPFN method (Optimal Latin Hypercube Experimental Design–Parametric Modeling of Emitter–Fluid Dynamics Simulation–NSGA-II Genetic Algorithm Optimization), we selected the structural parameters of channel tooth height (E), angle (A), pitch of teeth (B), and flow channel depth (D) to construct 128 emitters. Through simulation, we obtained q, the flow index (x), and the structural resistance coefficient (Cs) under the pressure (H) ranging from 0.02 to 0.15 MPa. The results showed that the rated flow rate (q_0.1) and x values of 128 emitters range from 0.50 to 0.85 L/h and 0.461 to 0.480, respectively. Since Cs is negatively correlated with x, to obtain the combination of the flow channel structural parameters with the optimal hydraulic performance (x = min f(E, A, B, D)) and the optimal anti-clogging performance (Cs = min g(E, A, B, D)), the flow channel structural parameters are optimized by using the NSGA-II genetic algorithm to obtain the Pareto frontier solution. The optimal combinations of channel structural parameters corresponding to the q_0.1 values of 0.62, 0.71, and 0.82 L/h with x of 0.470, 0.466, and 0.463 are obtained using the weighting method. Cs values are 0.131, 0.446, and 0.619, respectively. The limit laying length of the configured emitter is 150–180 m. According to the flow field cloud diagram before and after optimization, it can be found that increasing the high-velocity area and high-turbulent-kinetic-energy area in the main stream and decreasing the low-velocity area and low-turbulent-kinetic-energy area in the tooth base and downstream face can help reduce x and Cs, and thus improve the hydraulic performance and anti-clogging performance of the small flow rate emitter.

Variable flow emitters are used in subsurface drip irrigation to address challenges in soil moisture transport. This study investigates the impact of flow channel structural parameters on the hydraulic performance and anticlogging ability of emitters using computational fluid dynamics (CFD) simulations and experimental tests. The results show that the realizable k–ε turbulence model can be used to simulate the flow field inside the variable flow emitter flow channel. The nRMSE between the measured (q_m) and simulated (q) values of the flow rate is 11.23%, and the relative error between the measured (x_m) and simulated (x) values of the flow index is 4.66%, which gives a high simulation accuracy. A polar analysis shows that the tooth angle (A) has the smallest effect on the effluent flow rate at 0.1 MPa (q_0.1), x, and particle passage rate (η) of the variable flow emitter. Flow channel depth (D), tooth spacing (B), and tooth height (E) have a different order of precedence in the influence of the three indices, which are D > B > E > A, B > E > D > A and E > B > D > A, respectively. The value of η is positively correlated with the mean flow velocity (v) and the mean turbulent kinetic energy (k) in the flow channel, and η tends to increase and then decrease with the increase of x. The retention time of the particles in the flow channel is closely related to the magnitude of v and k. Three multivariate lin ear regression equations (R² = 0.883–0.995) were constructed for q_0.1, x, and η versus the flow channel structural parameters. The optimal design combination of channel structure parameters for different scenarios was determined using the scipy.optimize.minimize function in Python 3.8.0. The research results provide a reference for the optimal design of variable flow emitters.