The accurate estimation of soil hydraulic parameters (θ_s, α, n, and K_s) of the van Genuchten–Mualem model has attracted considerable attention. In this study, we proposed a new two-step inversion method, which first estimated the hydraulic parameter θ_s using objective function by the final water content, and subsequently estimated the soil hydraulic parameters α, n, and K_s, using a vector-evaluated genetic algorithm and particle swarm optimization (VEGA-PSO) method based on objective functions by cumulative infiltration and infiltration rate. The parameters were inversely estimated for four types of soils (sand, loam, silt, and clay) under an in silico experiment simulating the tension disc infiltration at three initial water content levels. The results indicated that the method is excellent and robust. Because the objective function had multilocal minima in a tiny range near the true values, inverse estimation of the hydraulic parameters was difficult; however, the estimated soil water retention curves and hydraulic conductivity curves were nearly identical to the true curves. In addition, the proposed method was able to estimate the hydraulic parameters accurately despite substantial measurement errors in initial water content, final water content, and cumulative infiltration, proving that the method was feasible and practical for field application.

Reasonable planning of water delivery schedules for canal systems can reduce losses caused by water seepage and improve the utilization efficiency of irrigation water. Empirical methods of water delivery scheduling for canal systems usually cause problems such as insufficient discharge, excessively delayed water delivery, and large losses under given water requirements. In this study, a canal water delivery scheduling model was set up, and a customized algorithm based on particle swarm optimization was proposed. Typical heuristic algorithms often become trapped in local optima and often search inefficiently under numerous constraints; however, the proposed algorithm can overcome these typical problems. The proposed method was evaluated for two typical canal irrigation systems, and the results showed that the algorithm is robust and efficient and can quickly meet the water delivery optimization schedules for canal irrigation systems. Compared with empirical methods, the algorithm reduced the leakage loss of delivered water from 7.29% to 5.40%, and 8.97% to 7.46% for the two tested canal systems. The discharge of the main canal is relatively stable, which can reduce the difficulty of head gate adjustment. The proposed optimization algorithm can provide practical and efficient water delivery schedules for irrigation canal systems.

The application of additives (e.g., wheat straw (WS) or rapeseed-oil residue (RR)) to soils is a common agronomic practice, used for improving soil water retention. Through a laboratory investigation, this study examined the effects of RR and WS on infiltration, evaporation, water distribution, and water retention. The results indicated that the migration rate of the wetting front, as well as the accumulative infiltration and evaporation, decreased with the amount of applied additives. RR was more effective than WS for infiltration and evaporation suppression. Furthermore, in the Kostiakov model, the value of a decreased with an increasing amount of additive, becoming smaller than the corresponding control treatment (CK) value; by contrast, the value of n decreased with increasing amount of applied additive, remaining larger than the CK value. In the Rose model, the values of A for the soils mixed with additives were smaller than those of the CK, and decreased with increasing amount of additive. Moreover, compared with the CK, the maximum water content for the RR-applied soils increased by 17.84% after infiltration and by 47.66% after evaporation. In addition, the water retention coefficients (calculated as the soil moisture after evaporation divided by that after infiltration) for soils mixed with RR were the highest, indicating that RR is more effective than WS for improving the water retention of the soil layer.

Film hole irrigation is an advanced low-cost and high-efficiency irrigation method, which can improve water conservation and water use efficiency. Given its various advantages and potential applications, we conducted a laboratory study to investigate the effects of soil texture, bulk density, initial soil moisture, irrigation depth, opening ratio (ρ), film hole diameter (D), and spacing on cumulative infiltration using SWMS-2D. We then proposed a simplified model based on the Kostiakov model for infiltration estimation. Error analyses indicated SWMS-2D to be suitable for infiltration simulation of film hole irrigation. Additional SWMS-2D-based investigations indicated that, for a certain soil, initial soil moisture and irrigation depth had the weakest effects on cumulative infiltration, whereas ρ and D had the strongest effects on cumulative infiltration. A simplified model with ρ and D was further established, and its use was then expanded to different soils. Verification based on seven soil types indicated that the established simplified double-factor model effectively estimates cumulative infiltration for film hole irrigation, with a small mean average error of 0.141–2.299 mm, a root mean square error of 0.177–2.722 mm, a percent bias of −2.131–1.479%, and a large Nash–Sutcliffe coefficient that is close to 1.0.

Hydroponic experiments were conducted to investigate the effects of varying particle sizes (5 μm, 0.1 μm, and 0.08 μm) and concentrations (0, 50, 100, 200, 500, 1000, and 2000 mg/L) of polystyrene plastic microspheres (PS-MPs) on the germination and growth of P. sativum seeds in order to investigate the effects of the microplastic environment on crop seed germination and seedling growth. The findings demonstrated that PS-MPs significantly harmed P. sativum seed germination. Specifically, low concentrations or high particle sizes weakened or promoted P. sativum seed germination, indicating varying susceptibilities to PS-MP treatment at different particle sizes. The strongest inhibitory effect on growth was observed under small particle size (0.08 μm) and high concentration (2000 mg/L). Stress had less of an impact on P. sativum in environments with low concentrations of PS-MPs, but in environments with medium and high concentrations, physiological and biochemical indicators like germination rate, stem length, and root length were significantly impacted. Furthermore, the particle size and concentration of PS-MPs had an impact on the growth effect of P. sativum seedlings. The indices of P. sativum seed germination were significantly impacted by the particle size of PS-MPs, despite the fact that PS-MP concentration was low. When the concentration of PS-MPs is low, the effects of varying PS-MP particle sizes on seed germination and root length are as follows: 0.1 μm > 5 μm > 0.08 μm. The concentration of PS-MPs had a significant impact on how it affected the buildup of dry matter and photosynthetic pigment.

Infiltration parameters and Manning’s roughness are essential input parameters for surface irrigation simulation models. Multiple points of infiltration experiments require time-consuming, costly data collection and problematic calculations of field mean infiltration parameters. This study estimated the field mean infiltration parameters and Manning’s roughness values on a regional scale, on the basis of closed-end furrow irrigation experiments conducted at 45 experimental sites on the Guanzhong plain, and evaluated the influence of Manning’s roughness on advance trajectory and performance indicators of closed-end furrow irrigation. Then, we present a functional normalization of the Kostiakov equation and pedotransfer function (PTF) to estimate the normalization factors. The proposed method can be used to estimate the field mean infiltration parameters using PTF and the represented Manning’s roughness to determine the optimal discharge of closed-end furrow irrigation. The results revealed that the advance trajectory and performance of closed-end furrow irrigation was not sensitive to variations of Manning’s roughness, which can be adopted as a representative value (i.e., 0.075) of the maize field. The normalization method was proven to be feasible for the Kostiakov equation, and the PTF reliably estimated the normalization factors. Last, the optimized values of the inflow discharge were determined using the proposed method, with various combinations of furrow lengths and bottom slopes in the study area, and also compared with the inflow discharge determined based on infiltration parameters and Manning’s roughness, which were inversed by SIPAR_ID. The results indicated that the inflow discharge values obtained through using the two methods were approximately equal, proving that the proposed method was reliable and easy to use in practice.