In order to effectively suppress the negative effects of salt ions contained in saline soils on agricultural soil quality and crop growth, this study took advantage of the water-saving properties and better soil improvement properties of poly-γ-glutamic acid (γ-PGA). By carrying out various experiments, the following relationships have been found. (1) The lab experiment studies the effect of the γ-PGA application on the infiltration of sandy loam soil. The application rates of γ-PGA are 0%, 0.1%, 0.2%, and 0.3%, respectively. (2) HYDRUS-1D is used to simulate water infiltration of sandy loam soil under multiple factors (bulk density, γ-PGA application rate, and the application depth of γ-PGA). (3) The effect of γ-PGA on soil solute (Cl⁻) transport is also explored in this paper. The results show that bulk density and the application depth of γ-PGA (p < 0.01) have higher effects on cumulative infiltration than the application amount of γ-PGA (p < 0.05). A lower γ-PGA application rate will increase the proportion of unavailable soil water by 3%. The established empirical models have good results. Furthermore, when the γ-PGA application rate is 0.3% (0.02-cm² min⁻¹), the Cl⁻ hydrodynamic dispersion coefficient is the highest. The study recommends applying the γ-PGA at 1.4 g cm⁻³, 5–20 cm, and 0.2%. The results of this study are conducive to an in-depth understanding of the physicochemical properties of poly-γ-glutamic acid, improving the utilization rate of salinized land, achieving agricultural water and fertilizer conservation and yield enhancement, and guaranteeing sustainable land use and sustainable development of agroecological environment.

With the constant advancement of irrigation technology and the continuous expansion of irrigation areas, non-point source pollution (NPS) caused by agricultural activities has posed a persistent threat to ecosystems and biological safety. Against this backdrop, it is imperative to lay scientific foundations for green, sustainable, and high-quality agricultural development through a thorough review of the relevant research progress. In this study, bibliometric methods are adopted to comprehensively analyze and visualize the current state and key literature on agricultural irrigation and NPS pollution from 2010 to July 2024. The focus of this study is specifically on summarizing the research hotspots and development trends of different irrigation methods and the mechanisms behind their impacts on NPS pollution. The results indicate that publications from the United States and China account for 63.8% of the total, but the fragmentation of research efforts remains, suggesting a necessity to strengthen international and regional collaboration. There are three institutions with the highest publication output, namely Northwest A&F University, Hohai University, and the Chinese Academy of Sciences. The subjects identified as the key areas of research on irrigation-related NPS pollution (IRR-NPS) include precision irrigation, rapid water pollution response, spatiotemporal management, interdisciplinary integration, wastewater treatment, and crop models. Regarding future research, it is necessary to focus attention on real-time precision irrigation, standardized crop models, data accuracy, spatiotemporal pollution coordination, pollution purification technology development, interdisciplinary integrated governance, and the innovative applications of soil improvement technologies. In addition to offering theoretical support and practical guidance for the management of agricultural NPS pollution, this study also provides management and technical support for policymakers, which is beneficial for advancing agricultural irrigation technology and environmental preservation.

Mastering crop evapotranspiration (ET) and improving the accuracy of ET simulation is critical for optimizing the irrigation schedule and saving water resources, particularly for crops cultivated in a greenhouse. Taking greenhouse-grown tomato under drip irrigation as an example, two weighing lysimeters were used to monitor ET at two seasons (2019 and 2020), whilst meteorological factors inside the greenhouse were measured using an automatic weather station. Then the path analysis approach was employed to determine the main environmental control factors of ET. On this basis, an improved Priestley-Taylor (IPT) model was developed to simulate tomato ET at different growth stages by considering the influence of environmental changes on model parameters (e.g., leaf senescence coefficient, temperature constraint coefficient and soil evaporative water stress coefficient). Results showed that the average daily ET varied from 0.06 to 6.57 mm d⁻¹, which were ~0.98, ~2.58, ~3.70 and ~3.32 mm/d at the initial, development, middle and late stages, respectively, with the total ET over the whole growth stage of ~333.0 mm. Net solar radiation (R_n) and vapor pressure deficit (VPD) were the direct influencing factors of ET, whereas air temperature (T_a) was the limiting factor and wind speed (u₂) had a little influence on ET. The order of correlation coefficients between meteorological factors and ET at two seasons was R_n > VPD > T_a > u₂. The IPT model can accurately simulate ET in hourly and daily scales. The root mean square error of hourly ET at four stages changed from 0.002 to 0.08 mm h⁻¹ and daily ET varied from 0.54 to 0.57 mm d⁻¹. The IPT coefficient was close to the recommended PT coefficient (1.26) when the average T_a approaches 26 °C and LAI approaches 2.5 cm² cm⁻² in greenhouse conditions. Our results can provide a theoretical basis for further optimization of greenhouse crop irrigation schedules and improvement of water use efficiency.

Working to simplify mechanistic models on the basis of reliability for estimating crop evapotranspiration (ET) in a greenhouse is still worthwhile for horticulturists. In this study, four ET models (Penman–Monteith, Priestley–Taylor, and Shuttleworth–Wallace models, and the Crop coefficient method) were parameterized after taking the restriction effect of resistance parameters in these models on ET into account, named as PA-PM, PA-PT, PA-CC, and PA-SW, respectively. The performance of these four parameterized models was compared at different growth stages, as well as the entire growing season. Tomatoes that were ET-grown in a solar greenhouse without a heating device were measured using weighting lysimeters during 2016–2017 and 2019–2021, in which data from 2016 were used to adjust the model parameters, and data from the other four study years were used to examine the model performance. The results indicated that the PA-PT and PA-CC models have a better performance in estimating tomato ET at four growth stages, while the PA-PM and PA-SW performed well only at the development and middle stages. Compared to the ET that was measured with the weighting lysimeters, the ET that was predicted using the PA-PM model was 27.0% lower at the initial stage, and 8.7% higher at the late stage; the ET that was computed using the PA-SW model was 19.5% and 13.6% higher at the initial and late stages, respectively. The PA-PT model yielded the lowest root mean square error and the highest index of agreement against the other models over the entire growing season, indicating that the PA-PT model is the best recommended model for estimating tomato ET in a solar greenhouse.