The scientific use of sodium carboxymethyl cellulose (CMC) to improve the production capacity of saline–alkali soil is critical to achieve green agriculture and sustainable land use. It serves as a foundation for the scientific use of CMC to clarify the water and salt transport characteristics of CMC-treated soil. In this study, a one-dimensional soil column infiltration experiment was carried out to investigate the effects of different CMC dosages (0, 0.2, 0.4, 0.6, and 0.8 g/kg) on the infiltration characteristics, infiltration model parameters, water and salt distribution, and salt leaching of saline–alkali soil in Xinjiang, China. The results showed that the final cumulative infiltration of CMC-treated soil increased by 8.63–20.72%, and the infiltration time to reach the preset wetting front depth increased by 1.02–3.96 times. The sorptivity (S) in the Philip infiltration model and comprehensive shape coefficient (α) in the algebraic infiltration model showed a trend of increasing first and then decreasing with CMC dosage, revealing a quadratic polynomial relationship. The algebraic model could accurately simulate the water content profile of CMC-treated soil. CMC enhanced the soil water holding capacity and salt leaching efficiency. The average soil water content, desalination rate, and leaching efficiency were increased by 5.18–15.54%, 21.17–57.15%, and 11.61–30.18%, respectively. The effect of water retention and salt inhibition on loamy sand was the best when the CMC dosage was 0.6 g/ kg. In conclusion, the results provide a theoretical basis for the rational application of CMC to improve saline–alkali soil in arid areas.

The Leaf Area Index (LAI) strongly influences crop biomass production and yields. The variation characteristic of LAI and the development of crop growth models can provide a theoretical basis for predicting crops’ water consumption, fruit quality and yields. This paper analyzes the relationship between measurements of aboveground grape biomass and trends in LAI and dry biomass production in grapes grown in the Turpan area. The LAI changes in grapes were estimated using the modified logistic model, the modified Gaussian model, the log-normal model, the cubic polynomial model, and the Gaussian model. Universal models of LAI were established in which the applied irrigation quota was applied to calculate the maximum LAI. The relationship between the irrigation quota and biomass production, yields, and the harvest index was investigated. The developed models could accurately predict the LAI of grapevines grown in an extremely arid area. However, the Gaussian and cubic polynomial models produced less accurate results than the other models tested. The Michaelis–Menten model analyzed the relationship between biomass and LAI, providing a numerical method for predicting dynamic changes in grapevine LAI. Moreover, the crop biomass increased linearly with the irrigation quota for quotas between 6375 and 13,200 m³/hm. This made it possible to describe the grape yield and harvest index with a quadratic polynomial function, which increases during the early stages of the growing season and then decreases. The analyses of the relationship between yield and harvest index provide important theoretical insights that can be used to improve water use efficiency in grape cultivation and to identify optimal irrigation quotas.

A novel method, ECWS, is proposed for measuring soil initial salinity content (b), based on the soil electrical conductivity EC and soil moisture content WS. This pioneering model rigorously establishes and incorporates the inherent potential correlation among soil bulk conductivity ($E{C}_a$), soil solution conductivity (${EC}_w$), volume water content (${\theta }_c$), and soil salinity content (SSC). First of all, in order to delve the deeper relationship between $E{C}_a$, ${EC}_w$, ${\theta }_c$ and SSC, the soil salinity conductivity conversion coefficient ${\rho }_a$ and soil leaching solution salinity conductivity conversion coefficient ${\rho }_w$ were employed based on the formula of parallel conducting channels of the soil–water system, and a new measurement model of salinity content was constructed. After that, a mathematical analysis method was used to transform the coefficients of multiple sets of regression equations into matrices to solve ${\rho }_a$, ${\rho }_w$ and b. Finally, to validate the accuracy of the proposed ECWS method, verification tests were conducted by utilizing TDR and PWMER sensors. The results with different salinity contents showed that the b size obtained by ECWS model were ${\mathrm{K}}_2{\mathrm{SO}}_4$ (1.84 g/kg), NaCl (1.91 g/kg), and KCl (1.92 g/kg). The maximum deviation was less than 0.08 g/kg (relative error less than 5%). The results showed that the influence of different anions and cations on the measurement of salinity content Cl⁻ is greater than that of ${\mathrm{SO}}_4^{2-}$, and K⁺ is greater than that of Na⁺. This study revealed the relationship between soil electrical conductivity and soil salinity content to a certain extent, and realized the transformation between them, which provided a new method for the measurement of soil salinity content, and also provided a reference for related research on the measurement of soil salinization.

Evaluating the performance of AquaCrop models under the drip irrigation of maize with soil conditioners is of great significance for improving coastal saline–alkali land crop management strategies. This study aimed to evaluate the performance of an AquaCrop model for maize growth simulation under different soil conditions (humic acid (HA) and sodium carboxymethyl cellulose (CMC)) and dosages and different levels of irrigation in the Shandong coastal saline–alkali area, China, and to optimize the amount of irrigation. Three years of experiments were carried out in the growing season of maize (Ludan 510) in 2019, 2020, and 2021. The dosages of HA were 5, 15, 25, and 35 g/m², the dosages of CMC were 1, 2, 3, and 5 g/m², and the levels of irrigation from 2019 to 2021 were all 120 mm. The model was calibrated with data from 2019, and the model was verified with data from 2020 to 2021, according to the recommended corn parameters in the AquaCrop model manual. The results showed that the model had a good simulation effect on canopy coverage, with a root-mean-square error (RMSE) of less than 15.2%, and the simulated aboveground biomass and yield were generally low. The simulated value of soil water content was generally high, with some treatments having errors of more than 15.0%. The simulation effect of irrigated maize from 2019 to 2020 was better than maize in 2021. The simulation effect of HA was better than that of CMC, while the simulation effect of a low-gradient modifier was better than that of high-gradient conditioner when compared with CMC. In conclusion, the AquaCrop model could be a viable method for predicting maize development under different soil conditioners in this area. The suitable levels of irrigation under HA and CMC treatments were 47.0–65.9 mm and 61.0–92.4 mm, respectively, according to the principle of high yield and water use efficiency. The results provided a reference for optimizing the drip irrigation of maize under the application of soil conditioners in coastal saline–alkali areas.

The contradiction between water demand and water supply in the Yellow River Delta restricts the corn yield in the region. It is of great significance to formulate reasonable irrigation strategies to alleviate regional water use and improve corn yield. Based on typical hydrological years (wet year, normal year, and dry year), this study used the coupling model of AquaCrop, the multi-objective genetic algorithm (NSGA-III), and TOPSIS-Entropy established using the Python language to solve the problem, with the objectives of achieving the minimum irrigation water (IW), maximum yield (Y), maximum irrigation water production rate (IWP), and maximum water use efficiency (WUE). TOPSIS-Entropy was then used to make decisions on the Pareto fronts, seeking the best irrigation decision under the multiple objectives. The results show the following: (1) The AquaCrop-OSPy model accurately simulated the maize growth process in the experimental area. The R² values for canopy coverage (CC) in 2019, 2020, and 2021 were 0.87, 0.90, and 0.92, respectively, and the R² values for the aboveground biomass (BIO) were 0.97, 0.96, and 0.96. (2) Compared with other irrigation treatments, the rainfall in the test area can meet the water demand of the maize growth period in wet years, and net irrigation can significantly reduce IW and increase Y, IWP, and WUE in normal and dry years. (3) Using LARS-WG (a widely employed stochastic weather generator in agricultural climate impact assessment) to generate future climate scenarios externally resulted in a higher CO₂ concentration with increased production and slightly reduced IW demand. (4) Optimizing irrigation strategies is important for allowing decision makers to promote the sustainable utilization of water resources in the study region and increase maize crop yields.

The northern foothills of Yinshan Mountain are situated in northern China’s agricultural and pastoral ecotone, serving as a crucial ecological barrier. To comprehensively assess the impact of grassland resource restoration in this region since the initiation of the Grain-for-Green conversion project in 2000, this study analyzes the spatiotemporal characteristics of precipitation use efficiency (PUE) and investigates climate-driven factors during 2001–2021. The results showed that the grassland types at the north foot of Yinshan could be divided into four categories: warm-arid, warm subtropical semidesert (WSS), warm temperate-arid, warm temperate zonal semidesert (WZS), warm temperate-semiarid, warm temperate typical steppe (WTS), and warm temperate-subhumid forest steppe (WFT). The NPP of the four grassland species were 151.34 (WSS), 196.72 (WZS), 283.33 (WTS), and 118.06 gC·m⁻² (WFT), and correspondingly, the PUE of the four grassland species were 0.66 (WSS), 0.66 (WZS), 0.80 (WTS), and 0.57 gC·m⁻²·mm⁻¹ (WFT). From 2001 to 2021, PUE in grassland showed an overall upward trend, rising from 0.57 to 0.99 gC·m⁻²·mm⁻¹. The trend analysis found that the vegetation ecological area of the northern foot of Yinshan became better, of which 54.36% was improved and 15.72% was degraded. It is worth pointing out that WSS had the highest degree of improvement, while WFT was in a degraded state. The climate driving force analysis shows that the regional contribution of precipitation is 19.57%, temperature is 28.33%, potential evapotranspiration is 13.65%, wind speed is 10.79%, and saturated vapor pressure is 27.66%.