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.

Soil amendments such as humic acid (HA), a biochar-based microbial agent (M), and vermicompost (V) can improve soil quality and promote crop growth. However, it remains unclear whether the co-application of the three soil amendments (HMV) has a synergistic effect on alleviating soil quality deterioration obstacles caused by dry sowing and wet emergence technology in Xinjiang cotton fields. A three-year field experiment was conducted in saline–alkali soils using plastic-film-mulched drip irrigation in Xinjiang, China. Through the orthogonal experiment method, the application amounts of HA, M and V were 75 kg ha⁻¹, 75 kg ha⁻¹ and 225 kg ha⁻¹ respectively in 2021. In 2022, three application amount gradients were used for HA, M and V: 60 kg ha⁻¹, 90 kg ha⁻¹ and 120 kg ha⁻¹ respectively. In 2023, the application amounts of HA, M, and V were 60 kg ha⁻¹, 120 kg ha⁻¹, and 120 kg ha⁻¹. It should be pointed out that V contains HA in the range of 20–35%. This study aimed to explore the improvement effect of a single or combined application of HA, M, and V on soil quality and cotton emergence rate using dry sowing and wet emergence technology in Xinjiang cotton fields. The results showed that the single and combined applications of HA, M, and V improved the soil quality and water–heat–salt environment of the cultivated layer. In the combined application, the cotton seedling emergence rate and yield increased by 1.9–22.8% and 7.0–54.1%. Therefore, it is recommended to jointly apply HA, M, and V to promote cotton seedling emergence and increase yield using dry sowing and wet emergence technology in Xinjiang cotton fields.

Optimal winter irrigation may be an alternative method for controlling soil salinization under seasonal freezing and thawing conditions in the Yellow River Delta. However, few studies have focused on optimal winter irrigation based on the dynamics of soil water, heat, and salt during the freezing–thawing period in this region. Taking the seedling stage of winter wheat in the Yellow River Delta as the research object and using observation data of hydrothermal salt from the Shandong agricultural high-tech demonstration base from 17 October 2019 to 15 June 2021, a numerical simulation of the hydrothermal coupling process of freeze–thaw soil in the experimental area was carried out through the Simultaneous Heat and Water Model (SHAW). The simulation results of the model were evaluated according to the mean error (ME), root mean square error (RMSE), and Nash efficiency coefficient (NSE). The results showed that the SHAW can well simulate the soil moisture (SM), soil temperature, and soil salt during the growth of winter wheat in this region: the SM at the depth of 0–80 cm with an ME < 0.038 cm³ cm⁻³, RMSE < 0.064 cm³ cm⁻³, and NSE > 0.669; the soil temperature with an ME < 1.311 °C, RMSE < 1.493 °C, and NSE > 0.738; and the soil salinity with an ME < 0.005 g kg⁻¹, RMSE < 0.014 g kg⁻¹, and NSE > 0.607. Moreover, the model was used to simulate the distribution of soil water and salt in the winter wheat seedling stage under different winter irrigation methods during wet, normal, and dry years. It was suggested that the appropriate winter irrigation amount was 80 mm in wet years and normal years and 100 mm in dry years, which could be beneficial to winter wheat growth during the seedling stage. These results provide a reference for irrigation optimization in the Yellow River Delta and other similar areas.