With the increasing shortage of land resources and people’s attention to the ecological environment, the application of microbial fertilizer with natural soil microorganisms as the main component has attracted increasing attention in saline agriculture. In this study, two salt-tolerant strains, YL07 (Bacillus atrophaeus) and YL10 (Planococcus soli), were isolated from maize (Zea mays L.) rhizosphere soil with a saturated conductivity (EC_e) of 6.13 dS/m and pH of 8.32 (Xinjiang, China). The effects of B. atrophaeus WZYH01 (YL07) and Planococcus soli WZYH02 (YL10) on the growth and development of maize (Zea mays L.) under salt stress (EC_e = 5.9 dS/m) were further studied. The results showed that compared with uninoculation, inoculation with B. atrophaeus WZYH01 and Planococcus soli WZYH02 significantly improved maize growth performance, biomass yield, and antioxidant levels under salt stress, and the effect of Planococcus soli WZYH02 was more prominent than the effect of B. atrophaeus WZYH01. Moreover, inoculation with B. atrophaeus WZYH01 and Planococcus soli WZYH02 protected maize from salt stress by regulating plant hormone [IAA and abscisic acid (ABA)] levels and increasing nutrient acquisition. In addition, the tested strains were most efficient for maize growth and health, increasing the content of K⁺ accompanied by an effective decrease in Na⁺ in maize tissues. The transcription levels of salt tolerance genes (ZMNHX1, ZMNHX2, ZMHKT, ZMWRKY58, and ZMDREB2A) in inoculated maize were also dramatically higher than the transcription levels of the specified salt tolerance genes in uninoculated maize. In conclusion, B. atrophaeus WZYH01 and Planococcus soli WZYH02 can alleviate the harmful effects of salt stress on crop growth, thereby promoting sustainable agricultural development.

Background: The application of base fertilizer is significant for reducing agricultural costs, non-point source pollution, and increasing crop production. However, the existing fertilization decision methods require many field observations and have high prices for popularization and application. Methods: This study proposes an innovative model integrating machine learning (ML) and swarm intelligence search algorithms to overcome the above issues. Based on historical data for maize, rice, and soybean crops, ML algorithms including random forest (RF), extreme random tree (ERT), and extreme gradient boosting (XGBoost) were evaluated for predicting crop yield. Coupled with the cuckoo search algorithm (CSA), the prime fertilization decision model (FDM) was established to discover the optimal fertilization strategy. Result: For all three crops, the yield simulation accuracy of the ERT model was the highest, with an R² and RRMSE of 0.749, 0.775, and 0.744, and 0.086, 0.051, and 0.078, respectively. Considering soil nutrient and fertilization characteristics as the determinants of yield and optimizing fertilization strategies, the proposed model can increase the average yield of maize, rice, and soybean in the study area by 23.9%, 13.3%, and 20.3%, respectively. Conclusions: The coupling model of ERT and the CSA constructed in this study can be used for the intelligent and rapid decision-making of the base fertilizer application for crops considered in the present study.

Soil salinization is one of the main hazards affecting the sustainable development of agriculture in the Hetao Irrigation District (HID) of Inner Mongolia. To grasp the water and salt transport patterns and spatial–temporal distribution characteristics of the HID at the regional scale, the improved Soil and Water Assessment Tool with a salinity module (SWAT-Salt) model was used to establish the distributed water and salt transport model for the watershed in this study. The results demonstrated that the modified model could more accurately represent the process of water and salt changes in the HID. The coefficient of determination (R²) in the simulation of streamflow and discharge salt loading was 0.83 and 0.86, respectively, and the Nash–Sutcliffe efficiency (NSE) was 0.80 and 0.74, respectively. Based on this, different hydrological processes (surface runoff, lateral flow, groundwater, soil seepage) as well as spatial–temporal distribution characteristics of water salinity in groundwater and soil were analyzed in the HID. Differences in groundwater and soil salinity in different land uses and soil types were also compared. Of these, surface runoff and lateral flow salt discharge loading are concentrated in the southwestern portion of the basin, while groundwater salt discharge loading is concentrated in the eastern as well as southwestern portions of the basin. The salt discharge loading from groundwater accounts for about 98.7% of the total salt discharge loading from all hydrological pathways and is the major contributing part of salt discharge from the irrigation area. Soil salinity increases gradually from west to east. Groundwater salinity (2946 mg/L) and soil water electrical conductivity (0.309 dS/m) were minimized in the cropland. Meanwhile, rational allocation of irrigation water can appropriately increase the amount of salt discharge loading. In conclusion, the model could provide a reference for the investigation of soil salinization and water–salt management measures in irrigation areas.