To explore the potential of smashing ridge tillage irrigation, it is necessary to investigate how smashing ridge tillage technology with mulched drip irrigation affects soil water, salinity, and cotton yield in saline fields. We conducted a two-year (2020–2021) field experiment to study the effects of different smashing ridge tillage depths on soil bulk density, moisture, salinity, dry matter production, yield, and its constituents (effective bolls, 100-bell weight). There were three smashing ridge tillage depths: A (20 cm), B (40 cm), and C (60 cm), with traditional tillage as the CT. The results showed that all of the smashing ridge tillage could reduce soil bulk density, improve the utilization and uptake of deep soil water during the rapid growth period, and reduce the soil salt content. Compared with the CT treatment, the average soil bulk density of the 0–60 cm soil layer in treatments A, B, and C in 2020 and 2021 decreased by 3.05%, 5.87%, 10.09%, and 1.65%, 4.48%, and 8.49%, respectively. The average soil water content in the 0–120 cm soil layer at the flowering and boll stage decreased by 3.68%, 6.28%, 9.04%, and 3.59%, 6.52%, and 9.98%, respectively; the soil salt content in the 0–120 cm soil layer at the boll opening stage decreased by 4.21%, 6.75%, 11.95%, and 5.47%, 24.25%, and 54.13%, respectively. Cotton dry matter production and yield tended to increase with an increasing depth of smash ridge tillage. Treatment C obtained the maximum dry matter production, seed cotton yield, effective bolls, and 100-boll weight. The dry matter production at the boll opening stage was significantly increased by 17.16% and 15.91%, and the yield was significantly increased by 65.24% and 84.14% in treatments C in 2020 and 2021, respectively, compared to CT. The smashing ridge tillage of 60 cm can optimize the structure of the soil tillage layer and also reduce soil salinity and increase yield, which is the suitable depth of smashing ridge tillage for saline cotton fields in the south of Xinjiang. The findings of the study can provide some theoretical basis and practical experience for the improvement of saline soils and sustainable agricultural development in South Xinjiang, China.

Soil salinization is an important factor contributing to the deterioration of soil environment and low crop yield in arable land. In this study, the effects of five fulvic acid applications (0 (CK), 150 (T1), 300 (T2), 450 (T3), and 600 (T4) kg·ha⁻¹) on soil physicochemical properties, humus content of each component, and cotton (Xinluzao No. 82) growth were investigated. It was confirmed that fulvic acid improved soil water-stable macroaggregates, moisture distribution, and desalinization. 0–20 cm soil relative desalinization rate was significantly increased by 2–11.75%. The pH value decreased by 0.09–0.21. The soil organic matter content was significantly increased compared to CK (p < 0.05), with 7.5–26.93% increase in organic matter content in 0–20 cm soil layer. Soil humification was increased to different degrees, with the most significant increase in humic carbon content in T3 treatment (p < 0.05). There was a significant increase in leaf area index (LAI), stem diameter, and plant height of cotton (p < 0.05). Cotton yield increased by 3.64–8.36% compared to CK (p < 0.05). Correlation analysis showed that cotton yield was significantly correlated with the soil textures of saline soils. The best improvement was achieved with 450 kg·ha⁻¹ fulvic acid. The results of this study can provide a theoretical basis for the improvement of saline soils in arid zones to enhance crop growth and yield.

In this study, the influence of the relevant parameters of a subsurface pipe layout on soil water and salt transport in a cotton field under mulched drip irrigation is explored. Based on the measured data of the changes in the groundwater level and salt in the field, the DRAINMOD numerical model has been used for simulating the water and salt dynamics of a salinized cotton field under subsurface pipe drainage. The results of the investigation show that the DRAINMOD model can accurately simulate the changes in the hydrological conditions and the salt-leaching process in the study area. The average deviation between the simulated and measured values of the groundwater depth in 2013 and 2014 was −1.72 cm and 2.43 cm, the average absolute deviation was 3.84 cm and 2.43 cm, the root mean square error was 5.14 cm and 3.63 cm, and the correlation coefficient was 0.87 and 0.94, respectively. The average deviation between the simulated and measured values of soil salinity in 2013 and 2014 was −0.68 g/kg and −1.86 g/kg, the average absolute deviation was 1.60 g/kg and 1.99 g/kg, the root mean square error was 1.95 g/kg and 2.99 g/kg, and the correlation coefficient was 0.82 and 0.86, respectively, which are all within the acceptable error range. After validation, the model was used to simulate and analyze the desalination process of a cotton field in the study area for 27 different subsurface pipe layout modes. The projection pursuit classification model has been combined with the accelerated genetic algorithm based on real-number coding. The comprehensive benefits of the subsurface pipe layout were evaluated using the construction cost, average desalination rate, and relative yield of cotton as the evaluation indices. The results show that C₁₁ (buried depth 2.1 m, spacing 30 m) is the optimal layout of the subsurface pipe. The results of this study can provide theoretical support and scientific guidance for the popularization and application of subsurface pipe salt discharge technology and drip irrigation under film in the arid inland areas of northwest China.

Against the global push for “carbon peak and carbon neutrality” and Xinjiang’s role as a major arid-region agricultural base in China, balancing agricultural development with low-carbon transitions remains challenging due to its fragile ecology and resource-intensive farming. However, county-scale dynamics of cultivated land carbon emission intensity (CEI) and its drivers in Xinjiang are understudied, limiting targeted mitigation. This study analyzed Xinjiang’s cultivated land CEI (2000–2020) using the Geographically and Temporally Weighted Regression and Stochastic Impacts by Regression on Population, Affluence and Technology (GTWR-STIRPAT) model, geodetector, and spatiotemporal analysis, with counties as units. Data included 30 m-resolution land use data and socioeconomic statistics. Results showed CEI rose from 0.270 to 0.377 t/hm², with marked spatial differences: northern Xinjiang saw fluctuating growth and a 58.65 km northeastward shift of emission gravity, while southern Xinjiang had lower western CEI (ecological constraints) and higher eastern CEI (agricultural expansion). Key drivers were total sown area (TSAC), agricultural film usage (UAPF), and rural agricultural population (RAP). Factor interactions (machinery power × sown area, q = 0.844) non-linearly amplified CEI. The GTWR-STIRPAT model (R² = 0.97) outperformed OLS and captured heterogeneity—mechanization/area expansion dominated northern CEI, while film use/population mattered more in the south. Region-specific strategies are needed: northern Xinjiang should optimize machinery energy and control area expansion; southern Xinjiang, strengthen ecology and promote low-carbon inputs; eastern Xinjiang, leverage efficient oasis agriculture. This study supports precise carbon management in Xinjiang and similar arid regions globally.