Effect of green manure returning pattern on water utilization of spring wheat under reduced irrigation in arid irrigation areas

Response of plastic film mulched maize to soil and atmospheric water stresses in an arid irrigation area

An accurate groundwater level (GWL) forecast at multi timescales is vital for agricultural management and water resource scheduling in arid irrigated areas such as the Hexi Corridor, China. However, the forecast of GWL in these areas remains a challenging task owing to the deficient hydrogeological data and the highly nonlinear, non-stationary and complex groundwater system. The development of reliable groundwater level simulation models is necessary and profound. In this study, a novel ensemble deep learning GWL predictive framework integrating data pro-processing, feature selection, deep learning and uncertainty analysis was constructed. Under this framework, a hybrid model equipped with currently the most effective algorithms, including the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) for data decomposition, the genetic algorithm (GA) for feature selection, the deep belief network (DBN) model, and the quantile regression (QR) for uncertainty evaluation, denoted as CEEMDAN-GA-DBN, was proposed for the 1-, 2-, and 3-month ahead GWL forecast at three GWL observation wells in the Jiuquan basin, northwest China. The capability of the CEEMDAN-GA-DBN model was compared with the hybrid CEEMDAN-DBN and the standalone DBN model in terms of the performance metrics including R, MAE, RMSE, NSE, RSR, AIC and the Legates and McCabe’s Index as well as the uncertainty criterion including MPI and PICP. The results demonstrated the higher degree of accuracy and better performance of the objective CEEMDAN-GA-DBN model than the CEEMDAN-DBN and DBN models at all lead times and all the wells. Overall, the CEEMDAN-GA-DBN reduced the RMSE of the CEEMDAN-DBN and DBN models in the testing period by about 9.16 and 17.63%, while it improved their NSE by about 6.38 and 15.32%, respectively. The uncertainty analysis results also affirmed the slightly better reliability of the CEEMDAN-GA-DBN method than the CEEMDAN-DBN and DBN models at the 1-, 2- and 3-month forecast horizons. The derived results proved the ability of the proposed ensemble deep learning model in multi time steps ahead of GWL forecasting, and thus, can be used as an effective tool for GWL forecasting in arid irrigated areas.

Conservation tillage is widely recognized as an important way to improve soil quality, ensure food security and mitigate climate change. However, relatively little attention has been paid to the subject in terms of sustainable evaluation of environmental and economic benefits of the combination of no tillage and straw returning for maize production in arid irrigated areas. In this study, grain yield (GY) and water use efficiency based on grain yield (WUEGY), soil carbon emission characteristics and economic benefits were investigated, and a sustainability evaluation index based on the above indicators was assessed in maize production under a wheat–maize rotation system from 2009 to 2012. Four wheat straw returning approaches were designed: no tillage with 25 to 30 cm tall wheat straw mulching (NTSMP), no tillage with 25 to 30 cm tall wheat straw standing (NTSSP), conventional tillage with 25 to 30 cm tall wheat straw incorporation (CTSP), and conventional tillage without wheat straw returning (CTP). The results showed that NTSMP treatment could effectively regulate water consumption characteristics of maize fields and meet the water conditions for high grain yield formation, thus gaining higher GY and WUEGY. NTSMP increased GY and WUEGY of maize by 13.7–17.5% and 15.4–16.7% over the CTP treatment, and by 5.6–9.0% and 2.3–11.2% over the CTSP treatment, respectively. Meanwhile, compared with CTP, the NTSMP treatment could effectively reduce carbon emissions from maize fields, where average soil carbon emission fluxes (ACf), carbon emission (CE) and water use efficiency based on carbon emission (WUECE) were reduced by 17.7–18.9%, 11.1–11.2% and 8.8–12.8% and carbon emission efficiency (CEE) was increased by 10.2–14.7%. In addition, the NTSMP and NTSSP treatments could effectively increase total output and reduce human labor and farm machinery input, resulting in higher economic benefit. Among them, the NTSMP treatment was the most effective, net income (NI) and benefit per cubic meter of water (BPW) were increased by 16.1–34.2% and 19.1–31.8% over the CTP treatment, and by 13.2–13.3% and 9.8–15.6% over the CTSP treatment, respectively. The sustainability analysis showed that the NTSMP treatment had a high sustainability evaluation index and was a promising field-management strategy. Therefore, no tillage with 25 to 30 cm tall wheat straw mulching is a sustainable maize-management practice for increasing economic benefits and improving environmental impacts in arid irrigated areas.

Irrigated areas are major vectors of agricultural development and components of ecosystems. The groundwater level maintains the irrigated areas’ ecology safety and sustainable development. Under the influence of irrational irrigation practices—such as flood irrigation or extreme water saving without consideration of ecological impact—different areas within an irrigation district may experience anomalies in groundwater levels (either too deep or too shallow). It is of great significance to carry out research on water resource allocation and future water-saving strategies, taking into consideration groundwater depths. In this study, a method for the optimal allocation of irrigation water resources that considered groundwater level was used to regulate irrational irrigation practices and to reveal the future direction of water saving. Helan County in Ningxia province, an ecologically fragile and arid irrigated area, was selected as a case study. Multiple scenarios of different water use and different degrees of water-saving were analyzed. The results showed that non-engineering water-saving measures (such as adjusting the planting structure and controlling the amount of irrigation for rice) had better benefits compared to engineering measures (such as efficient water-saving irrigation and channel lining). When implementing only one water-saving measure, the strategy of replacing 75% of the rice area with corn yielded the best results. This approach can reduce the irrigation water shortage rate to 11% and increase by 4.58% the acreage where the groundwater level is reasonable. When multiple water-saving measures are implemented together, the most effective strategy for future water-saving efforts involves the joint implementation of several measures: replacing 75% of the rice area with corn, limiting irrigation for rice to no more than 11.85 thousand m3/ha, adopting high-efficiency water-saving irrigation in 90% of the pump-diverted water irrigation region and 40% of the channel-diverted water irrigation region, and maintaining the channel’s water utilization coefficient at 0.62. This strategy can keep the irrigation water shortage below 3.66% and increase the acreage where the groundwater level is reasonable, by 4.58% per year. The conclusions and research approaches can provide references for the formulation of water-saving measures for irrigated areas’ sustainable development.

No-tillage with total green manure incorporation: A better strategy to higher maize yield and nitrogen uptake in arid irrigation areas

Grain yield and N uptake of maize in response to increased plant density under reduced water and nitrogen supply conditions

How to quantify the impact of climate change and human activities on groundwater is not only a hot topic of current research but also a key point of water resource management in arid irrigated areas. Therefore, this paper analyzes the changes in the trends of land use, climate, and groundwater extraction in the Yanqi Basin in recent years and uses the distributed hydrological model MIKE‐SHE to quantitatively analyze the impacts of these three factors on groundwater resources. The results show that: 1. The Nash coefficients of the simulated and observed groundwater levels during the verification period are 0.84, 0.79 and 0.76; the correlation coefficient between the simulated and observed soil moisture is 0.86. Although there are some uncertainties in the simulation, the results prove that the model can be used to simulate arid irrigated areas. 2. The effects of these three factors on groundwater levels are 5, 12.5 and 82.5%, respectively, and have caused the regional average groundwater level to decrease by a maximum of 0.07, 0.23 and 1.79 m, respectively. The effects of these three factors on the interactions between surface water and groundwater were 7.04, 3.63 and 89.33%. Groundwater extraction has become the main influencing factor of regional groundwater resources changes due to its more direct influence. 3. The influence of groundwater extraction has a strong spatial distribution characteristic and 10% of the study area has been greatly impacted by the groundwater extraction. Base on the above results, integrating multidisciplinary knowledge to establish the relationship between ecological environment and groundwater changes can provide strategies for the sustainable development of groundwater.

Long periods of surface water irrigation and water and salt movement have slow and continuous influence on the evolution of soil salinization in a closed hydrogeological unit of arid irrigation areas. It is of more application value to study the evolution process of soil salinization from the perspective of regional medium and long terms in the regional scale for the sustainable development of irrigated areas. In this study, the spatial–temporal evolution of soil salinization and dominant factors for soil salination, and the relationship between soil salination and the groundwater buried depth were studied through spatial interpolation and statistical analysis with long-time observed data of a closed hydrogeological unit in the Jingtaichuan Electric-Lifting Irrigation Area in Gansu Province, China. The results showed that from 2001 to 2016, the soil salt content, the groundwater mineralization, and the surface irrigated water amount in the study area enhanced slowly, while the groundwater buried depth decreased; the salinization degree in the study area was increasing slowly; there was a positive correlation between the soil salt content and the groundwater mineralization, while a negative correlation existed between the soil salt content and either the surface irrigated water amount or the groundwater buried depth; the groundwater buried depth had the strongest impact on the spatial distribution of the soil salt content; the increase rate of the soil salt content lowered as the groundwater buried depth increased, which met the logarithmic relationship; soil salination was actively developed in regions with a low groundwater buried depth below 2.5 m, and soil salinization became evident in regions with a groundwater buried depth below 5 m; 15.0 m was a critical groundwater buried depth that caused the increase or the decrease in the soil salt content. The research results provide a new way to predict the development trend of soil salinization in the medium and long terms and provide a theoretical basis for the development of salinization prevention and control measures in irrigated areas, which is of great significance to maintaining a harmonious soil and water environment in irrigated areas.

Irrigation areas in arid regions are vital production areas for grain and cash crops worldwide. Grasping the temporal and spatial evolution of planting configurations across several years is crucial for effective regional agricultural and resource management. In view of problems such as insufficient optical images caused by cloudy weather in arid regions and the unclear spatiotemporal evolution patterns of the planting structures in irrigation areas over the years, in this study, we took the Santun River Irrigation Area, a typical arid region in Xinjiang, China, as an example. By leveraging long time-series remote sensing images from Sentinel-1 and Sentinel-2, the spectral, index, texture, and polarization features of the ground objects in the study area were extracted. When analyzing the index characteristics, we considered several widely used global vegetation indices, including the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Soil Adjusted Vegetation Index (SAVI), and Global Environment Monitoring Index (GEMI). Additionally, we integrated the vertical–vertical and vertical–horizontal polarization data obtained from synthetic aperture radar (SAR) satellite systems. Machine learning algorithms, including the random forest algorithm (RF), Classification and Regression Trees (CART), and Support Vector Machines (SVM), were employed for planting structure classification. The optimal classification model selected was subjected to inter-annual transfer to obtain the planting structures over multiple years. The research findings are as follows: (1) The RF classification algorithm outperforms CART and SVM algorithms in terms of classification accuracy, achieving an overall accuracy (OA) of 0.84 and a kappa coefficient of 0.805. (2) The cropland area classified by the RF algorithm exhibited a high degree of consistency with statistical yearbook data (R2 = 0.82–0.91). Significant differences are observed in the estimated planting areas of cotton, maize, tomatoes, and wheat, while differences in other crops are not statistically significant. (3) From 2019 to 2024, cotton remained the dominant crop, although its proportional area fluctuated considerably, while the areas of maize and wheat tended to remain stable, and those of tomato and melon showed relatively minor changes. Overall, the region demonstrates a cotton-dominated, stable cropping structure for other crops. The newly developed framework exhibits exceptional precision in categorization while maintaining impressive adaptability, offering crucial insights for optimizing agricultural operations and sustainable resource allocation in irrigation-dependent arid zones.

Effects of oxygenated brackish water on germination and growth characteristics of wheat

Non-growing season irrigation and farmland subsurface drainage play a crucial role in salt leaching and salinization control in arid irrigation areas. This study aimed to investigate the reduction of autumn irrigation quotas and drainage discharge while maintaining soil moisture retention and reducing soil salinization. Field experiments were conducted with different autumn irrigation quotas (160 mm for SD1, 180 mm for SD2, and 200 mm for SD3) combined with subsurface drainage (1.5 m drain depth and 45 m spacing). A control treatment (referred to as CK) without subsurface drainage received 200 mm of irrigation. The results showed that, after 31 days of autumn irrigation, the groundwater depth in all three subsurface drainage plots stabilized to 1.5 m, with the CK being 0.2–0.3 m shallower compared to the SD plots. The mean soil water content in the 0–150 cm soil layer of the SD1, SD2, SD3, and CK after autumn irrigation was 0.36, 0.39, 0.41, and 0.42 cm3cm−3, respectively. The combination of autumn irrigation and subsurface drainage significantly reduced the soil salt content. The mean desalination rates in the root zone (0–60 cm) soil layer were 57.5%, 53.7%, 51.9%, and 45.1% for the SD3, SD2, CK, and SD1, respectively. The mean desalination rate of 60–150 cm was not significantly different between the SD2 and SD3 (p > 0.05), and both were significantly higher than that of the SD1 and CK (p < 0.05). The drainage discharge was 31, 36, and 40 mm in the SD1, SD2 and SD3, respectively. The amount of salt discharge through the drain pipe increased with increasing irrigation quota, which was 1.22 t/ha, 1.41 t/ha, and 1.50 t/ha for the SD1, SD2, and SD3, respectively. Subsurface drainage is an effective way to prevent salt accumulation in the soil, and an autumn irrigation quota of 180 mm is recommended for leaching of salinity in the Hetao Irrigation District. These findings provide valuable insights into optimizing irrigation practices and managing soil salinization in arid regions.

Characterizing spatial and temporal variability of soil salinity is tremendously important for a variety of agronomic andenvironmental concerns in arid irrigation areas. This paper reviews the characteristics and spatial and temporal variationsof soil salinization in the Ili River Irrigation Area by applying a geostatistical approach. Results showed that： （1） the soilsalinity varied widely, with maximum value of 28.10 g/kg and minimum value of 0.10 g/kg, and was distributed mainly atthe surface soil layer. Anions were mainly SO4^2- and Cl^-, while cations were mainly Na^＋ and Ca^2＋; （2） the abundance ofsalinity of the root zone soil layer for different land use types was in the following order： grassland 〉 cropland 〉 forestland.The abundance of salinity of root zone soil layers for different periods was in the following order： March 〉 June 〉 September;（3） the spherical model was the most suitable variogram model to describe the salinity of the 0-3 cm and 3-20 cmsoil layers in March and June, and the 3-20 cm soil layer in September, while the exponential model was the most suitablevariogram model to describe the salinity of the 0-3 cm soil layer in September. Relatively strong spatial and temporalstructure existed for soil salinity due to lower nugget effects; and （4） the maps of kriged soil salinity showed that higher soilsalinity was distributed in the central parts of the study area and lower soil salinity was distributed in the marginal parts.Soil salinity tended to increase from the marginal parts to the central parts across the study area. Applying the krigingmethod is very helpful in detecting the problematic areas and is a good tool for soil resources management. Managingefforts on the appropriate use of soil and water resources in such areas is very important for sustainable agriculture, andmore attention should be paid to these areas to prevent future problems.

No tillage and previous residual plastic mulching with reduced water and nitrogen supply reduces soil carbon emission and enhances productivity of following wheat in arid irrigation areas

Crop yield is limited by water and nitrogen (N) availability. However, in Hexi Corridor of northwestern China, water scarcity and excessive fertilizer N in wheat (Triticum aestivum L.) production causes serious conflicts between water and N supply and crop demand. A field experiment was conducted from 2016 to 2018 to evaluate whether reducing of irrigation and fertilizer N will reduce grain yield of wheat. There were two irrigation quotas (192 and 240 mm) and three fertilizer N rates (135, 180, and 225 kg N ha−1). The results showed that reducing irrigation to 192 mm and N rate to 180 kg N ha−1 reduced water uptake, water uptake efficiency, and N uptake of spring wheat as compared to local practice (i.e., 240 mm irrigation and 225 kg N ha−1 fertilizer). Whereas, it improved water and N utilization efficiency, and water and N productivity. Consequently, the irrigation and N rate reduced treatment achieved the same quantity of grain yield as local practice. The path analysis showed that interaction effect between irrigation and N fertilization may attributable to the improvement of grain yield with lower irrigation and N rate. The enhanced water and N utilization allows us to conclude that irrigation quota at 192 mm coupled with fertilizer N rate at 180 kg N ha−1 can be used as an efficient practice for wheat production in arid irrigation areas.

Increased groundwater extraction leads to the decrease of the extent of wetlands due to the implementation of a water-saving transformation project in an arid irrigation area. The application of integrated mitigation tools and strategies in China have increasing significance. In this study, an integrated approach (SWAT-MODFLOW) was followed; it is based on a soil and water assessment tool (SWAT) coupled with a modular three-dimensional finite difference groundwater model (MODFLOW). Recharge and evaporation values were estimated by SWAT and were then used to simulate groundwater in a MODFLOW model. Calibration (over the years 2000–2010) and validation (over the years 2010–2016) were performed, based on observed groundwater-level data; results showed that the combined SWAT-MODFLOW provides more accurate simulation and prediction of the dynamic changes of surface water and groundwater in irrigation areas than results from individual MODFLOW models. This method was applied to the Yanqi Basin, which is one of the most appropriate arid agricultural basins for modeling lake wetland and groundwater in China. The correlation coefficients (R2) between the simulated and real groundwater level are 0.96 and 0.91 in SWAT-MODFLOW and MODFLOW, respectively. With the gradual increase in the extraction to 248%, 0.62 × 108 m3 of groundwater discharged into the lake became −2.25 × 108 m3. The lake level drops 1.3 m compared with the current year, when the groundwater exploitation increases by 10 × 108 m3/year. Overall, the results of the coupling model offer scientific evidence for agricultural water management and lake recovery, so as to enhance the water use coordination.