The aim of this study was to investigate the impact of water and nitrogen regulation on the characteristics of water and fertilizer demands and the yield, quality, and efficiencies of the water and nitrogen utilization of peanuts cultivated under mulched drip irrigation in a desert–oasis region. The experiment, conducted in Urumqi, Xinjiang, centered on elucidating the response mechanisms governing peanut growth, yield, quality, water consumption patterns, and fertilizer characteristics during the reproductive period under the influence of water and nitrogen regulation. In the field experiments, three irrigation levels were implemented, denoted as W₁ (irrigation water quota of 22.5 mm), W₂ (irrigation water quota of 30 mm), and W₃ (irrigation water quota of 37.5 mm). Additionally, two nitrogen application levels, labeled N₁ (nitrogen application rate of 77.5 kg·ha⁻¹) and N₂ (a nitrogen application rate of 110 kg·ha⁻¹), were applied, resulting in seven treatments. A control treatment (CK), which involved no nitrogen application, was also included in the experimental design. The results indicate a direct correlation between the increment in the irrigation quota and increases in farmland water-related parameters, including water consumption, daily water consumption intensity, and water consumption percentage. The nitrogen harvest index (NHI) demonstrated a higher value in the absence of nitrogen application compared to the treatment with elevated nitrogen levels. The application of nitrogen resulted in an elevation in both nitrogen accumulation and nitrogen absorption efficiency within pods and plants. When subjected to identical nitrogen application conditions, irrigation proved to be advantageous in enhancing water-use efficiency (WUE), nitrogen partial factor productivity (NPFP), and the yield of peanut pods. The contribution rate of water to pod yield and WUE exceeded that of nitrogen, while the contribution rate of nitrogen to nitrogen-use efficiency (NUE) was higher. The total water consumption for achieving a high yield and enhanced water- and nitrogen-use efficiencies in peanuts cultivated under drip irrigation with film mulching was approximately 402.57 mm. Taking into account yield, quality, and water- and nitrogen-used efficiencies, the use of an irrigation quota of 37.5 mm, an irrigation cycle of 10–15 days, and a nitrogen application rate of 110 kg·ha⁻¹ can be regarded as an appropriate water and nitrogen management approach for peanut cultivation under mulched drip irrigation in Xinjiang.

The development and utilization of unconventional water resources has become a strategy to alleviate the agricultural water crisis in many countries and regions. To understand the research progress, hot spots, and future trends in the field of unconventional water agricultural irrigation (UWAI), this paper systematically analyzes 6738 publications based on the core database of Web of Science 1990–2023 using the scientific bibliometric analysis software CiteSpace, VOSviewer, and Scimago Graphica. The results showed that the research on UWAI is always rapidly developing. Soil science, crop science, and bioengineering are the main disciplines involved. Most research on WUAI has occurred in China and the United States. Countries with higher levels of development tend to have more influence. Collaboration among authors is fragmented, and collaboration between authors and states needs to be strengthened. Through keyword analysis, the research hotspots are summarized as follows: (1) The effects of traditional and emerging pollutants brought by unconventional water irrigation on soil physicochemical properties, crop growth, and groundwater quality; (2) the health threats caused by pollutants entering the food chain and groundwater; (3) unconventional water utilization technologies, including rainwater harvesting agriculture, precision agriculture, and urban agriculture. Future research hotspots will focus on the mechanisms of pollutant solute transport and transformation in the water–soil–crop system under non-conventional water irrigation conditions and crop physiological responses. We suggest that the research on traditional and emerging pollutants in unconventional water should be strengthened in the future, and the risk control system of unconventional water irrigation should be improved. International cooperation should be strengthened, especially with poor countries in arid regions, to promote the formation of unified international standards and guidelines for non-conventional water irrigation in agriculture.

Reasonable development and utilization of brackish-water resources can alleviate the pressure of freshwater scarcity in dryland areas and safeguard crop growth, but there are significant differences in brackish-water ions in different regions. Thus, exploring the mechanism of brackish-water irrigation considering brackish-water ionic differences on the growth and development of saline and alkaline dryland crops has an important production guidance value. In this study, the ionic differences in irrigated brackish water were characterized by sodium adsorption ratio using under-membrane drip-irrigated cotton as the research object, and three levels of mineralized irrigation water were designed, which were 3 g·L⁻¹ (T3), 5 g·L⁻¹ (T5), and 7 g·L⁻¹ (T7), respectively. Three different levels of sodium adsorption ratio (SAR) were set under each level of mineralization, which were 10 (mmol·L⁻¹)¹/² (S10), 15 (mmol·L⁻¹)¹/² (S15), and 20 (mmol·L⁻¹)¹/² (S20). The local freshwater irrigation was used as a control treatment. The results showed that brackish-water irrigation increased soil salt accumulation and soil water content, induced oxidative damage and disruption of ionic homeostasis in the cells, and decreased leaf photosynthetic rate. Brackish-water irrigation also significantly reduced dry matter mass by 11.04–50.12%. Reduced irrigation water SAR (S10 and S15) enhanced antioxidant enzyme activities such as superoxide dismutase (SOD), peroxidase (POD), and catalase (CAT), and reduced malondialdehyde (MDA) content by 14.29% and 9.09%, respectively, compared with high irrigation water SAR (S20). Leaf K⁺ uptake was increased by 5.29% and 1.57% in S10 and S15, respectively, compared with S20, while Na⁺ uptake was significantly suppressed. The K⁺/Na⁺ ratio increased by 45.07%, which resulted in improved leaf photosynthetic efficiency by 25.25% and 11.91%, and significantly enhanced dry matter accumulation by 24.81% and 11.20%, respectively. In addition, compared with T3S20, the T5S10 treatment reduced the irrigation water SAR. It contributed to a significant increase in SOD, POD, and CAT activities by 30.42%, 60.70%, and 99.20%, respectively, and in plant K⁺ content and K⁺/Na⁺ by 2.48% and 38.85%, respectively, although the irrigation water mineralization increased by 66.67%. Reducing SAR could enhance photosynthesis and dry matter accumulation through the dual regulation of “antioxidant damage + ion homeostasis” in salt-stressed cotton, laying a foundation for the realization of stable and high yields of cotton under brackish-water irrigation, and providing a new perspective for the management of brackish-water resources and the sustainable development of agriculture in Xinjiang and other arid regions.

Plastic mulching is widely used in arid and semi-arid cotton systems to improve soil hydrothermal conditions and water–nutrient use efficiency. However, residual mulch and its potential contribution to microplastic inputs pose growing environmental and soil-quality risks, highlighting the need for high-resolution and automated approaches to support plastic waste management, targeted retrieval, and precision field operations. Taking a mulched cotton field in Alar, Xinjiang, as the study area, this study proposes a novel plastic mulch extraction method that integrates Unmanned Aerial Vehicle (UAV)-based hyperspectral imagery with deep learning semantic segmentation. The Jeffries–Matusita (JM) distance was employed to select highly separable optimal bands and their combinations for discriminating plastic mulch, bare soil, and cotton canopy, which were then used to drive UNet, DeepLabV3+, and PSPNet models for plastic mulch mapping. The results indicate that the PSPNet model driven by the 402 nm single-band reflectance, Normalized Difference Index (NDI) (861 nm, 410 nm), and NDI (757 nm, 676 nm) achieved the best performance for plastic mulch identification (Intersection over Union (IoU) = 80.28%), significantly outperforming the RGB-based model (IoU = 76.51%). This study enables accurate, spatially explicit assessments of residual mulch, providing actionable evidence for plastic waste monitoring and management, while supporting sustainable agriculture and precision farmland management.