Search Results (1,310)

Climate change and population growth are intensifying water scarcity in arid regions, yet previous analyses focusing on a single driver may not fully capture the compounded effects of climatic and anthropogenic factors. This study integrates water-balance analysis, trend analysis, and correlation-based statistical analysis to examine the combined effects of hydroclimatic anomalies and socioeconomic activities on water resource dynamics in ecologically vulnerable Northwest China. Our results show that despite increasing precipitation, warming-associated increases in evapotranspiration, together with irrigation-based water use accounting for 89.8% of total consumption, have offset the potential runoff gains, suggesting that agricultural water use is a major anthropogenic contributor to regional water stress. Based on these findings and a comparative review of representative arid-region practices in Israel, Australia, and Saudi Arabia, we propose a technology-market-institution tripartite governance framework for Northwest China. This framework is intended to support more proactive adaptation in regional water management and to provide a context-specific reference for advancing SDG 6 and SDG 13 in dryland regions. Full article

As just transition debates extend into agricultural land use, this study examines landscape transition in Huwei Township, Taiwan, through a procedural justice lens. To address severe land subsidence, the state has promoted a shift from paddy rice cultivation to dryland farming, but the transition remains politically contested. Based on a systematic review of 55 empirical studies (2020–2026) and 12 semi-structured interviews, the study identifies a key mismatch in problem attribution: official accounts emphasize irrigation, whereas farmers point to urban development pressures and infrastructure burdens. The findings also show that cultivation-decoupled subsidies enable landowners to capture compensation while shifting operational risks onto tenant farmers and other vulnerable groups. The study argues that a socially sustainable transition depends on incorporating local knowledge and redesigning subsidy eligibility and risk-sharing rules to strengthen procedural justice, representativeness, and accountability. Full article

Surface water extent (SWE) is a key indicator of the regional water balance in dryland environments. However, the hydrological processes regulating SWE responses remain poorly constrained. Focusing on the Mu Us Sandy Land (MUSL), this study integrates multi-source remote sensing and hydrological datasets to investigate the long-term evolution of SWE and, critically, to distinguish the hydrological linkages between SWE dynamics and water storage variability in endorheic and exorheic regions during 1987–2024. An improved water extraction method was implemented on the Google Earth Engine platform, and SWE dynamics were interpreted within a water-balance framework supported by attribution analysis using machine learning. The results show that total SWE exhibited a significant increasing trend (7.95 km² yr⁻¹, p < 0.05) during 1987–2024, primarily driven by permanent SWE, while fundamentally different hydrological regimes governed SWE evolution. In the endorheic basin, SWE exhibited strong co-variation with subsurface water storage, with soil moisture and groundwater storage changes occurring concurrently with SWE changes. In contrast, no synchronous increase in SWE with groundwater storage was observed in the exorheic region. Instead, SWE expansion was mainly associated with accelerated groundwater storage depletion and reservoir construction. These contrasting patterns indicated that SWE dynamics in the endorheic basin were primarily controlled by subsurface water storage, whereas in exorheic regions they were largely driven by human-induced water redistribution rather than increases in total water storage. These findings highlight the importance of integrated surface–subsurface water management for sustaining long-term water security under climate change and increasing human water regulation. Full article

Analyzing the dominant drivers of the Food-Water-Land-Ecosystem (FWLE) nexus in the future is important for improving sustainable development in dryland ecosystems. However, the future trajectories of food–water–land–ecosystem interactions in typical drought-prone regions remain poorly understood. To address this gap, this study coupled the Gray Multi-Objective Programming with Patch-generating Land Use Simulation (GMOP-PLUS) model and applied spatial analysis methods (including longitudinal and zonal statistical analysis, trade-off synergy analysis, and redundancy analysis) to examine the spatiotemporal differentiation patterns of the FWLE nexus in Xinjiang under different development scenarios. Over the past two decades, water yield in Xinjiang’s agricultural landscapes has declined by 57.4%, primarily due to land-use and land-cover changes. Under the 2030 sustainable development scenario, a custom optimization developed via the GMOP model that balances economic and ecological objectives, crop production and habitat quality are projected to increase by 47.9% and 55.1%, respectively. Moreover, redundancy analysis results indicate that the driving contribution of precipitation on the FWLE nexus is expected to reach 76.9% by 2030. These findings provide a clear delineation of priority spatial units for improvement within Xinjiang agro-ecosystem and offer a strategic pathway for balancing ecological conservation and economic development. Full article

Understanding the dynamics of soil organic carbon (SOC) in farmland is crucial for assessing soil health, quantifying ecosystem potential for SOC enrichment, and guiding sustainable agricultural management. Existing research on SOC sequestration and mineralization has focused mainly on the topsoil layer (0–20 cm), whereas systematic evidence on how deep SOC (>20 cm) responds to agricultural management, and on strategies to enhance deep carbon sequestration, remains limited. This study uses long-term fixed-site monitoring data from 120 farmland plots across 21 typical farmland ecosystem stations and farmland–complex ecosystem stations within the Chinese Ecosystem Research Network (CERN) over 17 years (2004–2020). Using spatial analysis, we characterize the spatiotemporal dynamics of SOC below 20 cm along soil profiles across seven major geographical zones in China. We then estimate the heterogeneous effects of fertilization and straw-management practices (S, straw returning; SCF, straw returning with chemical fertilizer; OF, organic fertilizer; OCF, organic fertilizer with chemical fertilizer), tillage modes, and farmland types on SOC in the 20–40 cm, 40–60 cm, and 60–100 cm layers using a panel fixed-effects model. The results indicate pronounced vertical heterogeneity in SOC below 20 cm and a clear spatial gradient. The 60–100 cm layer shows a significant increase in SOC content during the study period, with a cumulative increase of 4.07%. Relative to single organic inputs, the co-application of organic and inorganic materials improves deep soil SOC enhancement efficiency. Compared with reduced tillage and no-tillage, conventional tillage is less conducive to SOC enhancement in layers shallower than 60 cm, yet it has a significant positive impact on SOC in the 60–100 cm layer. Compared with dryland and irrigated land, paddy fields are less favorable for SOC enhancement below 20 cm. Consequently, regarding agricultural practice, a composite tillage regime combining “surface conservation tillage with periodic deep tillage” should be promoted to foster deep SOC enhancement. Full article

Background: Tartary buckwheat (Fagopyrum tataricum) serves as an excellent model for studying plant water adaptation mechanisms due to its exceptional drought tolerance. While aquaporins (AQPs) mediate the transmembrane transport of water and solutes in plants, their fine-tuned regulatory networks underlying stress resilience in Tartary buckwheat remain largely elusive. Methods: Here, we combined bioinformatics and transcriptomics to systematically identify 30 highly conserved FtAQP genes at the genome-wide level. Results: Cross-validated by qRT-PCR, our analysis revealed their distinct expression patterns across different organs. Based on our transcriptomic data, we hypothesize that FtAQP family members potentially participate in a coordinated whole-plant water management network through differential spatiotemporal expression. Specifically, the robust transcription of FtAQP8, FtAQP12, and FtAQP28 in roots is associated with the initial water uptake process. As water undergoes long-distance transport, the synergistic upregulation of FtAQP13, FtAQP17, FtAQP20, and FtAQP29 in the stem suggests a potential role in facilitating critical lateral water flow. Furthermore, during reproductive development, FtAQP27 exhibits extreme tissue specificity in floral organs, implying its possible involvement in maintaining local osmotic homeostasis. Furthermore, the promoter regions of FtAQPs are highly enriched with cis-acting elements responsive to light, abscisic acid (ABA), and cold stress, suggesting they are intimately regulated by a coupling of endogenous phytohormones and environmental cues. Conclusions: Ultimately, this study provides valuable insights into the potential molecular basis of multidimensional water regulation in Tartary buckwheat, and identifies candidate genetic targets for improving water use efficiency in dryland agriculture through the precise manipulation of aquaporins. Collectively, while these observational findings provide valuable predictive models, future in vivo experimental validations are required to confirm their exact biological functions. Full article

Accurately identifying medium- and low-yield farmlands (MLYF) and diagnosing their constraints are essential for targeted improvement of productivity and national food security. However, traditional evaluation is usually limited by coarse spatial resolution and high labor costs, and a methodological gap remains between large-scale MLYF classification and system constraints diagnosis. To address the current methodological gaps, this study developed a comprehensive framework to determine the spatial distribution of MLYF in northern China and clarify their key constraints. The framework combined the Spatio-Temporal Random Forest (STRF) algorithm with vegetation indices (VIs), climate, and soil data to delineate MLYF and uses interpretable machine learning to diagnose major constraints. The model showed high explanatory power and ensured the reliability of attribution results. The results showed that MLYF exhibited obvious spatial heterogeneity, accounting for 48.66% of the total cultivated land in the study area. These MLYF are primarily concentrated in the northwestern Loess Plateau (LP), the central Along the Great Wall (ATGW) region, and the peripheries of the Huang-Huai-Hai (HHH) Plain. In addition to spatial classification, our analysis revealed significant differences in constraint mechanisms: soil structural, nutrient, and salinization constraints predominantly restrict productivity in the HHH Plain, whereas water stress and soil erosion are the primary drivers of yield gaps in the LP and ATGW regions. These findings provide new data and insights for understanding the spatial heterogeneity of farmland quality in typical dryland agricultural regions in northern China, and offer a scientific basis for targeted land improvement and regional agricultural sustainability. Full article

Waterlogging and secondary salinization are major drivers of land degradation in irrigated dryland regions, undermining soil productivity and long-term sustainability. Pakistan’s Indus Basin Irrigation System (IBIS), one of the world’s largest irrigation networks, supports national food security over approximately 16.7 million hectares (Mha). However, large-scale canal irrigation, combined with flat topography, monsoonal recharge, and inefficient water management, has disrupted groundwater balance, leading to persistent shallow water tables and widespread land degradation. Currently, nearly one-third of the irrigated area is affected by groundwater depths of less than 3 m. This review synthesizes six decades of waterlogging development and management in the IBIS, analyzing the evolution of drainage infrastructure, salinity control strategies, groundwater exploitation, and institutional reforms within a land sustainability perspective. Although large-scale interventions—including 61 Salinity Control and Reclamation Projects (SCARPs) and major outfall systems—initially reclaimed substantial areas, long-term performance has been constrained by governance fragmentation, inadequate operation and maintenance, and environmentally problematic effluent disposal. The Indus Basin experience underscores the need to move beyond infrastructure-centered solutions towards more integrated land–water governance and adaptive management to enhance land system resilience in irrigated regions facing growing climatic and resource pressures. Full article

Durum wheat production in the Mediterranean basin faces increasing climate variability and thus the need for computationally efficient tools to support agronomic decision-making at regional scale. Process-based crop models such as AquaCrop provide mechanistically sound yield estimates but require substantial computation time when screening large numbers of soil–climate–management combinations. The present study addresses this constraint by developing and evaluating five machine learning (ML) surrogate models—Linear Regression (LR), Multilayer Perceptron (MLP), Support Vector Machine for regression (SMOreg), RandomTree, and Reduced Error Pruning Tree (REPTree)—trained to emulate the AquaCrop-GIS response surface for durum wheat (Triticum durum Desf.) grain yield across the Capitanata plain (Southern Italy). A dataset of 342 instances was constructed by crossing 25 soil profiles, three sowing dates, and two irrigation regimes across 15 climatic grid cells (2014–2023), evaluated by stratified 10-fold cross-validation. The MLP achieved the highest accuracy (R = 0.983; R² = 0.966; RMSE = 0.083 t ha⁻¹); the four interpretable models were clustered at R = 0.891–0.907 (RMSE = 0.192–0.203 t ha⁻¹). All models converged on consistent agronomic signals: standard sowing (1 November) yielded +0.53 t ha⁻¹ over late sowing (15 November), supplemental irrigation added +0.17 t ha⁻¹, and fine-textured soils produced superior yields. The convergence of directional signals across linear, kernel-based, and tree-based architectures confirms that ML surrogates trained on process-model outputs can efficiently emulate AquaCrop response surfaces and deliver actionable management guidance for durum wheat producers and agricultural planners in Mediterranean dryland farming systems. Full article

Extensive animal production systems based on dryland pastures in Mediterranean regions have low profit margins. Improvements in soil fertility or grazing management and stocking rates are recognized strategies for reversing this situation and to ensure long-term agricultural sustainability. This article aims to assess whether this strategy of possible intensification of sheep production has a significant impact on soil compaction, which is a manifestation of soil functionality degradation resulting from trampling. An experimental design with four treatments was implemented (with and without dolomitic limestone application; continuous grazing with low stocking rates, CG-LSR, and deferred grazing with high stocking rates, DG-HSR). The study involved cone index (CI, in kPa) measurements (48 sampling areas, 12 in each treatment) on eight dates during two annual pasture/grazing cycles (2023/2024 and 2024/2025). Other soil parameters, the presence of trees and grazing preferences were also monitored and correlated with CI. The main results showed: (i) significantly higher soil compaction under CG-LSR than under DG-HSR; (ii) a negative and significant effect of soil moisture content (SMC) on CI (r = −0.381; p < 0.05); (iii) a significant CI increase in preferential grazing areas, but only in the topsoil layer (0–10 cm) and with a very weak correlation coefficient (r = 0.172; p < 0.05); and (iv) no significant differences in CI under and outside tree canopy areas (UTC and OTC, respectively) for the depth range of 0–30 cm. These results are good indicators of the desired and sustainable intensification of extensive livestock grazing systems. Full article

Accurately characterizing the spatiotemporal variations in precipitation in China’s drylands is important for solving water scarcity in the region, guaranteeing security in the ecological environment, and conducting precise drought disaster management. To reduce the uncertainty in the existing precipitation products, we developed a two-stage machine-learning framework combining extreme gradient boosting (XGBoost) and random forest (RF) residual corrections. Based on the ground-based observation data from 1030 meteorological stations and numerous high-precision precipitation products (GPM IMERG Final V6, MSWEP V2, CMFD 2.0, TerraClimate), a monthly fused precipitation dataset (XGB-RF) for China’s drylands was produced during the 2001–2020 period at the 0.1° resolution. The validation results showed that the XGB-RF had a monthly Kling–Gupta Efficiency (KGE) of 0.941, and it improved 20.6–62.2% relatively with that of input individual products. For the dataset as a whole, we found very consistent, reliable performance in all seasons and topography, in particular in winter time and data-scarce western areas where individual products have large biases. More importantly, the XGB-RF was employed for drought monitoring based on the 1-month Standardized Precipitation Index that calculated the median KGE of 0.888, which made good drought trend tracking and drought features possible. Notably, the KGE for the mean drought intensity was 0.757, which was higher than that of independent original products. This study provides a high-resolution precipitation forcing dataset and demonstrates the effectiveness of two-stage machine learning strategies in enhancing hydroclimatic monitoring and drought risk assessment in arid and semi-arid regions. Full article

Sowing space and depth critically influence wheat canopy architecture, yet their layer-specific effects remain poorly understood. This two-year field study evaluated the effects of three sowing spaces (1.5, 3.0, 4.5 cm) and three sowing depths (2, 3, 6 cm) on canopy projection area, leaf inclination angle, leaf area distribution, and leaf area index (LAI) of dryland wheat (Triticum aestivum ‘Ningmai 13’) in Luhe, Nanjing, China, using image-based phenotyping with manual validation. Narrow spacing (1.5 cm) with intermediate depth (3 cm) produced the largest canopy projection area (0.239–0.245 m²) and an increase in leaf erectness in the middle canopy layer (+23% above average). The highest LAI values (4.23–4.28 m² m⁻²) were achieved with narrow spacing (A₁B₁, A₁B₂), demonstrating that dense canopies can be established under dryland conditions. Grain yield (g/plant) was measured as a supporting agronomic indicator; the highest yield per plant (14.36 g/plant) was observed in A₃B₁. Image-based measurements showed excellent agreement with manual methods (R² > 0.97 for all traits), validating the phenotyping pipeline. These findings contribute to a deeper understanding of how sowing parameters shape wheat canopies in dryland systems. Full article

Timely and accurate mapping of tree species is essential for forest resource inventory, biodiversity conservation, and sustainable ecosystem management, particularly in dryland environments where structural heterogeneity, spectral similarity, and data scarcity complicate classification. This study develops a machine learning-based framework implemented in Google Earth Engine to map dominant tree species in the Elnour Natural Forest Reserve (ENFR), Blue Nile, Sudan, using multi-temporal and multi-sensor remote sensing data. Multi-temporal Landsat 5 TM, Landsat 8 OLI, and Sentinel-2 MSI imagery were integrated with vegetation index (NDVI), topographic variables derived from a digital elevation model (DEM), and field observations. The performance of Random Forest (RF), Support Vector Machine (SVM), Classification and Regression Trees (CART), and an unweighted ensemble approach was evaluated across four reference years (2008, 2013, 2018, and 2021). Results show that RF and SVM consistently achieved high classification performance, with overall accuracy (OA) ranging from 85.0% to 92.0% and Kappa coefficients (κ) from 0.81 to 0.89, while maintaining stable and ecologically realistic species-area estimates. CART showed greater sensitivity to class imbalance and overestimated minor species (OA = 72.0–80.0%, κ = 0.65–0.74), whereas the ensemble approach amplified misclassification of rare classes (OA = 78.0–84.0%, κ = 0.70–0.78). The integration of Sentinel-2 data improved species discrimination due to enhanced spatial and spectral resolution, particularly in the red-edge region; however, algorithm selection remained the dominant factor controlling performance. Feature importance analysis identified near-infrared (NIR), shortwave infrared (SWIR), and NDVI variables as the most influential predictors. Multi-temporal analysis revealed declining class separability, reflected by decreasing MCC values, and a shift in species composition, including a decline in Acacia seyal (Delile) and an increase in Sterculia setigera Delile. These patterns indicate increasing ecological complexity driven primarily by anthropogenic pressures, with climatic variability acting as an additional stressor. Full article

Tree and shrub planting is a widely used strategy to restore degraded semi-arid grasslands. Although nutrient resorption is a key adaptation to nutrient-limited environments, its dynamics at decadal scales remain poorly understood. In this study, we measured species-averaged nitrogen resorption efficiency (NRE) at both community and functional group levels, together with soil nutrients, across 20- and 40-year shrub-planted sites and a 40-year tree-planted site in Inner Mongolia, China. At the community level, green and senesced leaf nitrogen (N) concentrations, NRE, and aboveground biomass did not differ significantly among sites. However, clear differences emerged at the functional group level: Poaceae exhibited higher NRE than forbs and lower senesced leaf N than both forbs and Fabaceae. As restoration progressed, Poaceae replaced forbs as the dominant group, coinciding with increased soil nutrient availability. Notably, NRE in Poaceae declined with increasing soil nutrients, suggesting a shift toward greater reliance on direct soil nutrient uptake. This shift, combined with the production of low-nitrogen litter by dominant Poaceae species, may ultimately slow soil nutrient accumulation. Our findings highlight the importance of functional group dynamics in regulating long-term nutrient resorption and cycling and suggest that managing Poaceae dominance could enhance long-term soil nutrient enrichment and biodiversity in restored semi-arid grasslands. Full article

Enhancing nitrogen use efficiency (NUE) in cereal crops is a major challenge for dryland systems that rely heavily on synthetic nitrogen (N) inputs. Microbial biostimulants have recently emerged as promising alternatives for cost-effective N inputs in wheat through foliar colonization and endophytic biological N fixation. Methylobacterium symbioticum strain SB23 (also known as BlueN or Utrisha N) is a pink-pigmented, obligately aerobic, Gram-negative, facultative methylotrophic bacterium demonstrated to potentially reduce N chemical fertilization and improve yields in various crops. A field trial consisting of large replicated 2.3 ha plots of Australian Prime Hard (APH) wheat cv. Rockstar was established in south central New South Wales, Australia, to evaluate the foliar application of M. symbioticum strain SB23 under both standard and reduced N regimes for winter wheat maturing in late spring. Application of the SB23 biostimulant significantly increased wheat leaf chlorophyll concentration at 30 and 60 days after application (DAA) and promoted biomass accumulation at 60, 90 and 120 DAA in contrast to the untreated control, with the strongest positive response under reduced N input. Specifically, the 75% N + biostimulant treatment improved biomass by up to 23% and grain yield by 14% relative to the reduced-N control, demonstrating potential supplemental fertility without yield loss. Correlation analyses revealed that mid-season chlorophyll was strongly associated with biomass and carbon assimilation (r = 0.87 and 0.84, respectively), while biomass at 60 DAA was highly correlated with grain spike weight (r = 0.81), suggesting a strong association of improved crop vigor and yield with inoculation. At harvest, SB23 enhanced biomass nitrogen accumulation and nitrogen use efficiency, with the 75%N + biostimulant treatment achieving the highest plant N uptake (25% above the reduced-N control) and the greatest partial factor productivity of nitrogen (51.8 kg grain kg⁻¹ N applied), while both 100%N treatments showed the lowest efficiency. Collectively, these findings suggest that Methylobacterium symbioticum SB23 improves NUE through enhanced crop performance thereby providing a supplementary N source and delivering a cost–benefit advantage of approximately A$170 ha⁻¹ under reduced N application. Full article