Search Results (263)

Assessing the impacts of large-scale photovoltaic plants on soil properties is critical for sustainable land management in arid regions. This study examined a 50 MWp utility-scale PV facility in Siziwangqi, Inner Mongolia, northern China, a cold semi-arid desert steppe where renewable-energy development overlaps with fragile wind-eroded ecosystems. A spatially resolved sampling design contrasted soils at key micro-positions relative to the panels, namely Front, Under, and Behind, with adjacent natural Controls. By integrating laser diffraction analysis, multifractal modeling, and the EPIC erodibility equation, we evaluated soil particle-size probability redistribution and EPIC-estimated intrinsic erodibility. Compared with the silt-dominated surface soil of the natural steppe, soils within the photovoltaic plant exhibited fine-particle retention and lower information and correlation dimensions (D1 and D2), indicating stronger local clustering and a less even particle-size probability distribution. The EPIC-estimated erodibility factor decreased from 0.452 in the Control to approximately 0.361 in the PV micro-locations. These associations should be interpreted as texture- and SOC-based model estimates rather than direct measurements of wind erosion. Overall, multifractal parameters provide complementary proxy descriptors for detecting PV-induced particle sorting and potential changes in intrinsic soil erodibility, underscoring the need for field validation and adaptive management in dryland PV landscapes. These findings provide physical soil evidence for evaluating the environmental sustainability of dryland PV development and for supporting adaptive land management in solar farms. Full article

Balancing food production, ecosystem service value, and agricultural irrigation water consumption is a major challenge for sustainable agricultural development in China. However, quantitative evidence at the national scale remains limited on whether crop planting structure optimization derived from models can effectively achieve coordination among these three objectives. Existing studies mainly focus on individual crops or localized regions and rarely integrate the spatiotemporal evolution, influencing factors, and multi-objective optimization of major staple crops within a unified framework. This study developed a progressive framework integrating spatiotemporal evolution analysis, influencing factor identification, and planting structure optimization for wheat, rice, and maize. Spatial autocorrelation analysis, center-of-gravity shift analysis, and pixel-based image differencing were applied to reveal crop evolution patterns across China from 2000 to 2025. A five-dimensional indicator system comprising 17 quantitative indicators was developed through multiple experiments using four large language models, and the Random Forest algorithm was employed to identify key influencing factors and their relative importance. Based on these factors, optimization constraints were constructed, and a multi-objective fuzzy linear programming model combined with the NSGA-II algorithm was used to determine optimal crop area allocation across 28 provincial-level regions. The three staple crops exhibited a significant pattern of northward shift, eastward expansion, and southern contraction. Precipitation and market accessibility were common core influencing factors, ranking among the top five factors in all nine Random Forest models. Crop-specific factors, including soil available phosphorus for rice, accumulated active temperature for wheat, and soil pH for maize, explained differences in spatial responses among crops and provided a scientific basis for optimization modeling and coordinated improvement of food production, ecosystem service value, and irrigation water consumption. The optimized scheme increased total grain output by 3.6%, improved ecosystem service value by 11.0%, and reduced irrigation water consumption by 45.7% compared with the actual planting structure, all 28 provinces achieved improvement or stability in the three indicators simultaneously. Based on optimized crop allocation patterns, national planting structures were summarized into regional models, including a rice–maize dual-core system in Northeast China, wheat–maize rotation in the Huang-Huai-Hai Plain, rice-dominated systems in the middle and lower Yangtze River Basin and South China, water-efficient dryland farming in Northwest China, and a diversified balanced system in Southwest China. These findings provide a quantitative reference for optimizing China’s staple crop planting structure. Full article

Accurate mapping of terraces is important for farmland protection, food security, and the management of terrace abandonment. However, extracting terraces from high-resolution remote sensing imagery remains challenging because of complex backgrounds, fragmented ridge structures, weak boundary contrast, and high similarity between terraces and surrounding land-cover types. This study proposes AMF-Deep, an Attention Multi-scale Fusion DeepLabv3+ model, for binary terrace extraction across representative terrace regions and morphologies. AMF-Deep was evaluated using 0.61 m high-resolution remote-sensing imagery from five representative terrace regions in China, encompassing diverse terrace morphologies and background conditions. In the proposed model, MobileNetV2 is adopted as the backbone to reduce computational cost, a modified multi-scale ASPP module is used to capture terrace morphology at different spatial scales, CBAM enhances low-level boundary and texture features, ECA refines high-level channel responses before ASPP, and NAM recalibrates semantic features after ASPP. A residual decoder is further introduced to support low-level feature transfer and boundary recovery. Experimental results show that AMF-Deep achieved the highest regional performance in Huangling, with mIoU, mPA, Accuracy, Recall, and Precision of 89.47%, 95.17%, 96.70%, 94.99%, and 93.68%, respectively. Stepwise ablation experiments further indicated that the modified ASPP module, the differentiated attention arrangement, and the residual decoder contributed to the final performance. Qualitative seasonal visualization suggests that winter images and dryland terraces in Shexian remain more challenging because of weak texture contrast and high background similarity. These results indicate that AMF-Deep has potential for terrace extraction across representative regions, although further quantitative seasonal evaluation and cross-region validation are still needed. Full article

Water scarcity strongly constrains the revegetation of disturbed mine substrates in cold-arid regions. This study compared soil-moisture dynamics between a microtopographic reclamation area and an adjacent flat reclamation area in an abandoned stone quarry within the Kalamaili Ungulate Nature Reserve, northwestern China. The integrated restoration treatment, established in 2018, combined linear trenches 30–40 cm deep and spaced 60 cm apart, native seed sowing, transferred seed-bank topsoil, and water-retaining material. From January to December 2024, sensor-derived apparent volumetric water content was monitored at five depths—0–10, 10–20, 20–30, 30–40, and 40–50 cm—at 10 min intervals using sensors operated with the manufacturer’s standard calibration. Following quality control and temporal synchronization, 41,475 valid timestamps were retained for each monitored profile. The study provides a continuous full-year, multi-depth record from an unirrigated, cold-arid reconstructed quarry substrate, a setting that remains underrepresented in previous micro-catchment and dryland-restoration research. However, this study is based on a single hydrological year and does not directly measure plant physiological responses or long-term restoration outcomes. Therefore, elevated soil moisture should be interpreted as a preliminary indicator of restoration potential rather than a definitive measure of ecological recovery success. The monitored microtopographic profile maintained higher mean apparent water content than the flat profile during most of the year. The largest annual relative profile difference occurred at 10–20 cm depth, reaching 18.0%, followed by 11.7% at 0–10 cm, whereas differences decreased below 20 cm and reached 1.9% at 40–50 cm. During selected rainfall and probable snowmelt periods, the microtopographic profile exhibited larger wetting responses and slower post-event recession. These patterns indicate contrasting upper-profile wetting and drying dynamics between the two monitored reclamation configurations. However, runoff, infiltration, evaporation, snow accumulation, soil-water potential, and vegetation responses were not measured directly. Moreover, monitoring began several years after vegetation establishment, and trenching, seed addition, seed-bank transfer, and water-retaining material were not evaluated independently. The findings should therefore be interpreted as a site-specific post-establishment comparison rather than evidence of the isolated effect of microtopography. Replicated, multi-year studies are required to determine the mechanisms, effectiveness, and broader transferability of this integrated reclamation configuration. Full article

Understanding the spatiotemporal dynamics of dominant desert-steppe species under climate change is critical for sustainable management of arid ecosystems. Sophora alopecuroides L., a perennial drought-tolerant leguminous herb with substantial medicinal and forage value, is an important component of these ecosystems. Based on 137 occurrence records and 10 environmental variables, we applied the MaxEnt model and ArcGIS to simulate suitable habitats and range shifts of S. alopecuroides across multiple periods, including the Last Interglacial (LIG), Last Glacial Maximum (LGM), Mid-Holocene (MH), present, and the 2050s and 2070s under four representative concentration pathways (RCP 2.6, 4.5, 6.0, and 8.5). The model showed high predictive performance, with an area under the receiver operating characteristic curve (AUC) greater than 0.9. Temperature annual range (Bio7), elevation, precipitation of the warmest quarter (Bio18), mean temperature of the driest quarter (Bio9), and annual mean temperature (Bio1) were identified as key environmental drivers, indicating that the distribution of this species is mainly shaped by temperature regimes, seasonal water availability, and topography. Under current conditions, the total suitable habitat accounts for 28.9% of China’s territory, closely matching the known distribution. Since the LIG, the total suitable area has fluctuated slightly, being larger during the LIG and LGM, smaller during the MH, and close to the present level thereafter. The distribution centroid remained within the Jinta Basin of the Hexi Corridor, Gansu Province, with only limited displacement from the LIG to the present, suggesting that this area, together with the Tianshan–Junggar Basin margins and the Alxa Plateau–Helan Mountain region, constitute key topographically and climatically inferred potential refugial areas for S. alopecuroides. Future projections indicate that suitable habitat may expand slightly under low-emission scenarios but contract under medium- to high-emission scenarios, with centroid shifts varying among pathways. These findings highlight the potential importance of such bioclimatically inferred putative refugia in maintaining the distribution of dominant dryland herbs and provide a spatially explicit basis for monitoring, germplasm conservation, and adaptive management of S. alopecuroides under ongoing climate change. Full article

In dryland ecosystems, the restoration of Salix psammophila shrubs plays a vital role in wind erosion control and sand stabilization. However, the temporal dynamics of soil particle-size distribution and erodibility during S. psammophila restoration remain poorly understood. To address this gap, we established a chronosequence of S. psammophila plantations in the Hobq Desert—a temperate desert in northern China—that had been restored for 6, 12, 15, 25, and 35 years, with adjacent shifting sand dunes serving as the control (CK). At each of the six sites, ten replicate plots were established, and soil samples were collected from the 0–20 cm layer, yielding a total of 60 samples. Multifractal parameters and the soil erodibility K factor were calculated to quantify the effects of stand age on particle-size distribution and erodibility. Principal component analysis (PCA) and a Random Forest model were then applied to factors associated for the observed changes. Compared with CK, soil nutrient and fine particle contents increased significantly with increasing shrub age, whereas pH and sand content declined continuously. Specifically, under S. psammophila plantations, organic carbon (OC), total nitrogen (TN), total phosphorus (TP), available phosphorus (AP), and alkali-hydrolysable nitrogen (AHN) contents increased continuously with stand age, while the soil texture became progressively finer. During long-term S. psammophila restoration, the ranges of the multifractal parameters D₀, D₁, D₂ and D₁/D₀ were 0.82–0.91, 0.59–0.71, 0.50–0.58, and 0.70–0.78, respectively. S. psammophila restoration exhibited pronounced multifractal characteristics, which reduced the heterogeneity of the soil particle-size distribution and made the distribution more uniform, thereby resulting in a more stable soil structure and a more balanced ratio of fine to coarse particles. The soil erodibility K factor indicated that soil erosion resistance gradually increased with stand age, with a 23.71% reduction at 35 years compared with CK. Random Forest analysis identified organic carbon (OC), total nutrients (TN, TP), pH, soil particle-size fractions (clay, silt, sand), D₁, D₂, and vegetation characteristics (aboveground biomass, AGB; plant density, PD) as important predictor variables for soil erodibility (p = 0.01, R² = 0.961). These findings provide new insights into the mechanisms by which long-term S. psammophila restoration improves soil structural stability and erosion resistance, offering a scientific basis for optimizing vegetation restoration and sustainable desert ecosystem management in arid regions. Full article

Agricultural expansion is usually viewed as ecological pressure in dryland agro-pastoral ecotones, yet regional greening can also emerge alongside agricultural restructuring. This paradox is particularly important where cropland expansion, ecological restoration, and water-limited landscapes overlap. Using the Mu Us Sandy Land of northern China, we examined whether greening from 2000 to 2020 represented genuine non-cropland recovery and whether its spatial organization was associated with agricultural restructuring. Annual fractional vegetation cover (FVC), land use/cover, and environmental data were used to decompose regional greening and construct an Agricultural Opportunity Space Index (AOSI) based on cropland boundary proximity, local cropland density, interface intensity, and agricultural core-area influence. Ecological non-cropland improvement accounted for 84.30% of the total FVC increase, whereas the change in cropland share contributed only 2.30%. Recovery was broad-based, with FVC rising across low, median, and high quantiles and 68.97% of valid ecological non-cropland pixels showing significant greening. Restoration occurrence and mean FVC change (ΔFVC) generally increased along the AOSI gradient, while high-opportunity zones hosted stronger, more recurrent, and more stable restoration hotspots. These findings suggest that agricultural restructuring shaped non-cropland recovery not primarily through land occupation, but through the spatial redistribution of land-use pressure and resource management conditions beyond cropland boundaries. Full article

Hydrological processes in dryland urban lakes are jointly shaped by landscape pattern dynamics and water resource scarcity, yet the spatial correspondence between landscape fragmentation and lake ecological water demand remains poorly understood. This study took Yuehai Lake, a typical dryland urban lake in Northwest China, as a case study. Landscape pattern analysis was integrated with a water balance model to quantify ecological water demand and its spatial correspondence with landscape metrics. The model coupled the Penman–Monteith equation, a depth-modified evaporation model, and a Darcy’s Law-based zonal seepage calculation. Results showed that: (1) the landscape structure remained highly stable over 2014–2022, with the Aggregation Index ranging from 95.07% to 95.28% and the Largest Patch Index from 90.20% to 90.70%; (2) the annual ecological water demand for maintaining ecosystem integrity was estimated at 2036.97 × 10⁴ m³, comprising inherent lake water volume of 1138.02 × 10⁴ m³ (55.9%), evapotranspiration of 659.72 × 10⁴ m³ (32.4%), and lakebed seepage of 239.23 × 10⁴ m³ (11.7%); and (3) evapotranspiration was concentrated between May and August, accounting for 80.5% of annual losses, with water surface evaporation dominating the flux at 91.5%. These findings suggest a spatial correspondence between landscape metrics and ecological water demand components, providing quantitative support for differentiated water supplementation strategies in dryland urban lakes. Full article

Carbon storage dynamics in dryland and semi-arid ecosystems remain a major uncertainty in global carbon cycle assessments, particularly in regions like the Yellow River Basin (YRB). Using the Arid Ecosystem Model (AEM), we simulated the spatiotemporal evolution of four major carbon pools—total carbon (TOTC), vegetation carbon (VEGC), soil organic carbon (SOC), and litter carbon (LTRC)—from 1981 to 2060 under factorial climate scenarios. During 1981–2020, TOTC increased by 0.09 Pg C (+3.54%), driven by gains in VEGC (+0.03 Pg C, +21.43%) and SOC (+0.06 Pg C, +2.78%). LTRC showed minimal net change but was highly sensitive to interannual variability. From 2021 to 2060, under the high-emission SSP5 scenario, TOTC is projected to increase by 0.114 Pg C (+4.81%), with VEGC contributing most of the gain (+23.87%). CO₂_only simulations showed similar increases, underscoring the dominant role of CO₂ fertilization. In contrast, warming and precipitation alone produced weaker and more variable effects. Spatially, upper YRB regions are expected to maintain strong sink capacity, while the Loess Plateau and central-western subregions remain vulnerable to warming and moisture decline. LTRC exhibited the highest variability across scenarios (−18% to +22%), highlighting its role as a sensitive indicator of sink stability. These findings emphasize the need to account for nonlinear climate–carbon interactions and regional heterogeneity. Region-specific, adaptive strategies that integrate ecological restoration and climate adaptation will be critical to enhancing carbon sinks and supporting China’s carbon neutrality targets in the Yellow River Basin. Full article

Foxtail millet (Setaria italica L.) is a key dryland cereal in northern China yet multi-regional information on its disease–pest complexes remains limited. Using a 2025 field-monitoring dataset from 84 sites in 11 provincial-level regions, this study assessed the occurrence spectrum, regional variation, relative pressure, provincial spatial patterns, complex types, and statistical co-occurrence associations of major diseases and insect pests observed during a single grain-filling-stage survey. Field records were standardized numerically, and richness metrics, relative pressure indices, complex-type classification, provincial mapping, and binary correlation analysis were applied. Foxtail millet blast, millet downy mildew, and foxtail millet sheath blight were the dominant field-diagnosed disease categories, with occurrence rates of 97.18%, 89.74%, and 73.02%, respectively. Stink bug-related records represented by the hyaline grass bug, double-spotted long-tarsed leaf beetle records, armyworm records, foxtail millet leaf beetle records, and Asian corn borer records were the most common insect pest variables. Northeast China showed higher disease richness and total disease–pest richness, while Northeast China and the North China–Huang-Huai region had higher total disease–pest pressure. Pest-dominated sites accounted for 47.62% of all sites. Co-occurrence analysis identified strong positive statistical associations, particularly between millet downy mildew and stink bug-related records. These results provide a grain-filling-stage monitoring baseline for foxtail millet disease–pest complexes and support integrated monitoring, regional surveillance, and exploratory classification-based interpretation. The findings should be interpreted as cross-sectional field-monitoring results rather than evidence of seasonal dynamics, causal disease–pest interactions, or validated control thresholds. Full article

Aiming at severe soil secondary salinization, poor water retention and insufficient salt tolerance of conventional acrylic-based modifiers in arid and semi-arid regions of China, a poly(ethylene glycol) maleate citrate (PEGMC) crosslinking monomer was synthesized through esterification, and a dual covalent–hydrogen crosslinked P(PEGMC/AA) hydrogel was fabricated via free radical copolymerization with acrylic acid (AA). The hydrogel was characterized by NMR, FTIR, SEM, TGA and elemental mapping, while its binding mechanism with saline–alkali ions was elucidated through DFT calculations and molecular dynamics simulations. Its amelioration performance was evaluated through swelling, soil water retention, desalination and pot germination experiments. The hydrogel exhibited outstanding water absorbency, salt resistance and dry–wet cycling stability, with swelling ratios of 712 g/g in deionized water and 285 g/g in 0.9% NaCl solution, and remained 200 g/g after four dry–wet cycles. It enhanced soil water retention remarkably (over 93% after 72 h). At 0.30% dosage, soil salt content declined from 7.1 g/kg to 1.3 g/kg with desalination efficiency exceeding 80%, owing to porous physical adsorption and chemical chelation toward Na⁺, Ca²⁺ and Mg²⁺, with a binding energy of −136.936 kJ/mol. Pot tests revealed that crop germination rate rose from 19% (blank) to 75% under severe saline–alkali stress. Meanwhile, the hydrogel inhibited nutrient leaching and favored soil-water conservation. This work first incorporated PEGMC monomer into agricultural hydrogels to construct a stable dual crosslinked network, clarifying its synergistic mechanisms for salt fixation and water retention macroscopically and microscopically. It provides a promising functional material and theoretical basis for green, efficient in situ amelioration of dryland saline–alkali soil. Full article

Projecting the spatiotemporal dynamics of the potential distribution of dominant species under climate change is essential for desertification control and vegetation restoration in drylands. Here, we modeled the current (1970–2000) and future (2080–2100) suitable habitats of Caragana korshinskii Kom, an ecologically important shrub species in northwestern China, by constructing an ensemble of eight species distribution models on the Biomod2 platform using three CMIP6 Shared Socioeconomic Pathways (SSP126, SSP370, SSP585) and 40 environmental variables representing climate, soil, topography and drought conditions. Key environmental drivers were identified through variable importance ranking and response curves, while area changes, spatial patterns, and centroid shifts in suitable habitats were quantified. The ensemble model demonstrated good to excellent predictive performance (mean AUC > 0.9, mean TSS > 0.5). Soil base saturation (t-bs) and soil moisture contributed the most (>38%), highlighting the dominant role of edaphic factors. The current total suitable habitat of C. korshinskii is approximately 182.2 × 10⁴ km², with all future scenarios projecting a consistent decline. Under SSP585, habitat loss reached 9.8% with contraction (30.5 × 10⁴ km²) far exceeding expansion (12.6 × 10⁴ km²). The distribution centroid shifted markedly eastward with a minor southward fluctuation, establishing the Ordos–Bayannur region as a stable core habitat. Overall, our findings suggest that the distribution of C. korshinskii is strongly constrained by edaphic and moisture conditions, and future contraction of marginal habitats may compromise ecosystem services. Full article

Vegetation phenology is a sensitive indicator of climate change in China’s drylands (aridity index, AI < 0.65). However, accurate phenological monitoring remains challenging due to low signal-to-noise ratios, persistent soil background interference, and the scarcity of ground phenological sites. Existing global phenology products also perform poorly in hyper-arid and arid regions. This study developed an optimal phenology detection framework for China’s drylands by systematically evaluating various vegetation indices, noise-reduction techniques, fitting functions, and dynamic thresholds against ground observations, generating a dataset at 500-m resolution spanning 2001–2024. Specifically, we determined vegetation index thresholds to distinguish vegetated from non-vegetated pixels based on 453 field survey sites. Our results indicate that the Normalized Difference Phenology Index (NDPI) coupled with a 10% threshold and polynomial fitting provided the highest accuracy for Start of Season (SOS) (RMSE = 12.02 days). For End of Season (EOS), EVI2 combined with a 70% threshold and self-weighted double-logistic fitting yielded superior performance (RMSE = 19.89 days). Compared to the MODIS global phenology product (MCD12Q2), our dataset demonstrates significantly higher accuracy (higher R and lower RMSE) and broader spatial coverage, particularly in hyper-arid and arid regions. Spatiotemporal analysis reveals that SOS was earlier while EOS was later in more arid areas, potentially reflecting the opportunistic life strategies of ephemeral plants. Notably, a trend of delayed SOS was observed in these regions, which we potentially linked to the shifts in precipitation regimes under global change. This optimized framework and the resulting Chinese dryland phenology dataset provide a robust foundation for assessing ecosystem resilience and carbon cycle dynamics in water-limited environments. Full article

Soil wind erosion (SWE) remains a significant challenge to improving ecological environmental quality and achieving sustainable socioeconomic development in drylands of northern China. An in-depth understanding of the spatio-temporal variations and underlying mechanisms of regional SWE is a prerequisite for the scientific prevention and mitigation of erosion-related hazards. However, in regions with high variability in intra-annual climate, quantitative studies on the spatial heterogeneity and intra-annual variability of drivers of SWE are scarce. This knowledge gap poses challenges for policymakers in developing effective landscape management strategies that are spatially and temporally specific. Here, the dynamics of SWE in the Xilingol typical steppe of China were simulated using the Revised Wind Erosion Equation (RWEQ) at seasonal and annual scales during 2000–2020. Stepwise regression and geographically weighted regression (GWR) were employed to examine the spatial heterogeneity in the relationships between SWE and environmental variables. The results revealed that RWEQ simulations were significantly correlated with the frequency of dust storm events at the seasonal scale (R² = 0.807, p < 0.01). SWE in spring accounted for approximately two-thirds of the annual total, indicating that spring was the critical period for SWE control. High SWE intensity was concentrated in sandy soil regions, with the Otindag Sandy Land and Gahai Elesu Sandy Land being identified as priority areas for desertification prevention and control. Over the study period, SWE exhibited an overall decreasing trend at both seasonal and annual scales, suggesting an enhancement in the ecosystem’s capacity for windbreak and sand stabilization. The stepwise regression results indicated that climatic factors generally had greater explanatory power than topographic and landscape pattern variables. Wind speed showed the strongest association with SWE across different time scales, whereas the relationships of normalized difference vegetation index (NDVI) and precipitation with SWE exhibited clear seasonal dependence. The GWR results further revealed pronounced spatial heterogeneity and seasonal variability in both the direction and magnitude of the associations between SWE and climatic and landscape pattern variables. These findings provide scientific support for identifying priority areas for desertification prevention and for developing spatio-temporally targeted landscape management strategies in dryland sandy regions. Full article

Dryland sustainability depends on how vegetation productivity and water-use processes respond to climatic variability and human intervention. Focusing on Xinjiang, China, this study assessed eco-hydrological change from 2000 to 2023 using multi-source remote sensing and climatic datasets. We integrated vegetation productivity and water-use efficiency into a composite EcoIndex, combined anomaly-based diagnostics with eco-hydrological synchrony analysis, and used pixel-level random forest attribution to identify dominant climatic and anthropogenic controls. The results show clear regional differentiation. Northern Xinjiang remained primarily climate-driven and maintained relatively stronger vegetation–water coupling, whereas Southern Xinjiang exhibited more pronounced human-induced restructuring, especially in oasis and cultivated areas. Eastern Xinjiang functioned as a transitional zone with weak coupling and high sensitivity to multiple pressures. Across Xinjiang, 63.27% of the area was classified as climate-dominated, 22.41% as human-dominated, and 14.32% as mixed influence. The results indicate that improvements in vegetation condition do not necessarily imply improved eco-hydrological coordination, and that mixed-influence zones may represent early-warning areas of sustainability risk. This study provides a spatial diagnostic framework for supporting sustainable land and water management, regional adaptation planning, and resilience-oriented governance in arid and semi-arid regions. Full article