Search Results (1,590)

Cropland abandonment is a critical challenge for food security and the sustainable use of land resources, especially in the hilly and mountainous regions of southern China. To characterize the spatiotemporal evolution and associated-factor patterns of cropland abandonment in these regions, this study uses Ji’an City, Jiangxi Province, as a case study and develops an integrated framework for abandonment identification and monitoring, predictive-association analysis, relative risk assessment, and zoning management. Using Landsat remote sensing imagery from 1990 to 2024, crop phenological features, and multisource topographic, soil, climatic, ecological, and socioeconomic data, we identified and evaluated long-term cropland abandonment. The results show that, after incorporating phenological features and temporal filtering, annual land-use classification evaluated with spatially separated testing samples achieved a mean overall accuracy of 95.27% and a mean Kappa coefficient of 0.931, and the overall accuracy of cropland abandonment identification reached 91.58%, with a Kappa coefficient of 0.83. Cropland abandonment in Ji’an City showed an overall accelerating trend, with the abandonment rate exceeding 15% during 2020–2021. Spatially, abandonment was more intensive in the west than in the east and more severe in mountainous areas than in plains. Paddy field abandonment was the dominant form of cropland abandonment across the city, whereas dryland abandonment was more limited in extent but showed stronger interannual fluctuations. The predictive-association analysis indicated that tree cover consistently showed high predictive importance for both paddy fields and drylands. Paddy field predictions were more strongly associated with topographic position, surface roughness, and soil conservation, whereas dryland predictions showed stronger associations with aridity and population density. From 1993 to 2023, the model-estimated relative abandonment risk increased, and high-risk areas expanded from deep mountainous regions to low-hill areas. By coupling relative risk levels with associated-factor patterns, we divided paddy fields and drylands into four management zones: core protection zones, dynamic warning zones, integrated improvement zones, and ecological fallow zones. Differentiated management strategies were then proposed for each zone. This study provides methodological support and decision-making evidence for cropland abandonment monitoring, cropland protection, and differentiated land management in the hilly and mountainous regions of southern China. Full article

Against the backdrop of global warming and the “warming and moistening” trend in northwestern China, arid inland river basins are highly sensitive to climate change, with their vegetation dynamics strongly controlled by upstream snowmelt water supply. The Keriya River Basin, situated on the northern slope of the Kunlun Mountains and the southern edge of the Taklamakan Desert, exhibits pronounced vertical zonation in vegetation cover and relies heavily on upstream snowmelt water supply for its water resources. To date, there has been a lack of systematic research into the spatiotemporal evolution patterns of long-term NDVI time series in this basin, its multiscale climate responses, and, in particular, future vegetation projections based on CMIP6 multi-scenario analyses and machine learning methods. To address this, this study utilised MODIS NDVI remote sensing data, historical data from the CMIP6 BCC-CSM2-MR model, and monthly temperature, precipitation, and snow cover data for three SSP scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5) and systematically analysed the spatiotemporal differentiation characteristics of NDVI in the Keriya River Basin and its multiscale coupling relationships with climatic factors. A multi-model selection and forecasting framework was developed, integrating feature engineering with the XGBoost machine learning algorithm. The study innovatively introduced a physically constrained scenario scaling factor based on historical correlations and future climate mean values, thereby addressing the bias where machine learning models’ predicted NDVI means converged across different SSP scenarios. This enabled the monthly estimation of NDVI under various emission pathways from 2015 to 2100. The results indicate: (1) During the historical period (2001–2024), the basin’s annual average NDVI showed an overall slight increase; the annual pattern was unimodal, peaking in July and reaching its trough in January–February; NDVI was highest in summer and lowest in winter. (2) NDVI initially increases and then decreases with altitude; the highest NDVI values are observed in the 3000–4000 m altitude band; in the mid-altitude band, NDVI rose significantly after 2010 and peaked in 2017; the low-altitude band exhibits the greatest interannual stability. (3) During the historical period, both temperature and precipitation in the catchment exhibited high levels of fluctuation, with annual mean temperatures ranging from 1.90 to 3.92 °C and annual precipitation ranging from 434.5 to 621.0 mm. NDVI showed a strong positive correlation with temperature (R = 0.86), a relatively strong negative correlation with snow cover (R = −0.71), and virtually no correlation with precipitation, indicating that upstream snowmelt is heat-driven and water-dependent. (4) Under the future SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios, temperature increases are projected to be 0.83 °C, 2.68 °C, and 5.35 °C, respectively, whilst snow cover is projected to decrease by 2.0%, 14.3%, and 34.0%, respectively; The multi-year mean NDVI values predicted using the XGBoost model (validation R² = 0.9097) are 0.0726, 0.0683, and 0.0690, respectively, all characterised by strong seasonal fluctuations. Given that these future projections are based on a single CMIP6 model and a statistical forecasting framework, they are subject to a degree of uncertainty; however, the low-emission scenario (SSP1-2.6) still indicates a trend that is relatively more conducive to maintaining vegetation stability in this region and may provide preliminary scientific guidance for water resource management along the southern margin of the Tarim Basin. Full article

Dryland degradation increasingly compromises ecosystem stability, food production, and regional development. Clarifying its long-term evolutionary patterns and regional variations is therefore essential for formulating targeted management strategies. Leveraging GEE, we assembled multiple remote-sensing products together with land-cover information to establish a 1-km-resolution assessment framework describing vegetation conditions, drought pressure, potential soil salinity, and the ecological status associated with different land-cover types. An entropy-based weighting scheme was subsequently employed to derive the land degradation risk index (LDRI). This index enabled an assessment of changes in land degradation risk across arid Northwest China over the period 2001–2024, while also allowing the relative contributions of the principal driving factors to be evaluated. The analysis indicated that degradation risk generally weakened throughout the region, and shifts among risk categories occurred predominantly through stepwise movement between neighboring levels. Areas shifting toward lower-risk classes accounted for 75.36% of the total area experiencing risk-class changes. Nevertheless, High risk areas continued to be concentrated in the central-western desert belt, while the overall spatial pattern showed limited variation. Driver analysis indicated that vegetation productivity and hydrothermal conditions had relatively high explanatory power, while interactions among factors generally exhibited enhanced effects. Further stratified analysis showed that land degradation risk in vegetation zoning was mainly controlled by moisture conditions and vegetation productivity, whereas hydrothermal conditions exerted a stronger influence in non-vegetation zoning. The proposed LDRI provides a distinct analytical framework for the long-term monitoring and spatially differentiated management of land degradation risk across arid Northwest China, with broader implications for the scientific assessment and management of dryland degradation. Full article

Soil organic carbon (SOC) in mountain forest ecosystems exerts critical controls over regional carbon balance and climate feedback loops. This study collected 209 stratified topsoil (0–30 cm) samples across temperate mountain forests of Northeast China, conducted a boosted regression tree (BRT) modeling framework integrated with space-for-time substitution, and established 15 combined thermopluviometric sensitivity scenarios to simulate SOC shifts under diversified climate disturbances. Tenfold cross-validation yielded a model mean R² of 0.62, revealing mean annual temperature (MAT, RI = 35.31%) as the most influential predictor of SOC spatial variation, followed by elevation (ELE, RI = 19.11%), while single-season NDVI and soil particle fractions showed weak predictive capacity. Multi-scenario spatial simulation outputs demonstrated that simultaneous warming and aridification drastically reduce the coverage of high SOC zones, whereas increased precipitation can partially offset temperature-induced carbon mineralization losses. Terrain-mediated SOC spatial stratification remained stable across all climate backgrounds. This study quantifies the layered environmental association hierarchy of mountain SOC and generates spatially explicit modeled carbon sink projections under climate change. The terrain-dependent SOC response patterns provide operable differentiated carbon regulation guidance: humid low-lying convergence zones require long-term soil moisture conservation, while arid steep ridges need native mixed forest restoration to lift baseline carbon storage capacity, supporting precise watershed climate adaptation and targeted forest carbon sink management for temperate mountain regions. Full article

Arid mountain–oasis–desert regions exhibit strong terrain and surface-cover heterogeneity that is difficult to represent using conventional land-cover classification schemes. This study developed an ecosystem-oriented hierarchical framework for the Kashi region by combining a terrain-constrained Mountain extraction method with Automatic Deep Forest Shrinkage model (ADeFS). Nine ecosystem elements were defined: Mountain, Water, Forest, Cropland, Lake, Grassland, Desert, Ice, and Human. Mountain was first delineated as an independent physiographic element using a locally derived baseline surface, relative relief, slope, and topographic position, thereby reducing semantic overlap between terrain units and spectrally similar surface-cover classes. ADeFS was then adapted to classify the seven non-mountain classes, and Lake was subsequently separated from the unified Water class through visual interpretation. The results show that ADeFS achieved the highest accuracy, with an overall accuracy of 88.7% and a Kappa coefficient of 0.868. Independent field validation of the 2026 map yielded an overall accuracy of 86.2% and a Kappa coefficient of 0.825. From 2015 to 2026, the mountain-oasis-desert structure remained broadly stable, while Desert and Ice decreased and Forest, Grassland, and Cropland expanded. Ecosystem-element transitions were concentrated before 2021 and weakened thereafter. Landscape metrics showed that Desert remained the dominant matrix, Grassland had the highest patch density and edge density, and Cropland became increasingly aggregated within oasis agricultural areas. The framework provides an ecologically interpretable approach for ecosystem-element mapping and long-term monitoring in arid heterogeneous regions. Full article

Brackish water utilization provides an alternative strategy for alleviating freshwater scarcity in arid agricultural regions; however, the synergistic regulation of salinity, irrigation, and nitrogen management remains unclear. A two-year field experiment was conducted in 2025 and 2026 to investigate the effects of water–nitrogen–salinity interactions on growth, yield formation, resource use efficiency, and fruit quality of pigment pepper (Capsicum annuum L.) under arid conditions in Xinjiang, China. An L9(3³) orthogonal experimental design was adopted with three levels of brackish water salinity, irrigation amount, and nitrogen application rate. The comprehensive production performance of different management strategies was further evaluated using a combined weighting Cloud–TOPSIS approach. The results showed that water–nitrogen–salinity interactions significantly regulated pigment pepper growth, yield formation, and resource utilization, with consistent responses observed across the two experimental years. Increasing irrigation water salinity reduced leaf chlorophyll content (CHL) and nitrogen balance index (NBI), whereas flavonoid content (FLAV) exhibited an increasing trend under moderate salinity stress. Low-salinity irrigation combined with appropriate water and nitrogen inputs maintained higher photosynthetic capacity and nitrogen nutritional status. Yield, water use efficiency (WUE), and partial factor productivity of nitrogen (PFPN) were jointly affected by salinity, irrigation, and nitrogen supply. Excessive salinity significantly reduced crop productivity, while optimized irrigation and nitrogen management alleviated salt stress effects. The T2 treatment (1 g L⁻¹ salinity, 2400 m³ ha⁻¹ irrigation, and 300 kg ha⁻¹ nitrogen application) achieved the highest yield and maintained favorable WUE and PFPN values in both years. Fruit quality responses demonstrated that moderate salinity promoted capsaicinoid accumulation, whereas excessive salinity restricted biomass production and quality improvement. Correlation analysis revealed that photosynthetic nitrogen metabolism indicators were closely associated with yield formation, while flavonoid accumulation showed stronger relationships with quality attributes. The Cloud–TOPSIS evaluation identified T2 as the optimal management strategy under the experimental conditions by balancing yield, quality, and resource use efficiency. These findings indicate that coordinated regulation of irrigation water salinity, water supply, and nitrogen input is essential for achieving efficient brackish water utilization and sustainable pigment pepper production in arid regions. Full article

High-dimensional environmental covariates are increasingly available for digital soil mapping (DSM), but their effective use depends on both the feature-selection strategy and the predictive model architecture. However, systematic evidence remains limited regarding how different metaheuristic feature-selection methods interact with standalone and hybrid learning models across multiple soil and groundwater prediction tasks. This study systematically evaluated the interactions between 10 metaheuristic feature-selection algorithms and 13 predictive models, including random forest (RF), convolutional neural network (CNN), recurrent architectures, CNN–recurrent neural network (RNN) hybrids, squeeze-and-excitation (SE)-enhanced hybrids, and iTransformer-based hybrids, across four prediction tasks involving soil organic carbon (SOC), soil–water extract electrical conductivity (ECe), apparent electrical conductivity (ECa), and groundwater level (GWL) in Xinjiang, China. A total of 149 candidate environmental covariates were considered for ECe, SOC, and ECa, whereas 122 candidate covariates were considered for GWL. The results showed that no single feature-selection method consistently performed best across all four targets; instead, predictive performance depended on the interaction among the feature-selection strategy, predictive architecture, and target variable. CNN–RNN hybrid architectures generally achieved higher predictive performance than standalone models, although their benefits varied among prediction targets. The best-performing combinations yielded coefficient of determination (R²) values of 0.9826, 0.6981, 0.8429, and 0.8085 for GWL, SOC, ECe, and ECa, respectively. These findings indicate that target-specific compatibility, rather than aggressive dimensionality reduction or a universally superior algorithm, is a key determinant of predictive performance in high-dimensional DSM. By demonstrating that feature-selection effectiveness is jointly influenced by model architecture and target characteristics, this study provides a methodological reference for developing target-specific digital soil mapping models in arid regions. Full article

Open-pit coal mining in arid and desert regions causes extensive and persistent surface disturbance, yet long-term monitoring of disturbance and backfilling processes remains challenging. Existing time-series change detection approaches can identify spectral changes but provide limited information on mining disturbance types and their evolution pathways. To address this issue, an automated surface disturbance detection method (Auto-SD) was developed for open-pit coal mines in arid and desert environments. This method integrates disturbance-type identification and temporal information extraction using the tasseled cap brightness (TCB) component to characterize changes associated with surface material exposure and accumulation. Using Landsat imagery from 1990 to 2023, Auto-SD was applied to 89 open-pit coal mines in China’s arid and desert regions, achieving an overall classification accuracy of 0.84. The cumulative disturbed area reached 423.10 km², while the internal dumping area reached 94.25 km², indicating limited backfilling recovery. Disturbance intensified after 2006, whereas backfilling lagged behind, forming a trajectory of rapid expansion, delayed recovery, and gradual stabilization. Spatially, mining areas exhibited a progressive transition from external dumping to internal dumping and backfilling. Furthermore, cumulative pit area generally followed an S-shaped growth pattern with mining duration. These findings provide new insights into long-term mining landscape evolution and support ecological restoration assessment and sustainable resource management in arid mining regions. Full article

Cashmere goats are widely distributed in the cold, arid and semi-arid remote regions of northern China. Their main economic value is the production of precious cashmere fibers. The differentiation of second hair follicle (SHF) stem cells into hair follicle lineages plays a crucial role in SHF regeneration as well as in the morphogenesis and growth of cashmere fibers; however, its precise molecular mechanism is still unclear. In this study, we found that N⁶-methyladenosine (m⁶A)-circHECA recruiting FUS promoted the differentiation of SHF stem cells into hair follicle lineages through stabilizing FOXM1 mRNA in SHF stem cells, thereby activating the NOTCH pathway in cashmere goats. Furthermore, we confirmed that the m⁶A modification of circHECA is required for the FUS/FOXM1-mediated NOTCH signaling pathway to facilitate the differentiation of SHF stem cells into hair follicle lineages via transfecting circHECA m⁶A-deficient mutants. Our results contribute to elucidating the functional mechanism of m⁶A-circHECA in the differentiation of SHF stem cells into hair follicle lineages in cashmere goats. Full article

To gain a deeper understanding of the lower-atmospheric dynamic characteristics in the transition zone on the northeastern margin of the Tibetan Plateau, high-resolution wind profile data collected by a Doppler wind lidar (DWL) at Yinchuan Hedong International Airport from 2021 to 2023 were used to analyze the vertical structure, seasonal variations, and diurnal characteristics of the low-height wind field and wind shear in this region. The results indicate that (1) the data acquisition rate (DAR) below 1.5 km is generally high, exceeding 90% during most periods, and decreases monotonically with height; the 90% DAR contour height exhibits clear seasonal and diurnal variations, with the largest diurnal amplitude in summer and the smallest in winter. (2) The middle- and low-height wind fields are jointly modulated by topographic forcing and local circulations. Below 0.4–0.7 km, north–northeast and south–southwest winds prevail across all seasons, which is consistent with the blocking and splitting effects of the Helan Mountains. At 42 m, the wind direction shows a marked diurnal transition that may reflect the combined influence of the Helan Mountains’ bypass flow, mountain–plain circulation, and thermal contrasts between the Yellow River and surrounding desert/plain surfaces. (3) Horizontal wind speeds are predominantly concentrated below 6 m s⁻¹, and the development height of this low-wind-speed zone varies seasonally. The vertical velocity statistics show weak positive values in parts of the observed layer, but these signals are interpreted cautiously because vertical-velocity retrieval is subject to additional uncertainty. (4) The low-level wind shear intensity reaches its peak below 100 m and generally exhibits a U-shaped vertical distribution; severe wind shear below 100 m occurs most frequently from nighttime to early morning during May–October, whereas its occurrence frequency is lowest in winter. These findings provide observational evidence for aviation meteorological support and boundary-layer studies in semi-arid regions of Northwest China. Full article

Cropland fragmentation affects regional ecosystem functioning and biodiversity conservation, making the spatio-temporal coupling between cropland fragmentation (CLF) and habitat quality (HQ) a key basis for land management and ecological protection. However, the reported CLF–HQ coupling differs in sign between regions, and the mechanism underlying this divergence remains unclear. This study evaluated the spatio-temporal dynamics of CLF and HQ and their interrelationship across Hunan Province, China, at the township scale (∼2450 units) from 1994 to 2024. CLF was measured by a four-dimensional index consisting of Scale (SPI), Natural Endowment (NPI), Aggregation (API), and Convenience (CPI), with weights determined by a combined AHP–Entropy method; HQ was modelled with InVEST. The relationship between the two was analysed through bivariate spatial autocorrelation, Mantel tests, Random Forest, redundancy analysis (RDA), and XGBoost–SHAP. CLF peaked in urban fringes and was lowest in the western mountains; mean HQ declined modestly, mainly before 2014. Spatially, CLF and HQ were negatively associated (bivariate Moran’s I between −0.38 and −0.51, $p<0.001$), opposite in sign to the positive coupling found in arid Northwest China. Decomposition of the composite index showed that the negative association was carried largely by the natural-endowment dimension, in which slope and elevation were the dominant indicators. Structural fragmentation dimensions uniquely explained only 1.5–5.4% of HQ variance, and controlling for terrain reduced the CLF–HQ correlation by 67–96%. The sign also reversed under an alternative directional standardisation, so it is not robust to a defensible analytical choice. In these data, the observed negative coupling is largely consistent with a shared topographic gradient rather than a direct fragmentation effect. For management, this correlational evidence suggests that reducing fragmentation alone may do little to improve habitat quality; conservation is better guided by the underlying terrain and land-use gradients. More broadly, CLF–HQ assessments become more reliable when cropland structure is separated from natural endowment and terrain confounding is accounted for. Full article

As an endemic woody plant resource widely distributed in the arid regions of northwestern China, Hippophae rhamnoides subsp. sinensis Rousi possesses both ecological and medicinal value. Elucidating the effects of climate and soil conditions on the composition, structure, and function of its root-associated bacterial communities is of practical significance for the development and utilization of these indigenous plant resources in this water-limited region. In this study, root samples of H. rhamnoides were collected from 12 sampling sites in the arid region of Northwest China. High-throughput amplicon sequencing was employed to examine bacterial composition, alpha and beta diversity, molecular co-occurrence networks, and PICRUSt-based functional prediction. Mantel tests and redundancy analysis (RDA) were further applied to identify what is associated with shaping bacterial community structure. The main results are summarized as follows: (1) There were significant differences in the number of ASVs among the various sampling sites, with the P8 site having the highest number (435) and the P5 site having the lowest (156). The dominant phyla in the community were Proteobacteria, Actinobacteria, and Cyanobacteria. (2) Both α and β diversity showed significant differentiation among the 12 sampling sites. The Ace and Chao1 indices of P8 sampling sites were the highest, while P5 sampling sites were the lowest. In terms of community aggregation, P7 and P12 sampling sites showed tighter clustering, whereas P4 and P5 sampling sites showed more scattered bacterial assemblages. (3) Functional prediction suggested that metabolism, environmental information processing, and genetic information processing functional potential may all be dominant across 12 sampling sites. The abundance of environmental information predicted that the processing functional potential of the P9 sampling site was higher than that of the other 11 sampling sites, while the genetic information processing functional potential was low. (4) Cluster analysis grouped the 12 sampling sites into two groups: sampling sites P10, P11, and P12 were grouped into Group 1, while the remaining 9 sampling sites were grouped into Group 2. (5) RDA revealed that altitude was associated with shaping the root endophytic bacterial community structure of H. rhamnoides, followed by soil total nitrogen. Taken together, the H. rhamnoides populations investigated and sampled in this study were predominantly distributed in neutral-to-alkaline arid soils. The relevant environmental factors are significantly correlated with the alpha diversity patterns of root endophytic bacteria of this species, and they can modulate the community assembly processes, functional allocation characteristics, and co-occurrence network structures of these root endophytic bacteria. Full article

Degraded industrial sites in arid and semi-arid regions often suffer from loose surface substrates, weak water-retention capacity, high wind-erosion risk, and poor early vegetation establishment. Combining photovoltaic (PV) deployment with ecological utilization may improve near-surface habitats by shading, reducing wind speed, and regulating soil heat and moisture. This study investigated an ash storage yard of a coal-fired power plant in Ordos, Inner Mongolia, China, by comparing soil temperature, soil moisture, and near-surface wind-speed responses under three representative fixed PV tilt angles of 36°, 43°, and 50°, together with the corresponding early plant-growth suitability. A multi-physics model coupling near-surface airflow, water-vapor transport, and porous-media hydrothermal migration was established. A Gaussian suitability function combined with AHP-CRITIC weighting was used to construct a model-based comprehensive growth index (CGI) from soil temperature and moisture, while short-term field monitoring was used to validate afternoon soil hydrothermal trends. Among the three scenarios, the 36° configuration produced the widest horizontal heat–moisture-affected zone and the highest CGI values for alfalfa and Elymus nutans, reaching 0.7741 and 0.6875, respectively. Relative to the outside reference area, the rear PV zone reduced the near-surface wind speed by 33–40% and increased the plant heights of alfalfa and Elymus nutans by 49.4% and 37.8%, respectively. A first-order PVsyst assessment showed that the 43° configuration achieved the highest specific energy yield of 1814 kWh kWp⁻¹ year⁻¹, whereas the annual grid-connected output at 36° was only 0.59% lower. These findings indicate that the 36° configuration may provide a favorable compromise between early vegetation establishment and photovoltaic electricity generation among the tested scenarios. By linking renewable-energy production with microenvironment regulation and early vegetation establishment, the proposed framework provides a decision basis for the multifunctional and sustainable reuse of degraded industrial land. Nevertheless, the results represent a site-specific, single-season assessment and should not be interpreted as a universal optimum. Full article

Clonal selection is essential for adapting wine production to specific terroirs. To characterize clonal variation in berry quality, five ‘Merlot’ clones (M343, VCR1, VCR3, VCR13, and VCR101) grown in the Hexi Corridor region of Gansu Province, China, were evaluated during the 2024 growing season. An integrated analysis of morphological, physicochemical, phenolic, metabolomic, and volatile traits was conducted to provide a multidimensional assessment of clone-specific berry quality under the arid conditions of this region. VCR1 exhibited the highest total soluble solids and anthocyanin contents, alongside the largest number of annotated metabolites. VCR101 had the highest titratable acidity and the most diverse flavonoid profile, whereas VCR13 showed the highest total soluble sugar content. VCR3 had the highest tannin and total phenolic contents and also exhibited the greatest number and total semi-quantitative relative abundance of volatile compounds. These findings demonstrate that, under arid conditions, the clones differ in their accumulation patterns of sugars, organic acids, phenolics, and aroma-related metabolites. Exploratory principal component analysis identified VCR3 and VCR1 as promising candidates for production objectives emphasizing different quality attributes. These findings provide practical information for clonal selection, germplasm evaluation, wine grape breeding, and region-specific cultivation under arid environmental conditions. Full article

Hydrothermal variability, rising evaporative demand, and drought extremes increasingly threaten crop production in semi-arid regions, yet their relative contributions to maize yield variability remain unclear. Here, we examined maize yield responses to growing-season climatic conditions in Lyuliang City, North China, during 2005–2024 using yield statistics and ChinaMet climate data. Trend analysis, Pearson correlation, candidate regression models, standardized coefficients, and generalized additive models were used to identify dominant climatic predictors. Maize yield showed no significant temporal trend during the study period (Sen’s slope = 0.01 t ha⁻¹ yr⁻¹, p = 0.58), whereas growing-season potential evapotranspiration tended to increase (2.81 mm yr⁻¹, p = 0.06). Diurnal temperature range declined significantly (−0.04 °C yr⁻¹, p = 0.01), and minimum SPEI also decreased significantly (−0.04 yr⁻¹, p = 0.01), indicating intensified extreme dry conditions. Maize yield was most strongly correlated with aridity index (r = 0.72, p < 0.001) and water deficit (r = 0.72, p < 0.001), suggesting that hydrothermal balance explained yield variability better than precipitation or temperature alone. The highest-ranked regression model included aridity index, growing-season temperature, diurnal temperature range, and minimum SPEI, explaining 70% of interannual yield variation. Aridity index was the strongest positive predictor, whereas diurnal temperature range had a significant negative association with yield. Although extreme dry conditions intensified over time, minimum SPEI was not directly associated with annual yield, implying that drought impacts may depend on drought timing, crop phenology, and management buffering. These findings highlight the importance of maintaining favorable hydrothermal balance and reducing risks from increasing evaporative demand and temperature variability to support climate-resilient maize production in Lyuliang City and climatically similar rain-fed semi-arid regions of North China. Full article