The implementation of ridge-furrow with plastic film mulching has the potential to enhance crop yields and water productivity, particularly in black soil regions. However, the synergistic impacts of combining ridge-furrow with plastic mulching alongside with various organic amendments on maize yield and nitrogen fertilizer utilization efficiency remain unclear. Using ¹⁵N-labeled tracing technology, we investigated fertilizer-N recovery of maize, distribution, fertilizer-N residual in soil, and nitrogen fertilizer loss across six treatments: non-mulched flat with non-organic amendment (FN), non-mulched flat with straw amendment (FS), non-mulched flat with biochar amendment (FBC), ridge-furrow with plastic mulching without organic amendment (RN), ridge-furrow with plastic mulching with straw amendment (RS), and ridge-furrow with plastic mulching with biochar amendment (RBC). The results revealed that ridge-furrow with plastic mulching in comparison to non-mulched flat, led to a significant increase in maize dry biomass accumulation, yield, and the rate of fertilizer-N recovery in maize (NRE) by 8.57%–12.36%, 10.08%–15.13%, and 2.22%–3.18%, respectively. The rate of fertilizer-N residual in soil (NSR) and fertilizer-N loss (NLS) decreased by 0.5%–2.04% and 0.78%–3.21%, respectively. In addition, the straw and biochar amendments under different planting methods promoted NRE in plants and NSR in soil, reducing NLS. Compared with non-organic amendment treatments, the inclusion of straw and biochar amendments resulted in increased NRE and NRS by 1.64%–6.20% and 0.12%–2.18%, while NLS decreased by 1.76%–7.78%. Biochar amendment treatment exhibited significantly higher nitrogen accumulation and NRE compared to the straw amendment treatment. Overall, ridge-furrow with plastic mulching combined with biochar amendment proved to be an effective method to enhance nitrogen fertilizer utilization of maize in the black soil regions, improving both yield and nitrogen fertilizer utilization efficiency.

Understanding spatiotemporal variation in soil CO₂ flux (FCO₂) along elevational gradients is essential for predicting carbon–climate feedback in alpine ecosystems. However, how temperature- and moisture-related factors jointly regulate daily-scale FCO₂ and how their contributions vary with elevation remain unclear, particularly in the Sejila Mountains (Southeastern Tibetan Plateau). We conducted continuous in situ measurements of daily-scale FCO₂, air temperature (Ta), relative humidity (RH), soil temperature (ST, 0–10 cm), and volumetric soil water content (SW) across five elevational bands (3000–4200 m) in 2024–2025. Across both years, FCO₂ showed a unimodal seasonal cycle and a robust nonmonotonic spatial pattern, with the highest efflux at 3000 and 4200 m and peak rates exceeding 5.0 µmol CO₂ m⁻² s⁻¹. Cumulative carbon loss at 4200 m (909.90 g C m⁻²) exceeded that at mid-elevation sites. Linear mixed-effects models identified Ta as the most consistent positive predictor; the ST × SW interaction was not significant, indicating that temperature and moisture effects are largely additive at the daily scale. Piecewise regression revealed nonlinear SW thresholds (θ) in the FCO₂ response, with θ varying nonmonotonically with elevation. Multiple linear regression further showed that thermal predictors (Ta, ST) explained substantially more variance than moisture predictors (RH, SW), and the relative importance of thermal drivers increased with elevation. These results challenge the common expectation of a monotonic decline in soil respiration with elevation and suggest that, when SW remains above critical thresholds, warming may amplify soil carbon losses at high elevations on the Tibetan Plateau.

Against the backdrop of ongoing climate change, the Qinghai–Tibet Plateau, a region highly sensitive to climatic variation, exhibits intricate spatiotemporal patterns in reference crop evapotranspiration (ET_O), with significant implications for regional water-resource planning. This study selected four agro-climatic zones across the plateau region (TSA, TSH, TAZ, and WCH). Long-term daily observations from 28 meteorological stations were used to estimate ET_O via the FAO 56 Penman–Monteith equation. This extensive dataset enabled robust trend analysis using the Mann–Kendall test, alongside a cloud-model framework, and analyses of sensitivity and contributions to evaluate ET_O’s spatiotemporal evolution, its distributional uncertainty, and the underlying drivers. Results reveal pronounced regional heterogeneity in the interannual variability of ET_O. Annual ET_O declined in TSH and TSA (trend rates of −1.12 and −6.58 mm·10a⁻¹, respectively) and increased in TAZ and WCH (15.76 and 10.74 mm·10a⁻¹, respectively). At monthly and seasonal timescales, ET_O exhibited an unimodal pattern, with the greatest stability in winter and spring and lower stability in summer and autumn. The cloud-model parameter He indicates that ET_O stability is greatest in TSH and weakest in WCH, with He values of 7.15 and 12.29 mm, respectively. Contribution-rate analyses identify T_max and T_mean as the principal determinants of rising ET_O across all study zones, reflecting the largest individual contributions. Temperature-related factors together account for the majority of ET_O variability across the regions, with their absolute contributions ranging from 5.61% to 8.63%, well above those of aerodynamic factors (0.62–1.78%). Stability assessments indicate that ET_O is generally more unstable than its meteorological drivers, with substantial regional disparities, implying that ET_O evolution cannot be explained by a single controlling factor. Overall, the study characterizes the uncertainty in ET_O variations under complex terrain, highlights the value of the cloud model for refined hydrological assessments, and provides a scientific basis for adaptive agricultural water-resource management in the region.

Accurately assessing and predicting vegetation dynamics is of great significance for evaluating regional hydrological and ecological environments. This study focuses on the climate-sensitive Qinghai-Tibetan Plateau (QTP), aiming to reveal the spatiotemporal patterns, underlying driving mechanisms, and future trends of vegetation dynamics. The historical turning points of greening trends were identified using the running slope difference method, and the SHapley Additive exPlanations (SHAP) method was employed to analyze the key driving factors. An Xtreme Gradient Boosting (XGBoost) prediction model was constructed and validated, and then coupled with Coupled Model Intercomparison Project Phase 6 (CMIP6) multi-model ensemble data to project seasonal vegetation changes under different Shared Socioeconomic Pathways (SSP). The main conclusions are as follows: (1) Vegetation on the QTP showed an overall greening trend with significant spatial heterogeneity. Approximately 47.25% of the area exhibited no trend shift (NS), while 29.42% experienced a shift from greening to browning (GB), with most shifts occurring between 1990 and 2010. (2) Soil moisture and precipitation were the dominant driving factors, with contributions significantly higher than those of temperature, wind speed, and other variables, and they exhibited nonlinear interactive effects with the Normalized Difference Vegetation Index (NDVI). (3) In the future, vegetation is projected to show an overall increasing trend, with stronger responses in spring and autumn. The regional average rate of change is highest in spring, especially under the SSP5-8.5 scenario (17.8% for 2030–2060 and 26.4% for 2061–2100); in autumn, although the regional average rate of change is small, the internal spatial variability is significant. The humid regions in the eastern and southeastern parts of the QTP demonstrated more active greening across all seasons except winter, and high-emission scenarios are expected to exacerbate regional and seasonal differences. This study systematically reveals the adaptive dynamics and future scenarios of vegetation dynamics on the QTP, providing scientific support for the adaptation of alpine ecosystems to global change and the management of regional ecological security barriers.

This study addresses the challenge of estimating reference crop evapotranspiration (ET_O) in Xizang Plateau irrigation districts with limited meteorological data by proposing a coupled LASSO-BP model that integrates LASSO regression with a BP neural network. The model was applied to three irrigation districts: Moda (MD), Jiangbei (JB), and Manla (ML). Using ET_O values calculated by the FAO-56 Penman–Monteith (FAO-56PM) model as a benchmark, the performance and applicability of the LASSO-BP model were assessed. Short-term ET_O predictions for the three districts were also conducted using the mean-generating function optimal subset regression algorithm. The results revealed significant multicollinearity among six meteorological factors (maximum temperature, minimum temperature, average temperature, average relative humidity, sunshine duration, and average wind speed), as identified through tolerance, variance inflation factor (VIF), and eigenvalue analysis. The LASSO-BP model effectively captured the interannual variation of ET_O, accurately identifying peaks and troughs, with trends closely aligned with the FAO-56PM model. The model demonstrated strong performance across all three districts, with evaluation metrics showing MAE, RMSE, NSE, and R² values ranging from 4.26 to 9.48 mm·a⁻¹, 5.91 to 11.78 mm·a⁻¹, 0.92 to 0.96, and 0.82 to 0.94, respectively. Prediction results indicated a statistically insignificant declining trend in annual ET_O across the three districts over the study period. Overall, the LASSO-BP model is a reliable and accurate tool for estimating ET_O in Xizang Plateau irrigation districts with limited meteorological data.