Cover crops are vital for sustainable orchard management, but their differential impacts on soil microbial communities and inter-domain networks across soil profiles remain unclear. To address this, we conducted a field experiment in a semi-arid apple orchard on China’s Loess Plateau, comparing three treatments—clear tillage (CT), natural grass (NG), and Medicago sativa (MS)—across two soil depths (0–10 cm and 10–20 cm). Soil physicochemical properties and microbial biomass were analyzed, and bacterial (16S rDNA) and fungal (ITS2) communities were characterized via high-throughput sequencing. Results showed that MS treatment significantly increased key soil nutrients, elevating soil organic carbon (SOC) by 25% and total nitrogen (TN) by 30% in the topsoil relative to CT. Microbial α-diversity was significantly enhanced under cover crops, with the Shannon index increasing by 8.5% for bacteria and 15.2% for fungi in MS topsoil. Co-occurrence network analysis revealed that MS fostered the most complex and stable microbial networks, marked by a 40% increase in nodes and a 55% increase in edges compared to CT, alongside strengthened fungal–fungal associations. These findings quantitatively demonstrate that legume cover crops like Medicago sativa are most effective in enhancing soil fertility and microbial network complexity in semi-arid orchards, with benefits extending into the subsurface soil.

The fusion of Raman and visible–near-infrared (VIS–NIR) spectroscopy provides a promising pathway for rapid and non-destructive soil analysis. However, conventional fusion strategies often fail to properly balance modality discrepancies and complementary information. To address this limitation, this study develops an adaptive Gated Ridge Regression fusion model (Fusion_GatedRidge) for predicting soil organic matter (SOM). A total of 246 soil samples collected from a dryland agricultural region in Shanxi Province were analyzed using laboratory Raman and VIS–NIR spectroscopy. After standard preprocessing, three baseline fusion frameworks—EarlyFusion_Ridge, AE_LatentFusion, and WeightedLate_Fusion—were implemented for comparison with the proposed gated fusion method. Under fivefold cross-validation, Fusion_GatedRidge achieved the best performance, with an R² of 0.83, RMSE of 2.01 g·kg⁻¹, and RPD of 2.39. Compared with single-modality models, R² increased by up to 18.6% and RMSE decreased by up to 23.0%. The gating mechanism dynamically adjusted the contributions of Raman and VIS–NIR features, leading to more stable predictions with residuals largely within −2 to 2 g·kg⁻¹. Overall, the proposed model demonstrates that adaptive modality weighting enhances the exploitation of complementary spectral information and significantly improves SOM prediction accuracy. These findings offer a concise and effective multimodal fusion framework for laboratory-based soil nutrient assessment.

Soil total carbon (TC) and total nitrogen (TN) are key indicators of soil fertility and ecosystem stability, particularly in dryland agroecosystems. However, how rotational tillage combined with straw return affects aggregate formation and aggregate-associated TC and TN stabilization remains insufficiently understood. In this study, we aimed to clarify how rotational tillage affects aggregate structure, stability, and the spatial distribution of TC and TN, thereby revealing internal processes driving nutrient stabilization in dryland farming systems. A long-term field experiment was conducted at the Shenfeng site of Shanxi Agricultural University, China, including three rotational tillage systems with straw return: T1 (two years of no tillage (NT) + one year of deep tillage (DT)), T2 (two years of conventional tillage (CT) + one year of DT), and T3 (two years of DT + one year of CT). Soil aggregates were separated into total mechanical aggregate (TMA), 0.25–2 mm MA, and 2–10 mm MA, and they were further fractionated into water-stable aggregates (WM, Wm, and Wf) for TC and TN analysis. The results showed that aggregate stability, TC, and TN were positively correlated and decreased with soil depth, indicating strong surface enrichment. TC was mainly enriched in 0.25–2 mm MA, whereas TN was concentrated in 2–10 mm MA, and water-stable macroaggregates (WM) acted as the dominant reservoirs for R_C and R_N. Relative to the 2016 baseline (CK), TC in 2022 tended to be higher under rotational tillage with straw return, while NT-containing systems better maintained TN across the 0–60 cm profile. Among the treatments, T1 provided the most balanced performance, with a higher MWD and GMD, lower D, and improved aggregate-associated TC and TN retention. These findings suggest that rotational tillage with straw return, particularly the NT–NT–DT sequence, can support aggregate stability and is associated with improved aggregate-mediated TC and TN retention in the Loess Plateau dryland winter wheat system.

Background/Objectives: GDSL lipase is crucial for plant growth and the response to environmental stress. While GDSL gene families have been identified in some plants, a comprehensive genome-wide identification and analysis in M. domestica is lacking. Methods: In this study, we identified 131 MdGDSLs belonging to four subfamilies, distributed across 17 chromosomes in the M. domestica genome. Results: The molecular weights (MWs) of these MdGDSLs range from 14.44 to 83.26 kDa, with isoelectric points (PI) spanning from 4.32 to 10.09. Members within the same subgroup exhibit similar gene structures and conserved motifs. Furthermore, whole genome duplication (WGD) significantly contributes to the evolution of the MdGDSL gene family. The promoter regions of MdGDSL genes contain numerous cis-elements associated with abiotic stress and hormone responses. Additionally, RNA-seq data indicate that MdGDSL genes respond to high concentrations of CaCl₂ and cold stress treatments. Conclusions: This study provides a theoretical foundation for further investigation into the mechanisms underlying growth, development, and lipid metabolism in response to stress in M. domestica.

The spatiotemporal heterogeneity of hydrothermal conditions during the spring sowing period profoundly shapes cropping layouts and sowing strategies. Using NASA’s GLDAS remote sensing reanalysis, we developed a continuous agricultural climate risk index that integrates three remotely driven indicators—spring sowing window days (SWDs) derived from a “continuous suitable-day” logic, the hydrothermal coordination degree (D value), and a comprehensive suitability index (SSH_SI)—thus advancing risk assessment from single metrics to a multidimensional framework. Methodologically, dominant periodic structures of spring sowing hydrothermal risk were extracted via a combination of wavelet power spectra and the global wavelet spectrum (GWS), while spatial trend-surface fitting and three-dimensional directional analysis captured spatial non-stationarity. The index’s spatial migration trajectories and centroid-evolution paths were then quantified. Results reveal pronounced gradients along the Great Wall Belt: SWD displays a “central-high, terminal-low” pattern, with sowing windows restricted to only 3–6 days in northeastern Inner Mongolia and western Liaoning but extending to 11–13 days in the central plains of Inner Mongolia and Shanxi; SSH_SI and D values form an overall “south-west high, north-east low” pattern, indicating more favorable hydrothermal coordination in southwestern areas. Temporally, although SWD and SSH_SI show no significant downward trend, their interannual variability has increased, signaling rising instability, whereas the D value declines markedly in most regions, reflecting intensified hydrothermal imbalance. The integrated risk index identifies high-risk hotspots in eastern Inner Mongolia and northern North China, and low-risk zones in western provinces such as Gansu and Ningxia. Centroid-shift analysis further uncovers a dynamic regional adjustment in optimal sowing patterns, offering scientific evidence for addressing spring sowing climate risks. These findings provide a theoretical foundation and decision support for optimizing regional cropping structures, issuing climate risk warnings, and precisely regulating spring sowing schedules.

Long-term conservation tillage can alter soil properties and crop performance in a depth-dependent manner. We evaluated a 2 × 2 factorial experiment combining tillage practice (conventional plowing vs. no-tillage) and organic manure application (without vs. with organic manure) in rainfed winter wheat on the Loess Plateau. Soil nutrients, aggregate stability, N₂-adsorption-derived surface and pore properties, and bulk density were measured at 0–10, 10–20, and 20–40 cm, and grain yield was analyzed from 2011 to 2025. Two-way ANOVA showed that no-tillage generally increased surface-soil nutrient contents and aggregate stability, with the strongest responses at 0–10 cm. Significant tillage × organic manure application interactions for several nutrient properties indicated manure-dependent responses to tillage. These effects generally weakened with soil depth, demonstrating pronounced vertical stratification. Management effects on N₂-adsorption-derived surface and pore properties were limited, whereas no-tillage increased bulk density mainly within the upper 20 cm. Based on the descriptive 15-year yield analysis, NT had the numerically highest mean and cumulative yields and the lowest interannual variability. Overall, no-tillage enhanced surface-soil fertility and aggregate stability, but nutrient stratification and increased soil consolidation should be considered when optimizing long-term management.