The application of organic fertilizer affects soil microbes and enzyme activities. In this study, we explored the effects of various long-term different fertilization treatments (manure, M; chemical fertilizer, NP; manure + chemical fertilizer, MNP; and no fertilizer, CK) on bacterial community structure and soil sucrase, urease, and alkaline phosphatase activities in Shaping, Hequ, China. High-throughput sequencing was used to amplify the third to the fourth hypervariable region of the 16S ribosomal RNA for analysis of the bacterial community structure. Enzyme activities were determined by colorimetry. Soil treated with MNP had the highest bacterial Abundance-based Coverage Estimator index and enzyme activities. The principal coordinates analysis results showed significant differences among the various fertilization treatments (p < 0.001). Proteobacteria, Actinobacteria, Acidobacteria, Gemmatimonadetes, and Chloroflexi were consistently dominant in all soil samples. The redundancy analysis and Monte Carlo permutation tests showed that the soil bacterial communities were significantly correlated with alkali-hydrolyzable nitrogen, organic matter, urease, and alkaline phosphatase. Our results reveal the fundamentally different effects that organic and inorganic fertilizers have on soil bacterial communities and their functions.

Exploring crop yield stability and the relationship between the water–fertilizer effect and annual precipitation type in a broomcorn millet–potato–spring corn rotation system under long-term fertilization on chestnut cinnamon soil in loess tableland can provide a scientific basis for rational fertilization in the northwest Shanxi region in years with different precipitation. This study was based on a 33-year long-term fertilizer experiment, using four fertilizer treatments: no fertilizer as control (CT), single fertilizer nitrogen (N), single organic fertilizer (M), and nitrogen fertilizer with organic fertilizer (NM). The results showed that broomcorn millet and maize had the highest yield in wet years, while potatoes had the highest yield in normal years and the yield under NM treatment was the highest. The sustainable yield index (SYI) values for potato and maize were higher than the SYI for the broomcorn millet during years with different precipitation and the SYI for the NM treatment was the highest. The water use efficiency of NM treatment was the highest. The yield of broomcorn millet and maize was affected by nitrogen fertilizer, organic fertilizer, and precipitation during the growth period, while the potato yield was mainly affected by nitrogen fertilizer and organic fertilizer. Therefore, the rotation of potato–maize and the rational allocation of organic and inorganic fertilizer (NM) is the best planting system in this region.

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.

Optimizing field cropping practices to improve nitrogen use efficiency is imperative to promote intensive and sustainable wheat production. As a cultivation method commonly adopted in arid and semi-arid regions globally, the ridge–furrow mulching system (RFMS) is capable of efficiently harvesting rainfall, reduce evaporation losses, enhancing soil moisture levels in the root zone, and boosting crop productivity. However, the combined effects of varying ridge–furrow ratios (RD), ridge heights (RH), and nitrogen application rates (RN) on nitrogen fertilizer bias productivity (PFPN) under the influence of climatic conditions, soil types, and field management practices remain poorly understood due to a lack of systematic evaluation. This study conducted a meta-analysis of 462 comparative datasets from 98 research projects to reveal the interactive effects of RFMS and nitrogen fertilizer across climatic gradients. The results showed that RH, RD, and RN increased by 23.78%, 22.37%, and 23.07% respectively (p < 0.05), with the most significant enhancement of PFPN being demonstrated by RH. The most significant improvement in PFPN was observed when RD = 1:1, R < 10 cm, and RN > 200 kg∙hm⁻², with PFPN increasing by 27.7%, 29.50%, and 29.32% respectively (p < 0.05). Climatic and soil physico-chemical factors and field management practices are the key factors influencing the RFMS. When average annual evapotranspiration (AE) < 1000, RN > 200 has the best effect on nitrogen utilization efficiency, while under the condition of AE > 1500, RN < 100 is more effective. In terms of mulching strategy, full mulching of ridges and furrows is recommended in areas with severe drought and low temperatures, while mulching only ridges or furrows is more appropriate in areas with relatively mild climate. The present study provides a scientific basis for the optimal design of ridge–furrow mulching configuration and nitrogen application level. This is achieved by considering climatic conditions, soil fertility, and field management in agro-ecosystems in arid and semi-arid areas.

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.