Soil microorganisms are crucial for nutrient cycling, with abundant and rare taxa playing distinct roles. However, the mechanisms by which soil microbes influence nutrient cycling under different crop types remain unclear. In this study, we investigated the network structure, diversity, and microbial composition of croplands in the Yellow River Delta, focusing on four primary crops: soybean, maize, cotton, and sorghum. The findings revealed that the co-occurring network structure of sorghum planting-soils exhibited greater complexity than other crop types. Bacterial alpha diversity in cotton-planting soil is the highest and susceptible to environmental variations. The diversity of both rare and abundant taxa responds differently to soil nutrients depending on the crop type. While abundant taxa play a crucial role in soil multi-nutrient cycling, rare taxa are key drivers of variations in nutrient cycling expression. The diversity of rare taxa showed a strong correlation with critical nutrients. Structural equation modeling revealed that the alpha diversity of rare bacterial and fungal taxa significantly influenced the soil multi-nutrient cycling index (MNC). Specifically, higher Shannon indices of rare bacterial taxa were associated with lower MNC, while the opposite was true for soil fungi. Soil organic carbon and soil total nitrogen are the key factors influencing alpha diversity in rare bacterial and fungal taxa. Moreover, this study provides new insights into the role of rare soil microbial diversity in the nutrient cycling of agricultural ecosystems.

The accuracy of the crop coefficient approaches recommended by the FAO-56 guidelines for evapotranspiration (ET) in saline environments is limited due to complex soil–water–crop interactions, highlighting the need for advanced methods to improve ET estimation for water management in saline-alkali lands. To improve ET estimation for wheat, maize, and soybean in the Yellow River Delta, China, three machine-learning algorithms—gradient-boosting decision tree (GBDT), random forest (RF), and extreme gradient-boosting regression (XGBoost)—were applied alongside single- and dual-crop coefficient approaches (K_c-ET_o). The results showed that increasing the input variables did not necessarily improve the ML model performance. The ML models outperformed K_c-ET_o approaches, particularly for summer crops (maize and soybean), with the mean absolute error reduced by 26.4% to 80.9%, R² increased by 5.6% to 11.2%, and root mean square error (RMSE) decreased by 22.4% to 98.1%. RF and XGBoost were more accurate than GBDT, with R² increasing by 3.2% to 5.4% and RMSE decreasing by 22% to 57%. Scenario simulations showed increased ET with intensified emission scenarios for RF and GBDT, similar to K_c-ET_o approaches. However, XGBoost simulated a significantly lower ET in high-emission scenarios, indicating potential unreliability for scenario predictions beyond the training dataset, especially in a saline-alkali environment with an increasingly complex background.

Straw return and tillage practices can alter the soil properties and regulate the bacteria communities, which mediate nitrogen (N) transformation and accumulation. This study aims to elucidate the mechanisms of microbially driven N retention, providing a foundation for soil management strategies. A field experiment was conducted in 2019–2022, six treatments were set up, including rotary tillage with/without straw (RTSR and RTNS), deep tillage with/without straw (DTSR and DTNS), subsoiling with/without straw (STSR and STNS). Soil properties, N pools/fractions and bacterial communities were measured. The results showed that straw return and tillage practices ameliorated soil environment (reducing bulk density (by 7–8% via DTSR and STSR) and salinity (with 57% and 26% increase in DTSR and STSR compared with RTSR, while rotary tillage significantly reduced salinity), increasing soil organic matter (via RTSR treatment, with 5–16% significant increase in two years) and effectively promoting N accumulation. The number of OTUs and the α-diversity significantly increased in 2022 compared with 2021. Specifically, tillage was the main driver of bacterial α-diversity, but there was no significant influence on bacterial β-diversity. Mental test results showed that N availability is a pivotal environmental factor shaping the bacteria α- and β-diversity. Structural equation modeling revealed that SON accumulation directly drove N accumulation via the “environmental improvement–specific microbial community structure” pathway. STSR is the optimal treatment for promoting N accumulation by maintaining active SON levels, which is an effective strategy for sustainable N management in the Yellow River Delta (YRD).

Long-term grape cultivation alters soil acid–base properties and nitrogen transformation processes, yet the mechanisms underlying the changes in soil physical and chemical properties as well as nitrogen components remain unclear. In this study, four grape vineyards with cultivation ages of 0.5 (newly established, Y0.5), 4 (Y4), 16 (Y16), and 22 years (Y22) in Penglai, Yantai, China, were selected as research subjects. Soil nitrogen pools, key nitrogen hydrolase activities, and chiA gene community assembly, as well as their internal regulatory relationships were investigated. The results revealed that cultivation age significantly reshaped soil nitrogen speciation: Y16 vineyards exhibited the highest concentrations of total nitrogen, mineral nitrogen, microbial biomass nitrogen, and labile hydrolyzable ammonium nitrogen (HAN), representing a critical inflection point of soil nitrogen supply capacity. With prolonged cultivation to 22 years, soil nitrogen pools shifted toward recalcitrant stable organic nitrogen (stable-SON), accompanied by significant accumulation of amino sugar nitrogen (ASN). Activities of N-acetyl-β-D-glucosaminidase (NAG), urease, amidase and leucine aminopeptidase were markedly suppressed in Y16 soils. Both Chao1 richness and Shannon diversity indices of chiA gene communities reached the minimum values at 16 years, and PCoA confirmed distinct community separation of Y16 from other age groups. Soil pH, ammonium nitrogen and nitrate nitrogen were the dominant environmental filters governing chiA community composition. Actinomycetota and Pseudomonadota dominated the chiA-harboring microbiota, with acid-tolerant Streptomyces significantly enriched in Y16, while keystone genera Nonomuraea and Pseudoxanthomonas mediated the conversion between labile and stable organic nitrogen pools. Redundancy analysis demonstrated that chiA community composition (25.5% explanation), chiA α-diversity (19.3% explanation), and NAG activity (14.6% explanation) were the primary drivers of soil organic nitrogen fraction variation. Results support a four-level cascading framework wherein stand-age-induced soil acidification and altered N inputs drive shifts in chiA-harboring microbial diversity and community structure, which in turn modulate nitrogen hydrolase activities and ultimately govern the reciprocal transformation between labile and stable organic nitrogen pools, resulting in stage-specific nitrogen supply capacity. This study clarifies the microbial gene–enzyme–nitrogen pool coupling mechanism in chronosequence vineyards and provides theoretical guidance for targeted nitrogen management of aged vineyards.