Search Results (346)

Dissolved organic carbon (DOC) represents the most readily available and crucial carbon source for soil microorganisms, influencing their community structure, nutrient cycling, and metabolic functions. However, the interplay between functional genes and the organic components of DOC remains poorly understood. In this study, a 33-year fertilization experiment on black soil was carried out, setting up five fertilization treatments: unfertilized control (CK), nitrogen and potassium (NK), nitrogen, P and potassium (NPK), NPK plus straw (NPKS), and NPK plus manure (NPKM). The variation characteristics of soil DOC composition and carbon-degrading functional gene abundance under different fertilization treatments were systematically analyzed. The study found that applying chemical fertilizers combined with organic materials significantly increased soil organic carbon (SOC) and DOC contents in the thin-layer black soil of Gongzhuling. The soil DOC in this region is primarily derived from external inputs (Fresh plant-derived materials). Parallel factor analysis identified four fluorescent components: C1 as visible fulvic acid-like substances, C2 as humic acid-like substances, C3 as ultraviolet fulvic acid-like substances, and C4 as long-wavelength humic-like substances. Among these, NPK plus straw significantly enhanced the fluorescence intensity of the humic acid-like component (C2) and the total fluorescence intensity. The fluorescence intensity of the humic acid-like component increased by 36.0–208.9%, and the total fluorescence intensity increased by 23.8–270.9% compared to the CK. Moreover, the study found that the phylum composition of carbon-degrading microorganisms remained stable under different fertilization treatments. However, NPK plus straw significantly reduced the total abundance of carbon-degrading genes and influenced the composition and transformation of DOC by regulating the expression of key carbon-degrading genes ICL and abfA. These results offer new insights into the mechanisms by which fertilizer management affects the composition and stability of DOC in black soils via microbial functional gene pathways. Full article

Calonectria ilicicola (anamorph: Cylindrocladium parasiticum) is a globally important soil-borne fungal pathogen, causing Cylindrocladium black rot (CBR) in peanuts (Arachis hypogaea) and red crown rot (RCR) in soybeans (Glycine max), two legume crops central to global food security. Under favorable conditions, these diseases can cause yield losses of 15–50%, with severe epidemics causing substantial economic damage. A defining feature of C. ilicicola is its production of melanized microsclerotia that persist in soil for up to seven years, complicating long-term disease management across major production regions worldwide. The recent spread of RCR into the U.S. Midwest highlights the adaptive potential of the pathogen and underscores the urgency of updated, integrated control strategies. This review synthesizes current knowledge on disease symptoms, pathogen biology, the life cycle, isolation techniques, and molecular diagnostics, with particular emphasis on recent genomic and multiomics advances. These approaches have identified key virulence-associated genes and core pathogenicity factors, providing new insights into host–pathogen interactions and enabling more targeted resistance breeding through marker-assisted selection and the use of wild germplasm. We critically evaluate integrated disease management strategies, including host resistance, chemical and biological control, cultural practices, and physical interventions, highlighting their complementarities and limitations. By integrating classical pathology with emerging molecular and ecological innovations, this review provides a comprehensive background for developing more effective and sustainable management approaches for CBR and RCR. Full article

The unique environmental settings and increasing human activity in Northeast China have intensified gully erosion, threatening food security and sustainable development. However, systematic studies of environmental thresholds driving gully erosion remain scarce. This study analyzed erosion gullies across four typical regions of Northeast China using Google Earth imagery (2011 to 2021) and field survey data (2021) to investigate the (1) conditions under which gullies most frequently form and develop and (2) conditions conducive to gully stabilization. Results showed that, in semi-humid areas, gullies mainly developed on cultivated land with a gradient of 6–15°, though catchment area thresholds varied. In contrast, in the semi-arid mountain and hilly area, developing gullies grew fastest in forested areas with low vegetation coverage. Overall, while there were differences across the four regions, gullies were most likely to form on cultivated land, while stabilized gullies were concentrated in forested areas. These findings indicate that the conversion of cultivated land to forested land slows the development of erosional gullies. In addition, rainfall promotes the formation of new gullies and inhibits the growth of eroded gullies by reducing the effective drainage area. The results provide a theoretical basis for the prevention and control of gully erosion. Full article

Potato black scurf, caused by Rhizoctonia solani, is a widespread soil-borne disease in major potato-producing regions that reduces potato yield and tuber marketability. This study evaluated the field growth-promoting effects and disease-control efficacy of Bacillus velezensis HZ33 on the potato cultivars Xindaping and Longshu 7 and assessed its impact on rhizosphere microbial communities. Field trials showed that the application of HZ33 significantly enhanced potato growth and increased the chlorophyll content, yield, and commercial tuber rates. HZ33 also raised key soil nutrient levels. Its control efficacy against potato black scurf exceeded that of the chemical fungicide azoxystrobin. Application of HZ33 reduced the relative abundance of Rhizoctonia associated with black scurf and increased the relative abundance of beneficial fungi and bacteria. The microbial community structure correlated with both soil chemical properties and the disease index for potato black scurf. Overall, B. velezensis HZ33 appears to be a promising biocontrol agent for suppressing potato black scurf while improving potato yield. Full article

The transfer price of land management rights, as a key component of deepening rural reform at the 20th National Congress, profoundly influences the direction of agricultural production. Analyzing the land transfer management rights price differences can provide a deep understanding of regional transfer patterns and promote efficient land transfer. This study employs the SES framework to investigate factors of land transfer price differences by integrating correlation regression with the Boosted Regression Tree model. The results showed that (1) resource units determine land transfer management rights prices, with agricultural output value and net arable land income serving as core determinants. (2) City W is in the nascent land market, where the resource systems (RS) exert stronger influence. Key drivers include the transportation accessibility index and the proportion of flexible land. Compared to County K, where the land market exhibits full competition, the primary drivers of price shift from the resource systems to the governance systems and actors. Land transfer participants and the number of rural economic organizations become the main factors. Within the same Eastern black soil region, the transfer price differed by several thousand yuan per hectare. This disparity stems from differences in the two driving structures, necessitating the precise implementation of land transfer policies. Full article

Soil salinization is an escalating global concern threatening agricultural productivity and ecological sustainability, particularly in coastal regions where complex interactions among hydrological, climatic, and anthropogenic factors govern salt accumulation. The vertical differentiation and spatial heterogeneity of salinity drivers remain poorly resolved. We present an integrated modeling framework combining ensemble machine learning and spatial statistics to investigate the depth-specific dynamics of soil salinity in the Yellow River Delta, a vulnerable coastal agroecosystem. Using multi-source environmental predictors and 220 field samples harmonized to 30 m resolution, the hybrid Gray Wolf Optimizer–Random Forest–XGBoost model achieved high predictive accuracy for surface salinity (R² = 0.91, RMSE = 0.03 g/kg, MAE = 0.02 g/kg). Spatial autocorrelation analysis (Global Moran’s I = 0.25, p < 0.01) revealed pronounced clustering of high-salinity hotspots associated with seawater intrusion pathways and capillary rise. The results reveal distinct vertical control mechanisms: vegetation indices and soil water content dominate surface salinity, while total dissolved solids (TDS), pH, and groundwater depth increasingly influence middle and deep layers. By applying SHAP (SHapley Additive Explanations), we quantified nonlinear feature contributions and ranked key predictors across layers, offering mechanistic insights beyond conventional correlation. Our findings highlight the importance of depth-specific monitoring and intervention strategies and demonstrate how explainable machine learning can bridge the gap between black-box prediction and process understanding. This framework offers a generalizable framework that can be adapted to other coastal agroecosystems with similar hydro-environmental conditions. Full article

The sustainability of wheat-maize rotation systems in the North China Plain is challenged by the over-reliance on chemical fertilizers, which leads to the decline of soil organic matter and structural degradation, particularly in the unique Shajiang black soil (Vertisol). While straw return is widely recommended to mitigate these issues, the synergistic mechanisms of its long-term combination with chemical fertilizers on soil nutrient stoichiometry and aggregate stability remain inadequately quantified. A long-term field experiment was conducted with the five fertilization treatments including: (1) no fertilizer or straw (CK), (2) chemical fertilizer alone (NPK), (3) straw return chemical fertilizer (NPKS), (4) straw return with 10% straw-decomposing microbial inoculant combined with chemical fertilizer (10%NPKS), and (5) straw return with 20% straw-decomposing microbial inoculant combined with chemical fertilizer (20%NPKS) in the Shajiang black soil (Vertisol) region to investigate the effects of straw return combined with chemical fertilizers on soil organic carbon (SOC), total nitrogen (TN) and total phosphorus (TP) stoichiometry, aggregate stability, and crop yield in winter wheat-summer maize rotation systems of North China Plain. Our study demonstrated that the co-application of straw with a straw-decomposing microbial inoculant is a highly effective strategy for enhancing soil health and crop productivity, with its efficacy being critically dose-dependent. Our results identified the 10%NPKS treatment as the optimal practice. It most effectively improved soil physical structure by significantly increasing the content of large macroaggregates (>0.5 mm) and key stability indices (MWD, GMD, WA), while concurrently enhancing nutrient cycling, as evidenced by elevated SOC, TN, and shifted C/P and N/P stoichiometry. Multivariate analyses confirmed strong positive correlations among these soil properties, indicating a synergistic improvement in soil quality. Crucially, these enhancements translated into significant yield gains, with a notable crop-specific response: maize yield was maximized under the 10%NPKS treatment, whereas wheat yield benefited sufficiently from NPKS treatment. A key mechanistic insight was that 20%NPKS treatment, despite leading to the highest SOC and TN, induced a relative phosphorus limitation and likely caused transient nutrient immobilization, thereby attenuating its benefits for soil structure and yield. We conclude that co-applying straw with a 10% microbial inoculant combined with chemical fertilizer represents the superior strategy, offering a sustainable pathway to synergistically improve soil structure, nutrient availability, and crop productivity, particularly in maize-dominated systems. Full article

This study investigates the effects of different tillage practices on soil quality and maize yield in black soil farmland. Based on an eight-year continuous field plot experiment initiated in 2017, we examined the impacts of five tillage methods: conventional tillage (CT), no-tillage with straw mulching (NTS), subsoiling tillage with straw mulching (STS), harrow tillage with straw mulching and incorporation (HTS), and moldboard plowing tillage with straw incorporation (MPS). The focus was on soil structure, hydrothermal characteristics, organic matter, and nutrient content within the 0–40 cm soil layer, as well as maize dry matter accumulation and grain yield. The results indicate that, in 2023, compared to CT, STS significantly improved the soil structure and hydrothermal characteristic quality index (SHQI) in the 0–40 cm soil layer. Additionally, NTS, STS, HTS, and MPS significantly enhanced the soil organic matter and nutrient quality index (ONQI) in the 0–40 cm soil layer. NTS and STS increased the soil quality index (SQI) by 9.0% to 16.6% compared to the other treatments. Additionally, NTS, STS, HTS, and MPS significantly enhanced the soil organic matter and nutrient quality index (ONQI) in the 0–40 cm soil layer. In 2024, NTS and STS increased the soil quality index (SQI) by 9.0% to 16.6% compared to the other treatments. Furthermore, NTS and MPS significantly improved the SHQI in the 0–40 cm soil layer compared to CT. NTS and STS also significantly enhanced the ONQI in the 0–40 cm soil layer, while NTS, STS, and MPS increased the SQI by 7.3% to 22.6% compared to the other treatments. STS and MPS treatments significantly increased both hundred-kernel weight and grain yield compared to CT and NTS. Correlation and redundancy analyses revealed that SHQI in the 10–40 cm soil layer is a crucial factor affecting dry matter accumulation, yield, and its components in maize. In summary, in the semi-humid region of Northeast China, STS and MPS are cultivation techniques that optimize black soil quality and enhance maize grain yield. Full article

The accurate mapping of soil texture, a key determinant of soil’s hydrological and nutritional behavior, is essential for agricultural drought assessment, yet the application of multi-temporal satellite data for this purpose remains largely unexplored. In this study, we first identified the optimal prediction period by evaluating the performance of single-date imagery (satellite images captured on individual observation dates). Subsequently, dual-phase imagery (DPI) was developed to increase mapping accuracy. Finally, these refined predictions quantified soil texture’s response to drought and its corresponding thresholds. Results demonstrated that: (1) the bare soil period in April provided peak prediction accuracy for all texture fractions (Sand: R² = 0.617, RMSE = 10.21%; Silt: R² = 0.606, RMSE = 8.648%; Clay: R² = 0.604, RMSE = 1.945%); (2) Significant accuracy gain from DPI using April-August imagery fusion (Sand: R² = 0.677, RMSE = 9.386%; Silt: R² = 0.660, RMSE = 8.034%; Clay: R² = 0.658, RMSE = 1.807%); (3) sand content was the most critical factor influencing crop drought stress, with a threshold of 31%. By integrating multi-temporal satellite observations with quantitative drought evaluation for high-resolution soil texture mapping and precision agricultural management in Northeast China’s black soil region. Full article

Understanding how carbon is stored and stabilized in temperate agricultural soils is central to addressing one of the defining environmental challenges of our time—climate change. In this review, we bridge quantitative bibliometric insights with a qualitative synthesis of the mechanisms, regional differences, management practices, and models governing soil organic carbon (SOC) dynamics. We systematically analyzed 481 peer-reviewed publications published between 1990 and 2024, retrieved from Scopus and Web of Science, using bibliometric tools such as VOSviewer to map research trends, collaboration networks, and thematic evolution. The bibliometric analysis revealed a marked increase in publications after 2010, coinciding with growing global interest in climate-smart agriculture and carbon sequestration policies. Comparative synthesis across temperate sub-regions—such as the humid temperate plains of Europe, the semi-arid temperate zones, and the temperate black soil region of Northeast China—reveals that the effectiveness of common practices varies with soil mineralogy, texture, moisture regimes, and historical land-use. Reduced tillage (average SOC gain of 0.25 Mg C ha⁻¹ yr⁻¹), cover cropping (0.32 Mg C ha⁻¹ yr⁻¹), and organic amendments such as compost and biochar (up to 1.1 Mg C ha⁻¹ yr⁻¹) consistently enhance SOC accumulation, but with region-specific outcomes driven by these contextual factors. Recognizing such heterogeneity is essential for developing regionally actionable management recommendations. Recent advances in machine learning, remote sensing, and process-based modeling are enabling more accurate and scalable monitoring of SOC stocks, yet challenges remain in integrating micro-scale stabilization processes with regional and global assessments. To address these gaps, this review highlights a multi-method integration pathway—combining field measurements, mechanistic modeling, data-driven approaches, and policy instruments that incentivize adoption of evidence-based practices. By combining quantitative bibliometric analysis with regionally informed mechanistic synthesis, this review provides a holistic understanding of how knowledge about SOC in temperate agroecosystems has evolved and where future opportunities lie. The findings underscore that temperate agricultural soils, when supported by appropriate scientific practices and enabling policy frameworks, represent one of the most accessible natural climate solutions for advancing climate-resilient and sustainable food systems. Full article

Straw incorporation, as a widely recommended agronomic practice, has been continuously enhancing global crop production and soil–water conservation. However, the absence of a direct predictive capability for the long-term residual biomass of incorporated straw, based on management practices, constrains an accurate assessment of its effectiveness for soil conservation. To address these knowledge gaps, this study conducted systematic 4-year in situ monitoring of decomposition pits with varying incorporation amounts (A6 with 6 kg ha⁻¹, A8 with 8 kg ha⁻¹, A10 with 10 kg ha⁻¹, A12 with 12 kg ha⁻¹, and A14 with 14 kg ha⁻¹) and burial depths (D1 with 0–10 cm, D2 with 10–20 cm, D3 with 20–30 cm, D4 with 30–40 cm, D5 with 40–50 cm) to analyze long-term decomposition dynamics. Furthermore, time-dependent equations for post-incorporation residual biomass were developed based on management variables (incorporation amount and burial depth) to enhance the accuracy of soil loss prediction. The results showed that the higher incorporation amounts accelerated decomposition, with the residual straw ratios (RSRs) reduced by 27.4–62.2% compared to lower amounts at equivalent burial depths. Decomposition slowed with depth, and the RSR increased significantly with greater burial depth, rising at rates of 0.2–1.2% cm⁻¹ (p < 0.05). The RSR decreased significantly with longer incorporation duration at rates of 6.9–18.6% a⁻¹ (p < 0.05), with deeper soil layers exhibiting greater decline rates than shallower depths. The relationship between RSR and landfill amount (m), burial depth (d), and landfill years (a) is represented as follows: RSR = 10^1.62 a⁻¹ m^−0.54 d^0.45 (R² = 0.76). Based on this equation, the soil loss ratios (SLRs) under continuous straw incorporation for 4 years were estimated, and the results suggest that constant straw incorporation exerts cumulative effects, progressively reducing the SLR. This study provides the theoretical foundation for promoting and managing straw incorporation practices. Full article

In order to investigate the current status of soil heavy metal pollution, ecological risk, and risk sources in the black soil area of the Eastern Inner Mongolia Province, topsoil (0–20 cm) samples from farmland in the black soil area (N = 163) were collected to determine the contents of seven heavy metals. The levels of soil heavy metal pollution and ecological risk in the study area were evaluated by combining the geo-accumulation index, potential ecological risk index, and static environmental carrying capacity; the positive matrix factorization (PMF) model was used to identify the pollution sources and contributions of heavy metals in the soil and analyze the risk levels to adults and children. The soil was predominantly weakly acidic, with mean values of Cr, Ni, Cu, As, Cd, Pb, and Zn of 61.77, 26.77, 17.07, 12.11, 0.08, 12.61, and 85.71 mg·kg⁻¹. The mean concentrations of heavy metals exceeded the background values, except for Pb, the mean concentration of which was lower than the soil background. Ni concentrations of 6.21% at the sampling sites exceeded the risk screening value for agricultural soils. The geo-accumulation index showed that Cr (55.15%) and As (54.00%) were mainly mild pollutants; the static environmental carrying capacity indicated that the soils were slightly polluted by Ni, As, and Zn; and the potential ecological risk indices of Cd, Ni, and As were at moderate levels. The PMF model analyzed three pollution sources: mixed agricultural practice–transportation sources (39.46%), mineral-related activity sources (27.01%), and pesticide–fertilizer agricultural practices (33.53%). The human health risk assessment indicated that 46.58% of sampling sites posed a carcinogenic risk to children, with Ni as the main carcinogenic element. In conclusion, the potential contamination of As, Cd, Ni, Cr, and Zn in the Eastern Inner Mongolia farmland black soil area should be further studied. Full article

Soil organic matter (SOM) is a key indicator of cropland quality and carbon cycling. Accurate SOM mapping is essential for sustainable soil management and carbon sink assessment. This study investigated the effects of interannual climatic variability on SOM prediction using remote sensing and machine learning. Youyi Farm in the Sanjiang Plain, Heilongjiang Province, was selected as the study area, covering three representative years: 2019 (flood), 2020 (normal), and 2021 (drought). Based on multi-temporal Sentinel-2 imagery and environmental covariates, Random Forest models were used to evaluate single- and dual-period combinations. Results showed that combining bare-soil and crop-season images consistently improved accuracy, with optimal combinations varying by year (R² = 0.544–0.609). Incorporating temperature, precipitation, and elevation enhanced model performance, particularly temperature, which contributed most to prediction accuracy. Feature selection further improved model stability and generalization. Spatially, SOM showed a pattern of higher values in the northeast and lower in the central region, shaped by topography and cultivation. This study innovatively integrates interannual climatic variability with remote sensing temporal combination and feature selection, constructing a climate-adaptive SOM mapping framework and providing new insights for accurate inversion of cropland SOM under extreme climates, highlights the importance of multi-temporal imagery, environmental factors, and feature selection for robust SOM mapping under different climatic conditions, providing technical support for long-term cropland quality monitoring. Full article

Gully erosion poses a serious threat to soil fertility and agricultural sustainability in Northeast China’s black soil region. Accurate and efficient mapping of erosion gullies is critical for enabling targeted soil conservation and precision land management. In this study, we developed a texture-enhanced deep learning framework for automated gully extraction using high-resolution GF-1 and GF-2 satellite imagery. Key texture parameters—specifically mean and contrast features derived from the gray-level co-occurrence matrix (GLCM) under a 5 × 5 window and 32 gray levels—were systematically optimized and fused with multispectral bands. We trained and evaluated three convolutional neural network architectures—U-Net, U-Net++, and DeepLabv3+—under consistent data and evaluation protocols. Results demonstrate that the integration of texture features significantly enhanced extraction performance, with U-Net achieving the highest overall accuracy (90.27%) and average precision (90.87%), surpassing DeepLabv3+ and U-Net++ by margins of 6.06% and 9.33%, respectively. Visualization via Class Activation Mapping (CAM) further confirmed improved boundary discrimination and reduced misclassification of spectrally similar non-gully features, such as field roads and farmland edges. The proposed GLCM–CNN integrated approach offers an interpretable and transferable solution for gully identification and provides a technical foundation for large-scale monitoring of soil and water conservation in black soil landscapes. Full article

Excess fertiliser and sub-optimal irrigation threaten soil health in greenhouse vegetable systems on black soils. This study explored how water–fertilizer regimes shape soil aggregate structure, stability, and soil organic carbon (SOC) sequestration in a meadow black soil eggplant system in Heilongjiang, China. Using a randomized block design with drip irrigation, three treatments were tested: conventional water and fertilizer (WF), conventional water with 20% fertilizer reduction (W80%F), and 20% water reduction with conventional fertilizer (80%WF). Results showed that 80%WF significantly increased macro-aggregate proportion, improved stability (mean weight diameter, MWD; geometric mean diameter, GMD), enhanced total organic carbon (TOC) content, and strengthened carbon sequestration, whereas W80%F weakened aggregate stability and reduced SOC in deeper layers. Water availability was the dominant factor for aggregate formation and SOC in surface and middle layers, while nutrients were more influential at depth. These findings demonstrate that moderate water reduction is more effective than fertilizer reduction in improving soil structure and carbon sink capacity, providing a scientific basis for precision water–fertilizer management and sustainable greenhouse agriculture in black soil regions. Full article