Cropland carbon stocks driven by soil characteristics, rainfall and elevation

Variability analysis of soil organic carbon content across land use types and its digital mapping using machine learning and deep learning algorithms

Soil organic carbon (SOC) plays a crucial role in carbon cycle management and soil fertility. Understanding the spatial variations in SOC content is vital for supporting sustainable soil resource management. In this study, we analyzed the variability in SOC content across eleven different types of land use in the mining basin of Provence in southeastern France. We modelled this variability spatially using machine and deep learning regression. Four algorithms were tested: random forest (RF), support vector machine (SVM), extreme gradient boosting (XGBoost), and deep neural networks (DNNs). These integrated 162 soil samples and 21 environmental covariates, including climatic parameters, lithology, topographical features, land cover, remote sensing data, and soil physicochemical parameters. The results clearly show a large variability in SOC content across land use types, with forests revealing the highest values (mean of 69.3 g/kg) and arable land the lowest (mean of 8.9 g/kg). The Pearson correlation coefficients (R) indicate that land cover, topography, lithology, environmental indices, and clay content are the main factors influencing the SOC content. The XGBoost model generated the best result (R2 = 0.73), closely followed by RF (R2 = 0.68) and DNN (R2 = 0.60), while SVM showed the weakest performance (R2 = 0.36). XGBoost and RF remain the best options for obtaining reliable results with a limited number of soil samples and reduced calculation time. The results of this study provide vital insights for managing soil organic carbon in southeastern France and for climate change mitigation in sustainable land management.

A prediction model of soil organic carbon into river and its driving mechanism in red soil region

Soil erosion contributes to the irreversible loss of soil organic carbon (SOC) into rivers (SOCR), posing risks to food security and carbon cycle assessments. Red soil regions, characterized by high carbon sink potential and selenium enrichment, are particularly vulnerable. However, existing studies largely rely on small-scale experiments, with limited understanding of basin-scale SOCR dynamics and their driving factors. This study integrates the Soil and Water Assessment Tool (SWAT) for sediment yield simulation and a Soil Organic Carbon Content (SOCC) model to quantify SOCR at the basin scale. A Random Forest-based prediction model was developed to explore the spatial-temporal variability and driving mechanisms of SOCR in the Dongjiang River Basin (DRB), a representative red soil region in southern China. Results indicate significant spatial-temporal heterogeneity, with higher SOCR observed in downstream, human-disturbed areas during flood seasons. The model demonstrates excellent performance (R²>0.9). Key drivers of SOCR variability include sediment yield, cultivated land area (CULT), and urban land area (TOWN), with urbanization showing stronger sensitivity than cultivation due to factors such as city size and impervious surfaces. The proposed framework reveals the dynamic change characteristics of SOCR and its driving mechanism, which has the potential to be generalized to other basins with similar studies, and provides a technical support for land resource management and carbon cycling in the erosion-prone red soil region.

The construction of international wetland urban ecological security pattern coupled with MSPA and ESF

Creating an ecological security pattern is crucial for balancing the sustainable development of areas where human activities and the natural environment intersect. Using Hefei, an internationally recognized Wetland City, as a case study, we extracted ecological sources through ecological service function (ESF) analysis and morphological spatial pattern analysis (MSPA) core area connectivity analysis. Based on these ecological sources, we developed an ecological resistance surface system and identified ecological corridors and nodes using circuit theory. The findings are as follows: (1) Ecological source areas: The primary ecological sources in Hefei are located in the water bodies, forested areas, and scattered grasslands in the central and eastern parts of the city. This ecological source area covers 978.96 km2, which constitutes 8.55% of the city's total area. (2) Ecological corridors: Hefei contains 43 ecological corridors with a total length of 940.3 km, averaging 21.87 km each. These corridors are crucial for maintaining ecological connectivity and facilitating species movement. (3) Ecological nodes: There are 13 significant ecological nodes in Hefei, including 6 ecological obstacle points and 7 ecological pinpoints. These nodes play a vital role in supporting ecological processes and ensuring habitat connectivity. (4) Evaluation metrics: The α and β values for the source identification method that integrates MSPA with ecological service functions were 2.15 and 0.8, respectively, which are higher than those of the control group. Conversely, the γ-value was 0.18, lower than that of the control group. These results indicate that the combined ecological source extraction method provides significant advantages in terms of ecological corridor integrity, connectivity, and ecological flow management.

Identifying environmental impacts on planktonic algal proliferation and associated risks: a five-year observation study in Danjiangkou Reservoir, China

Understanding the risks of planktonic algal proliferation and its environmental causes is crucial for protecting water quality and controlling ecological risks. Reservoirs, due to the characteristics of slow flow rates and long hydraulic retention times, are more prone to eutrophication and algal proliferation. Chlorophyll-a (Chl-a) serves as an indicator of planktonic algal biomass. Exploring the intricate interactions and driving mechanisms between Chl-a and the water environment, and the potential risks of algal blooms, is crucial for ensuring the ecological safety of reservoirs and the health of water users. This study focused on the Danjiangkou Reservoir (DJKR), the core water source of the Middle Route of the South-to-North Water Diversion Project of China (MRSNWDPC). The multivariate statistical methods and structural equation modeling were used to explore the relationships between chlorophyll-a (Chl-a) contents and water quality factors and understand the driving mechanisms affecting Chl-a variations. The Copula function and Bayesian theory were combined to analyze the risk of changes in Chl-a concentrations at Taocha (TC) station, which is the core water source intake point of the MRSNWDPC. The results showed that the factors driving planktonic algal proliferation were spatially heterogeneous. The main factors affecting Chl-a concentrations in Dan Reservoir (DR) were water physicochemical factors (water temperature, dissolved oxygen, pH value, and turbidity) with a total contribution rate of 60.18%, whereas those in Han Reservoir (HR) were nutrient factors (total nitrogen, total phosphorus, and ammonia nitrogen) with a total contribution rate of 73.58%. In TC, the main factors were water physicochemical factors (turbidity, pH, and water temperature) and nutrient factors (total phosphorus) with total contribution rates of 39.76% and 45.78%, respectively. When Chl-a concentrations in other areas of the DJKR ranged from the minimum to the uppermost quartile, the probabilities that Chl-a concentrations at the TC station exceeded 3.4 μg/L (the benchmark value of Chl-a for lakes in the central-eastern lake area of China) owing to the influence of these areas were all less than 10%. Thus, the risk of planktonic algal proliferation at the MRSNWDPC intake point is low. This study developed an integrated framework to investigate spatiotemporal changes in algal proliferation and their driving factors in reservoirs, which can be used to support water quality management in mega hydro projects.

Mapping Sub-surface Distribution of Soil Organic Carbon Stocks in South Africa's Arid and Semi-Arid Landscapes: Implications for Land Management and Climate Change Mitigation

Soil organic carbon (SOC) stocks are critical for land management strategies and climate change mitigation. However, understanding SOC distribution in South Africa's arid and semi-arid regions remains a challenge due to data limitations, and the complex spatial and sub-surface variability in SOC stocks driven by desertification and land degradation. Thus, to support soil and land-use management practices as well as advance climate change mitigation efforts, there is an urgent need to provide more precise SOC stock estimates within South Africa's arid and semi-arid regions. Hence, this study adopted remote-sensing approaches to determine the spatial sub-surface distribution of SOC stocks and the influence of environmental co-variates at four soil depths (i.e., 0-30 cm, 30-60 cm, 60-100 cm, and 100-200 cm). Using two regression-based algorithms, i.e., Extreme Gradient Boosting (XGBoost) and Random Forest (RF), the study found the former (RMSE values ranging from 7.12 t/ha to 29.55 t/ha) to be a superior predictor of SOC in comparison to the latter (RMSE values ranging from 7.36 t/ha to 31.10 t/ha). Nonetheless, both models achieved satisfactory accuracy (R2 ≥ 0.52) for regional-scale SOC predictions at the studied soil depths. Thereafter, using a variable importance analysis, the study demonstrated the influence of climatic variables like rainfall and temperature on SOC stocks at different depths. Furthermore, the study revealed significant spatial variability in SOC stocks, and an increase in SOC stocks with soil depth. Overall, these findings enhance the understanding of SOC dynamics in South Africa's arid and semi-arid landscapes and emphasizes the importance of considering site specific topo-climatic characteristics for sustainable land management and climate change mitigation. Furthermore, the study offers valuable insights into sub-surface SOC distribution, crucial for informing carbon sequestration strategies, guiding land management practices, and informing environmental policies within arid and semi-arid environments.

Unraveling the relationship between soil carbon-degrading enzyme activity and carbon fraction under biogas slurry topdressing

Biogas slurry, a by-product of the anaerobic digestion of biomass waste, predominantly consisting of livestock and poultry manure, is widely acclaimed as a sustainable organic fertilizer owing to its abundant reserves of essential nutrients. Its distinctive liquid composition, when tactfully integrated with a drip irrigation system, unveils immense potential, offering unparalleled convenience in application. In this study, we investigated the impact of biogas slurry topdressing as a replacement for chemical fertilizer (BSTR) on soil total organic carbon (TOC) fractions and carbon (C)-degrading enzyme activities across different soil depths (surface, sub-surface, and deep) during the tasseling (VT) and full maturity stage (R6) of maize. BSTR increased the TOC content within each soil layer during both VT and R6 periods, inducing alterations in the content and proportion of individual C component, particularly in the topsoil. Notably, the pure biogas slurry topdressing treatment (100%BS) compared with the pure chemical fertilizer topdressing treatment (CF), exhibited a 38.9% increase in the labile organic carbon of the topsoil during VT, and a 30.3% increase in the recalcitrant organic carbon during R6, facilitating microbial nutrient utilization and post-harvest C storage during the vigorous growth period of maize. Furthermore, BSTR treatment stimulated the activity of oxidative and hydrolytic C-degrading enzymes, with the 100%BS treatment showcasing the most significant enhancements, with its average geometric enzyme activity surpassing that of CF treatment by 27.9% and 27.4%, respectively. This enhancement facilitated ongoing and efficient degradation and transformation of C. Additionally, we screened for C components and C-degrading enzymes that are relatively sensitive to BSTR. The study highlight the advantages of employing pure biogas slurry topdressing, which enhances C component and C-degrading enzyme activity, thereby reducing the risk of soil degradation. This research lays a solid theoretical foundation for the rational recycling of biogas slurry.

Effects of elevation and geomorphology on cadmium, lead and chromium enrichment in paddy soil and rice: A case study in the Xiangtan basin of China

The distributions of heavy metals in paddy fields and rice along river valleys were studied to explore the key factors affecting the accumulation of heavy metals in the upstream terraces and downstream plains. Results from 975 sampling sites showed that elevation, growing season and soil organic matter (OM) had significant effects on the content of Cd and Pb in topsoil and rice. The content of Cd (0.47-0.66 mg kg-1) and Pb (49.9-68.6 mg kg-1) in paddy fields with low elevation (30-60 m) in the downstream plains was significantly higher than the content of Cd (0.29-0.38 mg kg-1) and Pb (43.9-56.3 mg kg-1) in the upstream terraces with high altitude (60-90 m). In the double-rice production area, late rice generally produced grains with higher Cd and Pb content than early rice. Soil Cd was positively increased with the content of OM, especially in the downstream plains. When elevation was used for principal component analysis, plains with low elevation were grouped together with high content of total and soluble Cd, OM and Pb in soil, as well as high content of Cd and Pb in late rice. Altitude is one of the key factors affecting Cd content in rice. Although content of Cr (93.7-138.0 mg kg-1) was significantly higher than that of Cd and Pb in soil, content of Cr was lower than that of Cd in rice. These results indicate that paddy fields with elevation of 30-60 m in the downstream plains had high risk to produce late rice with Cd and Pb content exceeding the food safety standard 0.2 mg kg-1, which may be resulted from the driving force of runoff on soil soluble Cd and Pb from terraces to alluvial plains in river valleys.

Patterns and drivers of carbon stock change in ecological restoration regions: A case study of upper Yangtze River Basin, China

Balancing ecology and human development has been a long and wide concern. The upper Yangtze River Basin (UYRB) of China has implemented large important ecological restoration projects since the last century. These restoration practices have changed land use patterns within the UYRB, consequently impacting the local carbon cycle. The most noteworthy project is the Grain for Green Program, which returns cropland to natural vegetation (forest and grassland). Yet the effects of restoration on land use change, carbon sequestration, and associated food production remain unclear. This study utilized remote sensing data and conversion coefficients to analyze the ecological-policy-induced land use changes of the UYRB from 2000 to 2020 and their impacts on terrestrial carbon sequestration. Linear regression, machine learning, and structural equation modeling (SEM) were utilized to evaluate the correlations between environmental and socio-economic factors and the distribution of carbon stocks. The results indicated positive effects of ecological activities on the UYRB, despite decreases in cropland. Over the past 20 years, the UYRB had sequestered carbon by a total amount of 1796 ± 926 Mt C. The spatial distribution of sequestered carbon demonstrated a strong correlation with slopes, followed by temperatures. The SEM results indicated that agricultural production and carbon sequestration were enhanced synergically under land use changes. This further demonstrated the effectiveness of these land policies in achieving a balance between crop productivity and ecology protection. We emphasized the importance of vegetation restoration in achieving carbon neutrality and the necessity to continue these projects. We suggested a more reasonable land management for the future UYRB based on the characteristics of each geographical subregion. This work serves as an example of effective land management to other locations worldwide perusing the harmony of ecological restoration and human development.