Agricultural soil organic carbon stocks in the north-eastern Iberian Peninsula: Drivers and spatial variability

The response of crop yield, carbon sequestration, and global warming potential to straw and biochar applications: A meta-analysis

Organic materials play an important role in improving crop yield. However, due to variations in natural and field management practices, the impact of straw incorporation (NS) and biochar addition (NB) on soil organic carbon (SOC) sequestration and global warming potential (GWP) remains uncertain. This meta-analysis synthesizes the findings from 112 published studies, encompassing 897 samples, to assess the effects of NS and NB on crop yield, SOC, and GWP. The results reveal that Northeast China has the highest SOC stocks (40.80 Mg ha-1) and annual SOC sequestration (4.27 Mg ha-1 yr-1) compared to other regions. Notably, the NS and NB differ in their effect sizes on improving crop yield (7.68 % and 8.23 %, respectively) and SOC (6.92 % and 30.72 %, respectively), with opposing effects on GWP (increasing by 37.69 % in NS and decreasing by 23.94 % in NB). Following organic material application, climatic conditions, crop and field type, and soil properties affected SOC content and GWP. The main factors influencing variations in crop yield, SOC, and GWP were mean annual temperature and precipitation, initial SOC content, and soil pH, accounting for 57.46 %-60.29 %, 54.75 %-58.52 %, and 61.81 %-65.11 %, respectively. Considering the need to balance food demand, soil fertility and environmental benefits, biochar emerges as a recommended strategy for advancing future agriculture goals. In summary, this study quantitatively assessed the impact of organic material on crop yield, SOC, and greenhouse gas emissions, offering a scientific foundation for optimizing these factors under diverse regional conditions.

Soil carbon sequestration potential of cultivated lands and its controlling factors in China

Understanding soil organic carbon (SOC) stocks and carbon sequestration potential in cultivated lands can have significant benefit for mitigating climate change and emission reduction. However, there is currently a lack of spatially explicit information on this topic in China, and our understanding of the factors that influence both saturated SOC level (SOCS) and soil organic carbon density (SOCD) remains limited. This study predicted SOCS and SOCD of cultivated lands across mainland China based on point SOC measurements, and mapped its spatial distribution using environmental variables as predictors. Based on the differentiation between SOCS and SOCD, the soil organic carbon sequestration potentials (SOCP) of cultivated land were calculated. Boosted regression trees (BRT), random forest (RF), and support vector machine (SVM) were evaluated as prediction models, and the RF model presented the best performance in predicting SOCS and SOCD based on 10-fold cross-validation. A total of 991 topsoil (0-20 cm) SOC measurements and 12 environmental variables explaining topography, climate, organism, soil properties, and human activity were used as predictors in the model. Both SOCS and SOCD suggested higher SOC levels in northeast China and lower levels in central China. The cultivated lands in China had the potential to sequester about 2.13 ± 0.96 kg m-2 (3.25 Pg) SOC in the top 20 cm soil depth. Northeastern China had the largest SOCP followed by Northern China, and Southwestern China had the lowest SOCP. The primary environmental variables that affected the spatial variation of SOCS were mean annual temperature, followed by clay content and normalized difference vegetation index (NDVI). The assessment and mapping of SOCP in China's cultivated lands holds significance importance as it can provide valuable insights to policymakers and researchers about SOCP, and aid in formulating climate change mitigation strategies.

Incorporating agricultural practices in digital mapping improves prediction of cropland soil organic carbon content: The case of the Tuojiang River Basin

Accurate mapping of soil organic carbon (SOC) in cropland is essential for improving soil management in agriculture and assessing the potential of different strategies aiming at climate change mitigation. Cropland management practices have large impacts on agricultural soils, but have rarely been considered in previous SOC mapping work. In this study, cropland management practices including carbon input (CI), length of cultivation (LC), and irrigation (Irri) were incorporated as agricultural management covariates and integrated with natural variables to predict the spatial distribution of SOC using the Extreme Gradient Boosting (XGBoost) model. Additionally, we evaluated the performance of incorporating agricultural management practice variables in the prediction of cropland topsoil SOC. A case study was carried out in a traditional agricultural area in the Tuojiang River Basin, China. We found that CI was the most important environmental covariate for predicting cropland SOC. Adding cropland management practices to natural variables improved prediction accuracy, with the coefficient of determination (R²), the root mean squared error (RMSE) and Lin's Concordance Correlation Coefficient (LCCC) improving by 16.67%, 17.75% and 5.62%, respectively. Our results highlight the effectiveness of incorporating agricultural management practice information into SOC prediction models. We conclude that the construction of spatio-temporal database of agricultural management practices derived from inventories is a research priority to improve the reliability of SOC model prediction.

Cropland carbon stocks driven by soil characteristics, rainfall and elevation

Soil organic carbon (SOC) can influence atmospheric CO₂ concentration and then the extent to which the climate emergency is mitigated globally. It follows the elucidation of the driving factors of cropland SOC stocks, which is fundamental to reducing soil carbon loss and promoting soil carbon sequestration. Here, we examined the influence of 16 environmental variables on SOC stocks and sequestration based on three machine learning soil mapping methods, i.e. multiple linear regression (MLR), random forest (RF) and extreme gradient boosting (XGBOOST), with 2875 observed soil samples from cropland topsoil across Hunan Province, China in 2010. We employed a structural equation model (SEM) to extricate the driving mechanisms of environmental variables on SOC stocks at the regional scale. Our results show that XGBOOST had the most reliable performance in predicting SOC stocks, explaining 66 % of the total SOC stock variation. Croplands with high SOC stocks were distributed in low-altitude and water-sufficient areas. The partial dependence of SOC on precipitation showed a trend of increasing and then slowly decreasing. In addition, the grid-based SEM results clearly presented the direct and indirect routes of environmental variables' impacts on cropland SOC stocks. Soil properties regulated by elevation, were the most influential natural factor on SOC stocks. Precipitation and elevation drove SOC stocks through direct and indirect effects respectively. Our SEM combined with machine learning approach can provide an effective explanation of the driving mechanism for SOC accumulation. We expect our proposed modelling approach can be applied to other regions and offer new insights, as a reference for mitigating cropland soil carbon loss under climate emergency conditions.

Spatio-Temporal Variation and Its Driving Forces of Soil Organic Carbon along an Urban-Rural Gradient: A Case Study of Beijing

Rapid urbanization has reshaped land cover and the ecological environment, potentially improving or deteriorating soil organic carbon (SOC). However, the response of SOC to urbanization has not yet been fully exploited. Herein, by using the land-use transfer matrix, the Sen & Mann-Kendall tests, the Hurst index, and a geographical and temporal weighted regression (GTWR) model, as well as an urban-rural gradient perspective, we assessed the dynamic response of SOC to Beijing's urbanization from 2001 to2015 and identified the main drivers. The results found that SOC stock decreased by 7651.50 t C during the study period. SOC density varied significantly along an urban-rural gradient, with high value areas mainly being located in remote mountainous rural areas and low value areas mainly being located in urban areas on the plains. There was an uneven variation in SOC density across the urban-rural gradient, with suburban areas (25-40 km away from urban cores) losing the most SOC density while urban areas and rural areas remained relatively unchanged. GTWR model revealed the spatio-temporal non-flat stability of various driving forces. Precipitation, the proportion of forest, the proportion of grassland, the population, distance to the urban center, the slope, and the silt content are the main factors related to SOC stock change. As a result, we suggest policy makers reconceptualize the uneven variation in the SOC between urban and rural areas, emphasize suburban areas as a target for controlling SOC loss, and take into consideration the spatial and temporal heterogeneity of the factors influencing SOC stock when evaluating policies.

Quantifying ecosystem service mismatches for land use planning: spatial-temporal characteristics and novel approach-a case study in Jiangsu Province, China

Land use and land cover changes associated with urbanization have had a significant influence on ecosystem services (ESs), but previous studies have insufficiently focused on the relationships between ES supply and demand; these relationships are seldom considered in the science-policy frameworks of land use planning. In this study, a specific supply-demand indicator was constructed to measure ES supply and demand and their disparity across multiple scales in Jiangsu Province from 2000 to 2018. High spatial heterogeneity and mismatches of ES supply and demand were found in water yield, grain production, carbon sequestration, soil conservation, heat regulation, and recreation services. At provincial scale, the supplies of carbon sequestration and heat regulation services were smaller than their demands. At the 1-km² grid scale, the ES supply and demand mismatches in urban areas were more serious than those in surrounding areas, especially for carbon sequestration and recreation services. Five ES supply-demand risk zones were identified based on the current status and trends of all ES supply and demand. Southern Jiangsu generally had high risks of ES mismatch, which should be reduced by strategic planning. Constructing the ES supply-demand indicator is a novel practice that assists in evaluating environmental issues and integrating them into further development decisions. This paper suggests that governments should reduce ES mismatches with reference to local conditions (economic development, industrial type, and ecological carrying capacity) and the actual situation of ES supply and demand.

Soil organic carbon accumulation rates on Mediterranean abandoned agricultural lands

Secondary succession on abandoned agricultural lands can produce climate change mitigation co-benefits, such as soil carbon sequestration. However, the accumulation of soil organic carbon (SOC) in Mediterranean regions has been difficult to predict and is subject to multiple environmental and land management factors. Gains, losses, and no significant changes have all been reported. Here we compile chronosequence data (n = 113) from published studies and new field sites to assess the response of SOC to agricultural land abandonment in peninsular Spain. We found an overall SOC accumulation rate of +2.3% yr^-1 post-abandonment. SOC dynamics are highly variable and context-dependent. Minimal change occurs on abandoned cereal croplands compared to abandoned woody croplands (+4% yr^-1). Accumulation is most prevalent within a Goldilocks climatic window of ~13-17 °C and ~450-900 mm precipitation, promoting >100% gains after three decades. Our secondary forest field sites accrued 40.8 Mg C ha^-1 (+172%) following abandonment and displayed greater SOC and N depth heterogeneity than natural forests demonstrating the long-lasting impact of agriculture. Although changes in regional climate and crop types abandoned will impact future carbon sequestration, abandonment remains a low-cost, long-term natural climate solution best incorporated in tandem with other multipurpose sustainable land management strategies.

Spatiotemporal patterns and drivers of ecosystem service supply and demand across the conterminous United States: A multiscale analysis

Land-use and land-cover changes associated with urbanization have significantly influenced biodiversity and ecosystem functions, as well as the supply and demand of ecosystem services (ESs). Assessing ESs and exploring their drivers are critical for regional land-use planning and ecological sustainability. In this study, the supply-demand matrix approach was used to quantify ES supply, demand, and their gap at multiple scales across the conterminous United States from 1940 to 2011. A new integrated measurement framework was proposed to offset ES deficits by identifying an optimal land-use conversion strategy. We focused on exploring the scale and spatial effects of the impacts of various drivers on ESs using ordination and regression analysis. The results showed that the expansion of developed land led to decreased ES supply and increased ES demand during the past seven decades, generating growing ES deficits at different scales, especially in highly urbanized metropolitan areas. To alleviate or offset ES deficits, promoting the intensive utilization of developed land and converting cropland, pasture, and barren land into forests would be the optimal land use strategies. Moreover, the drivers of ESs exhibited not only scale dependence but also spatial heterogeneity. The smaller the scale, the more diverse the drivers. The natural and socioeconomic drivers explained less variation at the metropolitan scale than at the state scale. Economic factors were key drivers for ESs at the state scale, while social factors were key drivers at the metropolitan scale. The regression coefficients for the drivers of ESs in the geographically weighted regression (GWR) model showed remarkable spatial heterogeneity. The GWR coefficients might have important implications for decision making in ES management. Localized and efficient land-use strategies and management policies are needed to reduce the ecological footprints of urban areas and thus achieve regional sustainability.