Patterns and trends of topsoil carbon in the UK: Complex interactions of land use change, climate and pollution

Comparison of direct and indirect soil organic carbon prediction at farm field scale

To advance sustainable and resilient agricultural management policies, especially during land use changes, it is imperative to monitor, report, and verify soil organic carbon (SOC) content rigorously to inform its stock. However, conventional methods often entail challenging, time-consuming, and costly direct soil measurements. Integrating data from long-term experiments (LTEs) with freely available remote sensing (RS) techniques presents exciting prospects for assessing SOC temporal and spatial change. The objective of this study was to develop a low-cost, field-based statistical model that could be used as a decision-making aid to understand the temporal and spatial variation of SOC content in temperate farmland under different land use and management. A ten-year dataset from the North Wyke Farm Platform, a 20-field, LTE system established in southwestern England in 2010, was used as a case study in conjunction with an RS dataset. Linear, additive and mixed regression models were compared for predicting SOC content based upon combinations of environmental variables that are freely accessible (termed open) and those that require direct measurement or farmer questionnaires (termed closed). These included an RS-derived Ecosystem Services Provision Index (ESPI), topography (slope, aspect), weather (temperature, precipitation), soil (soil units, total nitrogen [TN], pH), and field management practices. Additive models (specifically Generalised Additive Models (GAMs)) were found to be the most effective at predicting space-time SOC variability. When the combined open and closed factors (excluding TN) were considered, significant predictors of SOC were: management related to ploughing being the most important predictor, soil unit (class), aspect, and temperature (GAM fit with a normalised RMSE = 9.1%, equivalent to 0.4% of SOC content). The relative strength of the best-fitting GAM with open data only, which included ESPI, aspect, and slope (normalised RMSE = 13.0%, equivalent to 0.6% of SOC content), suggested that this more practical and cost-effective model enables sufficiently accurate prediction of SOC.

Mapping Soil Organic Carbon in Low-Relief Farmlands Based on Stratified Heterogeneous Relationship

Accurate mapping of farmland soil organic carbon (SOC) provides valuable information for evaluating soil quality and guiding agricultural management. The integration of natural factors, agricultural activities, and landscape patterns may well fit the high spatial variation of SOC in low-relief farmlands. However, commonly used prediction methods are global models, ignoring the stratified heterogeneous relationship between SOC and environmental variables and failing to reveal the determinants of SOC in different subregions. Using 242 topsoil samples collected from Jianghan Plain, China, this study explored the stratified heterogeneous relationship between SOC and natural factors, agricultural activities, and landscape metrics, determined the dominant factors of SOC in each stratum, and predicted the spatial distribution of SOC using the Cubist model. Ordinary kriging, stepwise linear regression (SLR), and random forest (RF) were used as references. SLR and RF results showed that land use types, multiple cropping index, straw return, and percentage of water bodies are global dominant factors of SOC. Cubist results exhibited that the dominant factors of SOC vary in different cropping systems. Compared with the SOC of paddy fields, the SOC of irrigated land was more affected by irrigation-related factors. The effect of straw return on SOC was diverse under different cropping intensities. The Cubist model outperformed the other models in explaining SOC variation and SOC mapping (fitting R2 = 0.370 and predicted R2 = 0.474). These results highlight the importance of exploring the stratified heterogeneous relationship between SOC and covariates, and this knowledge provides a scientific basis for farmland zoning management. The Cubist model, integrating natural factors, agricultural activities, and landscape metrics, is effective in explaining SOC variation and mapping SOC in low-relief farmlands.

Influences of Environmental Variables and Their Interactions on Chinese Farmland Soil Organic Carbon Density and Its Dynamics

Farmland is one of the most important and active components of the soil carbon pool. Exploring the controlling factors of farmland soil organic carbon density (SOCD) and its sequestration rate (SOCDSR) is vital for improving carbon sequestration and addressing climate change. Present studies provide considerable attention to the impacts of natural factors and agricultural management on SOCD and SOCDSR. However, few of them focus on the interaction effects of environmental variables on SOCD and SOCDSR. Therefore, using 64 samples collected from 19 agricultural stations in China, this study explored the effects of natural factors, human activities, and their interactions on farmland SOCD and SOCDSR by using geographical detector methods. Results of geographical detectors showed that SOCD was associated with natural factors, including groundwater depth, soil type, clay content, mean annual temperature (MAT), and mean annual precipitation. SOCDSR was related to natural factors and agricultural management, including MAT, groundwater depth, fertilization, and their interactions. Interaction effects existed in all environmental variable pairs, and the explanatory power of interaction effects was often greater than that of the sum of two single variables. Specifically, the interaction effect of soil type and MAT explained 74.8% of the variation in SOCD, and further investigation revealed that SOCD was highest in Luvisols and was under a low MAT (<6 °C). The interaction effect of groundwater depth and fertilization explained 40.4% of the variation in SOCDSR, and fertilization was conducive to SOCD increase at a high groundwater depth (<3 m). These findings suggest that low soil temperature, high soil moisture, and fertilization are conducive to soil carbon accumulation. These findings also highlight the importance of agricultural management and interaction effects in explaining SOCD and SOCDSR, which promote our knowledge to better understand the variation of SOCD and its dynamics.

Multiple soil map comparison highlights challenges for predicting topsoil organic carbon concentration at national scale

Soil organic carbon (SOC) concentration is the fundamental indicator of soil health, underpinning food production and climate change mitigation. SOC storage is highly sensitive to several dynamic environmental drivers, with approximately one third of soils degraded and losing carbon worldwide. Digital soil mapping illuminates where hotspots of SOC storage occur and where losses to the atmosphere are most likely. Yet, attempts to map SOC often disagree. Here we compare national scale SOC concentration map products to reveal agreement of data in mineral soils, with progressively poorer agreement in organo-mineral and organic soils. Divergences in map predictions from each other and survey data widen in the high SOC content land types we stratified. Given the disparities are highest in carbon rich soils, efforts are required to reduce these uncertainties to increase confidence in mapping SOC storage and predicting where change may be important at national to global scales. Our map comparison results could be used to identify SOC risk where concentrations are high and should be conserved, and where uncertainty is high and further monitoring should be targeted. Reducing inter-map uncertainty will rely on addressing statistical limitations and including covariates that capture convergence of physical factors that produce high SOC contents.

Exploring the likely relationship between soil carbon change and environmental controls using nonrevisited temporal data sets: Mapping soil carbon dynamics across China

The prediction of soil organic carbon (SOC) changes in response to environmental change is often limited by a scarcity of revisited temporal data, which constrains scientific understanding and realistic predictions of soil carbon change. The present study improved the potential of nonrevisited temporal data in the prediction of SOC stocks (SOCS) variations. We proposed a method to develop predictions of SOCS change using two independent temporal data sets (pertaining to the 1980s and 2010s) in China based on the digital soil mapping technique. Changes in SOCS over time at the site level were analyzed via the interpolation of missing SOCS values in each data set. Quantitative SOCS change predictions were generated by modeling the relationship between SOCS change and variables that represent changes in climate, vegetation indices, and land cover. The scale-dependent response of SOCS change to these environmental dynamics was assessed. On average, a slight increase was observed from 3.70 kg m^-2 in the 1980s to 4.53 kg m^-2 in the 2010s. The proposed approach attained moderate accuracy with an R² value of 0.32 and a root mean squared error (RMSE) of 1.73 kg m^-2. We found that changes in climate factors were dominant controls of SOCS change over time at the country scale. At the regional scale, the controlling factors of SOCS change were distinct and variable. Our case study may be of value in the application of independent temporal data sets to analyze soil carbon change on multiple scales. The method may be used to resolve questions of soil carbon change projections and provide an alternative solution to predict likely changes in soil carbon in response to future environmental change when no temporal data are available.

Decadal shifts in soil pH and organic matter differ between land uses in contrasting regions in China

Soil organic matter (SOM) and pH are critical soil properties strongly linked to carbon storage, nutrient cycling and crop productivity. Land use is known to have a dominant impact on these key soil properties, but we often lack the ability to examine temporal trajectories across extensive spatial scales. Large-scale monitoring programmes provide the data to evaluate these longer-term changes, and under different climatic conditions. This study used data from Chinese soil surveys to examine changes in soil pH and SOM across different land uses (dry farmland, paddy fields, grassland, woodland, unused land), with surface soil (0-20 cm) collected in the periods 1985-90 (Survey 1; 890 samples) and 2006-10 (Survey 2; 5005 samples) from two contrasting areas. In the southern part of China the mean pH of paddy soils fell sharply over the two decades between surveys - from pH 5.81 to 5.19 (p < 0.001), while dry farmlands in the northern sampling area fell slightly (from pH 8.15 to 7.82; p < 0.001). The mean SOM content of dry farmland soil rose in both areas and the mean SOM of paddy fields in the southern area also rose (all p < 0.001). Woodland soil pH in the south showed an increase from 4.71 to 5.29 (p < 0.001) but no significant difference was measured in the woodlands of the northern area, although the trend increased. The SOM content of woodland top soils rose in the northern (p = 0.003) and southern (p < 0.001) study areas. The implications and potential causes of these changes over the two decade timespan between surveys are discussed and suggestions made as to how large scale soil sampling campaigns can be designed to monitor for changes and potential controlling factors.