Dynamic soil properties for soil health (DSP4SH) database 1.0 - Phase 1 and 2 datasets.

The dynamic soil properties for soil health (DSP4SH) is a Science of Soil Health Initiative that was designed to collect, process, and publicize scientifically rigorous datasets that inform sound indicators and interpretations. The Soil and Plant Science Division of the United States Department of Agriculture - Natural Resources Conservation Service (USDA-NRCS) and university cooperators collected a suite of standardized soil health metrics across eight states (Oregon, Washington, Kansas, Minnesota, Illinois, Connecticut, North Carolina, and Texas) within five soil survey regions (Northwest, North Central, Northeast, Southeast, and South Central). The DSP4SH database provides a substantial dataset of soil health metrics assessed. The dataset is composed of dynamic soil properties (DSP) data collected from each management system or ecological state represented by one to three independent plot replicates. Each plot has a minimum of three pedons. Nine groups from the DSP4SH monitoring network provided datasets used in developing the database. The submitted data includes 37 laboratory measured parameters, 60 variables of layer/horizon descriptions, 41 variables for laboratory analysis conducted at the Kellogg Soil Survey laboratory, and 12 variables for the management systems. An additional 31 variables were developed for site or plot description. Additional variables were developed to normalize the dataset. In preparation for DSP assessment, all tables (except for dataset from KSSL lab) were categorized by management system or ecological state. The categories were business as usual (BAU), the reference condition (Ref) and the soil health management (SHM). The overarching goal of DSP4SH phase 1 and 2 dataset publication is to promote increased accessibility, further analysis of the data, and overall understanding of the benefits of surveying dynamic soil properties for soil health.

Regional environmental controllers influence continental scale soil carbon stocks and future carbon dynamics.

Understanding the influence of environmental factors on soil organic carbon (SOC) is critical for quantifying and reducing the uncertainty in carbon climate feedback projections under changing environmental conditions. We explored the effect of climatic variables, land cover types, topographic attributes, soil types and bedrock geology on SOC stocks of top 1 m depth across conterminous United States (US) ecoregions. Using 4559 soil profile observations and high-resolution data of environmental factors, we identified dominant environmental controllers of SOC stocks in 21 US ecoregions using geographically weighted regression. We used projected climatic data of SSP126 and SSP585 scenarios from GFDL-ESM 4 Earth System Model of Coupled Model Intercomparison Project phase 6 to predict SOC stock changes across continental US between 2030 and 2100. Both baseline and predicted changes in SOC stocks were compared with SOC stocks represented in GFDL-ESM4 projections. Among 56 environmental predictors, we found 12 as dominant controllers across all ecoregions. The adjusted geospatial model with the 12 environmental controllers showed an R2 of 0.48 in testing dataset. Higher precipitation and lower temperatures were associated with higher levels of SOC stocks in majority of ecoregions. Changes in land cover types (vegetation properties) was important in drier ecosystem as North American deserts, whereas soil types and topography were more important in American prairies. Wetlands of the Everglades was highly sensitive to projected temperature changes. The SOC stocks did not change under SSP126 until 2100, however SOC stocks decreased up to 21% under SSP585. Our results, based on environmental controllers of SOC stocks, help to predict impacts of changing environmental conditions on SOC stocks more reliably and may reduce uncertainties found in both, geospatial and Earth System Models. In addition, the description of different environmental controllers for US ecoregions can help to describe the scope and importance of global and local models.

Assessing soil organic carbon stock of Wisconsin, USA and its fate under future land use and climate change.

Carbon stored in soils contributes to a variety of soil functions, including biomass production, water storage and filtering, biodiversity maintenance, and many other ecosystem services. Understanding soil organic carbon (SOC) spatial distribution and projection of its future condition is essential for future CO2 emission estimates and management options for storing carbon. However, modeling SOC spatiotemporal dynamics is challenging due to the inherent spatial heterogeneity and data limitation. The present study developed a spatially explicit prediction model in which the spatial relationship between SOC observation and seventeen environmental variables was established using the Cubist regression tree algorithm. The model was used to compile a baseline SOC stock map for the top 30 cm soil depth in the State of Wisconsin (WI) at a 90 m × 90 m grid resolution. Temporal SOC trend was assessed by comparing baseline and future SOC stock maps based on the space-for-time substitution model. SOC prediction for future considers land use, precipitation and temperature for the year 2050 at medium (A1B) CO2 emissions scenario of the Intergovernmental Panel on Climate Change. Field soil observations were related to factors that are known to influence SOC distribution using the digital soil mapping framework. The model was validated on 25% test profiles (R2: 0.38; RMSE: 0.64; ME: -0.03) that were not used during model training that used the remaining 75% of the data (R2: 0.76; RMSE: 0.40; ME: -0.006). In addition, maps of the model error, and areal extent of Cubist prediction rules were reported. The model identified soil parent material and land use as key drivers of SOC distribution including temperature and precipitation. Among the terrain attributes, elevation, mass-balance index, mid-slope position, slope-length factor and wind effect were important. Results showed that Wisconsin soils had an average baseline SOC stock of 90 Mg ha-1 and the distribution was highly variable (CV: 64%). It was estimated that WI soils would have an additional 20 Mg ha-1 SOC by the year 2050 under changing land use and climate. Histosols and Spodosols were expected to lose 19 Mg ha-1 and 4 Mg ha-1, respectively, while Mollisols were expected to accumulate the largest SOC stock (62 Mg ha-1). All land-use types would be accumulating SOC by 2050 except for wetlands (-34 Mg C ha-1). This study found that Wisconsin soils will continue to sequester more carbon in the coming decades and most of the Driftless Area will be sequestering the greatest SOC (+63 Mg C ha-1). Most of the SOC would be lost from the Northern Lakes and Forests ecological zone (-12 Mg C ha-1). The study highlighted areas of potential C sequestration and areas under threat of C loss. The maps generated in this study would be highly useful in farm management and environmental policy decisions at different spatial levels in Wisconsin.