Abstract. Globally, soil is one of the largest terrestrial carbon reservoirs, with soil organic carbon (SOC) regulating overall soil carbon dynamics. Robust quantification of SOC stocks in existing global observation-based estimates avails accurate predictions in carbon climate feedbacks and future climate trends. In this study, we investigated global and regional SOC estimates, based on five widely used global gridded SOC datasets (HWSD, WISE30sec, GSDE, SoilGrids250m, and GSOCmap), a regional permafrost dataset from Mishra et al. (UM2021), and a global-scale soil profile database (the World Soil Information Service soil profile database, WoSIS) reporting measurements of a series physical and chemical edaphic attributes. Our comparative analyses show that the magnitude and distribution of SOC varies widely among datasets, with certain datasets showing region-specific robustness. At the global scale, the magnitude of SOC stocks simulated by GSDE, GSOCmap, and WISE30sec are comparable, while estimates of SoilGrids250m and HWSD are at the upper and lower ends, respectively. Global SOC stocks ranged from 577–1171 Pg C and 1086–2678 Pg C at 0–30 cm and 0–100 cm depth. The spatial distribution of SOC stocks varies greatly among datasets, especially in the northern circumpolar and Tibetan Plateau permafrost regions. In general, the UM2021 and WISE30sec perform better in the northern circumpolar permafrost regions, and GSDE performs better in China. SOC stocks estimated by different datasets also show large variabilities across different soil layers and biomes. Overall, GSOCmap performs well at 0–30 cm depth, while SoilGrids250m and GSDE perform better at multiple depths. Among the five gridded global datasets, SoilGrids250m exhibits a more consistent spatial pattern and depth distribution with WoSIS. Large uncertainties in existing global gridded SOC estimates are generally derived from soil sampling density, diverse sources and mapping methods for soil datasets. We call for future efforts for standardizing soil sampling efforts, cross-dataset comparison, proper validation, and overall global collaboration to improve SOC estimates. The data are available at https://doi.org/10.6084/m9.figshare.20220234 (Lin et al., 2022).
Abstract. Cultivation of the terrestrial land surface can create either a source or sink of atmospheric CO2, depending on land management practices. The Community Land Model (CLM) provides a useful tool for exploring how land use and management impact the soil carbon pool at regional to global scales. CLM was recently updated to include representation of managed lands growing maize, soybean, and spring wheat. In this study, CLM-Crop is used to investigate the impacts of various management practices, including fertilizer use and differential rates of crop residue removal, on the soil organic carbon (SOC) storage of croplands in the continental United States over approximately a 170-year period. Results indicate that total US SOC stocks have already lost over 8 Pg C (10%) due to land cultivation practices (e.g., fertilizer application, cultivar choice, and residue removal), compared to a land surface composed of native vegetation (i.e., grasslands). After long periods of cultivation, individual subgrids (the equivalent of a field plot) growing maize and soybean lost up to 65% of the carbon stored compared to a grassland site. Crop residue management showed the greatest effect on soil carbon storage, with low and medium residue returns resulting in additional losses of 5 and 3.5%, respectively, in US carbon storage, while plots with high residue returns stored 2% more carbon. Nitrogenous fertilizer can alter the amount of soil carbon stocks significantly. Under current levels of crop residue return, not applying fertilizer resulted in a 5% loss of soil carbon. Our simulations indicate that disturbance through cultivation will always result in a loss of soil carbon, and management practices will have a large influence on the magnitude of SOC loss.
Abstract Estimates of soil organic carbon (SOC) stocks are essential for many environmental applications. However, significant inconsistencies exist in SOC stock estimates for the U.S. across current SOC maps. We propose a framework that combines unsupervised multivariate geographic clustering (MGC) and supervised Random Forests regression, improving SOC maps by capturing heterogeneous relationships with SOC drivers. We first used MGC to divide the U.S. into 20 SOC regions based on the similarity of covariates (soil biogeochemical, bioclimatic, biological, and physiographic variables). Subsequently, separate Random Forests models were trained for each SOC region, utilizing environmental covariates and SOC observations. Our estimated SOC stocks for the U.S. (52.6 ± 3.2 Pg for 0–30 cm and 108.3 ± 8.2 Pg for 0–100 cm depth) were within the range estimated by existing products like Harmonized World Soil Database, HWSD (46.7 Pg for 0–30 cm and 90.7 Pg for 0–100 cm depth) and SoilGrids 2.0 (45.7 Pg for 0–30 cm and 133.0 Pg for 0–100 cm depth). However, independent validation with soil profile data from the National Ecological Observatory Network showed that our approach ( R 2 = 0.51) outperformed the estimates obtained from Harmonized World Soil Database ( R 2 = 0.23) and SoilGrids 2.0 ( R 2 = 0.39) for the topsoil (0–30 cm). Uncertainty analysis (e.g., low representativeness and high coefficients of variation) identified regions requiring more measurements, such as Alaska and the deserts of the U.S. Southwest. Our approach effectively captures the heterogeneous relationships between widely available predictors and the current SOC baseline across regions, offering reliable SOC estimates at 1 km resolution for benchmarking Earth system models.
The objective of this study was to predict and map SOC stocks at different depth intervals within the upper 1‐m depth using profile depth distribution functions and ordinary kriging. These approaches were tested for the state of Indiana as a case study. A total of 464 pedons representing 204 soil series was obtained from the National Soil Survey Center database. Another 48 soil profile samples were collected to better represent the heterogeneity of the environmental variables. Two methods were used to model the depth distribution of the SOC stocks using negative exponential profile depth functions. In Procedure A, the functions to describe the depth distribution of volumetric C content for each soil profile were fitted using nonlinear least squares. In Procedure B, the exponential functions were fitted to describe the depth distribution of the cumulative SOC stocks. The parameters of the functions were interpolated for the entire study area using ordinary kriging on 81% of the data points ( n = 414). The integral of the exponential function up to the desired depth was used to predict SOC stocks within the 0‐ to 1‐, 0‐ to 0.5‐, and 0.5‐ to 1‐m depth intervals. These estimates were validated using the remaining 19% ( n = 98) of the data. Procedure B showed a higher prediction accuracy for all depths, with higher r and lower RMSE values. The highest prediction accuracy ( r = 0.75, RMSE = 2.89 kg m −2 ) was obtained for SOC stocks in the 0‐ to 0.5‐m depth interval. Using Procedure B, SOC stocks within the top 1 m of Indiana soils were estimated to be 0.90 Pg C.
Core Ideas Midmorning N 2 O flux estimates were not consistent with near‐continual data for spring and fall applied urea. Local N 2 O sampling protocols must account for temporal changes in management and climatic conditions. Non‐alignment in soil temperature and N 2 O patterns in annual‐crop soils is consistent with Fick's Law. Non‐alignment between temperatuer and N 2 O emissions can occur when the soil is saturated with water. Near‐continuous automated data collection at anticipated max and min emissions may improve the accuracy of N 2 O estimates. A common approach for measuring N 2 O emissions is to collect midmorning or early evening gas samples from experiments utilizing the chamber‐based flux methodology. However, due to high spatial and temporal variability, N 2 O estimates based on midmorning or early evening sampling may not provide accurate estimates of total emissions. This study determined the impact of sampling collection timing on the precision and accuracy of N 2 O emissions estimates. Nitrous oxide emissions, air and soil temperatures, and soil moisture were measured for 21 d following the application of 224 kg urea‐N ha –1 on 20 Sept. 2017, 11 Oct. 2017, and 1 May 2018, at six time intervals (0130–0230, 0530–0630, 0930–1030, 1330–1430, 1730–1830, and 2130–2230 h) over a 24‐h period. Based on multiple daily measurements, point samples collected between 0930 and 1030 h (midmorning) were inconsistent in their ability to predict N 2 O emissions. However, samples collected between 2130 and 2230 h (early evening) were similar to average emissions. The number of randomly collected point samples to be within 20% of the mean 80% of the time over a 21‐d period ranged from 13 samples for fertilizer applied on 20 Sept. 2017 to 48 samples for fertilizer applied on 11 Oct. 2017. This research indicates that management and climatic variability affect N 2 O emissions, and that accurate sampling protocols vary across management and climates. To reduce uncertainty, N 2 O sampling protocol should be tested under conditions likely to occur and where possible, near‐continuous measurement systems should be adopted.
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