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Estimation of soil organic carbon (SOC) in arable land can help with the better understanding of the impact of human activities on soils and the environment. Knowledge of cultivation history can assist with studies on soil carbon balance and CO2 emissions. This study used historical cultivation data for the last 300 years and quantified the influence on the spatial distribution of SOC in arable land from northeastern China. More than 230 SOC observations were used to train the prediction model derived from boosted regression trees (BRT) where topography, temperature, precipitation, and length of cultivation were employed as SOC predictors. Two BRT models, one with all predictors except length of cultivation (MA) and the other with all predictors (MB) were generated and the models were compared for SOC prediction performance. The MB model accounted for 76% of SOC variability in the study area and showed a better prediction performance compared to the MA model which exhibited lower values for Lin's concordance correlation coefficient (LCCC) and coefficient of determination (R2) and the higher mean absolute prediction error (MAE) and root mean square error (RMSE) indices. The model assigned the highest variable importance to length of cultivation indicating that this variable was key to determining the status of SOC spatial distribution. Our analysis showed that the study area lost about 45% SOC during the past 300 years of cultivation in Northeastern China. The predicted maps from both models suggested a decreasing trend in SOC content from north to south in the study area. We conclude that the historical data in cultivation history were a key variable in SOC predictions on the arable lands and it should be considered as a major variable in future SOC mapping studies, especially in agricultural areas with a long history of cultivation.

Quantification of soil organic carbon (SOC) stocks and their spatial variations at regional scales is a foundation to adequately assess plant productivity and soil carbon sequestration potentials so as to establish better practices for land use and land management. This study evaluated the spatial variation of SOC stocks from 1982 to 2012 in Wafangdian, Liaoning Province, China. To map SOC stock, we used geographically weighted regression (GWR) and regression kriging (RK) methods and a large set of soil samples, in which nine topographic and remote sensing variables were observed. The GWR approach performed better than the RK approach as the former has smaller absolute mean errors (AME), mean errors (ME), root mean square errors (RMSE) in comparison with observational data. Our results indicated that SOC stocks have an increasing trend in northeast and southwest mountainous areas in our study periods. Land-use changes caused by returning cultivation land to forest promoted SOC accumulation. The total SOC stocks of cultivation land, grasslands and forests within 0–0.2 m of soils were estimated to be 5.25 and 5.40 Tg in 1982 and 2012, respectively. This study provided important information of spatial variations in SOC stocks to agencies and communities in this region to evaluate soil quality and assess carbon sequestration potentials and carbon credits.

Estimation of carbon and nitrogen stocks is important for quantifying carbon and nitrogen sequestration as well as greenhouse gas emissions and inventorying national carbon and nitrogen balances. For Liaoning province of China, we estimated the vertical distribution of soil organic carbon (SOC), soil total nitrogen (STN), bulk density (BD), and mapped their spatial distribution at five standard soil depth intervals (0–5, 5–15, 15–30, 30–60 and 60–100 cm) using nine environmental variables as predictors including precipitation, temperature, land use, elevation, system for automated geoscientific analyses (SAGA) wetness index, and Normalized Difference Vegetation Index (NDVI). The highest average contents of SOC and STN were 15.2 g kg − 1 and 1.6 g kg − 1 in the 0–5 cm soil layer, and 1.5 g kg − 1 SOC and 0.4 g kg − 1 STN in the 60–100 cm soil layer, respectively. The prediction precision for SOC, STN and BD all decreased with soil depth. Average SOC and STN stocks for 0–30 cm were 3.1 kg m − 2 and 0.5 kg m − 2 , respectively. For the top 1 m, SOC and STN were 4.5 kg m − 2 and 0.9 kg m − 2 , respectively. In total, the soils stored approximately 588 Tg SOC and 128 Tg STN within the top 1 m. The soils under forest had the highest amount of carbon (356 Tg) and nitrogen (58 Tg) followed by agriculture and wetland that contributed 34% and 48% of the total stock, respectively. > 91% of the total SOC and STN stocks were in Argosols and Cambosols. We adopted a digital soil mapping method to map the spatial distribution of SOC and STN stocks and predict their uncertainties. The estimation was validated with a 10-fold cross-validation procedure. The data and high-resolution maps from this study can be used for future soil carbon and nitrogen assessment and inventorying.