Effect of cultivation history on soil organic carbon status of arable land in northeastern China.

Abstract 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 CO 2 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. Highlights • Quantify the role of length of cultivation in SOC distribution in arable lands • Mapping spatial distribution of SOC and comparing the performance of models • Evaluating the potential applications of BRT model [ABSTRACT FROM AUTHOR]