Baseline map of organic carbon stock in farmland topsoil in East China

Abstract

Soil organic carbon (SOC) is important to soil fertility and the global carbon cycle. Accurate estimates of SOC stock and its dynamics are critical for managing agricultural ecosystems and carbon accounting under climate change, especially for highly cultivated regions. We extensively surveyed the SOC levels in 29,927 sites in Zhejiang province, an intensively cultivated region of East China, from year 2007 to 2008. We then estimated the spatial distribution of topsoil (0–30cm) organic carbon stock using a random forest (RF) model, which is a powerful machine learning algorithm with superior predictive performance over parametric statistical models. The final RF model contained 23 predictor variables, covering soil properties, vegetation, climate, topography, land cover, farming practices, and locations. The RF model showed high performance in predicting the SOC stock, with a coefficient of determination (R2) of 0.76 and a root mean square error (RMSE) of 10.63tCha−1. This performance was superior to the General Linear Model (GLM) (R2=0.35, RMSE=19.93tCha−1) and the ordinary kriging (OK) method (R2=0.57, RMSE=14.44tCha−1), and was equivalent to Boosted Regressing Trees (BRT) (R2=0.73, RMSE=11.26tCha−1). According to the variable importance evaluation, soil properties were the most important predictor variables, followed by climate and location, with relative importance values of 61%, 17%, and 14%, respectively. The predicted SOC stock ranged from 14.8 to 125.5tCha−1, with an average±standard deviation of 50.1±12.3tCha−1. The mean SOC level obtained from this survey was considerably lower than the value of 60.5tCha−1 reported for the same region in the Harmonized World Soil Database (HWSD), which is the most commonly used soil database worldwide. A large spatial discrepancy of SOC stock was observed between this survey and HWSD in regional and sub-regional levels. This study provided an updated regional baseline map of SOC levels for improving farmland management and refining carbon accounting under climate change.