Applying statistical methods to map soil organic carbon of agricultural lands in northeastern coastal areas of China

ABSTRACT

Soil organic carbon (SOC) is an important indicator to evaluate agricultural soil quality. Precise mapping SOC can help to facilitate soil and environmental management decisions. This study applied multiple stepwise regression (MSR), boosted regression trees (BRT) model, and boosted regression trees hybrid residuals kriging (BRTRK) to map SOC of agricultural lands in Wafangdian City, northeastern China. A 10-fold cross-validation procedure was used to evaluate the performance of the three models. The BRTRK method exhibited the best predictive performance and explained 78% of the total SOC variability. The distribution of SOC was mainly explained by elevation, followed by soil-adjusted vegetation index (SAVI), and topographic wetness index (TWI). We conclude that the BRTRK was the most accurate method in predicting spatial distribution of SOC. In addition, our study indicated that topographic variables as key factors to affect SOC should be considered in future SOC mapping.

KEYWORDS:

• Digital soil mapping
• environmental variables
• soil organic carbon
• coastal areas

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the Natural Science Foundation Project of Shenyang Agricultural University [880418058].