Modeling the effects of farming management practices on soil organic carbon stock at a county-regional scale

- The effects of farming management practices on SOC stock were quantified by DNDC.
- Field- and region-scale validation proved DNDC model to be valuable.
- MSF and refined modeling unit methods controlled the uncertainties of modeling.
- Crop rotation combined with N fertilizer reduction and carbon additions are wise.

Farming management practices are paramount for soil organic carbon (SOC) sequestration in carbon cycling at different scales. However, due to a lack of detailed data, estimating the impacts of different farming management alternatives on overall SOC stock remains inadequately quantified, especially at the county-regional scale. Here, an agricultural county-region, Yucheng County, which covers an area of 988.5 km2 in the North China Plain, was selected as a case to estimate the impacts of different farming management practices on SOC stock using explicit spatial information and the DeNitrification-DeComposition (DNDC) model. We constructed a database by creating 524 polygon-based homogeneous modeling units using climate, soil and farming management information, which were linked to the DNDC to support the county-regional-scale simulations. Four experiments lasting from 2003 or 2004 to 2010 and 247 soil samples collected in 2009 from a soil depth of 0-20 cm across the study area were used for field- and regional-scale model validation, respectively. Validation results indicated that the DNDC model was acceptable for modeling SOC stock in Yucheng County. Seven farming management scenarios were designed for predicting the SOC stock from 1980 to 2020. Simulation results indicated that the changes in farming management practices had strong effects on carbon sequestration, with the maximum SOC stock ranging from 3.99 to 6.34 Mt. C across the county. The simulation results improved our understanding of the comprehensive impacts of management practices on the soil C pool when upscaling to larger regional or national scales.

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