Modeling soil organic carbon stocks and changes in agricultural cropping systems using a decision support tool and...

Area of Science:

• Agricultural Science
• Soil Science
• Environmental Science

Background:

• Soil organic carbon (SOC) is crucial for soil health and agricultural sustainability.
• Accurate and accessible tools are needed to quantify SOC stocks, as changes are slow to detect.
• The COMET-Farm tool estimates changes in SOC (ΔSOC) but its accuracy for direct SOC stock quantification is uncertain.

Purpose of the Study:

• To evaluate the accuracy of the COMET-Farm tool in estimating SOC stocks on working farms.
• To compare COMET-Farm's predictions with field-measured SOC stocks under variable management practices.
• To identify factors influencing the discrepancies between modeled and measured SOC stocks.

Main Methods:

• Field measurements of SOC stocks (0-20 cm depth) were conducted on 15 farms in Maryland.
• COMET-Farm predictions using default DayCent inputs were compared with farmer-reported data.
• Expert-applied DayCent predictions were also compared with field measurements.

Main Results:

• COMET-Farm significantly underestimated SOC stocks (R² = 0.101, RMSE = 18.80 Mg ha⁻¹).
• Expert-applied DayCent provided more accurate SOC stock estimates (R² = 0.73, RMSE = 7.89 Mg ha⁻¹).
• Discrepancies were larger in poorly drained soils and with long-term manure application.

Conclusions:

• COMET-Farm requires improvements in model initialization and site-specific data (soil texture, drainage) for accurate SOC stock estimation.
• Enhanced COMET-Farm could serve as a valuable tool for non-experts to quantify SOC stocks, aiding low-carbon agriculture transitions.
• Further testing and refinement are necessary before widespread adoption for direct SOC stock assessment.