Estimating soil organic carbon stocks and spatial patterns with statistical and GIS-based methods.

Area of Science:

• Soil Science
• Environmental Science
• Geoscience

Background:

• Accurate soil organic carbon (SOC) quantification is vital for soil quality assessment, global carbon cycle modeling, and climate change impact studies.
• Up-scaling soil property data from local to regional scales introduces uncertainties, affecting the reliability of SOC stock estimations.
• Understanding these uncertainties is critical for effective environmental management and policy development.

Purpose of the Study:

• To evaluate uncertainties in soil organic carbon stock estimation arising from up-scaling soil properties from county to provincial scales.
• To compare the performance of four different methods (mean, median, Soil Profile Statistics (SPS), and pedological professional knowledge based (PKB)) in estimating SOC stocks.
• To assess the impact of soil classification level changes (Soil Species to Soil Group) on SOC stock estimation accuracy.

Main Methods:

• Employed four distinct methods for SOC stock estimation: mean, median, SPS, and PKB.
• The SPS method involved calculating SOC stock at the county scale using mean SOC density and area.
• The PKB method incorporated soil parent materials and spatial locations of soil profiles for county-scale SOC density calculation.

Main Results:

• Up-scaling soil classification from Soil Species to Soil Group reduced variation in estimated SOC stocks compared to variations among different estimation methods.
• The differences in estimated SOC stocks among the four methods were smallest at the Soil Species level.
• The PKB method demonstrated superior ability in characterizing spatial SOC distribution differences due to its consideration of soil profile spatial locations.

Conclusions:

• The choice of method significantly impacts SOC stock estimations, particularly at broader soil classification levels.
• The pedological professional knowledge based (PKB) method offers a more robust approach for capturing spatial heterogeneity in SOC distribution.
• Future research should leverage spatially explicit data and expert knowledge for more accurate regional SOC assessments.