Estimating soil organic carbon stocks in dehesa ecosystems (Toledo, Spain)

Dehesa ecosystems, traditional silvopastoral systems in the Iberian Peninsula, are characterized by scattered trees and pastures extensively grazed by livestock. These systems provide critical ecosystem services, including soil organic carbon (SOC) storage, which helps mitigate the negative impacts of livestock production and might support farm economic sustainability through potential carbon (C) credits. However, accurately estimating SOC in dehesa soils is challenging due to their high spatial variability caused by scattered trees and grazing patterns, which create SOC “fertility islands” under tree canopies.

This study evaluates how different grassland management practices affect SOC storage in dehesa soils and determines optimal methodologies for estimating SOC stocks despite soil heterogeneity. Research was conducted on an organically managed farm in Alcañizo (Toledo, Spain), comparing fields with rotational and semi-continuous grazing systems, which differ in grazing frequency and resting periods. SOC and bulk density were measured in soil samples collected from 0–10, 10–20, and 20–30 cm depths on a 20 × 20 m grid. Four geospatial methods were used to estimate SOC stocks: IDW (Inverse Distance Weighting), Ordinary Kriging (OK) with 6- and 12-point radio, and soil units zonation.

Results revealed that SOC stored between 10–30 cm depth (1,724 ± 825 g C m^-2) was comparable to that in the top 10 cm (1,876 ± 641g C m^-2), underscoring the need to sample at least 30 cm for comprehensive SOC estimation. Trees significantly increased SOC storage by 56% and 34% in soils under the trees compared to open grassland soils in the rotational and semi-continuous management systems, respectively. Regarding management practices, the arithmetic mean of SOC stocks (0-30 cm) was slightly higher under semi-continuous management (3,861 ± 1,286 g C m²) compared to rotational management (3,339 ± 1,334 g C m²).

While SOC estimates were similar across geospatial methods and arithmetic means due to the large number of sampling points, IDW best represented SOC accumulation under trees, and soil unit-based methods identified SOC accumulation due to topography. Conversely, OK with a 12-point radius poorly captured SOC heterogeneity. The choice of geospatial estimation method significantly influences SOC stock estimates.

In conclusion, future SOC assessments in dehesa ecosystems should account for their high spatial variability by increasing sampling density and applying diverse estimation methods. This approach will improve the reliability of SOC stock estimates, aiding both ecological studies and C credit calculations.