A coupled carbon, aggregation, and structure turnover (CAST) model for topsoils

In this work, a soil carbon, aggregation, and structure turnover (CAST) model was developed based on the concept suggested by many authors in the scientific literature that macro-aggregates are formed around particulate organic matter, followed by the release of micro-aggregates. A simplified mechanistic Nitrogen model was also developed. The CAST model was evaluated by field data of cropland to set-aside conversions of Critical Zone Observatories in Greece (fine textured Mediterranean) and Iowa (coarse textured humid continental). The model was calibrated successfully to capture the carbon content and the C-to-N ratio content of the pools comprising the three aggregate types (macro-aggregates: >250 μm, micro-aggregates: 53-250 μm, silt―clay sized aggregates: <53 μm) in both sites. The soil system reached maximum macro-aggregation/porosity and minimum bulk density after 7 and 14 years of cropland to set aside conversion in Greece and Iowa, respectively, according to the model results. From then onward, macro-aggregate disruption presented a constant inter-annual variability and any further SOC increase was due to micro-aggregation resulting in the increase of bulk density and decrease of porosity towards a stable value. Sensitivity and Monte Carlo uncertainty analysis on model parameters suggested that the most uncertain state variables affected by most model parameters is the carbon mass in the silt―clay size aggregates and the silt―clay mass itself. Higher size aggregates and their carbon content as well as soil structure parameters such as porosity and bulk density do not exhibit significant sensitivity to model parameters. Validation and improvement of the CAST model could assist in revealing the primary factors determining organic matter, aggregation, and structure turnover in different ecosystems. Obtaining this capability should allow better prediction of the response of the soil system to management practices, landuse changes, and climate change in order to design and optimize the appropriate measures/practices.