Calibration of the Rothamsted organic carbon turnover model (RothC ver. 26.3), using measurable soil organic carbon pools

Abstract

A fractionation scheme that provided the measurement of a labile pool (particulate organic carbon), a charcoal-carbon pool, and a humic pool by difference was tested as a means of initialising the Rothamsted organic carbon turnover model version 26.3. Equating these 3 fractions with the resistant plant material, inert organic matter, and humic pools of the model, respectively, gave good agreement between measured and modelled data for 2 long-term rotation trials in Australia using a soil depth of 30 cm. At one location, Brigalow Research Station in Queensland, there were 3 distinct soil types, two clays and a duplex soil, in a semi-arid, subtropical climate. At this site, continuous wheat with some sorghum was established after clearing land under brigalow (Acacia harpophylla) and continued for 18 years. The second location was near Tarlee, South Australia, and was established on existing agricultural land. One soil type (red brown earth) with 2 rotations (continuous wheat and wheat–fallow) were available over a period of 8 years.

The modelled and measured data were in good agreement for both locations but the level of agreement was substantially improved when the resistant plant material decomposition rate was reduced from 0.3 to 0.15/year. No other modifications were required and the resulting values provided excellent agreement between the modelled and measured data not only for the total soil organic carbon but also for the individual pools. Using this fractionation scheme therefore provides an excellent means of initialising and testing the Rothamsted model, not only in Australia, but also in countries with similar soil types and climate.

For the first time, the work reported here demonstrates a methodology linking measured soil carbon pools with a conceptual soil carbon turnover model. This approach has the advantage of allowing the model to be initialised at any point in the landscape without the necessity for historical data or for using the model itself to generate an initial equilibrium pool structure. The correct prediction of the changing total soil organic carbon levels, as well as the pool structure over time, acts as an internal verification and gives confidence that the model is performing as intended.