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Regional estimates of carbon sequestration potential: linking the Rothamsted Carbon Model to GIS databases

Falloon, P.D., Smith, P., Smith, J.U., Szabo, J., Coleman, K., Marshall, S.
Biology and fertility of soils 1998 v.27 no.3 pp. 236-241
estimation, carbon, geographical variation, geographic information systems, mathematical models, databases, soil organic matter, carbon dioxide, soil fertility, climate change, land use, biogeochemical cycles, land management, soil, meteorological data, prediction, Hungary
Soil organic matter (SOM) represents a major pool of carbon within the biosphere, it is estimated at about 1400 Pg globally, which is roughly twice that in atmospheric CO2. The soil can act as both a source and a sink for carbon and nutrients. Changes in agricultural land use and climate can lead to changes in the amount of carbon held in soils, thus, affecting the fluxes of CO2 to and from the atmosphere. Some agricultural management practices will lead to a net sequestration of carbon in the soil. Regional estimates of the carbon sequestration potential of these practices are crucial if policy makers are to plan future land uses to reduce national CO2 emissions. In Europe, carbon sequestration potential has previously been estimated using data from the Global Change and Terrestrial Ecosystems Soil Organic Matter Network (GCTE SOMNET). Linear relationships between management practices and yearly changes in soil organic carbon were developed and used to estimate changes in the total carbon stock of European soils. To refine these semi-quantitative estimates, the local soil type, meteorological conditions and land use must also be taken into account. To this end, we have modified the Rothamsted Carbon Model, so that it can be used in a predictive manner, with SOMNET data. The data is then adjusted for local conditions using Geographical Information Systems databases. In this paper, we describe how these developments can be used to estimate carbon sequestration at the regional level using a dynamic simulation model linked to spatially explicit data. Some calculations of the potential effects of afforestation on soil carbon stocks in Central Hungary provide a simple example of the system in use.