Spatially Explicit Modelling of Landscape Change at the Humid Forest Margin in Cameroon

In common with most remaining areas of humid tropical forest, the forest of southern Cameroon is being converted to agricultural land, and is subject to increasing pressure as population grows and markets develop. The Humid Forest Ecoregional Centre of the International Institute for Tropical Agriculture (IITA-HFC) has worked for nearly a decade on improving agricultural production in villages at the forest margin, both by introduction of new higher-yielding and disease resistant varieties and by improvements to farming systems. As part of the Alternatives to Slash and Burn (ASB) programme, undertaken jointly with other CGIAR centres, research has been specifically targeted to the reduction of forest conversion and the maintenance of sustainable mosaics of forest and cultivation, and thus of floral and faunal biodiversity. A new project is in progress to model landscape changes in response to population and market pressures, and to assess the impact of the introduction of new or improved farming techniques. Modeling is undertaken within the SIMILE environment, which links system dynamics modeling with spatially explicit landscapes. A series of biophysical models of farming systems are linked through land tenure relationships to models of villages and households which incorporate kinship and other linkages. Decision-making in respect to selection of land for cultivation, selection of crops and allocation of labour is modeled at the household level, and model outputs include indicators of human wellbeing, soil quality and maps of changing land-use. Building the models involves compilation of extensive data on crops and soils collected by IITA and other cooperating institutions, as well as collection of field data on distribution and ownership of land patches in four test villages in the Humid Forest Benchmark area of southern Cameroon. These villages span the gradient from low population density, a high proportion of remaining forest and extensive agricultural systems to high population density, no forest and more intensive agriculture. Extensive use is being made of GPS receivers linked to hand-held computers in field data collection. In order to extrapolate results of modeling in the three test villages to the remainder of the Benchmark, new land cover maps are being prepared using satellite remote sensing. This project is still at an early stage, but preliminary information on land tenure and land use patterns within the three test villages are presented, as well as some prototype models of typical farming systems. The applicability of this approach as an aid to INRM in other areas is assessed. Introduction A project to model landscape changes in villages at and near the forest edge in southern Cameroon is currently being undertaken at the International Institute for Tropical Agriculture Humid Forest Ecoregional Centre (IITA-HFC) in Yaounde, Cameroon. This project is funded by the European Union as part of the Alternatives to Slash and Burn (ASB) programme managed by the International Centre for Agroforestry Research (ICRAF). The intention is to model activities and landscapes in four representative villages, using the SIMILE modeling environment which is currently being used for similar studies in Indonesia and Zimbabwe, and to use the models to evaluate the impact of a range of interventions and policy options on rates of forest conversion to agriculture. Detailed geographic and socio-economic data is being collected from the test villages for incorporation in the models, together with extensive biophysical data already accumulated by IITA and other organisations. Preliminary models have been constructed, and are currently being parametrised and tested. The modeling initiative benefits from the active collaboration of staff from other CGIAR centres working in Cameroon, notably CIFOR (ACM project), ICRAF and ICLARM, and also from the close participation of the national partner organisation IRAD. This collaboration takes the form partly of sharing data, and