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FLORES is an international programme of research which aims to improve the livelihood of the rural poor at the forest margin, through the development of models that can be used to assess the likely impact of alternative policy options.
Typically, FLORES models combine biophysical aspects (crops, trees and so forth) with human socio-economics and decision-making at the household level.
Simile is used as the modelling environment for FLORES, since it has the required expressiveness for the complex modelling involved, the computational efficiency needed to run the highly-disaggregated models, and the intuitive user interface needed for a participatory modelling approach.
The aim of FLORES is to improve the livelihoods of the rural poor. It seeks to do this by developing tools to enable those responsible for formulating policy to explore the possible impacts of alternative policy options.
FLORES is a user-oriented framework for rigorously exploring alternatives that can be calibrated to various situations, and tested against empirical data. It is implemented as a participatory modeling process that has a simulation model as an outcome of each iteration.
FLORES is built on the notion that there is no need to reinvent the wheel, existing knowledge is explicitly incorporated, integrated and acknowledged through the use of participatory modeling and a growing library of easily interchangeable sub-models.
Simile was chosen for the implementation of FLORES because it was considered to meet a larger number of the requirements of FLORES than other candidate approaches.
The Mafungautsi State Forest is located in central Zimbabwe, near the town of Gokwe. Considerable numbers of people live next to the forest, and there is considerable pressure to exploit the forest resources: primarily fuel wood, poles for construction, and grass for thatch in the vlei (annually-flooded river beds).
CIFOR has been undertaking research in the area for a number of years, concentrating on the relationship between the people, the forest resources, and the Forestry Commission, as part of the ACM project. They decided to take a lead role in developing a FLORES model for this context, based on a several villages on the north-west margin of the forest for which they had socio-economic data. The model was based on an earlier version of the FLAC training model, adapted by the addition of crop and water submodels on the biophysical side, and the development of a novel strategic decision-making submodel. The process of adapting the training model was undertaken with outside assistance, primarily in four FLAC workshops held between Sept 2000 and February 2001, in response to design requirements specified locally. The model was further refined in April 2001 as a demonstrator for the World Bank Rural Week meeting, held in Washington DC, 23-26th April 2001.
|Acronyms and Links
ACM: the CIFOR-led Adaptive Co-Managament project
CGIAR: Consulatative Group for International Agricultural Research, an umbrella organisation for a number of international institutes concerned with agriculture and natural resources.
CIFOR: the Centre for International Forestry Research, a CGIAR centre
FLAC: The FLORES Local Adaptation and Calibration package, which aims to enable local groups to make their own FLORES models.
FLORES: Forest Land-Oriented Resource Envisioning System, an international programme concerned with modelling the interaction between people and their land resources at the forest margin.
The overall structure of the model is shown in the submodel diagram, which was taken directly from the Simile implementation of the model.
The model conforms closely to the standard FLORES model, with a left-hand Human component and a right-hand Biophysical component. Inside the human submodel three villages are represented, with each village containing a variable number of households. The number of households increases when a family moves into the area, and decreases when a household dissolves because of, for example, the death of the only male adult. The biophysical side is represented in terms of a large number (900+) patches, based on the digitisation as polygons of the field boundaries from an aerial photograph.
The dynamics and behaviour of each household is modelled in five submodels:
The dynamics on the biophysical side is modelled in terms of four components:
The GIS submodel contains fixed patch-level attributes (e.g. area, soil type), as well as the boundary coordinates for display purposes.
Households and patches are joined by a Tenure association. This allows for two types of tenure: (1) ownership of a patch by a household; and (2) access to common land. The main role of the tenure association is to channel information flows from patch to household and vice versa. Three types of information are transferred:
Using the model
The model is set up with a Control Panel that enables you to set various policy options. You simply move the sliders and continue running or restart the model .
The behaviour of the model can be displayed using a number of display tools, including graphs and maps. T he following diagrams are for illustrative purposes.
The two graphs below show how the model can be used to investigate how the community responds to changes in healthcare. The first shows the changes in the average number of adults per household in each of three villages. The orange lines show the standard run of the model. The blue lines reflect the effect of improved healthcare and health education: there is a slight improvement in the average household size. The green lines are for an increased incidence of AIDS: not a policy lever as such, but still something that the user of the model might wish to explore.
The following graph is for the same three cases, but shows wealth per household (in each of the three villages). No obvious effect of the healthcare/education policy is apparent, probably because a decrease in mortality is balanced by a decrease in resources per household.
This map shows how indicators of performance can be displayed spatially. The solid dark area is part of the Mafungautsi State Forest. The small polygons are fields in the Batanai village: at the time this was prepared, only about half of the field boundaries had been digitised. The colour shows variations in crop productivity, with lighter areas indicating higher crop productivity running from left to right through the centre, with lower productivity on the higher (hence drier) land above and below this belt.