to large-scale shifts from conventional agricultural
practices. As of now, these external influences are
exogenous variables and are not calculated within the
model.
5. STUDY AREA
The study area choosen to check the application of this
model is the Royal Kingdom of Thailand because land
use/cover in Thailand has undergone dramatic
modifications in the last couple of decades. Most
significantly the area of lands categorized as forest or
woodlands and wetalnds have declined by almost 14
million hectares (48.9496) during the period 1880-1980
(Richards, et al, 1993). In the same corresponding
period, cultivated land area has shown an astounding
increase by nearly ten-times, a net increase in area by
about 16.4 million hectares and has since risen by 1096
till 1990. Human population has also increased by
almost seven times over the century, thus contributing
to the spread of the agricultural area, as demands for
food went up. Also, during this period, the rise in
demands elsewhere has also led to a spurt in
production for export as Thailand responded to these
new demands by bringing much of the land under
cultivation.
6. DATA AND RESULTS
The period of 1980-1990 has been choosen for the
operationalizing the model. The model simulates the
area under the major crops within the agricultural land
use and the area under urban land use on an annual
basis. This period was opted for, because of two reasons
- one that it being a comparatively recent period in the
past, helps us to get a substantial amount of
information on some of the causes for changes
including quite detailed data at the sub-provincial
level. The second reason is that this has been a period
of rapid changes in the country's economic structure
and the model-run would help us to understand the
pitfalls and the better points in our assumptions.
Since a large amount of data was needed for the model
run and its subsequent validation, ranging from
location specific data (like yield values, etc) to data at
the provincial and national levels, we used the GIS
platform for integrating and developing the database
required for such a model run. Also, data are both
spatial, like soil maps, time-series land use maps
derived from remote sensing data, etc., and non-spatial
in extent requiring pre-processing of the data to suit
the needs of the model run.
The model run and the results obtained from the first
run of the model are shown in Fig 4. It shows the land
use map of 1990 having the major classes only and the
simulated crop combination results within the
agricultural area in the same year. The biophysical
module takes into account the possibility of growing
more than one crop in a given year. This simulated
result was aggregated to the three major combinations
of cropping pattern that can explain some of the macro
characteristics of agricultural land use. The cropping
combinations within one year, considered are - double
cropping of rice (cereal); a crop of rice and other non-
cereal crop; both non-cereal crops or a single non-cereal
crop.
7. DISCUSSION AND CONCLUSIONS
The results on an annual basis from the complete run
of the model, including all the modules will be
presented at the time of the conference.
Our aim in developing the "agent-based" model was to
mimic the change process by including all the major
forces that drive land use changes as well as the basic
bio-physiéal characteristics at the lowest level of
interaction (the land-lot size), to help in constructing
the possible land use change scenarios. Also, it would
help to evaluate our understanding of the land use
change mechanisms. In addition, the modular structure
of the model allows the introduction of various policies
of the government like the issue of subsidy and
discontinuance of some policies to be evaluated. These
can be introduced into the model based on its timetable
to help to understand the immediate and long-term
effects of such changes. This model als o will help us
evaluate the human responses to such changes.
The results from the first sub-model, the biophysical
module, shows that the yield values are very much
within the acceptable range of estimation and the
approach has a high potential in estimating yields.
The income sub-model depends heavily on the initial
data and the tuning of the model according to it.
Historical data can be used to develop scenarios of land
use changes and the model can also be validated with
such data. In addition to it, the use of remote sensing
images can be made to compare the estimated land
cover from the model, with the measured values. In
this case, care must be taken to maintain the spatial
resolution at acceptable levels of comparison.
The entire modeling approach is based on the GIS
platform. The use of GIS platform and its tools has
helped in analyzing the micro-information (spatial)
within the boundaries of the available macro-level
(non-spatial) data.
8. REFERENCE
1. Alcamo, J., Kreileman, G.J.J., Krol, M. and Zuidema,
G., 1994. Modelling the global society-biosphere-
climate system. 1. Model description and testing. Water
Air Soil Pollution, 76, pp. 1-35.
474 International Archives of Photogrammetry and Remote Sensing. Vol. XXXII, Part 7, Budapest, 1998
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