arriving at 
) 
e managed 
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or a group 
erred to as 
isions for 
Iso acts as 
der macro- 
| into the 
1, thereby 
atural and 
mperature, 
in the corp 
the yield 
actor. Land 
anagement 
antified by 
lity. Also, 
d the yield 
a feedback 
e in yields. 
the overall 
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1ajor crops 
oducts are 
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ered. 
taken into 
d fertilizer 
biophysical 
basket and 
its composition. 
7. Education levels contribute to the mobility of the 
populations. 
Economic Activity 
8. Market location may influence the choice of land use, 
especially for commercial landuse in and around the 
existing urban land areas. 
9. International trade is not simulated but is given for 
the seven major crops - this is to check the impact of 
changing agricultural policies of the government. 
10. GPP (gross provincial product) changes are 
proportional to changes in the GDP. 
11. Sector-wise GDP and GPP are considered to give 
due weightage to the government policies. These will be 
reflected in the estimates of these macro-economic 
factors. 
3.3 Additional Information Used 
In addition to the above landuse drivers, the experience 
of different researchers in arriving at qualitative 
conclusions on the land use practices in the different 
regions of the study area are also considered in 
charting out the behavioural patterns of the agents. 
4. MODEL DESCRIPTION 
The overall framework of the model is given below, in 
Fig 3. The model consists of four sub-models - the 
biophysical crop module, agricultural income 
estimation module, urban land use module and the 
land use decision module. All these four sub-models 
interact and have feedback loops, to determine the new 
course of action by the agent at the next time step. The 
model structure is sequential. The model calculations 
were carried out on a land unit basis, consisting of 1km 
square grids. 
4.1 Biophysical Crop Module 
The biophysical crop sub-model calculates the potential 
productivity of the land unit for the given conditions of 
soil, topography, water availability and climatic 
parameters. The distribution of water availability takes 
into account the soil conditions, amount of rain- 
received, and the existence of irrigation facilities. The 
main assumption of this sub-model is that there is a 
strong linkage between the climate and crop 
distributions. (Leemans, et al, 1993). The crop yield 
estimates are derived by modifying the approach 
described in the EPIC model (Sharpley, et al., 1990). 
The central concept of this approach is the growing 
period and the photosynthetic efficiency of the crops. 
The biomass and yield calculations are carried out on a 
day-to-day basis and the final yield takes into effect the 
fluctuations in water and nutrient availability and the 
resultant stress induced by them. 
International Archives of Photogrammetry and Remote Sensing. Vol. XXXII, Part 7, Budapest, 1998 
4.2 Agricultural Income Module 
The income estimation sub-model estimates the income 
per land unit from various agricultural and non- 
agricultural sources for people primarily resident in 
agricultural areas including the yield-related revenue 
and the cost of production. The model also accounts for 
the initial cost incurred in land conversion from other 
uses to agricultural lands. The other incomes 
considered are the non-yield-on-farm income and the 
off-farm income. These factors influence the decision 
making process, in case of fluctuating incomes from a 
given unit of the land. 
4.3 Urban Land use Module 
Urban land use is the other major land use that is 
primarily influenced by the activities of the human 
beings. Here, we estimate the urban land requirements 
as it competes with the agricultural areas due to 
increasing population pressures and the rise in the 
economic levels of the region. Economic activity leads to 
the build up in commercial and industrial areas, thus 
attracting more people to settle down in these areas. 
The land rent concept is used here to obtain the 
monetary equivalents for a given unit of land. Also, the 
. needs of the rising population are considered. The sub- 
model takes into account the locational value of the 
land-unit in assessing the new areas that will be 
urbanized. 
4.4 Land Use Decision Module 
The final step in the simulation is the land use decision 
module, which uses the estimated income & the 
existing landuse in the land unit under consideration 
as its input to predict the land use. We use the 'Profit 
Maximization Principle' as the guiding principle in 
deciding the land use for a given land unit. As 
fluctuations in income over a short time-frame is quite 
natural, we prescribe a band of income, instead of a 
single value comparison, to determine the shifts. In 
addition to the economic factor, the demographic 
condition and the land use history are considered to 
help in arriving at a reasonable estimate for the change 
in the land use patterns. The age distribution and 
educational levels of the population in the respective 
grids are used to derive the behavioural patterns that 
are liable to influence the decision making process. 
4.5 Trade and Government Policy 
Also, the model takes into account the external 
influences that are likely to effect shifts in the 
agricultural patterns. The main factor considered here 
is that the external demand generated from export 
policies of specific crops, like cassava in Thailand, lead 
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