Rajan, KS 
  
In addition to the above data, 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. 
2.2 Model Description 
The overall framework of the model is given below, in Figure 2. The model consists of four models - the bio-physical 
crop yield model, the rural income model, the urban land use model and the agent decision model. 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. 
Existing Land U Population/Labour 
_ i nd Use peser 
Climato- Physical Technology Rural- Urban T 
  
  
  
  
  
     
  
  
  
  
  
   
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
Factors | Market Land Use 
Price | pati ; Agricultural ENP 
patin Production Costs Economic 
| 
T Activity 
Bio- Physical Crop Yield p [Agricultural Income Urban Land Use 
Module Module Module 
v 
v 
  
  
  
  
Asncultud e ——3 [Lan d Use Decision Module > AGENTS of CHANGE: 
Demand- Supply land owners, farmers. 
  
  
  
  
  
  
  
  
  
hae : + 
Time = T + interval dish ip 
v * 
[Land Conditior |Population| [GNP| 
Figure 2. Framework of the AGENT-LUC Model 
  
The bio-physical crop yield 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 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 as described in the EPIC model (Sharpley and Williams, 1990). The central 
concept of this approach is the growing period and the photosynthetic efficiency of the crops. 
The agricultural income sub-model calculates the economic potential of the land unit, based on both the agricultural and 
non-agricultural revenues and expenditures. It also takes into account the accessibility, terrain conditions and current 
land use in calculating the costs. 
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. The model takes into account the locational value — neighbourhood 
and accessibility of the land-unit in assessing the new areas that will be urbanized. 
The final step in the simulation is the agent decision model, which uses the estimated income, urban land needs & the 
existing landuse in the land unit under consideration as its input to predict the land use. The “agent” is the decision 
maker in this model, where in the agent arrives at a decision taking into account the prevailing conditions in the 
respective grids. In addition to the economic factor, the demographic condition (age distribution and educational levels) 
and the land use history are considered to help in arriving at a reasonable estimate for the change in the land use 
patterns. In addition to the land use change decision, the model has a migration sub-model that simulates the changes in 
the population of each grid as a consequence of the changes in the economic welfare and the demographic distribution 
that exists in the grid. 
  
1214 International Archives of Photogrammetry and Remote Sensing. Vol. XXXIII, Part B7. Amsterdam 2000.