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3 AGRICULTURAL LAND USE/COVER 
Agricultural land use changes in the model are brought about by the agent by considering the economic potential of the 
agricultural yields and the competing demands from other land uses. The major components that influence the decision 
are described in this section. 
3.1 Biophysical Crop Model (BCM) 
Land evaluation and suitability analysis have long used the biophysical factors like climate and soil as its determining 
factors (FAO, 1978), but the influence of human factors is not so well studied and described. Also, there exists 
considerable gap between the potential suitability of a given area to its actual productivity. Recent advances in 
modelling crop-yields based on their phenology have yielded better results, though the majority of them are 
point/location-specific. 
  
(a) (b) 
Figure.3. (a) Spatial yield distribution of paddy crop in Thailand; 
(b) Sample villages for paddy and cassava yields 
The biophysical crop model is a spatial model that calculates the biomass and yield of the crop at each of the land grids. 
It is based on the approach as enumerated in the EPIC (Erosion/Productivity Impact Calculator) model (Sharpley, et al., 
1990) developed at USDA. EPIC is a point-based (a given farm, with no spatial correlation) model. But as most of our 
calculations are on a raster/grid system and our main focus is in getting reliable agricultural yields, we adopted only the 
concepts and mathematical relationships used to simulate the plant growth component. 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. It has been shown that EPIC performed well in predicting crop yields and runoff in humid regions 
(Williams, 1985), whereas showed that it was also fairly acceptable in the simulation of the dry land agricultural 
systems (Steiner et al., 1990). The spatial yield distribution of paddy crop, calculated by the model, in Thailand is 
shown in Figure.3 (a) and Figure.3 (b) shows the sample village points for which data was collected to verify the model 
results. 
3.2 Rural Income Model 
An 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. 
  
International Archives of Photogrammetry and Remote Sensing. Vol. XXXIII, Part B7. Amsterdam 2000. 1215