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The original EPIC is static with respect to management and technology. A single crop or rotation, tillage practice, 
conservation measure, crop planting and harvesting date, and machine sequence is specified prior to an EPIC simulation 
and cannot be varied during a simulation. The level of technology (such as plant genetic material and efficiency, plant 
varieties or cultivar, irrigation efficiencies, and so on) is also fixed. This was one of the main bottlenecks in the EPIC 
because it can not adopt the management as per the climatic and resources prevail in temporal time scale. Therefore, the 
"Spatial-EPIC" carries a component where all these management and technologies practices have been made dynamic. 
3.2 Generating * Fine" Resolution Data from “Coarse” Resolution Data 
As discussed before the model used for development is a field scale model hence the data requirement in terms of their 
resolution is a big gap. Therefore, the first question may come to readers mind is how to "spatialize" the point-based 
models? What data is appropriate for these models? The concept of “generators” helps to answer these questions. The 
weather and slope generators were used. These generators are used not to save data storage size but to provide high- 
resolution (temporal and spatial) data from coarse resolution data. These generators help in integration of data and 
knowledge to build a multi-scale GIS database. These "climate analog" models here used as a “Weather Generator” 
[Richardson (1981a)] serve to describe the initial domain or target area for a range of priority setting. 
3.3 Biophysical Computation 
The model is composed of physically based components for simulating plant growth, nutrient, erosion, and related 
process for assessing crop productivity, determining optimal management strategies, erosion and so on. Simultaneously 
and realistically, model simulates the physical processes involved using readily available inputs. Commonly used input 
data are weather, crop, tillage, soil-attributes and management parameters. The model runs on defined rather derived 
cell size data layers provided by the user depending on their availability. Figure 3 shows physical factors considered in 
computing a mathematical model to find the effects of crop productivity coming from different processes. How all these 
different processes affects overall crop productivity is being modeled while simulation is shown in figure 4. “Spatial- 
EPIC” is composed of physically based submodels for simulating weather, hydrology, erosion, plant nutrients, plant 
growth, soil tillage and management, and plant environment control. The model runs on daily time-step therefore, each 
model is linked subsequently and interactively with other sub models as explained in figure 4. In brief, the each sub 
module are dealt with their computation procedure. Weather: daily rain, maximum and minimum temperature, solar 
radiation, wind and relative humidity can be based on measured and data and/or generated stochastically. Hydrology: 
runoff, percolation, lateral subsurface flow are simulated. Erosion: it simulates soil erosion by wind and water (for this 
paper the erosion part has not been included). Nutrient Cycling: the model simulates, nitrogen and phosphorus 
fertilization, transformations, crop uptake and nutrient movement. Nutrient can be applied as mineral fertilizers, in 
irrigation water, or as animal manures. Soil: soil temperature responds to weather, soil water content and bulk density. It 
is computed daily in each soil layer. Tillage: the equipment used affects soil hydrology and nutrient cycling. The user 
can change the characteristics of simulated tillage equipment, if needed. Crop Growth: A single crop model capable of 
simulating major agronomic crops. Crop-specific parameters are available for most crops. The model also simulates 
crop grown in complete rotations. Plant Environment: It is capable of variety of cropping variables, management 
practices, and other naturally occurring processes. These include different crop characteristics, plant population, dates 
of planting and harvest, fertilization, irrigation, tillage and many more those are normally practiced in the field. 
4. STUDY AREA AND DATA USED 
The chosen study area is India, lies to the north of equator, between 8?4' and 37?6" North and 68°7° and 97?25' East. It is 
bounded in the south by the Indian Ocean, in the west by the Arabian Sea, in the east by Bay of Bengal, in the north-east, 
north and a part of the north-west by Himalayan ranges, and the rest of the north-west by the Great Indian Desert. The soil 
characteristics of Indian nation were obtained after digitization of survey of India soil map with many properties like 
soil texture, soil pH and soil depth. Slope information of the country was derived from 1km GTOPO (NGDC, 1997). 
Weather data were obtained and their surfaces were generated using World Meteorological Organization station falling 
around 230 in number scattered throughout India. Agricultural management data were obtained at state level where 
there numbers are more than 30 in total of entire India at 5 year interval which was used for coarse level whole country 
simulation of 50 km cell size. On the other hand we succeed in procuring time-series data from 1974-1994 for one of 
the Indian State Bihar for detailed study at finer resolution simulation of 10-km cell size. 
5. RESULTS AND DISCUSSION 
  
International Archives of Photogrammetry and Remote Sensing. Vol. XXXIII, Part B7. Amsterdam 2000. 1193