Priya, Satya
as well as policy makers to know the impact of differences between input and output spatially from one place/region to
other from better management, productivity and profitability viewpoint.
The basic tenet associated with this goal is to facilitate the data flow and consistency between the GIS and simulated
model. The specific objectives were to develop a spatial biophysical crop model from the point based process model,
and then model application and its validation.
2. DEVELOPMENT OF “SPATIAL-EPIC MODEL”
To understand what these crop needs are from point to point/pixel to pixel it is necessary to understand the relationship
between crop yield and both controllable (such as fertilizer nutrients) and uncontrollable (such as soil, topography)
factors. The effect of these factors on yield is complex and may change from point to point within a field. Recently, one
of the many challenges facing regional, national or global agricultural research is the simple understanding of potential
solutions to the constrains for achieving its solutions. Identification of opportunities and constraints is the task of
characterization. Modeling within a GIS offers a mechanism to integrate the many scales of data developed in and for
agricultural research. Data access, including modeling results, expands to a "decision system" or decision tool which
uses a mix of process models (where appropriate/possible) and biophysical data (growing season climate characteristics,
soils, terrain). An accurate spatial (and temporal) database enables the characterization of agroecosystems. This ability
is vital in the developing world for efficient resource allocation in agricultural research. Agroecosystems are complex
entities, which span several levels or scales, with different processes dominating each scale. Therefore, a dynamic
agroecosystem characterization requires biophysical characterization integrity to be maintained by addressing particular
objectives with specific information — information which may aggregate up - or down - scale (e.g. the aggregate
description of a complex of soils would deliver a sensible "regional" characterization). With spatially interpolated
climate data, digital elevation models, and low resolution soils data in place, agroecosystem characterization
commences with simple models used to differentiate growing season and off season characteristics. Other information -
usually much more difficult to acquire - becomes critical in refining target domains as resource access, land tenure,
cropping system, labor availability etc. dominate the land use system at higher resolutions.
GIS based modeling of an agroecosystem is expected to give a new approach in order to provide agricultural managers
with a powerful tool to assess simultaneously the effect of farm practices to crop production in addition to soil and
water resources. At present, most of the crop models are location specific (point based) in nature, but to understand the
impacts on the agricultural systems, it is necessary to have spatially explicit information. Therefore, development of
spatially or raster based biophysical crop model took long way in helping us to understand many intricacies of modeling
of large areas at coarse and fine resolution. To do this, Spatial Erosion Productivity Impact Calculator, [Spatial-EPIC]
(Satya and Shibasaki,1998) was developed which gave us a new direction to simulate crop production at regional scale
from microscopic simulation at each small piece of land in an efficient way, enables us to incorporate the environmental
issues. *Spatial-EPIC" is a crop simulation model developed to estimate the relationship between soil erosion and crop
productivity which has been implemented in GIS environment at 50km and 10 km grid size for a nation and region
respectively to have spatial distribution of crop output then the classical point based method.
3. INTEGRATED SYSTEM - DESIGN AND DEVLOPMENT
As we developed “Spatial-EPIC” after integration of EPIC (Williams and Sharpley, 1989) with GIS, a brief description
of “Spatial-EPIC” system files is warranted. “Spatial-EPIC” system file structure is comprised of text files, which
contain estimate of parameters of different physical processes modeled by “Spatial-EPIC”. These files include Basic
User-Supplied Data file, Crop Parameter File, Tillage Parameter File, Pesticide Parameter File, Fertilizer Parameter File,
Miscellaneous Parameter File, Multi-Run File, Output Variables File and Daily Weather Data File. In this study, a
system framework was designed using ArcView 3.1a, Arc/Info as a pre and post processor for data furnishing as well as
graphical display of “Spatial-EPIC”. Figure 1 and 2 shows a brief schematic presentation of crop modeling and
integrated model run process respectively under *Spatial-EPIC". Since the model runs outside GIS (after processing all
the GIS input layers in the form of array) hence it requires an interface to link finally for its proper display query and
attribute information of each cell. To do so, an in house written soft code was developed to meet their pre and post
processing file format requirement. A great amount of time spent comprehending the “Spatial-EPIC” file structure and
data requirements to make the model run. Also, spatial and locational databases were created to provide site-specific
information of the defined cell resolution.
3.1 Development of Dynamic Adaptations cum Management Loop
1192 International Archives of Photogrammetry and Remote Sensing. Vol. XXXIII, Part B7. Amsterdam 2000.
