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NATIONAL LEVEL SPATIAL MODELING OF AGRICULTURAL PRODUCTIVITY: STUDY OF 
INDIAN AGROECOSYSTEM 
Satya Priya 
Development Department, GIS Division 
Oyo Corporation, 2-61-5, Toro-Cho, 
Omiya, Saitama 330-8632, JAPAN 
satya-priya(@oyonet.oyo.co.jp 
ISPRS paper no 1301 
KEY WORDS: GIS, Modeling, Agroecosystem 
ABSTRACT 
Traditional decision support systems based on crop simulation models are normally site-specific. In order to address the 
effects of spatial variability from one place/region to other of soil conditions, and weather variables on crop production, 
spatial model namely "Spatial-EPIC" using Geographic Information System (GIS) was developed linking with 
biophysical agricultural management simulation models. With the development of this model any size of agroecosystem 
starting from a field to a country and even bigger can be modeled. A country level Indian agroecosystem was simulated 
as an application of model development and have been detailed with validation in this paper. It also helped to predict 
spatial yield variability on a farm level, region level, state level and so on as a function of soil water conditions under 
various weather regimes and management practices based on their socio-economic resources they prevail. GIS-based 
model differing in their resolutions (~50 km grid size and ~10 km grid size) were applied to two level study respectively 
at whole India level and then one of the Indian province called Bihar. Results showed that at both resolution level crop 
yield varied significantly as a function of the data detailed due to their resolution (pixel sizes) as well as function of 
seasonal climatic variation, soil water holding characteristics and provided crop management time-series information. 
1 INTRODUCTION 
Agroecosystem are overwhelmingly a complex process of air, water, soil, plants, animals, micro-organism and 
everything else in a bounded area that people have modified for the purposes of agricultural production. An 
agroecosystem can be of any specific size. It can be a single field, household farm or it can be the agricultural landscape 
of a village, region or nation. Some of the most important decision in agricultural production, such as what crops to 
grow and on how much land to allocate depends on the existing knowledge base of current and future physical 
conditions like soil and climate, yields and prices. Modeling of the various processes in the system helps us to 
understand its flow and intricacies. An important issue in agricultural environmental modeling is that all the basic units 
(water, soil and chemicals) have a spatial distribution, and since this distribution does affect the processes and dynamics 
of their interaction considerably, geographic information system (GIS) is emerging as an important tool in modeling. 
There have been a lot of studies on agricultural potential productivity but to relate actual crop productivity, however, 
only model-based simulations are not sufficient. Spatial biophysical model is still lacking to compute agricultural 
productivity at regional or national level although the estimates of farm productivity are being done using 
experimental/point based model. Site-specific management, or precision farming, is a strategy in which cropping inputs 
such as fertilizer are applied at varying rate across a field in response to variations in crop needs. 
Modeling within a GIS offers a mechanism to integrate many scales of data developed in and for agricultural research. 
Irrespective of the scale at which various crops, agriculture environment models operate, it is known that management 
practices geared towards conservation and productivity are initiated at the field level. At present, however, few 
agricultural producers are utilizing the true analytical power of GIS and computer simulation models, partly because the 
loose or less linkages developed to-date between GIS and mostly public-domain modeling software are extremely 
cumbersome to use or are esoteric. 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). Thus a need exists for an integrated, GIS modeling system to allow agricultural producers 
  
International Archives of Photogrammetry and Remote Sensing. Vol. XXXIII, Part B7. Amsterdam 2000. 1191