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GIS based crop production model and its applications  Satya Priya RMSI Pvt. Ltd., NOIDA, India satyap@riskinc.com Abstract Traditional decision support systems based on crop simulation models are normally site-specific. In policy formulation, however, spatial variability of crop production often need to be evaluated due to different soil conditions, weather conditions and agricultural practices within a target-region. To address the spatial variability, a spatial model “Spatial-EPIC”h “(Satya et. al., 1998) was developed based on a crop simulation model EPIC (Williams, J. R. and A. N. Sharpely., 1989) (Erosion Productivity Impact Calculator). Since site-specific crop simulation models require point-based or fine resolution data, it is”necessary to feed the fine resolution data at each grid-cell in order to “spatialize” crop simulation models. The authors proposed a method to generate fine resolution data from coarse resolution data which are usually available at regional or national level. In addition, since the original EPIC crop management practices are static in nature, a dynamic adaptation loop is added to evaluate the impacts of agricultural practice changes over temporal scale. Validation of the spatial EPIC was conducted at different spatial scales, i.e. National scale (approx. 50km cell-size) and regional scale (approx. 10km cell-size) in India. Results showed that at both resolutions level crop yield varied significantly as a function of seasonal climatic variation, soil water holding characteristics and applied crop management strategies. Also, the study successfully demonstrated model applicability in evaluating an impact of climate changes over major cereal crops productivity at national level taking spatial variability into account. Finally, the model were applied for Indian State Bihar, at national level it was applied to India and then for the entire globe for the major cereal crops. All above said results will be presented during the conferecne session. 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.  Fig. 1: Brief schematic presentation of modeling under 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 fertiliser 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 utilising 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 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.
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