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GIS based crop production model and its applications 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 digitisation 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 Organisation 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.  Fig. 7: Rough Spatial Validation of Rice in Crop 1990 - 91 Results and discussion The model developed described in the earlier part of paper was found capable for simulating an unlimited number of crop management strategies, based on the selection and data provided by the user. In contrast to a stand-alone original EPIC crop simulation model, where the management information given in the beginning continues for the total no. of simulations year, hence the trend of output used to be more or less static and doesn’t correspond to the actual farm practice. With the development of dynamic loop under “Spatial-EPIC” it got rectified. Now with this, during computation the model runs for each and every pixel following the rows and columns sequence with various multiple soil, climate, and management information provided in the form of layers. Two-year crop rotation was found appropriate for long term simulation. The crops selected in a row were maize-wheat-rice. Crop management option provided by user the model could be briefly seen from figure 1 on its right hand side given management table. Besides these there are many other information which need to be fed like start of simulation date, planting date, harvesting date, tillage time, irrigation timing its amount, fertilisation time and so on. Amount of fertiliser applied used was the reported state and district level time-series data procured during the study. The crop selected in sequence for modeling was rainfed maize (without irrigation), irrigated wheat and monsoon rice with one user specified assured irrigation. All possible measures explained above were taken into account to mimic the more realistic field practice. Yield simulation of the rainfed maize varied from 0.4 to 3.5 t/ha as shown in figure described below under validation section for its spatial distribution of productivity throughout India. The maize yield shows quite high potentiality but being a third cereal it is not grown so widely like rice and wheat. Yield distribution of irrigated wheat crop varied between 0.5 to 3.5 t/ha also shown in figure described below under validation section clears that only the northern part of India is the wheat belt. Because of the fact that the Indo-Gangetic plains form the most important wheat area. The cool winters and the hot summers are very conducive to a good crop of wheat, whereas the rice is being grown throughout India but the southern part of India is found favorable from agro-climatic conditions. Similarly yield variation of monsoon rice was found to be fluctuating from 0. 3 to 3.0 t/ha.  Fig. 8: Map 5.4 Comparison of Two Different Resolutions Impact Over Wheat Yield |