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Overview |
Crop Production |
Crop Pattern |
Crop Yield |
Irrigation |
Soil Management | Precision Farming |
Relevant Products |
Relevant Links
GIS based crop production model and its applications
Validation
The first approach used to evaluate “Spatial-EPIC” yield simulation was to compare the output at state level average reported data for the year 1995 values. Closeness between measured and predicted yield at state level is first and coarse level validation to see whether the simulated output is following the trend is of reported aggregate average. For doing this the simulated 0.5 degree pixel resolution falling under the state were averaged and their mean were compared with the reported state level average as shown in figure 5 to figure 7 for maize, wheat and rice crop respectively. Again to go further ahead at same resolution validation for whole India, the output for maize, rice and wheat for the year 1990 of these growing belts were compared by overlaying the district coverage. To extract the mean value of a district simulated yield; all pixels were overlaid with all India district boundaries, which are roughly 450 in number. All the pixels following under particular district were averaged and their computed means were compared with the average reported statistical value for these three crops. All of these results can not be presented in this paper due to limited allowed volume in terms of total page no. Therefore, to see the same output spatially distributed over the country simulated vs. reported yield of maize, wheat and rice, a rough cum spatial validation map are given from figure 8 to 11 (where fig. 5 to 7 corresponds their validation as well) to have more explicit understanding of the area and their correspondence between productivity. Although there were some places where model has simulated more or less yields in case of maize and rice but in general it gives a very nice comparison hence one can easily identify the model performance by seeing these three maps as shown in the above said figure. The reason for getting less and more yields especially in rice crop is due to the limitation of not having district wise time series data of entire nation instead we applied state level procured management data like fertiliser and others. But, with the above validation figures it is self evident that the model was quite successful for simulating any piece of land as India could be one of the best example of showing the diversity from one place to other in terms of climate, natural, economical as well as social conditions.
Under the scope of the paper presented in this paper only country level (low-resolution) results have been explained whereas detailed state level (high resolution) could not be illustrated due to space limitation. But to give a feeling on how high-resolution results differs and gives more accurate output can be sensed seeing figure 12 comparing impact of two different resolution input data over wheat yield.
Limitations
Validation of models with a high spatial resolution is difficult and in some cases impossible, as it is impossible to validate each pixel output to a field data unless it is really being conducted under the same project. However, historic analysis gives possibilities to validate the model assuming the reported input applied in a area and validating it with simulated results. Usually in developing world all the data reported which could be fetched are not lower than the district boundaries and size of those district also varies to a greater extent. But the multi-scale approach helps in simulating the developing world where data are always a limitation. If certain grid cells, at the coarse allocation scale, have more information then accordingly a cell size can be estimated and could be applied to model the area/region more realistically.
Conclusions
The methodology presented found to be encouraging that provides an opportunity to plant physiologist, a modeler and GIS user a common ground to discuss simulation results and further potential research directions. Simulated crop yield and other maps generated under different scale dependencies within India and Bihar can be used to better communicate model predictions. Hence, using this methodology a region/nation can be modeled for any crop productivity, which help researchers and decision-makers understand the status and extent of climate, soils and crop cum field management effects on global processes such as rice, wheat and maize production.
To evaluate “Spatial-EPIC” yield simulation validation were carried out in different pockets of India based on the major growing reasons. Two tier validations were done at two different cell resolutions, coarse and fine for whole India (0.5 degree cell size) as well as one of the Indian states Bihar (0.1 degree cell size) respectively. Validation results was found quite successful for wheat and maize productivity whereas in case of rice it was a bit under estimated in southern most part of India whereas the other places gave better correlation between the simulated and observed values. It is believed that the model can be used in simulating any piece of land since India is one of the best example of showing the diversity from one place to other in terms of climate, natural, economical as well as social conditions from model applicability viewpoint.
Hence, the “Spatial-EPIC” possesses immense potential as a farm management tool. However, further research should be focussed on improving the model prediction, and the field level interactions within the system. Also, availability of new agricultural land-use maps with seasonal crop delineation, and other information of the management practices will help in bettering the model results.
Acknowledgements
Thanks goes to Global Frontier Project (in Japanese, Mirai-Kaitaku) sponsored by Japan Society of Promotional Science for providing generous financial support in order to accomplish this study without financial strain.
References
- Satya Priya Shibasaki, Ryosuke and Shiro Ochi (1998) Modeling Spatial Crop Production: A GIS approach, Proceedings of the 19th Asian Conference on Remote Sensing, 16-20 Nov, 1998 held at Manila. pp A-9-1 to A-9-6.
- Crane, P.J. and L. P. Herrington. 1992. GIS applications. A wide spectrum not without problems. Photogrammetric Eng. and Remote Sens. 8:1092-1094.
- NGDC, 1997. GTOPO30, Global Land One-Km Base Elevation, (Average 30-Second Elevations Grids). National Geophysical Data Center 325 Broadway, Boulder, Colorado.
- Williams, J. R. and . Sharpley, A.N.,
(eds.), (1989). EPIC —Erosion/Productivity Impact Calculator: 1.
Model Documentation, USDA Technical Bulletin No. 1768.
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