4. CONCLUSIONS 
A prototype CGMS was developed which could assimilate 
spatial inputs of weather, soil, crop management, RS-derived 
crop distribution and link it with wheat simulation model 
WTGROWS to generate crop parameters and yield maps on a 
Windows platform. The crop simulation was carried out at each 
grid, in this case 5’X5’ size. 
A procedure for using RS derived crop spectral profile and 
CGMS derived LAI profiles for estimating the important crop 
management input of date of sowing at district level was 
demonstrated. The RS-CGMS-derived phenology, its variation 
amongst districts, and derived sowing dates were consistent : 
with results from various field studies. The incorporation 
district-wise dates of sowing and N fertilizer application 
improved yield prediction. 
The simulation model was able to capture the variability in 
wheat yield within and across districts and the grid-wise 
weights derived from wheat distribution map allowed for 
estimation of average district level yields. 
The developed CGMS could be an important tool in crop 
monitoring and yield prediction at different spatial scales. At 
very fine resolution, CGMS can capture spatial variability in 
yield at farm / group of farms level for implementing precision 
crop management and at very coarse resolution it can forecast 
change in crop growth patterns due to global climate change. 
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ACKNOWLEDGEMENTS 
The authors wish to acknowledge Dr N. Kalra and Dr P.K. 
Aggarwal of Centre for Applications of System Simulation, 
Indian Agricultural Research Institute, New Delhi for 
providing the model WTGROWS and sharing their expertise in 
its implementation. A great deal of encouragement and 
guidance was provided by Shri J.S. Parihar, Group Director, 
Agricultural Resources Group in carrying out this study.