1. Crop Yield Modeling through Spatial Simulation Model

2. Simulation Model-WOFOST WOFOST (WOrld FOod STudies, Supit et al.,1994) is particularly suited to quantify the combined effect of changes in CO2, temperature, rainfall and solar radiation, on crop development, crop growth and crop water use, as all the relevant processes are simulated separately while taking due account of their interactions

3. Yield map Simulated Grid Yield Yield Prediction Through Simulation

4. Spatial Data Generation Weather

5. Soil Types in India as per FAO soil map

6. Generation of Calibrated Crop Coefficient Name of the state Bihar Haryana and Punjab MP Rajasthan UP Calibrated Variety HD2733 PBW343 Malvasakti (HI8498) Raj3765 HD2285

7. Sowing Date Retrieval from Remote Sensing Sowing date: spectral emergence-7 days Time series NDVI (25 Oct-15 Dec) Wheat NDVI AWiFS Wheat mask State-wise wheat NDVI ISODATA Classification Plotting temporal NDVI of each class 3 rd order polynomial curve fit Spectral emergence (The Day with first positive change in NDVI which is greater than the soil NDVI) Sub-setting 8 Nov 28 Nov 8 Dec Non-wheat 2008-09

8. Grid LAI Generation Real time LAI (56 m) Average grid LAI (5 km)

9. LAI Forcing in WOFOST model Computing the correction factor CF= observed LAI through remote sensing/Model derived LAI on RS observation date

10. Spatial Wheat Yield for 2009-10 (5 km) Input Data  Interpolated Weather Data  Calibrated Crop Coefficient  Sowing Date from Remote sensing  LAI from Remote Sensing Rajasthan Punjab < 2.5 2.5-3.5 3.5-4.5 >4.5 Non-wheat Non wheat < 2 t/ha 2-3 t/ha 3-4 t/ha >4 t/ha

11. Exploring WARM (Water Accounting Rice model) for rice yield simulation WARM Downloaded from: http://www.robertoconfalonieri.it/software_download.htm WARM version 1.9.6

12. Data used for calibration Daily weather data Station latitude Rain fall, Tmax, Tmin and solar radiation Crop data Date of sowing GDDs to reach emergence GDDs from emergence to flowering GDDs from flowering to maturity Periodical LAI (4 times) Dry biomass at harvest and grain yield at harvest Soil data Bulk density OC Clay Sand Field capacity PWP K S Variety: PR 118 Location: Punjab Agricultural Univ, Ludhiana, Punjab, India Climate: Semiarid subtropic

13. Calibration Result LAI (m2/m2) Validation Result LAI (m2/m2) DOY N.B. Two days delay in flowering was observed, Harvesting date was same as observed

15. Converting Point WOFOST Model to Spatial Mode WOFOST-exe Spatial data for weather Spatial data for crop Spatial data for soil Spatial data for sowing date Batch mode for all grid Output for all grid FORTRAN

16. DataSource 1.Real time Weather Data Maximum & Minimum Temperature Rainfall Daily Incoming Solar Radiation Wind speed Relative humidity IMD website (~80 station) Computed from temperature* Climatic normal 2. Soil Data Soil texture Soil moisture constants Hydraulic properties FAO soil map (1: 5M) 3. Management Data Planting/sowing date Irrigation (Date & Amount) Fertilizer (Date & Amount) Remote sensing (SPOT-VGT/INSAT-CCD) Not required for potential simulation 4.Crop data Phenology Physiology Morphology Derived for a major variety in each state through calibration Input Data and Source *Solar radiation Where, A h and B h are the empirical constants and Ra is the extra terrestrial radiation (Duffie and Beckman,1980) (Hargreaves, 1985)

17. Crop Growth Simulation Model InputsProcessOutput Weather (Temperature, Rainfall, solar radiation) Soil Parameters (Texture, depth, soil moisture, soil fertility) Crop Parameters (Phenology, physiology, morphology) Management (DOS, irrigation, fertilizer) Phenological Development CO 2 Assimilation Transpiration Respiration Partitioning Dry matter Format Biomass, LAI, Yield Water Use Nitrogen Uptake

18. Choice of Simulation Models in FASAL The model needs to be sufficiently process based to simulate crop productivity over a range of environments, while being simple enough to avoid the need for large amounts location specific input data It should be possible to run the model spatially, in large number of grids. The user interface of the model should be simple enough for multi-disciplinary users. There needs to be a scope for assimilation of in-season remote sensing derived parameters. The source code should be open for any modification WOFOST model has been chosen because of the availability of source code and relatively less input requirement