Crop Yield Modeling through Spatial Simulation Model

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 Prediction Through Simulation Yield map Simulated Grid Yield

4. Spatial Data Generation Weather

5. Soil Types in India as per FAO soil map

6. Generation of Calibrated Crop Coefficient

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

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. Non-wheat < 2.5 2.5-3.5 3.5-4.5 >4.5 Punjab Rajasthan Non wheat < 2 t/ha 2-3 t/ha 3-4 t/ha >4 t/ha 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

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

12. Data used for calibration Daily weather data Station latitude Rain fall, Tmax, Tmin and solar radiation Variety: PR 118 Location: Punjab Agricultural Univ, Ludhiana, Punjab, India Climate: Semiarid subtropic Soil data Bulk density OC Clay Sand Field capacity PWP KS 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

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

14. Thank you

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

16. Input Data and Source *Solar radiation Where, Ah and Bh are the empirical constants and Ra is the extra terrestrial radiation (Duffie and Beckman,1980) (Hargreaves, 1985)

17. Crop Growth Simulation Model

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