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Improved Crop Yield Forecasting Using Downscaled Data

Utilize the JRC impact model for crop yield forecasting by running it with downscaled ERA-40 data and bias-corrected probabilistic ensemble hindcasts at seasonal-to-decadal scales. The JRC contribution involves running the model on Wheat, Maize, and EU-25 Countries, analyzing and verifying results, and comparing them with the EC-JRCCROP YIELDFORECASTING SYSTEM DEMETER. Downscaling enhances simulated crop parameter accuracy. R2=0.58, RMSE=3484 kg/Ha for ERA-40 data; R2=0.69, RMSE=3309 kg/Ha for probabilistic ensemble hindcasts.

vernon-lowery
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Improved Crop Yield Forecasting Using Downscaled Data

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  1. WP5.5: the JRC contribution SCOPE Use the JRC impact model (namely, crop yield forecasting model) by running firstlywith appropriately downscaled ERA-40 data (by WP3.1,WP5.1) and secondly with fully downscaled and bias corrected probabilistic ensemble hindcasts at of seasonal-to-decadal scales. JRC contribution: • Run (with both downscaled data set) the crop yield model on the two main European crops: Wheat , Maize and on EU-25 Countries (according to the data availability) • Analyze and verify the driven by downscaled ERA-40 results with the existing gridded station data set (by JRC) • Comparison of the results driven by downscaled and bias corrected probabilistic ensemble hindcasts with the two previous

  2. EC - JRCCROP YIELDFORECASTING SYSTEM

  3. DEMETER experience Downscaling improves simulated crop parameters skill R2=0.58 - RMSE=3484 kg/Ha R2=0.69 - RMSE=3309 kg/Ha

  4. DEMETER experience Probability Density Function (PDF)

  5. Preliminary results on existing data set - Winter wheat -

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