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Nataliia Kussul, Andrii Shelestov, Sergii Skakun, Oleksii Kravchenko

Nataliia Kussul, Andrii Shelestov, Sergii Skakun, Oleksii Kravchenko Space Resarch Institute NASU-NSAU, Ukraine. Forecasting winter wheat yield in Ukraine using 3 different approaches. Content. Description of methods NDVI-based Meteorological data based CGMS Comparison of results.

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Nataliia Kussul, Andrii Shelestov, Sergii Skakun, Oleksii Kravchenko

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  1. Nataliia Kussul, Andrii Shelestov, Sergii Skakun, Oleksii Kravchenko Space Resarch Institute NASU-NSAU, Ukraine Forecasting winter wheat yield in Ukraine using 3 different approaches

  2. Content • Description of methods • NDVI-based • Meteorological data based • CGMS • Comparison of results

  3. NDVI-based empirical model • NDVI-based regression models for forecasting winter wheat yields were built for each oblast dYі = Yі - Tі = f(NDVIі) = b0 + b1*NDVIі Min = 0.019 t/ha per year Max = 0.197t/ha per year Criteria Rel. eff. =

  4. Winter wheat yield forecasting • Cross-validation • leave-one-out cross-validation (LOOCV) • using a single observation from the original sample as the testing data, and the remaining observations as the training data • Criteria • RMSE on testing data

  5. Meteorological model • A non-linear model for winter wheat yield forecasting that incorporates climatic parameters was built for the Steppe agro-climatic zone. • To model the relationship between crop productivity (in particular winter wheat) and main climatic parameters • Maximum temperature • Minimum temperature • Average temperature • Precipitation • Soil moisture • 0-20 cm depth • Available for months: Sept, Oct, Apr, May, June • Methodology • Correlation analysis • Linear multivariate regression • Non-linear multivariate regression

  6. Non-linear effects Corr coef april - 0.75

  7. Gaussian processes regression

  8. CGMS • Results of Crop Growth Monitoring System (CGMS) adopted for Ukraine • The use of meteorological data from 180 local weather stations at a daily time step for the last 13 years (from 1998 to 2011) • The new soil map of Ukraine at the 1:2,500,000 scale • The new agrometeorological data (crop data) were collected and ingested into the CGMS system • Yield forecasting

  9. Comparison the results of NDVI-based regression model with CGMS Prediction for 2010, models are trained for 2000-2009

  10. Comparison the results of NDVI-based regression model with CGMS Prediction for 2010, models are trained for 2000-2009: error histogram

  11. Comparison of models • RMSE for predicting yield for 2010, models are trained for 2000-2009 • NDVI: 0.79 t/ha • For steppe zone: 0.61 t/ha • Error can be reduced ~1.3 times when NDVI averaged by winter wheat mask • CGMS-May: 0.37 t/ha • For steppe zone: 0.24 t/ha • CGMS-June: 0.30 t/ha • For steppe zone: 0.19 t/ha • Meteo: 0.86 t/ha • Problem of over-fitting • For steppe zone: 0.26 t/ha

  12. NDVI averaged by mask • Masks need to be estimated for each year • For steppe zone: • NDVI: 0.61 t/ha • NDVI-mask: 0.46 t/ha • CGMS-May: 0.24 t/ha • CGMS-June: 0.19 t/ha Kirovohradska obl.

  13. Geoportal: crop maps

  14. Thank you!

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