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**Forecasting winter wheat yield in Ukraine using 3 different approaches**

Nataliia Kussul, Andrii Shelestov, Sergii Skakun, Oleksii Kravchenko Space Resarch Institute NASU-NSAU, Ukraine

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**Content Description of methods Comparison of results NDVI-based**

Meteorological data based CGMS Comparison of results

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**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 = t/ha per year Max = t/ha per year Criteria Rel. eff. =

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**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

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**Forecast for 2010 Crop yield forecast centner/ha**

Crop yield observed centner/ha

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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

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Non-linear effects Corr coef april

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**Gaussian processes regression**

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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

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**Comparison the results of NDVI-based regression model with CGMS**

Prediction for 2010, models are trained for

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**Comparison the results of NDVI-based regression model with CGMS**

Prediction for 2010, models are trained for : error histogram

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Comparison of models RMSE for predicting yield for 2010, models are trained for 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

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**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.

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Geoportal: crop maps

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Thank you!

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