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

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Presentation on theme: "Nataliia Kussul, Andrii Shelestov, Sergii Skakun, Oleksii Kravchenko Space Resarch Institute NASU-NSAU, Ukraine Forecasting winter wheat yield in Ukraine."— Presentation transcript:

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

3 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 = 0.197 t/ha per year Criteria Rel. eff. =

4 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 5

6 6

7 7 Forecast for 2010 Crop yield observed centner/ha Crop yield forecast centner/ha

8 8 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

9 9 Non-linear effects Corr coef april - 0.75

10 10 Gaussian processes regression

11 11 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

12 12 Comparison the results of NDVI-based regression model with CGMS Prediction for 2010, models are trained for 2000-2009

13 13 Comparison the results of NDVI-based regression model with CGMS Prediction for 2010, models are trained for 2000-2009: error histogram

14 14 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

15 15 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.

16 16 Geoportal: crop maps

17 17 Thank you!


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