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

2. Content Description of methods Comparison of results 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 = t/ha per year
Max = t/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

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

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. Non-linear effects
Corr coef april

10. Gaussian processes regression

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

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

14. 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

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. Geoportal: crop maps

17. Thank you!