Presentation on theme: "Forecasting winter wheat yield in Ukraine using 3 different approaches"— Presentation transcript:
1 Forecasting winter wheat yield in Ukraine using 3 different approaches Nataliia Kussul, Andrii Shelestov, Sergii Skakun, Oleksii KravchenkoSpace Resarch Institute NASU-NSAU, Ukraine
2 Content Description of methods Comparison of results NDVI-based Meteorological data basedCGMSComparison of results
3 NDVI-based empirical model NDVI-based regression models for forecasting winter wheat yields were built for each oblastdYі = Yі - Tі =f(NDVIі) = b0 + b1*NDVIіMin = t/ha per yearMax = t/ha per yearCriteriaRel. eff. =
4 Winter wheat yield forecasting Cross-validationleave-one-out cross-validation (LOOCV)using a single observation from the original sample as the testing data, and the remaining observations as the training dataCriteriaRMSE on testing data
8 Meteorological modelA 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 parametersMaximum temperatureMinimum temperatureAverage temperaturePrecipitationSoil moisture0-20 cm depthAvailable for months: Sept, Oct, Apr, May, JuneMethodologyCorrelation analysisLinear multivariate regressionNon-linear multivariate regression
11 CGMSResults of Crop Growth Monitoring System (CGMS) adopted for UkraineThe 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 scaleThe new agrometeorological data (crop data) were collected and ingested into the CGMS systemYield 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 modelsRMSE for predicting yield for 2010, models are trained forNDVI: 0.79 t/haFor steppe zone: 0.61 t/haError can be reduced ~1.3 times when NDVI averaged by winter wheat maskCGMS-May: 0.37 t/haFor steppe zone: 0.24 t/haCGMS-June: 0.30 t/haFor steppe zone: 0.19 t/haMeteo: 0.86 t/haProblem of over-fittingFor 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/haNDVI-mask: 0.46 t/haCGMS-May: 0.24 t/haCGMS-June: 0.19 t/haKirovohradska obl.
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