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Journal of Automation and Information Sciences
SJR: 0.275 SNIP: 0.59 CiteScore™: 0.8

ISSN Druckformat: 1064-2315
ISSN Online: 2163-9337

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Journal of Automation and Information Sciences

DOI: 10.1615/JAutomatInfScien.v45.i6.70
pages 68-81

Winter Wheat Yield Forecasting: a Comparative Analysis of Results of Regression and Biophysical Models

Felix Kogan
National Oceanic and Atmospheric Administration Center for Satellite Applications and Research, Camp Springs (USA)
Nataliya N. Kussul
Institute of Space Research of National Academy of Sciences of Ukraine and State Space Agency of Ukraine, Kiev, Ukraine
Tatyana I. Adamenko
Ukrainian Hydro Meteorological Center, Kiev
Sergey V. Skakun
Institute of Space Research of National Academy of Sciences of Ukraine and National Space Agency of Ukraine, Kiev, Ukraine
Alexey N. Kravchenko
Institute of Space Research of National Academy of Sciences of Ukraine and State Space Agency of Ukraine, Kiev
Alexey A. Krivobok
Ukrainian Researcher Hydro Meteorological Institute, Kiev
Andrey Yu. Shelestov
National University of Life and Environmental Sciences of Ukraine, Kiev
Andrey V. Kolotii
Institute of Space Research of National Academy of Sciences of Ukraine and State Space Agency of Ukraine, Kiev
Olga M. Kussul
Institute of Space Research of National Academy of Sciences of Ukraine and National Space Agency of Ukraine, Kiev, Ukraine; National Technical University of Ukraine "Kiev Polytechnical Institute"
Alla N. Lavrenyuk
Institute of Space Research of National Academy of Sciences of Ukraine and National Space Agency of Ukraine, Kiev, Ukraine; National Technical University of Ukraine "Kiev Polytechnical Institute"

ABSTRAKT

Relative efficiency of using satellite data to winter wheat yield forecasting in Ukraine at region level is assessed. The efficiency of forecasting on the basis of empirical and biophysical models of agricultural crops is compared. As empirical yields models the linear regression models of yield dependency on 16-day index NDVI composite on the basis of MODIS data with spatial resolution 250 m (MOD 13) are considered as well as nonlinear regression model, in which daily meteorological data of 180 local meteorological stations are used as predictors. The empirical approach to prediction is compared with biophysical which is implemented in the system CGMS, adapted for the Ukraine and based on the WOFOST model. For parameters identification of the yield models the official statistical data is used of winter wheat yield at the regional level for the period of 2000−2009. Validation of models is done on independent data for 2010 and 2011. The obtained results showed that when training models for 2000−2009 and 2000−2010 years and validating for 2010 and 2011 respectively all three approaches show similar accuracy. Average root mean square prediction error is approximately 0.6 c/ha.


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