Probabilistic Yield Forecast Based on Aproduction Process Model

  • Kadaja Jüri 
  • Saue Triin 
  • Vii Peeter 
Conference paper
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 293)


A method for probabilistic forecast of agricultural yield depending on meteorological variability, i.e. forecast of agrometeorological resources, is discussed. Forecast is based on the category of meteorologically possible yield (MPY)–the maximum possible yield for a given variety in the existing meteorological conditions. The forecasting process is realized by a potato production process model POMOD, which applies the principle of maximum plant productivity and method of reference yields. The yield diversity, granting probabilistic distribution was obtained from series of model calculations,whereby the weather realizations for post–forecast period were gained from a century–long meteorological data series. Three examples realized for extremely different years are discussed. The results of such forecast, presented as a cumulative distribution, allow user to adjust and plan activities to thesufficiently assured yield level. Forecast of agrometeorological resources can be transformed to the forecast of real commercial yield (CY) by incorporating the efficiency coefficient of using meteorological conditions (CY/MPY).


Meteorological Condition Climatic Forecast Forecast Ensemble Probabilistic Forecast Efficiency Coefficient 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. Agroclimatic resources of Estonian SSR. Gidrometeoizdat, 1974, 174 p. (In Russian).Google Scholar
  2. Cantelaube P., Terres J.M. Seasonal weather forecasts for crop yield modelling in Europe. Tellus Ser A Dyn Meteorol. Oceanogr., 2005, 57: 476–487.Google Scholar
  3. Challinor A.J., Slingo J.M., Wheeler T.R., Dobias-Reyes F.J. Probabilistic simulations of crop yield over western India using the DEMETER seasonal hindcast ensembles. Tellus Ser A Dyn. Meteorol. Oceanogr., 2005, 57: 498–512.Google Scholar
  4. Dubrovsky M., Zalud Z., Trnka M., Pesice P., Haberle J. PERUN - The System for the Crop Yield Forecasting. in: XIV Czecho-Slovak Bioclimatological conference, 2–4. September 2002, Lednice, Czech Rep. CD-ROM proceedings.Google Scholar
  5. Kadaja J. Influence of fertilisation on potato growth functions. Agronomy Research, 2004, 2zz1): 49–55.Google Scholar
  6. Kadaja J., Tooming H. Potato production model based on principle of maximum plant productivity. Agric. For. Meteorol. 2004, 127(1–2): 17–33.CrossRefGoogle Scholar
  7. Kitse E. Mullavesi [Soil water]. Tallinn, Valgus, 1978, 142 p. [In Estonian].Google Scholar
  8. Kuchar L. The exponential polynomial model (EPM) of yield forecasting for spring wheat based on meteorological factors and phenophase. - Agric. For. Meteorol, 1989, 46: 339–348.CrossRefGoogle Scholar
  9. Sepp J. Effect of meteorological conditions of different periods on potato productivity and the probabilistic yield forecast. Proceedings of All-Union Research Institute of Agricultural Meteorology. Leningrad, Gidrometeoizdat, 1988, 23: 116–122. (In Russian).Google Scholar
  10. Sepp J., Tooming H. Productivity resources of potato. Gidrometeoizdat, Leningrad, 1991, 261 p. (In Russian).Google Scholar
  11. Tooming H. Climate change and estimation of ecologically founded yields. In: T. Kallaste and P.Kuldna (Eds.).Climate change studies in Estonia. Ministry of the Environment Republic of Estonia, SEI, Tallinn, 1998, pp. 141–152.Google Scholar
  12. Tooming H. Ecological principles of maximum crops productivity. Gidrometeoizdat, Leningrad, 1984, 264 p. (in Russian, summary in English).Google Scholar
  13. Tooming H. Evaluation of agrometeorological resources based on the potential productivity of crops. Journ. Agric. Met. (Jap.), 1993, 48 (5): 501–507.Google Scholar
  14. Tooming H. Kõiva P. Agroclimatic estimation of the potential and actually possible potato yield. Meteorologiya i Gidrologiya (Meteorology and Hydrology), 1979, (7): 105–109. (In Russian).Google Scholar
  15. Tooming H. Mathematical description of net photosynthesis and adaptation processes in the photosynthetic apparatus of plant communities. In: Setlik I. (Ed.), Prediction and Measurement of Photosynthetic Productivity. Pudoc, Wageningen, 1970, pp. 103–114.Google Scholar
  16. Tooming H. Mathematical model of plant photosynthesis considering adaptation. Photosynthetica, 1967, 1(3–4): 233–240.Google Scholar
  17. Tooming H. Prospects in forecasting the efficiency of changing the plant parameters and the estimation of maximum yield. In: Programming of agricultural crops yield. Kolos, Moscow, 1975, pp. 403–414. (In Russian).Google Scholar
  18. Tooming H. Solar radiation and yield formation. Gidrometeoizdat, Leningrad, 1977, 200 p. (In Russian, abstract in English).Google Scholar
  19. Tooming H. The method of model yields. Vestnik Sel'skokhozyaistvennoi Nauki (Reports of Agricultural Sciences), 1982, (3/306): 89–94. (In Russian).Google Scholar
  20. Zhukovsky E.E., BelchenkoG.G., Brunova T.M. Probability analysis of climate change Impact on potential productivity of agricultural ecosystems. Meteorologiya i Gidrologiya (Meteorology and Hydrology)m, 1992, (3): 70–79. (In Russian, abstract in English).Google Scholar
  21. Zhukovsky E.E., Sepp J., Tooming H. Probabilistic forecasts of possible yield. Meteorologija i Gidrologiya (Meteorology and Hydrology), 1990, (1): 95–102. (In Russian with English abstract).Google Scholar
  22. Zhukovsky E.E., Uskov I. B. About the principles of yield programming on the probabilistic basis. In Modeling and managing of processes in the agroecosystems. Institute of Agrophysics, Leningrad, 1984, pp. 116–126. (In Russian).Google Scholar
  23. Zhukowsky E.E., Sepp J., Tooming H., On the possibility of the yield calculation and forecasting calculation. Vestnik Sel'skokhozjaistvennoi Nauki (Reports of Agricultural Sciences), 1989, (5): 68–79. (In Russian, abstract in English).Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  1. 1.Department of Agricultural Engineering and TechnologyEstonian Research Institute of AgricultureSakuEstonia

Personalised recommendations