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Forecasting of Winter Wheat Yield: A Mathematical Model and Field Experiments

Author

Listed:
  • Igor Atamanyuk

    (Department of Applied Mathematics, Warsaw University of Life Sciences, 02-787 Warsaw, Poland
    Department of Higher and Applied Mathematics, Mykolaiv National Agrarian University, 54020 Mykolaiv, Ukraine)

  • Valerii Havrysh

    (Department of Tractors and Agricultural Machines, Operating and Maintenance, Mykolaiv National Agrarian University, 54020 Mykolaiv, Ukraine)

  • Vitalii Nitsenko

    (Department of Entrepreneurship and Marketing, Institute of Economics and Management, Ivano-Frankivsk National Technical Oil and Gas University, 76019 Ivano-Frankivsk, Ukraine
    SCIRE Foundation, 00867 Warsaw, Poland)

  • Oleksii Diachenko

    (Department of Information Technologies, Odessa State Agrarian University, 65012 Odessa, Ukraine)

  • Mariia Tepliuk

    (Department of Business Economics and Entrepreneurship, Kyiv National Economic University Vadym Hetman, 03057 Kyiv, Ukraine)

  • Tetiana Chebakova

    (Department of Business Economics and Entrepreneurship, Kyiv National Economic University Vadym Hetman, 03057 Kyiv, Ukraine)

  • Hanna Trofimova

    (Ukrainian Institute For Plant Variety Examination, 03041 Kyiv, Ukraine)

Abstract

An increase in world population requires growth in food production. Wheat is one of the major food crops, covering 21% of global food needs. The food supply issue necessitates reliable mathematical methods for predicting wheat yields. Crop yield information is necessary for agricultural management and strategic planning. Our mathematical model was developed based on a three-year field experiment in a semi-arid climate zone. Wheat yields ranged from 4310 to 6020 kg/ha. The novelty of this model is the inclusion of some stochastic data (weather and technological). The proposed method for wheat yield modeling is based on the theory of random sequence analysis. The model does not impose any restrictions on the number of production parameters and environmental indicators. A significant advantage of the proposed model is the absence of limits on the yield function. Consideration of the stochastic features of wheat production (technological and weather parameters) allows researchers to achieve the best accuracy. The numerical experiment confirmed the high accuracy of the proposed mathematical model for the prediction of wheat yield. The mean relative error (for the third-order polynomial model) varied from 1.79% to 2.75% depending on the preceding crop.

Suggested Citation

  • Igor Atamanyuk & Valerii Havrysh & Vitalii Nitsenko & Oleksii Diachenko & Mariia Tepliuk & Tetiana Chebakova & Hanna Trofimova, 2022. "Forecasting of Winter Wheat Yield: A Mathematical Model and Field Experiments," Agriculture, MDPI, vol. 13(1), pages 1-22, December.
    • Handle: RePEc:gam:jagris:v:13:y:2022:i:1:p:41-:d:1012920
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    References listed on IDEAS

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