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Title: Forecasting of Winter Wheat Yield: A Mathematical Model and Field Experiments
Authors: Атаманюк, Ігор Петрович
Atamanyuk, Igor
Гавриш, Валерій Іванович
Havrysh, Valeriy
Nitsenko, Vitalii
Diachenko, Oleksii
Tepliuk, Mariia
Chebakova, Tetiana
Trofimova, Hanna
Keywords: cropping system
mathematical model
wheat production
Issue Date: 2023
Publisher: Mykolaiv National Agrarian University
Warsaw University of Life Sciences
Ivano-Frankivsk National Technical Oil and Gas University
SCIRE Foundation
Odessa State Agrarian University
Kyiv National Economic University Vadym Hetman
Ukrainian Institute For Plant Variety Examination
Citation: Atamanyuk, I., Havrysh, V., Nitsenko, V., Diachenko, O., Tepliuk, M., Chebakova, T., & Trofimova, H. (2023). Forecasting of winter wheat yield: A mathematical model and field experiments. Agriculture (Switzerland), 13(1) doi:10.3390/agriculture13010041
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.
Appears in Collections:Публікації науково-педагогічних працівників МНАУ у БД Scopus
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