IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v13y2022i1p41-d1012920.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/13/1/41/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2077-0472/13/1/41/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Antonina Kalinichenko & Valerii Havrysh & Igor Atamanyuk, 2019. "The Acceptable Alternative Vehicle Fuel Price," Energies, MDPI, vol. 12(20), pages 1-20, October.
    2. Balat, Mustafa & Balat, Havva, 2009. "Recent trends in global production and utilization of bio-ethanol fuel," Applied Energy, Elsevier, vol. 86(11), pages 2273-2282, November.
    3. van der Velde, M. & Nisini, L., 2019. "Performance of the MARS-crop yield forecasting system for the European Union: Assessing accuracy, in-season, and year-to-year improvements from 1993 to 2015," Agricultural Systems, Elsevier, vol. 168(C), pages 203-212.
    4. Ali Mostafaeipour & Mohammad Bagher Fakhrzad & Sajad Gharaat & Mehdi Jahangiri & Joshuva Arockia Dhanraj & Shahab S. Band & Alibek Issakhov & Amir Mosavi, 2020. "Machine Learning for Prediction of Energy in Wheat Production," Agriculture, MDPI, vol. 10(11), pages 1-19, October.
    5. Oleg Bazaluk & Valerii Havrysh & Mykhailo Fedorchuk & Vitalii Nitsenko, 2021. "Energy Assessment of Sorghum Cultivation in Southern Ukraine," Agriculture, MDPI, vol. 11(8), pages 1-22, July.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Aloisio S. Nascimento Filho & Rafael G. O. dos Santos & João Gabriel A. Calmon & Peterson A. Lobato & Marcelo A. Moret & Thiago B. Murari & Hugo Saba, 2022. "Induction of a Consumption Pattern for Ethanol and Gasoline in Brazil," Sustainability, MDPI, vol. 14(15), pages 1-11, July.
    2. Filimonau, Viachaslau & Högström, Michaela, 2017. "The attitudes of UK tourists to the use of biofuels in civil aviation: An exploratory study," Journal of Air Transport Management, Elsevier, vol. 63(C), pages 84-94.
    3. Yasuda, Masahide & Matsumoto, Tomoko & Yamashita, Toshiaki, 2018. "Sacrificial hydrogen production over TiO2-based photocatalysts: Polyols, carboxylic acids, and saccharides," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 1627-1635.
    4. Bharathiraja, B. & Jayamuthunagai, J. & Sudharsanaa, T. & Bharghavi, A. & Praveenkumar, R. & Chakravarthy, M. & Yuvaraj, D., 2017. "Biobutanol – An impending biofuel for future: A review on upstream and downstream processing tecniques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 68(P1), pages 788-807.
    5. Zhang, Chen & Sun, Zongxuan, 2017. "Trajectory-based combustion control for renewable fuels in free piston engines," Applied Energy, Elsevier, vol. 187(C), pages 72-83.
    6. Andrea Patané & Giorgio Jansen & Piero Conca & Giovanni Carapezza & Jole Costanza & Giuseppe Nicosia, 2019. "Multi-objective optimization of genome-scale metabolic models: the case of ethanol production," Annals of Operations Research, Springer, vol. 276(1), pages 211-227, May.
    7. M'Arimi, M.M. & Mecha, C.A. & Kiprop, A.K. & Ramkat, R., 2020. "Recent trends in applications of advanced oxidation processes (AOPs) in bioenergy production: Review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 121(C).
    8. Sánchez, S. & Lozano, L.J. & Godínez, C. & Juan, D. & Pérez, A. & Hernández, F.J., 2010. "Carob pod as a feedstock for the production of bioethanol in Mediterranean areas," Applied Energy, Elsevier, vol. 87(11), pages 3417-3424, November.
    9. Jin, Wenxiang & Chen, Ling & Hu, Meng & Sun, Dan & Li, Ao & Li, Ying & Hu, Zhen & Zhou, Shiguang & Tu, Yuanyuan & Xia, Tao & Wang, Yanting & Xie, Guosheng & Li, Yanbin & Bai, Baowei & Peng, Liangcai, 2016. "Tween-80 is effective for enhancing steam-exploded biomass enzymatic saccharification and ethanol production by specifically lessening cellulase absorption with lignin in common reed," Applied Energy, Elsevier, vol. 175(C), pages 82-90.
    10. Diep, Nhu Quynh & Fujimoto, Shinji & Minowa, Tomoaki & Sakanishi, Kinya & Nakagoshi, Nobukazu, 2012. "Estimation of the potential of rice straw for ethanol production and the optimum facility size for different regions in Vietnam," Applied Energy, Elsevier, vol. 93(C), pages 205-211.
    11. Phanankosi Moyo & Mahluli Moyo & Donatus Dube & Oswell Rusinga, 2013. "Biofuel Policy as a Key Driver for Sustainable Development in the Biofuel Sector: The Missing Ingredient in Zimbabwe’s Biofuel Pursuit," Modern Applied Science, Canadian Center of Science and Education, vol. 8(1), pages 1-36, February.
    12. Tan, Raymond R. & Aviso, Kathleen B. & Barilea, Ivan U. & Culaba, Alvin B. & Cruz, Jose B., 2012. "A fuzzy multi-regional input–output optimization model for biomass production and trade under resource and footprint constraints," Applied Energy, Elsevier, vol. 90(1), pages 154-160.
    13. Yao, Yung-Chen & Tsai, Jiun-Horng & Wang, I-Ting, 2013. "Emissions of gaseous pollutant from motorcycle powered by ethanol–gasoline blend," Applied Energy, Elsevier, vol. 102(C), pages 93-100.
    14. Arkadiusz Piwowar & Maciej Dzikuć, 2019. "Development of Renewable Energy Sources in the Context of Threats Resulting from Low-Altitude Emissions in Rural Areas in Poland: A Review," Energies, MDPI, vol. 12(18), pages 1-15, September.
    15. Lenka Rumánková & Luboš Smutka, 2013. "Global sugar market - the analysis of factors influencing supply and demand," Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, Mendel University Press, vol. 61(2), pages 463-471.
    16. Starfelt, Fredrik & Daianova, Lilia & Yan, Jinyue & Thorin, Eva & Dotzauer, Erik, 2012. "The impact of lignocellulosic ethanol yields in polygeneration with district heating – A case study," Applied Energy, Elsevier, vol. 92(C), pages 791-799.
    17. Oleg Bazaluk & Valerii Havrysh & Vitalii Nitsenko & Tomas Baležentis & Dalia Streimikiene & Elena A. Tarkhanova, 2020. "Assessment of Green Methanol Production Potential and Related Economic and Environmental Benefits: The Case of China," Energies, MDPI, vol. 13(12), pages 1-25, June.
    18. Akroum-Amrouche, Dahbia & Abdi, Nadia & Lounici, Hakim & Mameri, Nabil, 2011. "Effect of physico-chemical parameters on biohydrogen production and growth characteristics by batch culture of Rhodobacter sphaeroides CIP 60.6," Applied Energy, Elsevier, vol. 88(6), pages 2130-2135, June.
    19. Vallinayagam, R. & Vedharaj, S. & Yang, W.M. & Roberts, W.L. & Dibble, R.W., 2015. "Feasibility of using less viscous and lower cetane (LVLC) fuels in a diesel engine: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 51(C), pages 1166-1190.
    20. Katia A. Figueroa-Rodríguez & Francisco Hernández-Rosas & Benjamín Figueroa-Sandoval & Joel Velasco-Velasco & Noé Aguilar Rivera, 2019. "What Has Been the Focus of Sugarcane Research? A Bibliometric Overview," IJERPH, MDPI, vol. 16(18), pages 1-15, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jagris:v:13:y:2022:i:1:p:41-:d:1012920. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.