IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v14y2024i9p1592-d1477044.html
   My bibliography  Save this article

Novel Model for Pork Supply Prediction in China Based on Modified Self-Organizing Migrating Algorithm

Author

Listed:
  • Haohao Song

    (College of Engineering, Northeast Agricultural University, Harbin 150030, China)

  • Jiquan Wang

    (College of Engineering, Northeast Agricultural University, Harbin 150030, China)

  • Gang Xu

    (College of Engineering, Northeast Agricultural University, Harbin 150030, China)

  • Zhanwei Tian

    (College of Engineering, Northeast Agricultural University, Harbin 150030, China)

  • Fei Xu

    (College of Arts and Sciences, Northeast Agricultural University, Harbin 150030, China)

  • Hong Deng

    (College of Arts and Sciences, Northeast Agricultural University, Harbin 150030, China)

Abstract

Pork supply prediction is a challenging task of significant importance for pig producers and administrators, as it aids decision-making and maintains the pork supply–demand balance. Previous studies failed to consider impact factors like the month-age transfer principle of pigs, epidemic factors, and the simultaneous import and export volumes of pork, leading to the absence of a quantitative prediction model for pork supply. In this background, we proposed a novel quantitative prediction model of pork supply that incorporates pork production and pork import/export volumes. First, a prediction model for pork production that takes into account the month-age transfer principle of pigs and epidemic factors was presented, along with a recursive model of the pig-herd system. A novel method based on a modified self-organizing migrating algorithm (MSOMA) was proposed for calculating the quantity of monthly newly retained sows (NRS). Furthermore, the pork-production prediction model considered the epidemic factor as a random disturbance term (RDT), and a prediction method based on MSOMA and a back-propagation neural network (MSOMA-BPNN) was introduced to predict such disturbance terms. Second, the proposed MSOMA-BPNN was employed to predict pork import and export volumes. The pork supply was subsequently determined based on the predicted pork production, as well as the pork import and export volumes. The proposed pork supply prediction model was applied to forecast China’s pork supply from 2010 to 2023. The results validate the high effectiveness and reliability of the proposed model, providing valuable insights for decision makers. The empirical results demonstrate that the proposed model is a promising and effective tool for predicting the pork supply. To our knowledge, this is a novel tool for pork supply prediction, considering the pig-herd system and pork import and export volumes from a systemic perspective. These features allow for consideration of the scientific formulation of a pig production plan, the establishment of early warning mechanisms to deal with epidemic situations and emergencies, and the regulation of pork supply and demand balance.

Suggested Citation

  • Haohao Song & Jiquan Wang & Gang Xu & Zhanwei Tian & Fei Xu & Hong Deng, 2024. "Novel Model for Pork Supply Prediction in China Based on Modified Self-Organizing Migrating Algorithm," Agriculture, MDPI, vol. 14(9), pages 1-30, September.
  • Handle: RePEc:gam:jagris:v:14:y:2024:i:9:p:1592-:d:1477044
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/14/9/1592/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2077-0472/14/9/1592/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Helen H. Jensen & Stanley R. Johnson & Seung Houll Shin & Karl D. Skold, 1989. "CARD Livestock Model Documentation: Poultry," Food and Agricultural Policy Research Institute (FAPRI) Publications (archive only) 88-tr3, Center for Agricultural and Rural Development (CARD) at Iowa State University.
    2. Reza Pourmoayed & Lars Relund Nielsen, 2022. "Optimizing pig marketing decisions under price fluctuations," Annals of Operations Research, Springer, vol. 314(2), pages 617-644, July.
    3. Helen H. Jensen & Stanley R. Johnson & Seung Houll Shin & Karl D. Skold, 1989. "CARD Livestock Model Documentation: Beef," Center for Agricultural and Rural Development (CARD) Publications 88-tr2, Center for Agricultural and Rural Development (CARD) at Iowa State University.
    4. Karl D. Skold & Eric Grundmeier & Stanley R. Johnson, 1989. "CARD Livestock Model Documentation: Pork," Center for Agricultural and Rural Development (CARD) Publications 88-tr4, Center for Agricultural and Rural Development (CARD) at Iowa State University.
    5. Jie Pang & Juan Yin & Guangchang Lu & Shimei Li, 2023. "Supply and Demand Changes, Pig Epidemic Shocks, and Pork Price Fluctuations: An Empirical Study Based on an SVAR Model," Sustainability, MDPI, vol. 15(17), pages 1-16, August.
    6. Karl D. Skold & Eric Grundmeier & Stanley R. Johnson, 1989. "CARD Livestock Model Documentation: Pork," Food and Agricultural Policy Research Institute (FAPRI) Publications (archive only) 88-tr4, Center for Agricultural and Rural Development (CARD) at Iowa State University.
    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. Manchanda, Sumit & Kliebenstein, James B. & McKean, James D., 1995. "Economic Comparison of Alternatives to Sulfamethazine Use in Pork Production," ISU General Staff Papers 199507010700001265, Iowa State University, Department of Economics.
    2. Liang, Jing, 2010. "Three essays on food safety and foodborne illness," ISU General Staff Papers 201001010800002782, Iowa State University, Department of Economics.
    3. Eswaramoorthy, K., 1991. "U.S. livestock production and factor demand: a multiproduct dynamic dual approach," ISU General Staff Papers 1991010108000010523, Iowa State University, Department of Economics.
    4. Jingjing Wang & Xiaoyang Wang & Xiaohua Yu, 2023. "Shocks, cycles and adjustments: The case of China's Hog Market under external shocks," Agribusiness, John Wiley & Sons, Ltd., vol. 39(3), pages 703-726, July.
    5. Grundmeier, Eric & Hayes, Dermot, 1990. "An Examination of the Likely Impact of the Withdrawal of Bovine Growth Promotants on the U.S. Beef Industry," 1990 Annual meeting, August 5-8, Vancouver, Canada 271032, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    6. Buhr, Brian L., 1993. "A Quarterly Econometric Simulation Model Of The U.S. Livestock And Meat Sector," Staff Papers 13465, University of Minnesota, Department of Applied Economics.
    7. Fabio C. Zanini & Philip Garcia, 1997. "Did Producer Hedging Opportunities in the Live Hog Contract Decline?," Finance 9712005, University Library of Munich, Germany.
    8. Metcalfe, Mark R., 2002. "Environmental Regulation And Implications For Competitiveness In International Pork Trade," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 27(1), pages 1-22, July.
    9. Tserenpurev Chuluunsaikhan & Jeong-Hun Kim & So-Hyun Park & Aziz Nasridinov, 2024. "Analyzing Internal and External Factors in Livestock Supply Forecasting Using Machine Learning: Sustainable Insights from South Korea," Sustainability, MDPI, vol. 16(16), pages 1-21, August.
    10. Liang, Jing & Fabiosa, Jacinto F. & Jensen, Helen H. & Miller, Gay Y., 2010. "Potential HPAI Shocks and Welfare Implications of Market Power in the U.S. Broiler Industry," 2010 Annual Meeting, July 25-27, 2010, Denver, Colorado 61496, Agricultural and Applied Economics Association.
    11. Mingyu Xu & Xin Lai & Yuying Zhang & Zongjun Li & Bohan Ouyang & Jingmiao Shen & Shiming Deng, 2024. "An Integrated Hog Supply Forecasting Framework Incorporating the Time-Lagged Piglet Feature: Sustainable Insights from the Hog Industry in China," Sustainability, MDPI, vol. 16(19), pages 1-24, 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:14:y:2024:i:9:p:1592-:d:1477044. 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.