IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v13y2023i9p1724-d1229613.html
   My bibliography  Save this article

Progress and Prospective in the Development of Stored Grain Ecosystems in China: From Composition, Structure, and Smart Construction to Wisdom Methodology

Author

Listed:
  • Yunshandan Wu

    (College of Biological and Agricultural Engineering, Jilin University, Changchun 130022, China
    These authors contributed equally to this work.)

  • Wenfu Wu

    (College of Biological and Agricultural Engineering, Jilin University, Changchun 130022, China
    School of Grain Science and Technology, Jilin Business and Technology College, Changchun 130507, China
    These authors contributed equally to this work.)

  • Kai Chen

    (College of Biological and Agricultural Engineering, Jilin University, Changchun 130022, China)

  • Ji Zhang

    (College of Biological and Agricultural Engineering, Jilin University, Changchun 130022, China)

  • Zhe Liu

    (College of Biological and Agricultural Engineering, Jilin University, Changchun 130022, China)

  • Yaqiu Zhang

    (College of Biological and Agricultural Engineering, Jilin University, Changchun 130022, China)

Abstract

Food security is intrinsically linked to maintaining optimal physical health and promoting active lifestyles. Stored Grain Ecosystems (SGEs) are complex systems comprising a range of grains, microorganisms, and environmental elements. To ensure sustainable grain storage and promote food-friendly SGEs, careful regulation and monitoring of these factors are vital. This review traces the evolution of the Eco-concept of stored grain in China, focusing on micro- and macro-structural composition, the Multi-field/Re-coupling structure, and Smart Construction of SGEs, while introducing the four development lines and Wisdom Methodology of SGEs. The current status and challenges of SGEs in China are also discussed. The Eco-concept of stored grain in China has progressed through the initial exploration period, formation and practice periods, and has now entered its fourth stage, marked by a shift to include interactions of multiple biological fields. This evolution extends beyond the traditional binary relationship and offers emerging technologies greater scope for scientific and intelligent theoretical analysis of grain storage practices. The Wisdom Methodology employs a multifaceted, Mechanism and Data-driven approach, incorporating four driving methods, and is now widely recognized as a leading strategy for researching Smart Grain Systems. Digital Twin technology enables precise simulations and mappings of real-world SGEs in a virtual environment, supporting accurate assessments and early warnings for issues concerning grain conditions. Driven by Mechanism and Data, Digital Twin solutions are a pioneering trend and emerging hotspot with vast potential for enhancing the intelligence and wisdom of future grain storage processes. Overall, this review provides valuable guidance to practitioners for advancing high-quality Smart Grain Systems, enhancing sustainable and intelligent grain storage practices.

Suggested Citation

  • Yunshandan Wu & Wenfu Wu & Kai Chen & Ji Zhang & Zhe Liu & Yaqiu Zhang, 2023. "Progress and Prospective in the Development of Stored Grain Ecosystems in China: From Composition, Structure, and Smart Construction to Wisdom Methodology," Agriculture, MDPI, vol. 13(9), pages 1-13, August.
    • Handle: RePEc:gam:jagris:v:13:y:2023:i:9:p:1724-:d:1229613
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/13/9/1724/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2077-0472/13/9/1724/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ma, Shuaiyin & Ding, Wei & Liu, Yang & Ren, Shan & Yang, Haidong, 2022. "Digital twin and big data-driven sustainable smart manufacturing based on information management systems for energy-intensive industries," Applied Energy, Elsevier, vol. 326(C).
    2. Shuyao Li & Wenfu Wu & Yujia Wang & Na Zhang & Fanhui Sun & Feng Jiang & Xiaoshuai Wei, 2023. "Production Data Management of Smart Farming Based on Shili Theory," Agriculture, MDPI, vol. 13(4), pages 1-26, March.
    3. F. Hammami & S. Ben Mabrouk & A. Mami, 2016. "Modelling and simulation of heat exchange and moisture content in a cereal storage silo," Mathematical and Computer Modelling of Dynamical Systems, Taylor & Francis Journals, vol. 22(3), pages 207-220, May.
    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. Lv, Zhihan & Cheng, Chen & Lv, Haibin, 2023. "Digital twins for secure thermal energy storage in building," Applied Energy, Elsevier, vol. 338(C).
    2. Taghizadeh-Hesary, Farhad & Dong, Kangyin & Zhao, Congyu & Phoumin, Han, 2023. "Can financial and economic means accelerate renewable energy growth in the climate change era? The case of China," Economic Analysis and Policy, Elsevier, vol. 78(C), pages 730-743.
    3. Song, Houde & Liu, Xiaojing & Song, Meiqi, 2023. "Comparative study of data-driven and model-driven approaches in prediction of nuclear power plants operating parameters," Applied Energy, Elsevier, vol. 341(C).
    4. Marcin Relich, 2023. "A Data-Driven Approach for Improving Sustainable Product Development," Sustainability, MDPI, vol. 15(8), pages 1-18, April.
    5. Cheng, Han & Kong, Xianguang & Wang, Qibin & Ma, Hongbo & Yang, Shengkang & Xu, Kun, 2023. "Remaining useful life prediction combined dynamic model with transfer learning under insufficient degradation data," Reliability Engineering and System Safety, Elsevier, vol. 236(C).
    6. Ma, Shuaiyin & Huang, Yuming & Liu, Yang & Liu, Haizhou & Chen, Yanping & Wang, Jin & Xu, Jun, 2023. "Big data-driven correlation analysis based on clustering for energy-intensive manufacturing industries," Applied Energy, Elsevier, vol. 349(C).
    7. Marcin Relich, 2023. "Predictive and Prescriptive Analytics in Identifying Opportunities for Improving Sustainable Manufacturing," Sustainability, MDPI, vol. 15(9), pages 1-14, May.
    8. Novoa-Muñoz, Francisco, 2019. "Simulation of the temperature of barley during its storage in cylindrical silos," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 157(C), pages 1-14.
    9. Larisa Vazhenina & Elena Magaril & Igor Mayburov, 2023. "Digital Management of Resource Efficiency of Fuel and Energy Companies in a Circular Economy," Energies, MDPI, vol. 16(8), pages 1-21, April.
    10. Ma, Shuaiyin & Huang, Yuming & Liu, Yang & Kong, Xianguang & Yin, Lei & Chen, Gaige, 2023. "Edge-cloud cooperation-driven smart and sustainable production for energy-intensive manufacturing industries," Applied Energy, Elsevier, vol. 337(C).
    11. Dimitrios Kalfas & Stavros Kalogiannidis & Olympia Papaevangelou & Katerina Melfou & Fotios Chatzitheodoridis, 2024. "Integration of Technology in Agricultural Practices towards Agricultural Sustainability: A Case Study of Greece," Sustainability, MDPI, vol. 16(7), pages 1-24, March.
    12. Wang, Jiayang & Wang, Qiang & Sun, Wenqiang, 2023. "Quantifying flexibility provisions of the ladle furnace refining process as cuttable loads in the iron and steel industry," Applied Energy, Elsevier, vol. 342(C).

    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:2023:i:9:p:1724-:d:1229613. 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.