IDEAS home Printed from https://ideas.repec.org/a/aes/dbjour/v14y2023i1p1-15.html
   My bibliography  Save this article

Prediction Model for Soybean Productivity

Author

Listed:
  • Ion GANEA

    (Moldova State University, Chisinau, Republic of Moldova)

Abstract

This paper presents a holistic approach to biological and agricultural research focused on the use of interconnected technologies in the context of climate change. Researchers from different countries have analyzed how smart technologies can help agriculture adapt to these changes. The most representative works in the field are analyzed. Among these technologies are graph database systems such as Neo4j, which have demonstrated success in predicting the studied phenomena. The paper describes the development of a soybean crop productivity prediction model using monthly and annual data of meteorological phenomena such as precipitation, air temperature, hydrothermal coefficient, soil moisture, and others. Some of the results of this promising research are also presented.

Suggested Citation

  • Ion GANEA, 2023. "Prediction Model for Soybean Productivity," Database Systems Journal, Academy of Economic Studies - Bucharest, Romania, vol. 14(1), pages 1-15.
    • Handle: RePEc:aes:dbjour:v:14:y:2023:i:1:p:1-15
    as

    Download full text from publisher

    File URL: https://www.dbjournal.ro/archive/34/34_1.pdf
    Download Restriction: no
    ---><---

    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:aes:dbjour:v:14:y:2023:i:1:p:1-15. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Adela Bara (email available below). General contact details of provider: https://edirc.repec.org/data/aseeero.html .

    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.