IDEAS home Printed from https://ideas.repec.org/a/epw/ejfood/v4y2022i2id20464.html

Productivity Analysis of Mango Based Agroforestry Systems in the Madhupur Sal Forest of Bangladesh

Author

Listed:
  • Md. Tanbheer Rana

    (Georg August University of Göttingen, German)

Abstract

The study was carried out in the Madhupur Sal forest of Bangladesh to perform the productivity analysis of mango-based agroforestry systems. In order to achieve the objective, three different mango-based agroforestry systems along with three control plots having three replications were randomly selected in three sites of the study area. The size of individual study plot was 33 decimals. The selected three mango-based agroforestry systems were viz. i) mango + pineapple+ ginger + papaya + banana + turmeric, ii) mango + pineapple + ginger + papaya + banana, and iii) mango + turmeric+ papaya + aroid based agroforestry systems. In order to calculate the productivity of respective mango-based agroforestry systems as well as sole cropping performance, data related to incurred cost, total yield and income from tree and crop components, soil samples for soil chemical properties changes were collected from each plot that produced BCR and LER. The results revealed that the total calculated cost of production (tk/ha) and total yield (tk/ha) from selected mango-based agroforestry systems were Tk. 202421, 186373 and 163631 along with Tk. 1076344, 956095 and 816520 respectively. The BCR and LER of the selected above mango-based agroforestry systems were 5.32, 5.13, 4.99 and 3.27, 2.76, 2.32 respectively. Therefore, mango-based agroforestry systems are more profitable than sole cropping system.

Suggested Citation

  • Md. Tanbheer Rana, 2022. "Productivity Analysis of Mango Based Agroforestry Systems in the Madhupur Sal Forest of Bangladesh," European Journal of Agriculture and Food Sciences, European Open Science, vol. 4(2), pages 24-29, March.
    • Handle: RePEc:epw:ejfood:v:4:y:2022:i:2:id:20464
    DOI: 10.24018/ejfood.2022.4.2.464
    as

    Download full text from publisher

    File URL: https://eu-opensci.org/index.php/ejfood/article/view/20464
    File Function: Abstract page
    Download Restriction: no
    File URL: https://eu-opensci.org/index.php/ejfood/article/download/20464/5099
    File Function: Full text
    Download Restriction: no
    File URL: https://libkey.io/10.24018/ejfood.2022.4.2.464?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:epw:ejfood:v:4:y:2022:i:2:id:20464. 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: Editor-in-Chief (email available below). General contact details of provider: https://eu-opensci.org/index.php/ejfood .

    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.