IDEAS home Printed from https://ideas.repec.org/a/eee/agiwat/v70y2004i2p83-96.html
   My bibliography  Save this article

Evaluation of pedotransfer functions in predicting the soil water contents at field capacity and wilting point

Author

Listed:
  • Givi, J.
  • Prasher, S.O.
  • Patel, R.M.

Abstract

No abstract is available for this item.

Suggested Citation

  • Givi, J. & Prasher, S.O. & Patel, R.M., 2004. "Evaluation of pedotransfer functions in predicting the soil water contents at field capacity and wilting point," Agricultural Water Management, Elsevier, vol. 70(2), pages 83-96, November.
  • Handle: RePEc:eee:agiwat:v:70:y:2004:i:2:p:83-96
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378-3774(04)00189-1
    Download Restriction: Full text for ScienceDirect subscribers only
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Liao, Kaihua & Lai, Xiaoming & Zhou, Zhiwen & Liu, Ya & Zhu, Qing, 2020. "Uncertainty analysis and ensemble bias-correction method for predicting nitrate leaching in tea garden soils," Agricultural Water Management, Elsevier, vol. 237(C).
    2. Mattar, M.A. & Alazba, A.A. & Zin El-Abedin, T.K., 2015. "Forecasting furrow irrigation infiltration using artificial neural networks," Agricultural Water Management, Elsevier, vol. 148(C), pages 63-71.
    3. Abdullah, Araz S. & Aziz, Moyassar Mohammed & Siddique, K.H.M. & Flower, K.C., 2015. "Film antitranspirants increase yield in drought stressed wheat plants by maintaining high grain number," Agricultural Water Management, Elsevier, vol. 159(C), pages 11-18.
    4. Łukasz Borek & Andrzej Bogdał & Tomasz Kowalik, 2021. "Use of Pedotransfer Functions in the Rosetta Model to Determine Saturated Hydraulic Conductivity (Ks) of Arable Soils: A Case Study," Land, MDPI, vol. 10(9), pages 1-22, September.
    5. Yamaç, Sevim Seda & Şeker, Cevdet & Negiş, Hamza, 2020. "Evaluation of machine learning methods to predict soil moisture constants with different combinations of soil input data for calcareous soils in a semi arid area," Agricultural Water Management, Elsevier, vol. 234(C).

    More about this item

    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:eee:agiwat:v:70:y:2004:i:2:p:83-96. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/agwat .

    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.