IDEAS home Printed from https://ideas.repec.org/a/spr/nathaz/v59y2011i1p447-463.html
   My bibliography  Save this article

Maize yield forecasting for Zimbabwe farming sectors using satellite rainfall estimates

Author

Listed:
  • Desmond Manatsa
  • Innocent Nyakudya
  • Geoffry Mukwada
  • Herbet Matsikwa

Abstract

Southern Africa rainfall station network is suffering from an unfortunate serious decline while climate-related food insecurity is worsening. In the current work, we demonstrate the possibility of exploiting the complementary roles that remote sensing, modeling, and geospatial data analysis can play in forecasting maize yield using data for the growing seasons from 1996/1997 to 2003/2004. Satellite-derived point-specific rainfall estimates were input into a crop water balance model to calculate the Water Requirement Satisfaction Index (WRSI). When these WRSI values were regressed with historical yield data, the results showed that relatively high skill yield forecasts can be made even when the crops are at their early stages of growth and in areas with sparse or without any ground rainfall measurements. Inferences about the yield at national level and small-scale commercial farming sector (SSCF) sector can be made at confidence levels above 99% from the second dekad of February. However, the most unstable models are those for the communal farming sectors whose inferences for yield forecast can only be made above the 95% confidence level from the end of February, after having recovered from a state of complete breakdown two dekads earlier. The large-scale commercial farming (LSCF) sector has generally the weakest fitting, but it is usable from the first dekad of February to the end of the rainy season. Validation of the national yield models using independent data set shows that an early estimation of maize yield is quite feasible by the use of the WRSI. Copyright Springer Science+Business Media B.V. 2011

Suggested Citation

  • Desmond Manatsa & Innocent Nyakudya & Geoffry Mukwada & Herbet Matsikwa, 2011. "Maize yield forecasting for Zimbabwe farming sectors using satellite rainfall estimates," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 59(1), pages 447-463, October.
  • Handle: RePEc:spr:nathaz:v:59:y:2011:i:1:p:447-463
    DOI: 10.1007/s11069-011-9765-0
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s11069-011-9765-0
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1007/s11069-011-9765-0?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
    ---><---

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

    References listed on IDEAS

    as
    1. Baez-Gonzalez, Alma D. & Jones, J. G. W., 1995. "Models of sorghum and pearl millet to predict forage dry matter production in semi-arid Mexico. 2. Regression models," Agricultural Systems, Elsevier, vol. 47(2), pages 147-159.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. Zhao Zhang & Yi Chen & Pin Wang & Shuai Zhang & Fulu Tao & Xiaofei Liu, 2014. "Spatial and temporal changes of agro-meteorological disasters affecting maize production in China since 1990," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 71(3), pages 2087-2100, April.
    2. Lin Liu & Bruno Basso, 2020. "Linking field survey with crop modeling to forecast maize yield in smallholder farmers’ fields in Tanzania," Food Security: The Science, Sociology and Economics of Food Production and Access to Food, Springer;The International Society for Plant Pathology, vol. 12(3), pages 537-548, June.
    3. Desmond Manatsa & Leonard Unganai & Christopher Gadzirai & Swadhin Behera, 2012. "An innovative tailored seasonal rainfall forecasting production in Zimbabwe," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 64(2), pages 1187-1207, November.

    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.

      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:spr:nathaz:v:59:y:2011:i:1:p:447-463. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.