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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
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    References listed on IDEAS

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    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.
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    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.

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