Comparative performance of scientific and indigenous knowledge on seasonal climate forecasts: A case study of Lupane, semi- arid Zimbabwe

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Research Paper 01/05/2013
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Comparative performance of scientific and indigenous knowledge on seasonal climate forecasts: A case study of Lupane, semi- arid Zimbabwe

Ignatius Chagonda, Adelaide Munodawafa, Francis T. Mugabe, Veronica Makuvaro, Philip Masere, Cyril T. F. Murewi
Int. J. Agron. Agri. Res.3( 5), 1-9, May 2013.
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Seasonal climate forecasting (SCF) is weather prediction over a period ranging from 3-6 months period. Forecasting can be done using scientific forecasts (SF) or indigenous knowledge forecasts (IKF) systems. Forecast results can be very fruitful to smallholder farmers in semi-arid areas where rainfall is highly variable. Effective use of SCF has faced challenges including: rainfall variability, access to forecast information, interpretation of forecast results and generation gap. There is limited research on comparative performance of the two forecasts. The research seeks to evaluate comparative performance of the two forecasting methods in predicting outcome of the following rainfall season. The study was carried out in Daluka and Menyezwa wards of Lupane district, south-western Zimbabwe, which receives annual average rainfall of 450-650 mm. Focus group discussions and personal interviews were used in 2008/09 and 2009/10 seasons to capture farmers’ experiences and knowledge on SCFs and their application. The predicted outcome of the IKF and SF were compared with actual rainfall recorded from the predicted period. Results indicated high dependence on the use of the IKFs by Lupane farmers in predicting the outcome of the following season’s rainfall. Both the IKF and SF predicted inadequate rainfall in the two consecutive seasons and the results concurred with recorded rainfall in Daluka ward in the two seasons and in Menyezwa ward in 2009/10 season only. Results demonstrate that in the absence of SF, farmers may use IKFs. It is imperative that the two forecasts complement each other to increase farmer adaptation to climate variability.


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