The application of seasonal rainfall forecasts and satellite rainfall estimates to seasonal crop yield forecasting for Africa
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H. Greatrex, 2012, "The application of seasonal rainfall forecasts and satellite rainfall estimates to seasonal crop yield forecasting for Africa", PhD Thesis, University of Reading, UK
Rain-fed agriculture is extremely important in sub-Saharan Africa, thus the ability to forecast and monitor regional crop yields throughout the growing season would be of enormous benefit to decision makers. Of equal importance to be able to assign a measure of uncertainty to the forecast, especially considering that many predictions are made in the context of a complex climate and sparse meteorological and agricultural observations.
This work investigates these issues in the context of an operational updating regional crop yield forecast, concentrating in particular on a case study forecasting Ethiopian maize. Part 1 of the work presents a detailed discussion of Ethiopia’s climate and agricultural systems.
As real-time ground based weather observations are sparse in Africa, Part 2 contains an investigation into remotely sensed satellite rainfall estimates. A daily TAMSAT calibration and the geostatistical process of sequential simulation were used to create a spatially correlated ensemble of Meteosat-derived rainfall estimates. The ensemble mean was evaluated as a daily deterministic rainfall product and was found to be as good as or better than other products applied in the same region. A validation of the full ensemble showed that they realistically estimated Ethiopian rainfall fields that agreed both with observed spatial correlations and input pixel level statistics.
Part 3 of the work includes a discussion on regional crop simulation modelling and presents a new parameterisation of the GLAM crop simulation model for tropical maize. GLAMMAIZE was then driven using individual members of the satellite ensemble; this was shown to exhibit the correct sensitivities to climate inputs and performed reasonably against yield observations.
Finally, Part 4 presented a new method of creating stochastic spatially and temporally correlated rainfall fields. This ‘regional weather generator’ was tested using a case study on Ethiopian April rainfall and a detailed discussion was included about future development plans.