Development and evaluation of model-based operational yield forecasts in the South African sugar industry.

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dc.contributor.advisor Schulze, Roland E.
dc.creator Bezuidenhout, Carel Nicolaas.
dc.date.accessioned 2012-05-18T10:32:18Z
dc.date.available 2012-05-18T10:32:18Z
dc.date.created 2005
dc.date.issued 2005
dc.identifier.uri http://hdl.handle.net/10413/5336
dc.description Thesis (Ph.D.)-University of KwaZulu-Natal, Pietermaritzburg, 2005. en
dc.description.abstract South Africa is the largest producer of sugar in Africa and one of the ten largest sugarcane producers in the world. Sugarcane in South Africa is grown under a wide range of agro-climatic conditions. Climate has been identified as the single most important factor influencing sugarcane production in South Africa. Traditionally, sugarcane mill committees have issued forecasts of anticipated production for a region. However, owing to several limitations of such committee forecasts, more advanced technologies have had to be considered. The aim of this study has been to develop, evaluate and implement a pertinent and technologically advanced operational sugarcane yield forecasting system for South Africa. Specific objectives have included literature and technology reviews, surveys of stakeholder requirements, the development and evaluation of a forecasting system and the assessment of information transfer and user adoption. A crop yield model-based system has been developed to simulate representative crops for derived Homogeneous Climate Zones (HCZ). The system has integrated climate data and crop management, soil, irrigation and seasonal rainfall outlook information. Simulations of yields were aggregated from HCZs to mill supply area and industry scales and were compared with actual production. The value of climate information (including climate station networks) and seasonal rainfall outlook information were quantified independently. It was concluded that the system was capable of forecasting yields with acceptable accuracy over a wide range of agro-climatic conditions in South Africa. At an industry scale, the system captured up to 58% of the climatically driven variability in mean annual sugarcane yields. Forecast accuracies differed widely between different mill supply areas, and several factors were identified that may explain some inconsistencies. Seasonal rainfall outlook information generally enhanced forecasts of sugarcane production. Rainfall outlooks issued during the summer months seemed more valuable than those issued in early spring. Operationally, model-based forecasts can be expected to be valuable prior to the commencement of the milling season in April. Current limitations of forecasts include system calibration, the expression of production relative to that of the previous season and the omission of incorporating near real-time production and climate information. Several refinements to the forecast system are proposed and a strong collaborative approach between modellers, climatologists, mill committees and other decision makers is encouraged. en
dc.language.iso en_ZA en
dc.subject Sugarcane--Research--South Africa. en
dc.subject Sugarcane--Yields--South Africa. en
dc.subject Sugarcane--Yields. en
dc.subject Crop yields--Forecasting. en
dc.subject Crop yields--Mathematical models. en
dc.subject Theses--Bioresources engineering and environmental hydrology. en
dc.title Development and evaluation of model-based operational yield forecasts in the South African sugar industry. en
dc.type Thesis en

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