Development and evaluation of model-based operational yield forecasts in the South African sugar industry.
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Date
2005
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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.
Description
Thesis (Ph.D.)-University of KwaZulu-Natal, Pietermaritzburg, 2005.
Keywords
Sugarcane--Research--South Africa., Sugarcane--Yields--South Africa., Sugarcane--Yields., Crop yields--Forecasting., Crop yields--Mathematical models., Theses--Bioresources engineering and environmental hydrology.