Sugar is an important global agricultural commodity and a significant input to the advanced industrialised world. Annual average global sugar production is around 120 million tonnes, with consumption around 118 million tonnes. Sugar is produced under a broad range of climatic conditions in some 120 countries and is one of the most heavily traded agricultural commodities (FAO,2001). Plants produce sugar as a storehouse of energy that is used as required. Approximately 70% of sugar is produced from sugar cane while the remaining 30% is produced from sugar beet (Sugar Knowledge International,2001). Thailand's cane and sugar industry is now one of the major sources of foreign income for the country. The value of sugar exports (around 35 billion baht or AUD $1.5 billion per annum) ranks among the top ten exported commodities of the Thai economy. Approximately 9.2% of annual global sugar production is exported from Thailand (WTO,2001). The sugar industry is extremely complex and comprises individual links and components in the supply and demand chain that are more delicately in balance than with most other commodity based industries. Thailand's sugar production has been characterized by greater extremes of variability than in most other sugar producing countries. A unique combination of pests, disease, climate, soils, problems with plant available moisture and the low technology basis of crop management has increased production risk and uncertainty for the crop. Total tonnage of cane and sugar is notoriously difficult to predict during the growing season and for a mature crop before the harvest. Accordingly, the focus of this research is on the development and testing of methods, algorithms, procedures and output products for Sugar Cane Crop Forecasting and Yield Mapping. The resulting spatial and temporal information tools have the potential to provide the basis of a commercially deployable decision support system for Thailand's sugar industry. The scope of this thesis encompasses several levels within a geographical hierarchy of scales; from regional, district, farm, and plot within a study area in northeastern Thailand. Crop forecasting at regional level will reduce production risk uncertainty while yield mapping and yield estimation at local, farm and plot scales will enable productivity to be improved by identifying, diagnosing the cause of and reducing yield variability. The research has three main objectives. These are to: Develop statistical analysis procedures and empirical algorithms expressing the relationship between yield potential and spectral response of sugar cane yield as a basis for mapping, monitoring, modeling, forecasting and management of sugar production in Thailand. Evaluate the validity of a technology based versus conventional approach to crop forecasting and yield mapping, commencing with a series of testable null-hypotheses and culminating in procedures to calibrate and validate empirical models against verifiable production records. Outcomes are used to review and evaluate existing and potential future approaches to regional crop forecasting, localised yield mapping and yield estimation tools for operational use within Thailand's sugar industry. Identify, evaluate and establish performance benchmarks in relation to the practicality, accuracy, timeliness, cost effectiveness and value proposition of a satellite based versus conventional approach to crop forecasting and yield mapping. The methodology involved time series analysis of recorded sugar cane yields and production outcomes paired with spectral response statistics of crops derived from satellite imagery and seasonal rainfall records over a three year period within four provinces, forty five component districts and 120 representative farms. Spectral statistics were derived from raw multi-spectral satellite imagery (multitemporal SPOT- VI at regional scale and Landsat 7 ETM+ imagery at local scale) acquired during the 1999 to 2001 sugar cane seasons. Crop area and production statistics at regional scale were compiled and furnished by the provincial sugar mill and verified through government agencies within Thailand. Selective cutting at sample sites within nominated fields owned by collaborating growers was undertaken to validate localised differences in productivity and to facilitate yield variance mapping. Acquisition, processing, analysis and statistical modeling of remotely sensed satellite spectral data, rainfall records and production outcomes were accomplished using an empirical approach. Resulting crop production forecasting algorithms were systematically evaluated for reliability by assessing accuracy, spatial and temporal variability. Long term rainfall and district sugar cane yield and production records were used to account for district and season specific differences between estimated and recorded yields, to generate error probability functions and to improve the accuracy and applicability of empirical models under more extreme conditions. Limitations on finding and length of records constrained the number of seasons and the area for which satellite imagery with contrasting levels of spatial and spectral resolution could be acquired. The absence of verifiable long term production records combined with limitations on the duration and area able to be covered by field trips meant that time series analysis of paired data was necessarily constrained to a three year period of record coinciding with the author's period of candidature. Accordingly, although a comprehensive set of well correlated district and month specific yield forecasting algorithms was able to be developed, temporal restrictions on data availability constrained the extent to which they could be subjected to thorough accuracy and reliability analysis and extended with confidence down to farm and field scale. A variety of approaches, using different parameter combinations and threshold values, was used to combine individual districts and component farms into coherent groups to overcome temporal data constraints and to generate more robust production forecasting algorithms, albeit with slightly lower levels of apparent accuracy and reliability. The procedures adopted to optimise these district groupings are systematically explained. Component differences in terrain, biophysical conditions and management approaches between district groupings are used to explain differences in production outcomes and to account for apparent differences between forecast versus actual yields between districts both within and between different groups. The outcomes of this research - particularly the data acquisition and analysis procedures, empirical modeling, error assessment and adjustment techniques, and the optimisation procedures used to facilitate grouping of districts - provide a practical basis for the deployment of an operational sugar cane production forecasting and yield mapping information system to facilitate planning and logistical management of production, harvesting, transportation, processing, domestic marketing and export of sugar from northeastern Thailand. At the local and farm level, yield maps and plot based yield estimates will assist users to improve productivity by recognising, identifying and responding to potential causes of within and between field spatial variability. However, before such an information system can be confidently deployed, additional resources will be required to obtain paired production records, spectral data fiom satellite imagery and biophysical input data over a longer period to ensure that the empirical models are operationally robust and to validate their accuracy under a wider range of conditions by comparing forecasts with actual outcomes over larger areas during the next few seasons.
|Date of Award||2004|
|Supervisor||Brian Button (Supervisor)|