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 fiom 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,
identiwing 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.
| Identifer | oai:union.ndltd.org:ADTP/218733 |
| Date | January 2004 |
| Creators | Onpraphai, Thaworn, n/a |
| Publisher | University of Canberra. Resource, Environmental & Heritage Sciences |
| Source Sets | Australiasian Digital Theses Program |
| Language | English |
| Detected Language | English |
| Rights | ), Copyright Thaworn Onpraphai |
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