| Summary: | 碩士 === 國立中興大學 === 土壤學系 === 84 === Farm land is the one of the irreversible fundamental elements
of agriculture. Excepting for breeding crops, it provides other
services such as greener environment, conserving natural
ecosystem, reserving energy resource and being an effective
space for national development. Recently, because of fast
economic developing and shortage of the urban space, country
farm have become a new spotlight for city and industry
extending. There is 36,000 km2 of total land area in Taiwan, in
which 880,000 ha, about 24.3% is the farm land. According to
one of CAO's publications, some 160,000 ha of farming land
will be released to support civil development.
This reseach aims at the discussion and comparison of
different evaluating methods in classify the farm land. We also
applied GIS as a tool to build an computer information system
for data analysis. With such a system, we could provide
information for decision making of the policy of land release.
We took Tainan county as an example, using two different
approaches. The first was the classification of agricultural
land, by using natural factors of farm as the basic index and
modifying these index by other relative auxiliary factors. The
second approach was named LESA, which unified natural and
economic factors to provide linear weighting access. The results
of the classification of agricultural land showed that most of
the good farm area would be preserved and classified into better
ranks, so the classification method is a good method when one
standed in the view point of preventing good farm area from
being released. The conclusions of LESA indicated that
different weights generated different results. Numerical
analysis also showed that LESA might generate peculiar values
at the edges when one assigned the extreme values of the
weighting factors a and b . It meant that we should
select weighting values carefully when using this approach. The
results of the taxonomy of farm land was closely related
with the methods of the approaches. They also affected
the utilizing types of farms. The more the data and opinions
were accepted, the better the outcome was more
objective.
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