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The information required for (a) is easily available with the CLIMEX model. Information about CLIMEX can be obtained from CSIRO Entomology (<http://www.ento.csiro.au/climex/climex.htm>). The CLIMEX model (Version 1.1) has historical records for thousands of locations on all continents.
Procedure. Select "Compare Location" from the menu options of CLIMEX. The "Compare Location" dialogue box appears. Select:
Cane toad in "Species" and Australia in "Location Set" Run model
A map (Figure 8.5a) or table can be generated. Figure 8.5a shows the locations where the cane toad has the required environment for survival and population buildup under the present climate conditions. Filled circles in the map indicate favorable locations—the larger the circle the more favorable the location. Crosses indicate locations not favorable to the long-term survival of cane toad.
The information required for (b) is also generated with the CLIMEX model.
Procedure. Select "Preference" from menu bar and then "Scenario" and "Geenhouse" from the dropdown windows. The "Select Greenhouse Scenario" dialogue box appears. Select "Edit," and the "Greenhouse Scenario" dialogue box opens. Edit greenhouse scenario maximum and minimum temperature and percent rainfall change (in this example, a 2°C rise in maximum and minimum temperature and 10 percent increase in rainfall is added, both for winter and summer). Select "Compare Location" from the menu options of CLIMEX. The "Compare Location" dialogue box appears. Select:
Cane toad in "Species" and Australia in "Location Set" Click on "Greenhouse" Run model
A table or a map (Figure 8.5b) is generated. Figure 8.5b shows the potential distribution of cane toad under a global warming scenario. Comparing the two maps shows that under the greenhouse effect, cane toad will spread in all coastal regions of South and Western Australia and in many inland areas of all states of mainland Australia.
FIGURE 8.5. Cane toad in Australia: (a) present distribution and (b) distribution under greenhouse effect scenario
2. A Company based at Orange, New South Wales, is engaged in a highly profitable apple production and processing business. The company is eager to expand its business to other areas. To buy more farms on which to grow quality apples, it wants information on sites/places throughout Australia that match the climate of Orange.
The information that the company based at Orange needs can be easily generated with CLIMEX. CLIMEX has historical records of climates for thousands of locations on all continents. CLIMEX allows comparison of the average climates of different locations. This is done by measuring similarities in temperature, rainfall, and relative humidity. CLIMEX allows the operator to select a location (called "Target Location") and compare its climate with the climate of each location in a location set. The climate similarities in each pair of locations are measured by a match index between 0 and 100.
Procedure. Select "Match Climates" from the menu options of CLIMEX. The "Match Climates" dialogue box appears. Select:
Orange in "Target Locations" and Australia in "Matching Location
Set" Run model
CLIMEX generates a map or a table (Table 8.5) that shows 228 places having some sort of similarity with Orange in terms of individual weather elements and overall climatic conditions. Table 8.5 provides the information that the company based at Orange required. It shows the 15 top places (out of 228) in Australia that are more than 70 percent similar to Orange in terms of climate.
Use of Decision Support Systems (DSS)
Much effort and money have been invested into the development of decision support and expert systems throughout the world. However, the rate of adoption by farmers does not seem to keep pace with the development of the systems (Kuhlmann and Brodersen, 2001). The adoption of decision support systems in developing countries at the individual-farmer level is negligible and is likely to remain so at least in the near future. At the dawn of the twenty-first century, not even a small portion of the farming community of developing countries is aware of computers and their use in agriculture. Illiteracy and abject poverty are the most significant impediments. The small size of farms will remain a major obstacle to the adoption of computers and
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Renewable energy is energy that is generated from sunlight, rain, tides, geothermal heat and wind. These sources are naturally and constantly replenished, which is why they are deemed as renewable.