Computer Aided Learning CALMet

There is considerable expertise available throughout the world on the study of climate change and climate variability and its impact on regional agriculture communities. However, the results of scenario analyses and scientific evaluations are often not effectively communicated widely to the appropriate user community. As electronic communication technology improves, it is possible to share information, experiences, and data more efficiently with many other people around the world. Computer-aided learning provides an opportunity to use CDs or the Internet to develop self-paced modules on any topic of interest. The use of computer-aided learning in meteorology has mainly been confined to the synoptic weather patterns and daily weather predictions (Floor, 2001). As the various systems become more widely used, modules will be developed and this method of learning will be expanded into other areas including agricultural meteorology (Spangler and Fulker, 2001). This could become a highly efficient method of teaching the agricultural meteorologists many of the skills and techniques necessary to assess the impact of climate change and climate variability on agriculture. Using such computer-aided learning tools, agricultural meteorologists could study various scenarios based on unique climate and agronomic features within their regions. Using these scenario analyses, the agricultural meteorologists could then become more aware of potential future conditions. These scientific results can form the basis of advisories issued to farmers and decision-makers on adaptation and mitigation measures to reduce the vulnerability to climate variability.

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