Examples of Professional Expertise Training

To show the application of the above logic some specific examples will be given of the types of applications that could be taught to agricultural meteorologists to empower them to make a contribution to decreasing the vulnerability of the community.


Crop-climate matching can be used to select the most suitable crop for specific climatic conditions. All the aspects of the situation need to be considered including the socio-economic acceptability of the alternatives to the community (FAO, 1990). An approach that can be taken is to characterise the specific crop requirements from the perspective of the climate (Doorenbos and Kasaam, 1979; Doorenbos and Pruit, 1992). The ecotope must also be described and defined in similar terms (Sys et al., 1991) so as to determine the potential of the environment for crop production. It is important at this stage to identify the limitations of both the cropping systems and the environmental conditions and also to clarify the predicted changes. The logic is then followed whereby the most limiting factor must be satisfied first and then the others can be considered. For example, in many climates the length of the growing season is a limiting factor for crop selection. A suitable crop or cultivar can be selected using the length of the frost-free season. With changes in the growing season due to climate variability, or trends over decades, it may be necessary to alter cropping patterns, crop types, and crop cycles to adjust to the climate extremes.


In operational agricultural meteorology much use has been made of climate based indices to assess and integrate the effect of the environment. Many indices have been developed from an empirical perspective and do not really represent the cause and effect relationships. Under these circumstances they will inevitability fail under some conditions so that the boundary conditions should be careful defined (Rosenberg et al., 1983). Among the most common is the use of thermal time calculations for prediction of flowering and maturity dates of crops (McMaster and Wilhelm, 1997). Such an index can be used with the long-term climate data to make recommendations for cultivar choice and select planting dates. However, it is difficult to include climate variability into such calculations and still make it easily understood at the farmers' level. This can be addressed by the use of deterministic crop models together with the long-term data to provide improved cropping recommendations (Singels and de Jager, 1991).


Risk can be defined in various ways and should be considered from the communities' perspective using the available long-term weather data. Communities face different circumstances and have different historical experiences so they can be classified as risk averse or risk susceptible (Anderson and Dilion, 1992). The climate data can be used together with modelling techniques to develop cumulative distribution curves of probabilities for obtaining a certain yield under certain conditions (Muchow et al., 1991). These curves have been used to advise farmers on selection of cropping systems or choice of crops or tillage practices or the management of natural resources. As seasonal outlooks improve (Mason et al., 1996), and become more readily available, they can make a contribution to reducing the vulnerability of the communities to extreme weather events (Walker et al., 2001). If these probabilities can be used in conjunction with seasonal outlooks (Hammer and Nichols, 1996), then it is possible to take into consideration some degree of climate variability in a specific place and to make a recommendation to the producers in that area.

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