Introduction
Location Map
Base Map
Database Schema
Conventions
GIS Analyses
Flowchart
GIS Concepts
Results
Conclusion
References

CONCLUSION

Changes in Mean Maximum Temperature

The CCCma model predicts increases in mean annual monthly maximum temperatures under both the A2a and B2a scenarios.  Under the A2a scenario, the temperature increase is predicted to be between 1.2 and 1.9°C depending on location within Ethiopia.  Interestingly, patterns of temperature change across Ethiopia differ in the two models, A2a and B2a.  The more optimistic carbon /climate scenario B2a model predicts maximum temperature increases of 1.1 to 1.6°C.  In our model case of the agricultural town of Abay, the A2a scenario predicts an increase of 1.5 °C and the B2a scenario predicts an increase of 1.1°C.  Interpreting the exact implications of this increase is beyond the scope of this project, and will best be assessed using local knowledge.  Ultimately, we can assert that crops that fail during heat waves should probably be replaced with more heat tolerant crops in each region of the country, as maximum temperatures will be an increasing stress due to greenhouse gas induced climate change.

Changes in Total Annual Precipitation

The CCCma model predicts changes in total annual precipitation under the A2a scenario ranging from an increase of 110 mm to a decrease of 192 mm depending on location.  The north, and some of the south, and eastern border regions are predicted to become wetter, while the central and western areas are predicted to become drier.  The more optimistic scenario B2a predicts changes in total annual precipitation ranging from an increase of 113 mm to a decrease of 193 mm depending on location.  The north and south regions of Ethiopia are predicted to become wetter, while the central portion is predicted to become drier.   A2a predictions are for a larger area to become drier than in B2a.  For the town of Abay, CCCma models predict drier conditions under both scenarios.  Annual precipitation in Abay is predicted to be 75 mm less under A2a and 100 mm less under B2a.  Farmers in Abay should thus prepare themselves for reduced rainfall, although farmers in different regions of Ethiopia may experience different changes.

Implications

The Ethiopian economy is based on agriculture, with agriculture directly supporting approximately 85% of the population in terms of employment and livelihood (Deressa and Hassan, 2009). 

Therefore, the implications of climate change to Ethiopia will be many.  The net loss of annual precipitation and increase in maximum temperatures predicted by the CCCma climate models may prove detrimental to Ethiopian crops (especially areas that do not utilize irrigation) and result in a reduction of crop production and revenue per hectare of land area.  Worldwide, climate change is expected to cause a shift in crop selection.   In a study by  Kaiser et al. (1993), in the Midwestern United States, future reductions in grain yields (excluding wheat) and faster depletion of groundwater sources are predicted as a result of climate change. 

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And in the country of Cameroon, climate change is predicted to cause an increase in the incidence of supra optimal temperatures (maximum temperature for growth) resulting in larger areas of Cameroon that are unsuitable for maize and sorghum cultivation.  Therefore, agriculture in Cameroon may have to return to other crop varieties such as groundnut and soybean to ensure food security in an uncertain future as a result of climate change (Meza and Silva, 2009). 

In large areas of Ethiopia, climate change is expected to be detrimental to crop production.  Therefore, the Ethiopian government and individual farmers should consider designing and implementing agricultural adaptation policies to counteract the harmful impacts of climate change.  Adaptation methods such as soil conservation, use of better adapted crop varieties, altering planting dates and use of irrigation may allow agricultural areas to be less susceptible to climate change (Deressa et al., 2009).  The use of technology, such as GIS analysis of global climate models demonstrated here, should also be implemented to provide local governments and farmers with crucial data (predicted values of precipitation, temperature, etc.) that may assist future agricultural adaptation methods.