Vulnerability, Impact and Adaptation

Central America

The Tropical Agricultural Research and Higher Education Center (CATIE) in collaboration with CIAT is conducting the VIA analysis in Central America. The study has two scales of implementation: a compilation of national level vulnerability indicators in Guatemala, El Salvador, Honduras, Nicaragua, Costa Rica, and Panama; and vulnerability analysis with emphasis on local adaptive capacity in one watershed per country in Honduras, Costa Rica, and El Salvador. The study focuses on water, agriculture, and ecosystem services.

Exposure: The average from 24 GCMs was used to calculate the changes in temperature and precipitation for the time horizons of 2030 and 2050 under the A1B scenario. The results show that in all countries an increase in temperature of 1°C or greater is likely to occur. The increase in temperature is expected to be accompanied by a reduction in rainfall and prolonged periods of drought causing stress on water resources and ecosystem services.

Figure 13: Results from the Exposure Analysis in Guatemala

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Sensitivity: In the eight countries in the region 18 crops were analyzed. For each country the changes in crop suitability were modeled based on the results from the exposure analysis highlighting the areas that are most likely to lose or gain in crop suitability for production. For instance, in El Salvador crops for subsistence (maize, beans and sorghum) and commercial crops (coffee and sugarcane) were evaluated based on the results from the climate analysis. The results show that the crops most sensitive to the changes in climate are beans, coffee and sugarcane losing in area of productivity. Figure 14 highlights the municipalities in El Salvador that are likely to gain or lose area that is suitable for crop production (over 70% of the municipalities are expected to lose in area).

Figure 14 : Changes in Crop Suitability in Municipalities in El Salvador

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To estimate the sensitivity of the population to the potential impacts of climate change on crop production a map was produced showing the proportion of the population in each municipality that is dependent on agriculture. Figure 15 presents the results for the municipalities in El Salvador, which shows that an average of 50% of the population is employed in the agriculture sector. Overlaying the results from Figure 14 with Figure 15 the municipalities that are most sensitive to climate change can be determined for example: the municipality of Nuevo Cuscatlán may lose 19% of its area for crop production but only 14% of the population is dependent on agriculture compared to the municipality of Alegria where 46% of the population is dependent on agriculture.  

Figure 15: Municipalities in El Salvador Dependent on Agriculture

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Adaptive Capacity: Three conditions were considered in the evaluation of the adaptive capacity of the population in the agriculture sector: the extent to which their basic needs are satisfied, the availability of resources to innovate and the capacity to take action. Indicators were selected to measure each condition. The results from Panama are presented in the Figure 16 below. The map shows that the areas with the lowest adaptive capacity are located on the Atlantic coasts, which is mainly due to their poor housing, water and sanitation conditions and limited access to education.

Figure 16 : Adaptive Capacity in the Municipalities of Panama

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Adaptation Options: Adaptation options were identified for the region based on the results from the analyses including:

  • Adaptation in the agriculture sector requires work at different scales from the farm level to the national government.
  • Conversion and diversification of production systems and crops such as agroforestry and drought resistant varieties.
  • Improved use and distribution of rainwater and groundwater resources, promoting a balance between subsistence and cash crops.
  • Investment in human resources to implement adaptation actions with emphasis on management and use of local knowledge to address impacts including, monitoring and predicting climate.
  • Investment in social resources such as facilitating the local production and commercialization of crops or developing community agreements for the management of water resources.
  • Income diversification to reduce the sensitivity of rural communities that depend on agriculture.
  • Management of financial resources including local government budgets, credits, incentives and insurance.
  • Consideration of non-climatic factors for adaptation such as examining and eliminating policies that promote the conversion or degradation of ecosystems that function as water recharge and regulation areas.

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*Results from the local analysis are expected in June 2014.