Uganda: Poverty Rates In Subcounties With Very High Wetland Use Impacts
In this map, all the wetlands at greatest risk of degradation are selected and overlaid with the poverty level in the surrounding subcounties. It displays the location of these wetland sample points and the poverty rate for the neighboring 60 rural subcounties.
As the map shows, highly impacted wetlands are spread widely across Uganda, and the proportion of the subcounty population falling below the rural poverty line includes all poverty levels. Wetlands with very high impacts from use are located in subcounties with lower poverty levels (shaded in green) mainly in the southwestern part of the country. But highly impacted wetlands are also situated within poorer subcounties, mostly north of Lake Kyoga in Lira, Amuria, Dokolo, and Amolatar Districts (shades of brown and yellow), but also in Jinja District, where farmers grow rice in wetlands.
This means that based on the existing data from the National Wetlands Information System and the most recent poverty map, there is no straightforward relationship between poverty levels and potential wetland degradation. High impact from wetland use occurs in both poor and better-off subcounties.
Nevertheless, the map can be useful to flag certain subcounties where close coordination between wetlands management and poverty reduction efforts could be beneficial for both wetlands and human well-being. For example, in subcounties with high poverty rates of 40-60 percent (shaded in light brown) and a great number of highly impacted wetlands, additional or more intensive use could threaten the future supply of benefits. This in turn could negatively impact poor families who depend on wetlands for their livelihoods or fall back on these resources in emergencies.
Improved wetlands management that results in a more optimal combination of products and services (one that lowers the overall impact on the wetland system while maximizing the revenue) could reduce the risk of resource degradation and negative well-being impacts for poor households. Conversely, creating new economic opportunities outside of the wetland sector may permit some families to reduce dependence on resource extraction with low returns and high impacts, resulting in both improvements in well-being and lower resource pressure on wetlands.
In subcounties with highly impacted wetlands but low poverty rates, the presence of alternative income-generating activities and livelihood strategies is more likely. This suggests that any strategy to change and optimize the combination of wetland uses or to restore a wetland could build on greater assets and capabilities of households in these subcounties.
This map represents just one example that analyzes the relationship between wetland use impacts and poverty. Other useful analyses are also possible. For example, a different map overlay could pinpoint where wetlands exposed to no or low impacts coincide with high poverty levels and could lead to further investigation of the reasons behind this pattern.
Sources: International boundaries (NIMA, 1997), district administrative boundaries (UBOS, 2006a), subcounty administrative boundaries (UBOS, 2002a), water bodies (NFA, 1996; NIMA, 1997; Brakenridge et al., 2006), combined impacts from all wetland uses (authors’ calculation based on WID, 2006), and rural poverty rate (UBOS and ILRI, 2008).