Recap: Social Change and Development | Africa with Audrey Smith
On Monday February 17, Audrey Smith (University of Florida) presented at the Social Change and Development in Africa (SCAD) Working Group meeting. Her talk, “Large-scale Land Acquisitions and Ecosystem Services: Impacts on Natural Woodlands and Energy Security in Ethiopia,” centered on her preliminary findings from her research on large-scale land acquisitions in Ethiopia. The overarching project goal was to examine changes in complex human and natural systems associated with large-scale land acquisitions. Globally, large-scale land acquisitions have increased in the past 10-15 years as economically rich countries began investing in land and resources elsewhere to combat domestic energy and agricultural crises. Smith’s research asks: What proportion of land cover in Ethiopia is forest/natural woodland? What is the change in forest/natural woodland cover from 2005 to 2018? Are large-scale land acquisitions driving forest/woodland loss? And Are large-scale land acquisitions impacting household energy security (i.e. availability and access of fuelwood)?
The study uses 16 sites in Ethiopia—8 that are sites of large-scale land acquisitions and 8 control sites. Her methods for data collection included household surveys, ecological data (tree species diversity/abundance), and remote sensing imagery. Preliminary results indicate that forest loss is occurring as a result of large-scale land acquisitions, and subsequent land conversion for commercial agriculture. Woodlands are then affected by the displacement and movement of rural people: fuelwood extraction begins in new areas, ecosystem services are impacted, and there is less access to natural resources. Smith noted that she plans to improve her current methods used for the study’s land cover classification as she continues analyzing data. Next steps for the project included, classifying land cover in 2005 data set, conducting an ecosystem services assessment with analysis, and analyzing land cover change, NDVI time series, and household survey data.