Forest Land Use Potential and Ecosystem Service Recovery in Africa (FLESRA)

Forest and Landscape Restoration has gained momentum as an approach to help reverse forest degradation and deforestation. However, it has been hindered by a lack of proper impact assessment and monitoring to enable timely adaptive management decision-making to ensure the success of projects. Scientific studies on forest restoration are often subject-specific with a focus on socioeconomic (profitability and markets), governance processes (stakeholder mapping, enabling policy environment-institutions), or biophysical settings (silviculture and site conditions among disturbance gradients), often ignoring the need to integrate all the dimensions. This dataset was developed as part of the FLESRA project (see link), which sought to empirically combine these three dimensions, in a geographically diverse Ethiopia, to help develop tools and recommendations for the successful decision-making, implementation and monitoring of forest and landscape restoration (FLR) initiatives with the potential to expand to the entire sub-Saharan Africa (SSA). The project focused on the interface of both site level (dominant use, single stakeholder, on-site effects-ecosystem services) and landscape level (enabling policy environment, multiple actors, beliefs-values-norms, and off-site effects-ecosystem services), using a combination of tools and methodologies. At the site level (WP 1-2), we analysed restrictions towards efficiency and economic viability of forest restoration strategies using cost-benefit analysis and the effect of soil, environmental variables, and management on the performance of various silvicultural strategies using dominant land use as reference. At the landscape level, we analysed potential coalitions, trade-offs, and synergies of multiple ecosystem services, core values, beliefs, and norms of multiple stakeholders across scales (WP 3). The dataset is tailored to be used by government bodies, finance institutions, NGOs, local community organisations, and research institutions to aid in decision-making towards cost-effective forest land restoration transition approaches and serve as a baseline for FLR monitoring in Ethiopia. Work packages in detail and justification In detail, the work packages are: WP 1: Silviculture (Institute of Forest Ecosystems _WO) WP 2: Environmental Cost-Benefit structures of restoration strategies (Institute of Forestry-WF) WP 3: Governance processes- stakeholder settings (Institute of Forestry-WF) Description of datasets per work package Work package 1: The forest inventory dataset includes measured vegetation attributes from 1,521 plots in several sites of woodlots, agroforestry, boundary plantings, natural forests, grazing lands, plantations, and exclosures, including assisted natural regenerated sites. The attributes are as detailed below: Site context information including site_id, name, location (region, district, village), assessment date, area, ownership, support, participants, restoration goals, planting year, stand age, former and surrounding land uses, restoration type, dominant species, seedling sources, laborers, work-days, silviculture activities, soil type, and other notes At plot-level assessment (seedlings and saplings) site_id, plot_id, species_id (local and scientific), and individual counts At plot-level assessment (trees) measurements of tree heights (m), diameter at breast height (DBH, cm), species_id, site_id, plot_id. The soil dataset is from 337 plots in the above restoration strategies includes site_id, plot_id, soil depth, and target soil variables (physical and chemical parameters like bulk density, soil organic carbon, total nitrogen, cation concentration, pH, sand, silt, and clay contents among others). Data was collected at two depths: 0-10 cm and 10-30 cm. The forest inventory and soil datasets also include plot coordinates. All the information for the study regions is currently captured in CSV format. Work package 2: FLESRA_household_assets_wellbeing_Dataset: The dataset (in Excel) contains information on 1,537 households from 30 villages in Ethiopia’s restoration landscapes. The attributes are based on the livelihood and well-being evaluation framework. The dataset includes (per respective sheet) household location, household head information, household restoration goals, household farm physical equipment, household non-farm physical assets, household social assets (group memberships), household livestock assets, household remittances, household incentives and subsidies, subjective well-being, impact of restoration restrictions, ecosystem service ranks per respective land use, and challenges and barriers to restoration. The household parameters are linked with the unique household ID, similar to that of the household patch level dataset. FLESRA_household_patch_level_Dataset: The dataset (in Excel) contains information on 5,645 patches owned by 1,537 households in the respective restoration landscapes in Ethiopia. The characterization of the farm system is based on the farm systems analytical framework of a peasant farm household. Within the farm system analysis, the farm system includes the management of numerous farm patches by an individual farmer. These patches represent land use with either only crop, pasture for grazing, fallow, tree-based systems, and mixed land cover (crops mixed with trees, pasture mixed with trees). The database includes patch location attributes, inputs (establishment and labour costs), and outputs(benefits). Note: all patches correspond to specific household IDs, so patch-level variables can easily be matched with the household_assets_wellbeing dataset. FLESRA_household_level_WTP_Dataset: The dataset (in Excel) contains the Willingness to Pay (WTP) of 2,616 households for the restoration of degraded landscapes in Ethiopia. The data includes: household location, household head general information, importance and sources of threats attached to forests, hypothetical restoration project including preferred restoration activities, bid values for restoration, and stated amount of willingness to pay for FLR upscaling. FLESRA_institutional_patch_level_Dataset: The dataset (in Excel) contains information on 281 patches owned by 134 actors (enterprises, schools, church-mosque, state, community, and NGOs) in Ethiopia. The data includes: Actor general information with actor_ids, actor category and location. Patch level information with patch_ids, patch size, ecological zones, perceived soil traits (fertility, drainage, and erosion), patch distances, and ownership information. Costs mainly include establishment, transaction, labour, and costs spent on tools. Stated output information, which includes market type, harvested quantity, and unit cost per strategy and product type. Note: all components are linked with a similar actor-id. Work package 3: FLESRA_actor_network_VBN_Dataset includes: Dataset (in Excel) contains information of 314 actors interviewed at different jurisdictional levels, i.e., federal, district, and village levels (forest landscapes) in Ethiopia. The data is based on various indicators obtained from various theoretical frameworks. The actor network includes actor_id and flow of financial, information, equipment, seeds/seedlings, and authority. Actor resources and value-beliefs-norms include actor_ids, actor category, scale of operation, role in forest restoration, actor invested and planned resources, restoration activities, and finances. Actor current and intended future restoration activities, actor perceived ecosystem services (ecosystem valued attributes), value orientations, actor preferences and priorities (management activities-resource extraction and resource use and access), actor beliefs (environment concern), norm, enabling policy environment, and restoration challenges of 489 interviewed actors. All attribute codes are explained under the sheet: variable dictionary.