From stories to maps: translating participatory scenario narratives into spatially explicit information

Abstract

To understand future land use change, and related ecological and social impacts, scenario planning has become increasingly popular. We demonstrate an approach for translating scenario narratives into spatially explicit land use maps. Starting from four previously developed scenarios of land use change in southwestern Ethiopia we developed a baseline land use map, and rules for how to modify the baseline map under each scenario. We used the proximity-based scenario generator of the InVEST software to model the prospective land cover changes to existing forest (53%), arable land (26%), pasture (11%), and wetlands (7%), under the four future scenarios. The model results indicate that forest cover area would remain essentially the same under the "gain over grain" and "biosphere reserve" scenarios. Coffee plantations would cover almost half the landscape (49%) in the "mining green gold" scenario, whereas arable land would expand and cover more than half of the landscape (57%) in the "food first" scenario. The approach presented here integrates future land use mapping with participatory, narrative-based scenario research to assess the social-ecological outcomes of alternative futures. The translation of narratives onto maps can help researchers and stakeholders better understand and communicate potential land use changes, and facilitate a more spatially nuanced approach to managing or adapting to broad scale socioeconomic changes. Our study constitutes a methodological contribution to the management of land use change, as well as a tool to facilitate transparent policy negotiation and communication at local, government, and NGO levels.

Plural valuation in southwestern Ethiopia: Disaggregating values associated with ecosystems in a smallholder landscape

“…We found that beneficiaries could be grouped into five clusters based on their ecosystem product rankings, and that these groups differed in their social–ecological context. Previous research had already shown that future development under different scenarios differed substantially between the four social–ecological kebele groups (Duguma et al., 2022). Smallholders in our study area, and in heterogeneous landscapes elsewhere, should thus not be treated as a homogeneous group, but rather differentiated based on their social–ecological context.…”

“…To investigate the role of social–ecological context, the 78 kebeles (smallest administrative units in Ethiopia) in our study area were clustered into four social–ecological groups, based on a range of ecological and social variables, including land use and land cover data, altitude, remoteness and wealth. The pasture–cropland, the khat–cropland, the woody vegetation and the accessible–wealthy group were characterized, respectively, by high availability of pasture and arable land, by high availability of khat (a popular plant stimulant) and arable land, by high extent of woody vegetation cover and by being relatively accessible and wealthy (Duguma et al., 2022). We then selected eight kebeles—two from each social–ecological group (Figure 1, Table S1).…”

Land use intensification causes the spatial contraction of woody-plant based ecosystem services in southwestern Ethiopia

“…Brief summaries of the storylines of these scenarios are presented in Table 1 (for details see refs. 34,35). The scenarios considered a wide range of plausible environmental, social, and economic changes.…”

Where Are the Trade-Offs in Multiple Ecosystem Services in the Process of Ecological Restoration? A Case Study on the Vegetation Restoration Area in the Loess Plateau, Northern Shaanxi

“…In addition, Wang et al [68] adopted the InVEST model to probe the water yield, NPP, and soil conservation, and their results show that the NPP displays a trade-off relationship with respect to water yield, agreeing with our discoveries from the loess ridge, loess hilly, and covered sand, loess hilly areas (Figure 6). Moreover, other regions, such as arid watersheds in northwestern China [79], the Qinghai-Tibet Plateau [80], North America [81], Europe [82], and Africa [83], were also investigated using the InVEST model. Challenges still exist in this work and can be solved in our future work.…”