ScenaLand: a simple methodology for developing land use and management scenarios

Scenarios serve science by testing the sensitivity of a system and/or society to adapt to the future. In this study, we present a new land use scenario methodology called ScenaLand. This methodology aims to develop plausible and contrasting land use and management (LUM) scenarios, useful to explore how LUM (e.g. soil and water conservation techniques) may affect ecosystem services under global change in a wide range of environments. ScenaLand is a method for constructing narrative and spatially explicit land use scenarios that are useful for end-users and impact modellers. This method is innovative because it merges literature and expert knowledge, and its low data requirement makes it easy to be implemented in the context of inter-site comparison, including global change projections. ScenaLand was developed and tested on six different Mediterranean agroecological and socioeconomic contexts during the MASCC research project (Mediterranean agricultural soil conservation under global change). The method first highlights the socioeconomic trends of each study site including emerging trends such as new government laws, LUM techniques through a qualitative survey addressed to local experts. Then, the method includes a ranking of driving factors, a matrix about land use evolution, and soil and water conservation techniques. ScenaLand also includes a framework to develop narratives along with two priority axes (contextualized to environmental protection vs. land productivity in this study). In the context of this research project, four contrasting scenarios are proposed: S1 (business-as-usual), S2 (market-oriented), S3 (environmental protection), and S4 (sustainable). Land use maps are then built with the creation of LUM allocation rules based on agroecological zoning. ScenaLand resulted in a robust and easy method to apply with the creation of 24 contrasted scenarios. These scenarios come not only with narratives but also with spatially explicit maps that are potentially