Several management actions are applied to restore ecosystem services in degraded Mediterranean rangelands, which range from adjusting the grazing pressure to the removal of grazers and pine plantations. Four such actions were assessed inQuercus cocciferaL. shrublands in northern Greece: (i) moderate grazing by goats and sheep; (ii) no grazing; (iii) no grazing plus pine (Pinus pinasterAiton) plantation in forest gaps (gap reforestation); and (iv) no grazing plus full reforestation of shrubland areas, also withP. pinaster. In addition, heavy grazing was also assessed to serve as a control action. We comparatively assessed the impact of these actions on key provisioning, regulating and supporting ecosystem services by using ground‐based indicators. Depending on the ecosystem service considered, the management actions were ranked differently. However, the overall provision of services was particularly favoured under moderate and no grazing management options, with moderate grazing outranking any other action in provisioning services and the no grazing action presenting the most balanced provision of services. Pine reforestations largely contributed to water and soil conservation and C sequestration but had a negative impact on plant diversity when implemented at the expense of removing natural vegetation in the area. Heavy grazing had the lowest provision of ecosystem services. It is concluded that degraded rangelands can be restored by moderating the grazing pressure rather than completely banning livestock grazing or converting them into pine plantations. Copyright © 2015 John Wiley & Sons, Ltd.

Investment in biology-based technological innovations is a key requirement for the development of modern agriculture/forestry. The expansion of innovative biological technologies includes changes in crops/cultivations, such as the transition from intensive monocultures to multiple crops of lower agrochemical inputs with the integration of woody trees/shrubs or animals, represented by Agroforestry. This innovative biological technology is further promoted at the European Union (EU) level by powerful institutions such as the Green Deal and the new CAP, mainly by tools such as ecoschemes and agri-environmental and climate measures (AECMs). The use of integrated regional spatiotemporal models, such as CLUE-S, to predict land use changes in the framework of Agroforestry is rather restricted. This paper examines Agroforestry as a vehicle that can contribute to achieving the rural development of the region of Thessaly, Greece. It sets a time horizon for reviewing the changes that are expected in the most important units of land uses of the rural landscape of the municipality of Mouzaki, western Thessaly plain, in the year 2040, which serves as model land for the region of Thessaly. It examines these changes with the effect of three (3) socio-economic scenarios: (a) a linear operating scenario (business as usual, BAU), (b) an ecological land protection (ELP) scenario, and (c) a rapid economic development (RED) scenario. These scenarios were introduced in the non-spatial module of the CLUE-S spatiotemporal model, while in the spatial module sixteen (16) characteristic landscape parameters were introduced as independent variables. The most important land use units, including traditional silvoarable and silvopastoral woodland systems, were the dependent variables. The simulations of the changes of the land use units showed that under the RED scenario, in the year 2040 the extent of the silvoarable systems is expected to increase significantly (57%) compared to the reference year of 2020, while the rest of the land use units under the other scenarios are mainly regulated by depopulation/abandonment of the rural areas and the processes of natural succession. The fact that the extent of silvoarable systems is increasing, in combination with the favorable institutional environment created by European rural policies, gives impetus to regional rural development through investments in the agricultural sector and mainly in Agroforestry systems.

Rural landscape evolution models are used as tools for the analysis of the causes and impact of land use changes on landscapes. The CLUE-S (the Conversion of Land Use and its Effects at Small regional extent) model was developed to simulate the changes in current land use, by using quantitative relationships between land uses and driving factors combined with a dynamic modeling of land use competition. One of the modules that build the “CLUE-S” is the non-spatial subset of the model that calculates the temporal evolution of the land use/cover under several socio-economic scenarios. The purpose of this research was to estimate the demands of land use in the area of Mouzaki, Thessaly plain, Greece with the ultimate goal of using them in the non-spatial module of the CLUE-S to predict the evolution of land uses in year 2040. These estimations are the quantitative prediction of the spatial change for all land use types at the aggregate level. Three models of forecasting the future land cover in the area were simulated, in order to obtain a clear view of the different land uses in the future. We distinguished three model-scenarios for calculating the demand-forecasts: (a) business as usual (BAU) scenario, that deals with a linear projection of the current land use demands, (b) rapid economic development (RED) scenario, and (c) ecological land protection (ELP) scenario. In the BAU scenario the land use demands for the year 2040 were calculated using linear interpolation utilizing historical data from 1960 to 2020. In the RED scenario, the demands were calculated by maximizing the economic benefit of land uses, and in the ELP scenario the demands were calculated by maximizing the environmental benefit of land uses. Furthermore, a multi-criteria analysis was performed to find the trade-offs between economic benefit maximization and environmental benefit optimization. It was found that the agricultural lands reach their maximum area under the RED scenario, while reaching their lower bound for the ELP scenario. The same goes for agroforestry systems. The grasslands reach their lower bound under the ELP scenario, while they achieve a higher value under the RED scenario. Concerning the silvopastoral woodlands, although an increase is foreseen under the BAU scenario, it appears that they reach their lower bound in the other two scenarios, RED and ELP. Forests receive intermediate values and cover a larger area under the ELP scenario compared with the RED scenario. The expected forest cover under the BAU scenario is higher. Moreover, sparse and dense shrublands receive their lower bound for both optimization scenarios, while the settlements reach the upper bound for the RED scenario and the lower one under the ELP scenario.