Agricultural land use in Europe has changed considerably in the last decades. However, our understanding of agricultural land use changes, especially changes in land use intensity, is limited because the evidence is fragmented. This paper presents a systematic review of case study evidence on manifestations and underlying drivers for agricultural land use change in Europe. We analyzed 137 studies that together report on 76 cases of intensification and 143 cases of disintensification. Observed changes were manifested as expansion or contraction of agricultural land as well as in changes of land management intensity, landscape elements, agricultural land use activity, and specialization/diversification. Economic, technological, institutional and location factors were frequently identified as underlying drivers, while demographic drivers and sociocultural drivers were mentioned less often. In addition, we found that farmers were very important as moderators between underlying drivers and manifestations of agricultural land use change. Farmer decisions differed between different farmer types, and according to their characteristics and attitudes. We found major land use change trajectories in relation to globalization of agricultural markets, the transition from a rural to an urban society, and the shift to post-socialism in central and eastern Europe

Model‐based global projections of future land‐use and land‐cover (LULC) change are frequently used in environmental assessments to study the impact of LULC change on environmental services and to provide decision support for policy. These projections are characterized by a high uncertainty in terms of quantity and allocation of projected changes, which can severely impact the results of environmental assessments. In this study, we identify hotspots of uncertainty, based on 43 simulations from 11 global‐scale LULC change models representing a wide range of assumptions of future biophysical and socioeconomic conditions. We attribute components of uncertainty to input data, model structure, scenario storyline and a residual term, based on a regression analysis and analysis of variance. From this diverse set of models and scenarios, we find that the uncertainty varies, depending on the region and the LULC type under consideration. Hotspots of uncertainty appear mainly at the edges of globally important biomes (e.g., boreal and tropical forests). Our results indicate that an important source of uncertainty in forest and pasture areas originates from different input data applied in the models. Cropland, in contrast, is more consistent among the starting conditions, while variation in the projections gradually increases over time due to diverse scenario assumptions and different modeling approaches. Comparisons at the grid cell level indicate that disagreement is mainly related to LULC type definitions and the individual model allocation schemes. We conclude that improving the quality and consistency of observational data utilized in the modeling process and improving the allocation mechanisms of LULC change models remain important challenges. Current LULC representation in environmental assessments might miss the uncertainty arising from the diversity of LULC change modeling approaches, and many studies ignore the uncertainty in LULC projections in assessments of LULC change impacts on climate, water resources or biodiversity.