Understanding how farmers perceive climate change risks and how this affects their willingness to adopt adaptation practices is critical for developing effective climate change response strategies for the agricultural sector. This study examines (i) the perceptual relationships between farmers' awareness of climate change phenomena, beliefs in climate change risks and actual adaptation behaviour, and (ii) how these relationships may be modified by farm-level antecedents related to human, social, financial capitals and farm characteristics. An extensive household survey was designed to investigate the current pattern of adaptation strategies and collect data on these perceptual variables and their potential antecedents from private landowners in Veszprém and Tolna counties, Hungary. Path analysis was used to explore the causal connections between variables. We found that belief in the risk of climate change was heightened by an increased awareness of directly observable climate change phenomena (i.e. water shortages and extreme weather events). The awareness of extreme weather events was a significant driver of adaptation behaviour. Farmers' actual adaptation behaviour was primarily driven by financial motives and managerial considerations (i.e. the aim of improving profit and product sales; gaining farm ownership and the amount of land managed; and, the existence of a successor), and stimulated by an innovative personality and the availability of information from socio-agricultural networks. These results enrich the empirical evidence in support of improving understanding of farmer decision-making processes, which is critical in developing well-targeted adaptation policies.

Impacts of socio-economic, political and climatic change on agricultural land systems are inherently uncertain. The role of regional and local-level actors is critical in developing effective policy responses that accommodate such uncertainty in a flexible and informed way across governance levels. This study identified potential regional challenges in arable land use systems, which may arise from climate and socio-economic change for two counties in western Hungary: Veszprém and Tolna. An empirically-grounded, agent-based model was developed from an extensive farmer household survey about local land use practices. The model was used to project future patterns of arable land use under four localised, stakeholder-driven scenarios of plausible future socio-economic and climate change. The results show strong differences in farmers' behaviour and current agricultural land use patterns between the two regions, highlighting the need to implement focused policy at the regional level. For instance, policy that encourages local food security may need to support improvements in the capacity of farmers to adapt to physical constraints in Veszprém and farmer access to social capital and environmental awareness in Tolna. It is further suggested that the two regions will experience different challenges to adaptation under possible future conditions (up to 2100). For example, Veszprém was projected to have increased fallow land under a scenario with high inequality, ineffective institutions and higher-end climate change, implying risks of land abandonment. By contrast, Tolna was projected to have a considerable decline in major cereals under a scenario assuming a de-globalising future with moderate climate change, inferring challenges to local food self-sufficiency. The study provides insight into how socio-economic and physical factors influence the selection of crop rotation plans by farmers in western Hungary and how farmer behaviour may affect future risks to agricultural land systems under environmental change.

Despite the Paris Agreement target of holding global temperature increases 1.5 to 2°C above pre-industrial levels, high-end climate change (HECC) scenarios going beyond 4°C are becoming increasingly plausible. HECC may imply increasing climate variability and extremes as well as the triggering of tipping points, posing further difficulties for adaptation. This paper compares the outcomes of four concurrent European case studies (EU, Hungary, Portugal, and Scotland) that explore the individual and institutional conditions, and the information used to underpin adaptation-related decision-making in the context of HECC. The focus is on (i) whether HECC scenarios are used in current adaptation-related decision-making processes; (ii) the role of uncertainty and how climate and non-climate information is used (or not) in these processes; and (iii) the information types (including socio-economic drivers) commonly used and their limitations in relation to HECC scenarios. Decision-makers perceive HECC as having a low probability or distant occurrence and do not routinely account for HECC scenarios within existing climate actions. Decision-makers also perceive non-climate drivers as at least as important, in many cases more important, than climate change alone. Whilst more information about the implications of particular sectoral and cross-sectoral impacts is needed, climate change uncertainty is not a significant barrier to decision-making. Further understanding of individual and institutional challenges brought about by the 'squeeze' between adapting to HECC scenarios or to lower levels of temperature change (as those agreed in Paris) is essential to better contextualise the use of climate change information.

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We present a simple model to disaggregate age structured population census data to a 1-km grid for Hungary. A dasymetric approach was used to predict the spatial distribution of population in different age groups by distinguishing residential preferences (in relation to accessible social, economic and green amenities) for working age groups (15-29, 30-49 and 50-64) and population dependencies for children and the elderly (aged 0-14 and 65+). By using open-access land cover data and fine-level population census data as inputs, the model predicts the likely spatial distribution of population and age structure for Hungary in 2011. The resulting map and gridded data provide information to support spatial planning of residential development and urban infrastructure. The model is less data-demanding than most existing approaches, but provides greater power for describing population patterns. It can also be used to create scenarios of future demographic change. ARTICLE HISTORY