Effects of extreme events on land-use-related decisions of farmers in Eastern Austria: the role of learning

Claudine Egger; Andreas Mayer; Bastian Bertsch-Hörmann; Christoph Plutzar; Stefan Schindler; Peter Tramberend; Helmut Haberl; Veronika Gaube

doi:10.1007/s13593-023-00890-z

Effects of extreme events on land-use-related decisions of farmers in Eastern Austria: the role of learning

Egger, Claudine; Mayer, Andreas; Bertsch-Hörmann, Bastian; Plutzar, Christoph; Schindler, Stefan; Tramberend, Peter; Haberl, Helmut; Gaube, Veronika 2023-06-01 00:00:00 European farm households will face increasingly challenging conditions in the coming decades due to climate change, as the frequency and severity of extreme weather events rise. This study assesses the complex interrelations between external framework conditions such as climate change or adjustments in the agricultural price and subsidy schemes with farmers’ decision-making. As social aspects remain understudied drivers for agricultural decisions, we also consider value-based characteristics of farmers as internal factors relevant for decision-making. We integrate individual learning as response to extreme weather events into an agent-based model that simulates farmers’ decision-making. We applied the model to a region in Eastern Austria that already experiences water scarcity and increasing drought risk from climate change and simulated three future scenarios to compare the effects of changes in socio-economic and climatic conditions. In a cross-comparison, we then investigated how farmers can navigate these changes through individual adaptation. The agricultural trajectories project a decline of active farms between −27 and −37% accompanied by a reduction of agricultural area between −20 and −30% until 2053. The results show that regardless of the scenario conditions, adaptation through learning moderates the decline in the number of active farms and farmland compared to scenarios without adaptive learning. However, adaptation increases the workload of farmers. This highlights the need for labor support for farms. Keywords Agent-based modeling · Learning · Scenario analysis · Adaptation · Austria · Agriculture 1 Introduction et al. 2017; IPCC 2022), such as droughts, heavy rainfall, or floods (Otto et al. 2018; Mukherjee et al. 2018; Trenberth Climate change threatens agricultural production not only 2018) that cause soil erosion (Borrelli et al. 2020) and yield through changing climatic conditions such as further increas- declines (Vogel et al. 2019). Human impacts on global ecosys- ing temperatures and changing rainfall patterns (Loarie et al. tems increased the frequency of severe extreme events world- 2009; Box et al. 2019) but also through severe extreme wide (Stott 2016; Ummenhofer and Meehl 2017; IPCC 2022), weather events (Zwiers et al. 2013; Stott 2016; Diffenbaugh which are also an emerging topic in science: While the first Bulletin of the American Meteorological Society, published in 2012, covered the occurrence of six extreme events (Her- Veronika Gaube was the supervisor of the project this paper was written for. ring et al. 2014), the most recent publication not only covers a * Claudine Egger multitude of extreme events but also focuses on their anthro- claudine.egger@boku.ac.at pogenic causes and effects (Herring et al. 2022). In contrast to the long-term impacts of climate change, of which the full Department of Economics and Social Sciences, Institute extent will only become apparent in the medium to long-term of Social Ecology, University of Natural Resources and Life Sciences, Schottenfeldgasse 29, 1070 Vienna, Austria future, extreme weather events have an immediate impact on regional agricultural and socio-economic systems (Gobiet Environment Agency Austria, Spittelauer Lände 5, 1090 Vienna, Austria and Truhetz 2008). This makes it essential to better under- stand the societal coping strategies of farmers to such events. Community Ecology and Conservation, Faculty of Environmental Sciences, Community Ecology Because occurrence and magnitude of extreme events rise and Conservation Research Group, Kamýcká 129, (Stott 2016; IPCC 2022), private and public decision-making CZ-165 00 Prague 6, Czech Republic Vol.:(0123456789) 1 3 39 Page 2 of 17 C. Egger et al. is increasingly challenged to develop suitable response strate- learning based on network experiences or beliefs but learn- gies. The damage potential of extreme events depends not ing from an individual perspective has rarely been studied only on their physical characteristics, but crucially also on (Brown et al. 2017). the response of the affected socio-ecological systems and the In order to investigate spatially explicit effects of indi- ability to adapt and transform through feedback mechanisms vidual decision-making, agent-based modeling represents an (Carpenter et al. 2012). The ability of actors (individuals and approach that is particularly suitable. While system-dynamic organizations) to overcome risks (e.g., food scarcity) imposed models follow a “top-down” approach in which changes of by changes in the natural environment through adaptation to a socio-ecological system emerge based on the overarch- new circumstances has been a key success factor for societal ing systemic relationships, agent-based models (ABMs) development ever since the hunter-gatherer age (Boserup simulate systemic transitions in a “bottom-up” process that 1965). starts with the individual agent. ABMs consider interac- Climate change poses long-time risks to agricultural tions and the feedback dynamics emerging from agent’s production through the gradual change in temperature and reaction to changes in their social and biophysical environ- precipitation and the risks of intermediate losses through ment when simulating the dynamics of a system (Verburg the rising frequency and severity of extreme events (IPCC et al. 2016). This predestines such models for the analysis of 2022). Depending on the governance level, there are dif- socio-ecological systems such as farming systems, as they ferent, complementary response strategies to address these can integrate the complex interrelations between land-use risks. (1) Reduce climate change in a collective societal decision-making and changes in external socio-economic, effort, e.g., through mitigation attempts that limit the tem- biophysical, and climatic conditions (Verburg et al. 2019). perature rise compared to the pre-industrial level to 1.5°C Recent agent-based applications that integrate climate- (Newell et al. 2021). (2) In addition, farmers can pursue related risks (van Duinen et al. 2016; Amadou et al. 2018; individual reaction strategies by adapting their decision- Zagaria et al. 2021) primarily focus on peer influence from making as response to short-term changes such as yield loss social networks. In combination with the agent’s goal orien- due to extreme events, and thereby anticipating changing tation towards profit maximization, these models consider agricultural conditions from a long-ranging changing cli- learning in a social and economic context but neglect other mate. This requires that farmers perceive climate change factors such as political or environmental impacts. While the as risk factor that needs to be considered in their decision- influence of stochastic extreme events on land-use manage- making, and several studies identify a relation of climate ment has been integrated in ABMs (Mayer et al. 2022; Egger change perception and related risk association (Osbahr et al. et al. 2022), the occurrence of extreme events as driver for 2011; Arbuckle et al. 2015; van Winsen et al. 2016; Schatt- individual learning has primarily been modeled for drought man et al. 2016; van Duinen et al. 2016; Shukla et al. 2019; perception and the subsequent transition to irrigated pro- Mitter et al. 2019; Zagaria et al. 2021). duction systems (van Duinen et al. 2016; Yang et al. 2020; Brown et al. (2017) stress that the decisions on adapta- Zagaria et al. 2021). With this study, we want to deepen tion to climate change are shaped by social, economic, the knowledge on learning from extreme events related to environmental, and political contexts. The influence of climate change. social factors such as gender or social relations in agri- As European farmers operate within boundaries set by cultural production systems remains understudied (David- socio-political guidelines such as CAP policies and their cli- son 2016), leaving significant gaps in related literature matic environment (Stoate et al. 2009; Peltonen-Sainio et al. contributions on farmers’ decision-making in relation to 2010; van Zanten et al. 2014; Pe’er et al. 2017), this paper climate change adaptation. The dominant assumptions investigates how farm households can navigate changes of economic rationality lack full apprehension of social coming from their socio-ecological framework conditions complexity and thereby limit the potential for integrating through individual adaptation investigating the following realistic forms of decision-making into land-use-based questions: adaptation models (Groeneveld et al. 2017; Brown et al. 2017). Many agricultural land-use models that focus on How do climatic and socio-economic scenarios impact climate change adaptation do not include any form of the agricultural development trajectories and how do learning (Brown et al. 2017), meaning that the integration farms respond to climate change impacts and changes in of learning in a functional way in which the experience of agricultural subsidy schemes? a changing environment results in adaptation (De Houwer How does the integration of “learning” triggered by et al. 2013). Similarly, Huber et al. (2018) identified learn- extreme events affect the development of farms? ing as underrepresented aspect in a review on European How can individual farms adapt to external framework land-use models. In cases where learning is integrated in changes? Which socio-economic effects does this have land-use models, the focus lies on the integration of social on farms? 1 3 Effects of extreme events on land‑use‑related decisions of farmers in Eastern Austria: the… Page 3 of 17 39 We use the SECLAND ABM (Mayer et al. 2022; Egger irrigation because water availability is generally limited, and et al. 2022) to explore the socio-ecological effects of adap- small-scale farming prevails profitable irrigation. Farmers tation within the existing agricultural production system reported that the recurring droughts and the lack of precipita- through the application of on-site farm improvement man- tion already had negative impacts on agricultural production agement actions. We apply the model to a study region in quantity (e.g., deficits in harvested grassland biomass, lower Eastern Austria and simulate agricultural trajectories for the crop yields) and quality (e.g., of summer grains) (Fessler years 2015–2053 in line with three distinct future scenarios 2021; Petz 2021). to evaluate the impact learning from extreme events has on agro-structural development. The study site, characterized 2.2 Conceptual modeling framework by cropland farming in the Eastern lowlands and livestock farming in the hilly areas of the Western parts (Fig. 1), The purpose of the SECLAND ABM (Egger et al. 2022) is belongs to a region in Austria predicted to experience notice- the study of systemic feedbacks between external changes able impacts of climate change in the upcoming decades in the socio-political, biophysical, and climatic framework (Haslmayr et al. 2018). conditions and the internal dynamics arising from the decision-making of farm agents and its effects on the land- use dynamics of the study region (Dullinger et al. 2020; 2 Methods Mayer et al. 2022). This application of the model features the integration of learning as model update that builds upon 2.1 Study region the interactions between the decision-making process and the occurrence of extreme weather events. Figure 3 depicts The study region consisted of the three districts Oberpul- an overview of the model’s structural relations between lendorf, Neunkirchen, and Wiener Neustadt Land located external and internal dynamics. A thorough description in Eastern Austria (Fig. 2). The area encompasses a total of the SECLAND model is available in Dullinger et al. area of about 2.818km , 107 municipalities, and a popula- (2020), Egger et al. (2022), and Mayer et al. (2022). The tion of about 203,000. Biogeographically, the study region ODD description (Müller et al. 2013) of the here presented is divided: the West situated on the outskirts of the Alps updated version of the model can be found in the supple- belonging to the Alpine and the Eastern parts being part of mentary material (Table SI2). the Continental region (EEA 2017). Elevation ranges from 169 (Northeast, Southeast) to 2056 m a.s.l (Northwest). Cor-2.3 Initialization respondingly, the annual average precipitation is between 400 and 2000 mm/year, and average annual temperatures 2.3.1 Farms and agricultural area range from +4° to +12° (Hiebl and Frei 2016). Farmers in the study region are facing rising risks of weather extremes, The ABM is initialized with 2990 farms that cultivate 76,220 especially droughts, that can hardly be compensated through patches (parcels of land) with a total area of 87,545 hectares Fig. 1 a View into the study region from the Mountain Semmering: showing the transition from the mountainous West towards the lowlands in the Eastern parts. b View over a rapeseed field in the Eastern lowlands (Oberpullendorf). 1 3 39 Page 4 of 17 C. Egger et al. Fig. 2 Location of the study region encompassing the districts Neunkirchen, Wiener Neustadt Land, and Oberpullendorf in the Eastern part of Austria (Natural Earth 2022). Fig. 3 Overview of the modeling process and functional relations between external settings (blue), internal dynamics (green), and the effects of extreme weather events (red) of the SECLAND ABM. 1 3 Effects of extreme events on land‑use‑related decisions of farmers in Eastern Austria: the… Page 5 of 17 39 (ha) agricultural land in 2015. Farm agents are distinguished We used qualitative data to understand farmers’ motiva- according to the following core characteristics: farming type, tions and relevant management actions, which is a common farming style, and production system. There are three farm- approach in agent-based modeling (Janssen and Ostrom ing types: cash crop farms, processing farms that engage 2006; Valbuena et al. 2008; Smajgl et al. 2011). We con- either in pig fattening or suckler production, and (cattle) ducted semi-structured interviews with 20 farmers (11 cash livestock farms focusing on meat or milk production (for crop and 9 livestock farms; Fessler 2021; Petz 2021) dur- an extensive description see Supplementary Tables SI2 and ing summer 2020 to identify and calibrate action sets with SI4). The farming style (Schmitzberger et al. 2005) represents probabilities. In addition, we held an online stakeholder the “value system” of farmers. The model incorporates five workshop with regional experts and decision-makers in July farming styles that are decisive for the decision-making of 2020, to discuss past and current agricultural trends, the role farmers: “innovative” (highly flexible and open to learning), of climate change-related extreme events (recurring droughts “yield optimizer” (prioritizes yield maximization), “sup- and heat waves) and assess the scenario storylines on future port optimizer” (tries to maximize income from subsidies), development of agriculture. The workshop was followed “idealist” (self-realization in farm work), and “traditionalist” up by two expert interviews, where important topics that (continuing the tradition). Without specific information on emerged during the workshop were discussed in more detail proportions, farming styles are randomly assigned among (see Supplementary Table SI6). the model’s farmer population. Farm management is either conventional or organic, which each includes five intensity 2.4 Internal processes levels that differ in labor demand, yields, prices and costs, respectively, gross margins, and subsidies. Farmland consists 2.4.1 Evaluation indicator and decision‑making of croplands and/or grasslands. Cropland is managed in three cropping cycles with sugar beet, rapeseed, and soy as lead Farms in the SECLAND ABM are setup as bounded rational crops. Grassland contains meadows, pastures, and mountain agents (Groeneveld et al. 2017) that pursue well-being rather pastures. Cattle livestock units (lu) are bound to grassland. than profit maximization. We defined well-being as satisfy - Livestock numbers are computed based on the forage provi- ing balance between (minimum) income and (maximum) sion (computed as area × intensity level) of meadows/pas- workload (per fulltime farm worker (full time equivalent, tures held by a farm. Porcine livestock numbers are assigned fte)). Farms invest labor to cultivate their patches (related to farms (in lu) (see Supplementary Table SI1). crops and livestock units) from which they generate agri- cultural income. Farm intensity levels are linked with spe- cific labor input demand and gross margin outputs. Farms 2.3.2 Input data use three satisfaction criteria to annually evaluate their well-being: (1) a minimum agricultural income (per fte) For the setup of the ABM, we relied on the complemen- that is based on reported averages per farm type (Grüner tary combination of quantitative and qualitative data. The Bericht NÖ 2017; SI2). (2) A workload maximum of 1800 main quantitative source was the Invekos (IACS) data h (per fte) that relates to the yearly workload of Austrian that we used for the initialization of farm structure. Farm employees (WKO Statistik 2020). (3) To account for income areas were based on spatially explicit land-use information comparison among peers, the hourly agricultural income on parcels provided by the Invekos GIS-data. The initial- (computed per farming type) needs to exceed a yearly com- ization year 2015 was the first year with comprehensive, puted minimum (average value - 1SD; see Supplementary parcel-specific land-use information. We chose the simula- Table SI2). Depending on the satisfaction (with either or tion period 2015–2053 as such to allow subsequent com- both), farms take a decision among ten possible actions that putations on crop cultures as moving average for the year affect their farm management to improve/maintain satisfac- 2050. Subsidies, farm, and livestock classification, num - tion by increasing/decreasing their workload and/or income bers, and densities (e.g., livestock units per hectare, lu/ha) (Table 1). Based on the synthesis of the qualitative data, all are drawn from the aggregated analysis of reported Inve- relevant actions were assigned with pre-defined probabilities kos numbers. We relied on production-region-related yield that depend on income-workload satisfaction, farming type, data from the bookkeeping farms (Buchführungsbetriebe). and farming style. For missing (either unreported or database secrets) values and to compute gross margins, we used price and cost and 2.4.2 Adaptive decision‑making labor requirement information on state/federal level (BAB 2021) and for socio-economic inputs (farmers age, off-farm Based on strategies from interviews with regional farmers income, etc.) data from Statistik Austria (see Supplementary (Fessler 2021; Petz 2021) as well as from previous pro- Tables SI3–SI5). jects in Austria (Freudenberg 2017; Perzl 2021) and France 1 3 39 Page 6 of 17 C. Egger et al. Table 1 Set of possible land-use decisions for individual farmers in SECLAND. Farms take land-use decisions based on their satisfaction with income and workload; probabilities depend on happiness, farm type, farming style and scenario. Actions Description Act1: Nothing Farms decide not to change their operations and postpone their decision into the next year Act2: Hire farm worker Farms hire an additional farm worker (10% of a full-time equivalent or 180 hours yearly) Act3: Intensification Farms with intensity level ≤ 4 increase their intensity level by +1 Act4: Extensification Farms with intensity level > 1 decrease their intensity level by−1 Act5: Organic production Farms switch their production style from conventional to organic, this is possible once in the 37 years simulation period Act6: Land-use change Farms decide between income gain (maximum income) or time saving (minimal time use) and switch their cropland between crop cycles accordingly Act7: Afforestation Farms plant forest on one of their grassland patches and decrease their suffer-counter Act8: Expansion Farms try to acquire 1 patch from the rental market to expand their farming size Act9: Reduction Farms dismiss additional workers if they are employed; Farms send 1 patch to the rental market to reduce their farming size Act10: Termination Farms pass their remaining areas to the rental market; patches change their patch unit to the one of the rental markets and set their intensity level to 0; Farms then track their “death” year and reason (termination) before switching their activity status to 0 (Mayer et al. 2022), we identified four actions relevant for we decided to link four adaptation types, based on the dis- adaptation: land-use change, organic production, extensifica- tinction made by Mitter et al. (2019), with the model’s five tion, and expansion. For example, farmers mentioned that farming styles. Schmitzberger et al. (2005) classify the farm- due to climate change, yield fluctuations are increasing and ing styles according to their agricultural decision-making, they need to compensate for this by expanding their farm ranking traditionalists as very static, idealists as static, sup- size. We decided against the introduction of new actions, as port and yield optimizers as equally dynamic, and innovative these would have had the potential to introduce new risks, as as very dynamic. Based on this assessment, we associated for example irrigation often presented as adaptation meas- the farming styles with adaptation types, for which we devel- ure to stabilize water supply for agricultural production oped risk-related adaptation functions: with a presumable (Wheeler et al. 2013; van Duinen et al. 2016; Zagaria et al. impact on the decision-making of innovative, lighter and 2021) induces risks such as soil salinization (Singh 2021). equal effects for both optimizer types but minor effects on A stakeholder workshop (Supplementary Table SI6) in the idealists and even less on traditionalists (Fig. 4a). This is study region and expert interviews confirmed that water represented via adaptation function: scarcity and decreasing ground water levels are already a (x) =(x∕37) problem for the region and would be amplified by irrigation projects. Therefore, we focused on the integration of adapta- where i denotes the four adaptation types 1–4 and α depends tion strategies considering on-site farm measures within the on the number of extreme events x. Each occurrence of x existing set of actions. The results of this study, however, increases α persistently. α reaches its maximum of 1 in the i i can give indication for the integration of new actions and (unlikely) case of 37 extreme events, meaning that one event decision-options in future model versions. occurred in each year of the modeling period. The root value In a recent study, Mitter et al. (2019) developed a typol- z symbolizes the inertia towards change; the higher its value ogy for farmers’ attitudes towards adaptation strategies the lower is the willingness to incorporate adaptation (see for a case study in Austria that builds upon their climate Fig. 4a). change perception and related climate risk association. They The α-functions affect the likelihood LAct of the afore- identified four types of farmers’ attitudes (climate change mentioned actions Act (k=4, 5, 6, 8) in the decision-making adaptors, integrative adaptors, cost-benefit calculators, and process, as their algorithm is altered by 1 + α (Fig. 4b). In climate change fatalists) that were determined by farmers’ the case of reaching its maximum value of 1 (37 extreme perception and appraisal of climate change as well as indi- events), the likelihood doubles. LAct (α ) reflects the new like- k i vidual and regional farm characteristics. Depending on their lihood of an action after the multiplication by (1+α ). respective attitudes, they show varying grades of inertia towards adopting climate change adaptation measures. To LAct = LAct ∗(1 + ) k i k i integrate adaptive decision-making in the SECLAND ABM, 1 3 Effects of extreme events on land‑use‑related decisions of farmers in Eastern Austria: the… Page 7 of 17 39 Fig. 4 A Functional relation- ship of for the adaptation types 1–4: the increasing value for the square root z reflects the increasing the resistance (inertia) towards adaptation; each occurrence of an extreme event x has a persisting effect on . b Illustrative visualization of the effects of on decision- making: increases the likeli- hood for the actions Act and Act with the rising number of extreme events x (for x 0–4). As a result of the stakeholder process, we have outlined 2.5 External settings three distinct scenarios to assess future agricultural develop- ment pathways for the study region (Table 2). As baseline, a 2.5.1 Scenarios business-as-usual (BAU) scenario depicted the continuation of current development trajectories with constant subsidies Farms are exposed to several risks such as environmental, and moderate impacts of climate change. In a high subsidy production, market, price, and political risks (Bard and Barry (HS) scenario, subsidies were used as a steering mechanism 2001; Huber et al. 2022). Subsidy schemes as well as mar- to target favorable socio-economic conditions for agricultural ket demand for agricultural products and prices are external production. It describes high societal market intervention to factors in the SECLAND model. While market demand and achieve climate goals and, correspondingly, low impacts from price play a subordinate role for this application, we assess climate change. In contrast, the free market (FM) scenario the variation of subsidy schemes in two distinct scenarios depicted the development of a society that forgoes controlling and focus on the model integration of production risk as intervention and abolishes all subsidies by 2052 to focus on result of climate change-induced extreme weather events. Table 2 Assumptions made to construct the scenarios driving the (business-as-usual), HS (sustainability driven high subsidy), and FM agent-based model: yields, prices, subsidies, preferences, and the (free-market & fast climate change) scenarios. Thresholds for work- probability of extreme events per annum (p.a.) distinguishing BAU load and agricultural income are assumed to remain constant. Scenarios BAU HS FM Yields Decrease: 5–25% Decrease: 0–10% Decrease: 15–50% Prices Constant conv: +15% Constant org: +25% Variable costs Constant Constant Constant Subsidies Constant Increase: conv. 50%/org. 75% in 5-year Decrease: conv./org. 100% in 5-year inter- intervals until 2030, thereafter constant vals until 2030, thereafter constant Workload Increase: 10% Increase: 5% Increase: 10% Extreme events Expected value: 10% p.a. (ran- Expected value: 7.5% p.a. (random nor- Expected value: 15% p.a. (random normal dom normal distribution, SD mal distribution, SD 2.5%) distribution, SD 2.5%) 2.5%) yields: decrease 30% yields: decrease 60% yields: decrease 40% Alterations in Constant Constant Constant, possibility to switch from org. to farmer’s decision conv. production matrices 1 3 39 Page 8 of 17 C. Egger et al. free market competition instead, which we combined with 3 Results more pronounced climate change. The effects of climate change were accounted for in two 3.1 Extreme events ways in the model and the scenarios (Mayer et al. 2022; Egger et al. 2022). For the effect of climate change on yield The 300 simulation runs led to a total of 1,080 extreme development, we used region-specific yield prognoses for events. The MC distribution of extreme events in Fig. 5a low (HS), moderate (BAU), and a large temperature increase shows overlaps between scenarios. On average, each (FM) from (Haslmayr et al. 2018) for the forecast of yields. 37-year run involved 3.73 (BAU), 2.81 (HS), and 4.26 (FM) Extreme weather events related with climate change that extreme events. In all scenarios, runs without extreme events lower the annual harvest also played a crucial role in the occurred. This is contrasted by a maximum of 11 events in modeling framework. While recent climate projections one run of the FM scenario (BAU, 8; HS, 7). The scenario foresee rising intensity of such events for the study region, comparison shows a broader variation with outliers (dia- substantial differences based on climate scenarios are not mond shaped points) for the FM scenario. The years most expected until the second half of the twenty-first century affected by extreme events vary greatly among scenarios (Gobiet and Kotlarski 2020). We therefore integrated sto- (Fig. 5b). While in the BAU scenario, the years 2032 and chastic weather extreme events with minor variations in 2052 were most affected by extreme events in 18 runs; this severity and occurrence among the scenarios (Table 2). number was lower for the HS, where the years 2025 and 2042 are hit in 12 and 13 runs. For the FM scenario, the extreme events occurred earlier, in 2021 and 2024, and in 2.6 Limitations and model evaluation higher number with 21 and 20 runs. For each year and run, the model tracked the occurrence of extreme events and we We relied on random distributions to bridge data gaps. We reused these modeling outputs from the non-adaptive sce- performed 100 Monte Carlo (MC) runs per scenario to take narios, as inputs for the adaptive scenarios. Having similar into account the stochasticity of these assumptions, but remain extreme events within (adaptive and non-adaptive) scenarios within the limits of our computer performance power. We eval- allowed us to study the effects of adaptation. uated the ABM calibration by comparing model results from the “Business-as-usual” (BAU) scenario with historic data for 3.2 Farms and areas key variables such as the number of active farms, agricultural area, or livestock numbers (Supplementary Figure SI7A and The mean across all runs and scenarios showed a decline B), in line with the validation by results approach proposed from the initial number of 2990 active farms in 2015 to 2059 by Troost and Berger (2015). To ensure transparency, we fol- in 2053. In the BAU scenario, the number of active farms lowed the ODD protocol (Grimm et al. 2006, 2010; Müller decreased by 31%, which was similar to the HS scenarios et al. 2013) to document model inputs and calibration. (2,085 farms, −30%). The FM scenario showed a stronger Fig. 5 Comparison of Monte- Carlo (MC) results of extreme events. a Box-plots showing the distribution of extreme events with mean numbers of occurrence per run varying between 3 and 4. b Statistical overview of years affected by extreme events compared for all MC runs and scenarios; the size of the circles is used as indicator for the occurrence of extreme events (small=low, medium=intermediate, large=high). Scenarios assessed: business-as-usual (BAU), a sustainability-driven high subsidy (HS) and low- subsidy free market (FM) scenario. 1 3 Effects of extreme events on land‑use‑related decisions of farmers in Eastern Austria: the… Page 9 of 17 39 decline to (1873 farms, −37%) in 2053 (Fig. 6a). The results The scenario outcomes reflected the range of results for agri- of the adaptive scenarios illustrated similar farm abandon- cultural area, as the difference between highest (HSA) and ment trends, but at lower rates, with 2141 (−28%) active lowest (FM) means of cultivated cropland in 2053 marks farms in 2053 in BAUA, and comparable lower, respectively, 11% of the total cropland area 2015. Grassland depicted higher numbers for HSA (2173; −27%) and FMA (1941; less scenario variation with consistent decreases of about −35%). In bivariate comparison for 2053, the adaptive −32% to −35% until 2053, again with lower rates (∆3%) of scenarios depicted significantly (p <0.01) higher values of abandonment in the adaptive scenarios (see Supplementary about +4% in MC means of active farms. The split of mean Table SI8). The highest area of grassland cultivated in 2053 active farms in 2053 by farm type (Fig. 6b) revealed higher was observed in the BAUA scenario. Compared to cropland, scenario sensitivity for farm types, with numbers varying the range of results for grasslands was less diverse, with dif- between −37% (HSA) and −61% (FM) for processing, −29% ferences of about 6% of 2015 grassland area. (HSA) and −39% (FM) for cash crop, and −25% (BAUA) and −33% (FM) livestock farms. The adaptation-related dif- 3.3 Eec ff ts from adaptive management ferences of mean active farms split by farm type varied from 3 (livestock farms) up to 6% (processing farms). We used four specific indicators (Fig. 7a–d) to assess the Corresponding to the decline of the numbers of farms, impact of the adaptation measures extensification, land-use also a decline of agricultural area was detected with change, organic farming, and expansion. The development means ranging from 69,848 (HSA) to 61,568 ha (FM) in trajectories for the shares of extensive areas and the shares of the year 2053 when compared to the initial 87,545 ha in organic farms in the adaptive scenarios highlight the influence 2015 (Fig. 6c). The mean decrease of agricultural areas and of extensification and organic farming (Fig. 7a, c). Extensive assumed subsequent forest transition of abandoned agricul- areas ranged from 30% to 40% in 2053, with noticeably higher tural areas (Fig. 6d) was considerably lower in the BAU shares for all adaptive scenarios (Fig. 7a). The corresponding (−25%) and HS (−23%) scenarios than in the FM (−30%). results of the FMA and BAU scenario underlined the effect of Again, the adaptive scenarios depicted differences of about adaptation. Across all scenarios, increasing shares of organic 4%, meaning fewer agricultural areas abandoned in 2053 and farms until 2053 was a dominant future development trend therefore also less forest transition. The decrease of cropland (Fig. 7c). Compared to 2015, the proportion of organic farms was smaller compared to grassland. Cropland declined by nearly doubled for the BAU and HS scenarios until 2053, −17% (HSA) to −28% (FM), with higher cropland cultiva- especially in the adaptation scenarios where this trend was tion in the adaptive scenarios. Trends were similar in all more pronounced. Despite low, absolute numbers in the FM scenarios, with stronger pronunciation for the FM scenarios. scenarios in 2053 (BAU, 545; BAUA, 590; see Supplementary Fig. 6 Comparison of the simulation results between scenario runs without adap- tation (BAU, HS, FM) and adaptation scenarios (indicated by the suffix A in the scenario code: BAUA, HSA, and FMA). Distribution of the Monte Carlo (MC) results 2053 for (a) active farms and (c) agricultural area; comparison of initial 2015 num- bers against 2053 means for (b) active farms split by farm type (proc=processing, crop=cash crop, ls= livestock) and (d) mean areas split by land-use type (forest, grass=grassland, crop=cropland). Scenarios assessed: Business-as-usual (BAU), a sustainability-driven high subsidy (HS), and low- subsidy free market (FM) scenario. 1 3 39 Page 10 of 17 C. Egger et al. Fig. 7 Cross comparison of four indicators related to the adapta- tion actions: (a) for extensifica- tion the mean development of shares of extensive (intensity level <3) areas; (b) for land- use change the mean cropland shares split by crop cycle in 2053; (c) for organic production the mean development of shares of organic farms; (d) for expan- sion the mean share of farms > 20ha in 2053 (with 20 ha being the Austrian average of agri- cultural area per farm in 2016). Scenarios assessed: business-as- usual (BAU), a sustainability- driven high subsidy (HS), and low-subsidy free market (FM) scenario; adaptation scenarios are indicated by the suffix A. Table SI8), nevertheless, the relative share of organic farms the scenario conditions were the decisive factor for the devel- increased even in these scenarios. opment of the mean share of farms with insufficient income In contrast, the comparison of the initial values of 2015 (Fig. 8a). While in the BAU settings the rate of farms with and with 2053 means for selected indicators showed marginal insufficient income remained stable over time, the favorable effects for the adaptation measures land-use change and expan- conditions of the HS scenarios slightly decreased this rate. In sion (Fig. 7b, d). The cropland shares among the crop cycles contrast, in the FM scenarios, the mean rate of farms unsatis- (cr1, cr2, cr3; see Supplementary Table SI1) revealed similar fied with income increased to nearly 50% by 2053. development for the BAU and HS scenarios with expanding The comparison of the development of the mean share of shares for crop cycles 2 and 3, contrary to the FM scenarios farms with excess workload showed similar scenario trends with higher shares of cr1 in 2053 (Fig. 7b). The similarity but consistently higher rates for the adaptive scenarios between the adaptive and non-adaptive scenarios showed that (Fig. 8b). Until about 2025 across scenarios, farms pursued the development of the crop cycles was primarily driven by the a reduction of workload with decreasing shares of farms scenario conditions. These favored the cropping of soy (lead that work more than 1800 h/fte/year. From then onwards, crop cr2) and rapeseed (lead crop cr3) in the BAU/HS scenar- the scenario conditions had a stronger effect, which was ios and upholded the production of root crops (lead crop cr1) in reflected in the continued declines under the favorable the FM scenarios. The shares of farms with a farm size of more conditions in the HS scenarios, decelerated and stagnant than 20ha remained merely stable among scenarios (Fig. 7d). development in the BAU scenarios, and a trend reversal While in 2015 about 44% of farms exceeded 20ha, this number with increasing numbers in the FM scenarios. In all adap- increased slightly towards 46%–47% in 2053. Interestingly, tive scenarios, the trajectories depicted higher workloads the results depicted the highest farm sizes in the HS scenarios, per full-time equivalent for the period between 2025 and compared to the lowest shares in the FM scenario, which indi- 2053; for the FM scenarios, this was already observed from cated that the high subsidy volumes and prices allowed more 2020 onwards. In both graphs, the impact of extreme events farms to increase their farm size. was visible in the punctual spikes of the trajectories. 3.4 Income and workload satisfaction 4 Discussion The impact of learning on farmer satisfaction was repre- sented by its impact on agricultural income and workload, This paper investigates the impact of learning from extreme with a positive effect for income (higher absolute numbers) weather events on land-use decision-making and subse- and a negative effect for workload (Fig. 8a–b). We found that quently agricultural development. For this purpose, we 1 3 Effects of extreme events on land‑use‑related decisions of farmers in Eastern Austria: the… Page 11 of 17 39 Fig. 8 Development of shares of farms 2015–2053 with insuf- ficient income (a), respectively, the share of farms excess workload (full-time workers (fte) working more than 1800 h/ year) (b). Scenarios assessed: business-as-usual (BAU), a sustainability-driven high sub- sidy (HS) and low-subsidy free market (FM) scenario; adapta- tion scenarios are indicated by the suffix A. integrated learning into the SECLAND ABM and simulated case study under climate change in New Zealand, Gawith future trajectories for three scenarios for a rural region in et al. (2020) identified that socio-economic constraints on eastern Austria. Despite the simulated, unilateral decline farmers’ decision-making resulted in considerably lower of active farms and agricultural area until 2053, the results adaptation rates compared to pure profit maximization, showed that adaptation made a difference by buffering this and concluded that economic models underestimated the evolution. However, its impact varied depending on the sce- realistic cost of adaptation. Although adaptation had a nario conditions and increasing labor demand emerged as positive effect on the number of active farms and agricul- limiting factor for adaptation. tural area under cultivation in 2053, the SECLAND ABM With the integration of learning in the SECLAND ABM, depicted a realistic picture on (social and economic) costs we build on the two essential factors of the decision-making and revealed that this did not translate to the well-being process: the farming style symbolizing the intrinsic motiva- of farms. Income satisfaction was predominantly driven tion of farmers, and well-being (a balance between income by socio-politic scenario conditions, and partially affected and workload). It is common in agent-based modeling to by the occurrence of extreme events but independent of address risk-related adaptation of an agent through imita- adaptation. This confirms empirical analyses on Italian, tion or orientation within its social network (van Duinen Hungarian, Slovakian, and Swiss farmers (Severini et al. et al. 2016; Amadou et al. 2018; Gawith et al. 2020; Zagaria 2016; Brunner and Grêt-Regamey 2016; Bojnec and Fertő et al. 2021; Marvuglia et al. 2022; Huber et al. 2022). How- 2019), demonstrating that direct payments from the first ever, this acknowledges only on the social aspect of learn- pillar of the Common Agricultural Policy (CAP) increase ing, while the SECLAND ABM integrates learning as socio- and stabilize farm income by partially offsetting produc- ecological process that builds on the farming style and the tion and market volatilities (Lurette et al. 2020). Further- occurrence of the extreme events. This model design brings more, our simulations highlighted workload as trade-off two advantages. First, the decision to model varying degrees factor for the decision-making of farms. On the one hand, of inertia towards adaptation depending on the farming style the already high initial burden presented a strong motive integrates heterogeneity among the farmer population. Sec- for farms to purse a reduction in their workload. Limita- ond, the increasing effect of extreme events on adaptation tion in the production factors capital and labor represent desire allows the consideration of gradual transition to adap- important constrains for diversification of farms, espe- tation but avoids conglomerated network effects. cially concerning family farms and the seasonal peaks While SECLANDs farm agents consider workload and during harvest season (Bowman and Zilberman 2013). On income as decisive factors for their actions, farmers are the other hand, did adaptation result in a higher workload commonly parameterized to seek profit maximization for farms, which is connected to the dominant adaptation (Filatova et al. 2009; Schreinemachers and Berger 2011; measures organic farming and extensification, as organic Zimmermann et al. 2015; van Duinen et al. 2016; Ama- and extensive farming (small-scale farming in general) are dou et al. 2018; Yang et al. 2020; Zagaria et al. 2021; associated with higher labor demand then conventional or Huber et al. 2022). Consequently, the consideration of highly mechanized industrial farming (BAB 2021). This non-financial aspects or limitations is underrepresented means that farms only managed to compensate environ- in the decision-making processes of farm agents. For a mental risks by increasing their individual workload. 1 3 39 Page 12 of 17 C. Egger et al. structure with scattered fields) and legal reasons (conflicts 4.1 Adaptation strategies with the water regulation framework, already existing water scarcity). As a result, this study offered an alternative We followed a participatory modeling approach that involved stakeholders into several phases of the project from approach towards climate change adaptation, compared to the generally in literature proposed introduction of irriga- data collection and scenario outline to the model definition (Hare 2011). On the one hand, participatory modeling can tion systems (Wheeler et al. 2013; van Duinen et al. 2016; Amadou et al. 2018; Yang et al. 2020; Zagaria et al. 2021). increase modeling performance and results (Gaube et al. 2009; Smetschka and Gaube 2020). On the other hand, it The simulation results showed varying degrees of effects from the adaptation measures. Future cropping patterns gives heterogenous stakeholders the opportunity to use the option space created by future scenarios as base for discus- depicted a shift from sugar beet towards rapeseed and soy cultivation, mainly influenced by scenario settings. In Europe, sions and to compare their future management options (Car- mona et al. 2013). Due to the COVID-19 pandemic, we had rapeseed is an essential crop for the production of biodiesel (van Duinen et al. 2016), which is as substitute for fossil fuels, to adapt the planned stakeholder activities (adopt one online workshop followed by expert interviews instead of recur- especially important in sustainability scenarios. Independent of the scenario, we observed an increase of soy cultivation in ring in person meetings), which unfortunately had an impact on the involvement of local stakeholder. Nevertheless, we 2053. The production of domestic, high-quality GMO-free soy has become a lucrative market for European farmers, managed to capture essential topics in our exchanges with regional stakeholders. During the interviews, farmers men- which competes with the cultivation of established crops in terms of cropland demand. Especially for organic production, tioned price pressure, the need to expand production, and subsequently rising workload of farm workers as well as soy is attractive due to its ability to fix atmospheric nitrogen in organic crop rotations; moreover, it generates good income concerns about the negative impacts from recurring droughts on agricultural production in the study region. Furthermore, due to its high price (Fogelberg and Recknagel 2017). Despite a trend towards farm enlargement within EU the discussion on future agricultural development during the stakeholder workshop revealed the desire for experiments countries (Jurkėnaitė and Baležentis 2020), the simula- tions suggested only a marginal increase for the share of and alternative support approaches as part of substantial reforms for the current agro-environmental framework to farms exceeding the 2016 Austrian average farm size of 20 ha (Grüner Bericht 2021). However, there are other factors promote a fundamental transformation of agriculture. We deducted four adaptation strategies organic produc- to consider for farms in terms of productivity and profit- ability than just possible economies of scale from a larger tion, extensification, expansion, and land-use change based on farm management strategies from the 20 interviews in farm size. An empirical analysis from Australia depicts that, especially for smaller farms, access to advanced pro- the study region supplemented with additional 40 inter- views with farmers in Austria and France. With the shift to duction technology plays a decisive role in increasing farm productivity (Sheng et al. 2015). For European countries, organic and extensive production, farms can decrease the pressure on production quantity; additionally, higher prices the farming type also plays a decisive role, as diverse and mixed farms oppose farm size growth, while specialized can increase their gross margins (Resare Sahlin et al. 2022; Garmendia et al. 2022). As farms in this application of crop and livestock farms showed the highest growth rates in the last decade (Jurkėnaitė and Baležentis 2020). On the the SECLAND ABM only consider the agricultural income as satisfaction criteria, they cannot substitute farm income other hand, the consideration of workload threshold in the decision-making is a limiting factor in the expansion of farm with off-farm work (Brunner and Grêt-Regamey 2016). Instead, they can decrease their external input costs (e.g., size. The analysis of EU Farm Accountancy Data Network (FADN) highlights the ability to rely on family labor capac- fertilizer, machinery) through an extensification strategy. Additionally, through organic production, extensification, ity as key determinant of farm profitability for small and medium farms (Kryszak et al. 2021). and by expansion of their farm size, farms can increase the inflow of subsidies from both pillar 1 (direct payments) and The results revealed the shift to organic production and decrease of production intensity as most decisive actions pillar 2 (rural and structural development) of the CAP frame- work. Land-use change gives farms a chance to change their for land-use and farm management. The dominant trend of increasing organic farms, which was even more pronounced agricultural products, and switch between crops and crops cycles, to adapt to a changing climatic and economic condi- in the adaptive versions, aligned well with historic devel- opments. Since Austria’s accession into the EU in 1995, tions (Wheeler et al. 2013; Marvuglia et al. 2022). We did not consider the strategic reduction of farm area (Wheeler the share of organic farms has been rising strongly. Austria reached the EU goal of 25% of organic agricultural areas et al. 2013). The exchange with local stakeholders revealed early on that irrigation systems are not a viable solution for in 2030, already in 2020 with a share of 26.4%, placing it among the EU countries with the highest shares of organic the region mainly for economic (small-scale agricultural 1 3 Effects of extreme events on land‑use‑related decisions of farmers in Eastern Austria: the… Page 13 of 17 39 farming (Kummer et al. 2021). Additionally, several stud- The earlier an extreme event occurs the sooner it triggers ies identified organic farmers as young, educated, better adaptive decisions of farmers, and the longer it influences informed, and less risk averse then conventional famers their decision-making. On the other hand, every extreme (Koesling et al. 2004; Flaten et al. 2005; Tzouramani et al. event has a negative impact on farm income and farmers’ 2014; Schattman et al. 2016; Läpple et al. 2016; Mitter et al. satisfaction, as farmers have to keep investing in damage 2019), meaning that they have higher sensibility for climate control limiting their options. We deliberately decided to change and are more willing to adapt their production; there- apply slight variations in the stochastic extreme event func- fore, ultimately more of them will exist in 2053. tions to capture these offsetting effects, which can be seen The comparison of the results for agricultural area showed in the compilation of the scenario results. In the HS sce- that losses of extensive land occur in all scenarios, although nario, there was less incentive for adaptation due to the low the extent of the decline differed between the scenarios. We frequency of extreme events. In the FM scenario, the high observed varying land sharing and land sparing strategies rate and severity of extreme events in combination with (Grass et al. 2021), as the reduction of subsidies in the FM declining subsidies decreased the efficiency of adaptation scenarios reduced total farmed area, but the remainder was actions. The apparent difference between the results with cultivated at higher intensity. By contrast, higher subsidies in and without adaptation in the BAU scenarios highlighted the HS scenarios kept more agricultural area under cultiva- efficient adaptation based on the interplay between subsidy tion, but this land was farmed with lower intensity. Irrespec- volume and extreme event frequency. Therefore, if adapta- tive of scenario narrative, adaptation led to higher shares of tion is implemented as reaction to a negative event, any extensive areas and generally more agricultural area in 2053. circumstances that lower farmers’ distress (e.g., compen- This has two implications for the sustainability of farm man- sation payments, “mild” climate change) also reduce their agement. First, the reduction of external inputs such as fer- perceived risk and consequently their desire for adapta- tilizers and pesticides serves as a risk management strategy tion. In addition, there is the temporal aspect of adaptation, (Ahsan and Roth 2010; Tzouramani et al. 2014; Schattman which have a greater effect at lower costs, the earlier they et al. 2016) that reduces a farmers’ exposure to fluctuating are implemented (Brunner and Grêt-Regamey 2016). production costs (Serra and Duncan 2016; Bojnec and Fertő For a shift towards ex-ante adaptation, the negative stimu- 2019). Second, organic and extensive farming reduces these lus must be substituted by an alternative driver. One way input costs but increases the revenues (prices, subsidies) to overcome this dilemma provides the focus on positive simultaneously. In addition, organic farmers consider envi- incentives as motivator for the implementation of adapta- ronmentally friendly agricultural practices to be effective tion measures ex-ante. Such a change in perspective would measures for climate change adaptation (Schattman et al. dissolve the constraint of climate-related risk perception and 2016; Mitter et al. 2019), which was confirmed by regional broaden the group of potentially adopting farmers. The sce- farmers during our interviews (Fessler 2021; Petz 2021). nario comparison provides valuable implications for socio- political measures that could guide such as result-based 4.2 From ex‑post to ex‑ante adaptation payments approach. The success and efficiency of the CAP measures and its reforms have been broadly discussed and Our results projected the decline of about one-third of the especially criticized for the shortcoming in support for envi- initially active farms accompanied by the loss of one-quar- ronment- and climate-friendly practices, despite its major ter of the initial agricultural area up until the year 2053. financial volume (Serra and Duncan 2016; Pe’er et al. 2019, Although a decline in agricultural production seems inevi- 2020; Scown et al. 2020). How effectiveness of the CAP table, the scenario comparison showed that the setting of can be increased in a target-oriented manner has yet to be the external framework conditions can slow down or accel- defined (Scown et al. 2020). To add to this discussion, we erate this trend. Independent of the scenario conditions, our investigate the potential impact of socio-political measures results highlighted the positive effect of adaptation on the on adaptation decision-making and agricultural develop- number of active farms as well as agricultural area. This ment in the “high subsidy” and the “free market” scenarios. corresponds with findings of Zagaria et al. (2021) and van The comparison of our results indicates that financial incen- Duinen et al. (2016), who based the adaptation mechanism tives alone are insufficient: despite the significant increase on farmers’ perception of drought risk for adaptation, for in subsidies in the HS scenarios, the superior results from case studies in Italy and the Netherlands. Whether adap- the adaptive BAU scenario depict comparable outcomes in tation measures incorporate the transition of an agricul- active farms and agricultural area and illustrate the potential tural production system or not, modeling frameworks that positive effect of adaptation on agro-structural development. build learning and adaptation on climate change-related On the other hand, synergies to existing farm manage- extreme events, first need a negative impact to create an ment practices play a role in the adoption of agro-environ- ex-post response that changes farmers’ decision-making. mental and climate measures of farmers (Fanchone et al. 1 3 39 Page 14 of 17 C. Egger et al. 2022). The findings of this study are in line with the EU for farmers. Furthermore, the comparison of the results farm-to-fork strategy that builds on organic and low-input against the socio-politic framework conditions revealed farming with the aim to strengthen regional production and the limited scope of financial incentives. consumption (Kummer et al. 2021). Our analysis also high- We identified the need for agricultural subsidy schemes lights workload as limiting factor for the decision-making of that increase the scope of action for farms, by supporting farms as their already high workload increased further with them in workload reduction in the long run. To increase successful adaptation. This crystallizes workload reduc- the resilience of farms and achieve successful long-term tion as an essential factor to incentivize management shifts. shifts towards sustainable development paths in agricul- Socio-political measures that provide temporal and seasonal ture, individual and societal efforts must complement each farm labor support have the potential to increase the sus- other. Future directives of the CAP should therefore focus tainability of farms, not only farm profitability but also the not only on economic and ecological but equally on social social well-being of farm households. objectives in order to create sustainable framework condi- For this first application of learning in the SECLAND tions for European farmers. ABM, we concentrated solely on the integration of learn- Supplementary Information The online version contains supplemen- ing and on aggregated dynamics on study region level. The tary material available at https://doi. or g/10. 1007/ s13593- 023- 00890-z . analysis of spatial heterogeneity as driver for adaptation (Delay et al. 2015) was beyond the scope of this paper, as Acknowledgements We thank all the local farmers and regional we focused on the comparison of results with and without experts for their participation in interviews and the stakeholder work- shop. Furthermore, we want to acknowledge the work of Martina adaptation. Nevertheless, we plan to address the implications Perzl, Bernadette Fessler, and Sabine Petz for conducting the farmer of learning in a heterogenous study region in future research. interviews. Last but not the least, we also thank the two anonymous reviewers for their excellent feedback that really helped to improve the manuscript. 5 Conclusion Authors' contributions Conceptualization: C.E., A.M., V.G.; model design: C.E.; analysis: C.E., A.M., V.G.; writing—original draft: European farmers are embedded in complex socio-ecologi- C.E.; writing—review and editing: all authors; funding acquisition: cal systems. Their decision-making is influenced not only by C.P, S.S., V.G. restrictions set by farm production capacity, but they also have Funding Open access funding provided by University of Natural to comply with the guidelines lined out by the national and Resources and Life Sciences Vienna (BOKU). This study was funded international subsidy schemes. Additionally, in the upcoming by the Climate and Energy Fund Austria as part of the project CHESS decades, farmers will face the challenge to adapt to chang- - Managing Climate cHange impacts on land use and EcoSystem Ser- vices, ACRP11 - CHESS - KR18AC0K14615. ing climatic conditions that have long-term effects as well as abrupt extreme events that affect agricultural production. Data availability The agent-based module (ABM) is written in Net- For this study, we integrated learning through climate logo 6.0.2. The open-source software is accessible on https://ccl. nor th change adaptation into the SECLAND ABM. In a novel wester n.edu/ ne tlogo/ inde x.shtml . The model code is not publicly avail- able but a thoroughly description can be found in the supplementary approach, we linked the effects of extreme events (envi- material. ronmental) and value-based characteristics (social and eco- A request for use for the Austrian IACS data can be made with nomic) on farmers’ decision-making to integrate learning the ministry for agriculture, regions, and tourism via https:// dafne. at/. as socio-ecological process into the model. This allowed The socio-economic data are publicly available from Statistik Aus- tria https://www .s tatis tik.at/ and Bundesanstalt für Agrarwirtschaft und us to use the particular advantage of agent-based modeling Bergbauernfragen BAB - Deckungsbeiträge und Kalkulationsdaten and simulate the effects of learning on socio-economic farm (agrarforschung.at). structure development as well as its spatial impacts on land- use dynamics across diverse future scenarios. Declarations From the scenario analysis, we conclude that adapta- Research involving human participants The study was conducted tion has a positive impact on both the number of active according to the guidelines laid down in the 1964 Helsinki Declara- farms and the agricultural area in 2053. However, suc- tion and its amendments. cessful adaptation does not necessarily mean a shift to an irrigation system, which is an infeasible solution in Consent to participate (include appropriate statements) All study par- ticipants gave informed consent to participate in the study. small-scaled, or grassland-dominated farming systems. We identified organic and extensive farming as successful and Consent for publication The authors affirm that human research par - well-established adaptation strategies to climate change. ticipants provided informed consent for this publication. Our results also depicted that adaptation was accompanied Competing interests The authors declare no competing interests. by an additional increase of the already high workload 1 3 Effects of extreme events on land‑use‑related decisions of farmers in Eastern Austria: the… Page 15 of 17 39 Open Access This article is licensed under a Creative Commons Attri- basin, Spain. J Environ Manage 128:400–412. https:// doi. org/ bution 4.0 International License, which permits use, sharing, adapta-10. 1016/j. jenvm an. 2013. 05. 019 tion, distribution and reproduction in any medium or format, as long Carpenter SR, Arrow KJ, Barrett S et al (2012) General resilience to as you give appropriate credit to the original author(s) and the source, cope with extreme events. Sustainability 4:3248–3259. https:// provide a link to the Creative Commons licence, and indicate if changes doi. org/ 10. 3390/ su412 3248 were made. The images or other third party material in this article are Davidson D (2016) Gaps in agricultural climate adaptation research. included in the article's Creative Commons licence, unless indicated Nat Clim Change 6:433–435. https://doi. or g/10. 1038/ nclim ate30 otherwise in a credit line to the material. If material is not included in 07 the article's Creative Commons licence and your intended use is not De Houwer J, Barnes-Holmes D, Moors A (2013) What is learning? permitted by statutory regulation or exceeds the permitted use, you will On the nature and merits of a functional definition of learn- need to obtain permission directly from the copyright holder. To view a ing. Psychon Bull Rev 20:631–642. https:// doi. or g/ 10. 3758/ copy of this licence, visit http://cr eativ ecommons. or g/licen ses/ b y/4.0/ .s13423- 013- 0386-3 Delay E, Piou C, Quenol H (2015) The mountain environment, a driver for adaptation to climate change. Land Use Policy 48:51–62. https:// doi. org/ 10. 1016/j. landu sepol. 2015. 05. 008 Diffenbaugh NS, Singh D, Mankin JS et al (2017) Quantifying the References influence of global warming on unprecedented extreme climate events. Proc Natl Acad Sci 114:4881–4886. https:// doi. org/ 10. 1073/ pnas. 16180 82114 Ahsan DA, Roth E (2010) Farmers’ perceived risks and risk manage- Dullinger I, Gattringer A, Wessely J et al (2020) A socio-ecological ment strategies in an emerging mussel aquaculture industry in model for predicting impacts of land-use and climate change on Denmark. Mar Resour Econ 25:309–323. https:// doi. org/ 10. regional plant diversity in the Austrian Alps. Glob Change Biol 5950/ 0738- 1360- 25.3. 309 26:2336–2352. https:// doi. org/ 10. 1111/ gcb. 14977 Amadou ML, Villamor GB, Kyei-Baffour N (2018) Simulating agri- EEA (2017) Biogeographical regions in Europe - European Environo- cultural land-use adaptation decisions to climate change: an ment Agency. https:// www. eea. europa. eu/ data- and- maps/ figur empirical agent-based modelling in northern Ghana. Agric es/b iogeo graph ical-r egion s-i n-e urope-2. Accessed 20 May 2021 Syst 166:196–209. https://d oi.o rg/1 0.1 016/j.a gsy.2 017.1 0.0 15 Egger C, Plutzar C, Mayer A et al (2022) Using the SECLAND model Arbuckle JG, Morton LW, Hobbs J (2015) Understanding farmer to project future land-use until 2050 under climate and socio- perspectives on climate change adaptation and mitigation: the economic change in the LTSER region Eisenwurzen (Austria). roles of trust in sources of climate information, climate change Ecol Econ 201:107559. https://d oi.o rg/1 0.1 016/j.e colec on.2 022. beliefs, and perceived risk. Environ Behav 47:205–234. https:// doi. org/ 10. 1177/ 00139 16513 503832 Fanchone A, Nelson L, Dodet N et al (2022) How agro-environmental BAB (2021) IDB Deckungsbeiträge und Kalkulationsdaten - Bunde- and climate measures are affecting farming system performances sanstalt für Agrarwirtschaft und Bergbauernfragen. https://idb. in Guadeloupe?: lessons for the design of effective climate agrar forsc hung. at/ defau lt. html. Accessed 27 Jan 2021 change policies. Int J Agric Sustain 20:1348–1359. https:// doi. Bard SK, Barry PJ (2001) Assessing farmers’ attitudes toward risk org/ 10. 1080/ 14735 903. 2022. 21368 36 using the “closing-in” method. J Agric Resour Econ 26:248– Fessler BM (2021) Entscheidungsverhalten in der Grünlandbe- 260. https:// doi. org/ 10. 22004/ ag. econ. 31153 wirtschaftung von Landwirten und Landwirt*innen aus den Bojnec Š, Fertő I (2019) Do CAP subsidies stabilise farm income in politischen Bezirken Neunkirchen und Wiener Neustadt Land. Hungary and Slovenia? Agric Econ 103–111. https:// doi. org/ Alpen-Adria-Universität Klagenfurt, Klima- und Umweltschutz- 10. 17221/ 190/ 2018- AGRIC ECON maßnahmen im Fokus Borrelli P, Robinson DA, Panagos P et al (2020) Land use and cli- Filatova T, Parker D, van der Veen A (2009) Agent-based urban land mate change impacts on global soil erosion by water (2015– markets: agent’s pricing behavior, land prices and urban land use 2070). Proc Natl Acad Sci 117:21994–22001. https:// doi. org/ change. Journal of Artificial Societies and Social Simulation 3:. 10. 1073/ pnas. 20014 03117 https:// www. jasss. org/ 12/1/ 3. html Boserup E (1965) The conditions of agricultural growth - the eco- Flaten O, Lien G, Koesling M et al (2005) Comparing risk perceptions nomics of agrarian change under population pressure. Georg and risk management in organic and conventional dairy farming: Allen and Unwin Ltd. empirical results from Norway. Livest Prod Sci 95:11–25. https:// Bowman M, Zilberman D (2013) Economic factors affecting diver - doi. org/ 10. 1016/j. livpr odsci. 2004. 10. 014 sified farming systems. Ecol Soc 18:. https:// doi. org/ 10. 5751/ Fogelberg F, Recknagel J (2017) Developing soy production in central ES- 05574- 180133 and northern Europe. Legum Crop Syst 109–124. https://doi. or g/ Box JE, Colgan WT, Christensen TR et al (2019) Key indica- 10. 1079/ 97817 80644 981. 0109 tors of Arctic climate change: 1971–2017. Environ Res Lett Freudenberg A (2017) Wahrnehmung und Stellenwert des Klimawan- 14:045010. https:// doi. org/ 10. 1088/ 1748- 9326/ aafc1b dels bei Landnutzungsentscheidungen von LandwirtInnen im Brown C, Alexander P, Holzhauer S, Rounsevell MDA (2017) Ennstal. Alpen-Adria-Universität Klagenfurt Behavioral models of climate change adaptation and mitiga- Garmendia E, Aldezabal A, Galan E et al (2022) Mountain sheep graz- tion in land-based sectors. WIREs Clim Change 8:e448. https:// ing systems provide multiple ecological, socio-economic, and doi. org/ 10. 1002/ wcc. 448 food quality benefits. Agron Sustain Dev 42:47. https:// doi. org/ Brunner SH, Grêt-Regamey A (2016) Policy strategies to foster the 10. 1007/ s13593- 021- 00751-7 resilience of mountain social-ecological systems under uncer- Gaube V, Kaiser C, Wildenberg M et al (2009) Combining agent-based tain global change. Environ Sci Policy 66:129–139. https://doi. and stock-flow modelling approaches in a participative analysis org/ 10. 1016/j. envsci. 2016. 09. 003 of the integrated land system in Reichraming, Austria. Landsc Carmona G, Varela-Ortega C, Bromley J (2013) Participatory model- Ecol 24:1149–1165. https://doi. or g/10. 1007/ s10980- 009- 9356-6 ling to support decision making in water management under Gawith D, Hodge I, Morgan F, Daigneault A (2020) Climate change uncertainty: two comparative case studies in the Guadiana river costs more than we think because people adapt less than we 1 3 39 Page 16 of 17 C. Egger et al. assume. Ecol Econ 173:106636. https:// doi. org/ 10. 1016/j. ecole Kryszak L, Guth M, Czyzewski B (2021) Determinants of farm profit- con. 2020. 106636 ability in the EU regions. Does farm size matter Agricultural Gobiet A, Truhetz H (2008) Klimamodelle, Klimaszenarien und ihre Economics – Czech 3:90–100. https:// doi. or g/ 10. 17221/ 415/ Bedeutung für Österreich. In: Böhm R, Godina R, Nachtnebel 2020- AGRIC ECON HP, Pirker O (eds) Auswirkungen des Klimawandels auf die Kummer S, Klingbacher E, Petrasek R et al (2021) Stärkung der Österreichische Wasserwirtschaft. Bundesministerium für Land- biologischen Landwirtschaft in Österreich bis 2030 Studie zu und Forstwirtschaft, Umwelt und Wasserwirtschaft und Österrei- Erfolgsfaktoren und Handlungsoptionen. Forschungsinstitut für chischer Wasser- und Abfallwirtschaftsverband (ÖWAV), Wien biologischen Landbau FiBL, Wien Gobiet A, Kotlarski S (2020) Future climate change in the European Läpple D, Renwick A, Cullinan J, Thorne F (2016) What drives inno- Alps. Oxf Res Encycl Clim Sci. https://doi. or g/10. 1093/ acr efor e/ vation in the agricultural sector? A spatial analysis of knowl- 97801 90228 620. 013. 767 edge spillovers. Land Use Policy 56:238–250. https:// doi. org/ Grass I, Batáry P, Tscharntke T (2021) Chapter six - combining land-10. 1016/j. landu sepol. 2016. 04. 032 sparing and land-sharing in European landscapes. Adv Ecol Res Loarie SR, Duffy PB, Hamilton H et al (2009) The velocity of climate 64:251–303. https:// doi. org/ 10. 1016/ bs. aecr. 2020. 09. 002 change. Nature 462:1052–1055. https:// doi. org/ 10. 1038/ natur Grimm V, Berger U, Bastiansen F et al (2006) A standard protocol for e08649 describing individual-based and agent-based models. Ecol Model Lurette A, Stark F, Lecomte L et al (2020) A model to explore which 198:115–126. https:// doi. org/ 10. 1016/j. ecolm odel. 2006. 04. 023 diversity is needed to design sustainable agricultural systems at Grimm V, Berger U, DeAngelis DL et al (2010) The ODD protocol: a the territorial level. Agron Sustain Dev 40:32. https://doi. or g/10. review and first update. Ecol Model 221:2760–2768. https://doi. 1007/ s13593- 020- 00634-3 org/ 10. 1016/j. ecolm odel. 2010. 08. 019 Marvuglia A, Bayram A, Baustert P et al (2022) Agent-based model- Groeneveld J, Müller B, Buchmann CM et al (2017) Theoretical foun- ling to simulate farmers’ sustainable decisions: farmers’ interac- dations of human decision-making in agent-based land use mod- tion and resulting green consciousness evolution. J Clean Prod els – a review. Environ Model Softw 87:39–48. https:// doi. org/ 332:129847. https:// doi. org/ 10. 1016/j. jclep ro. 2021. 129847 10. 1016/j. envso ft. 2016. 10. 008 Mayer A, Egger C, Loyau A et al (2022) Mountain pastures increase Grüner Bericht (2021) Grüner Bericht 2021 - Die Situation der öster- the resilience of livestock farming to extreme events in the reichischen Land- und Forstwirtschaft Gemäß §9 des Land- Ariège department. France. Agron Sustain Dev 42:49. https:// wirtschaftgesetzes. Bundesministerium für Landwirtschaft, doi. org/ 10. 1007/ s13593- 022- 00779-3 Regionen und Tourismus, Wien Mitter H, Larcher M, Schönhart M et al (2019) Exploring farmers’ Grüner Bericht NÖ (2017) DER GRÜNE BERICHT 2017 - Ber- climate change perceptions and adaptation intentions: empirical icht über die wirtschaftliche und soziale Lage der Land- und evidence from Austria. Environ Manage 63:804–821. https://doi. Forstwirtschaft 2017org/ 10. 1007/ s00267- 019- 01158-7 Hare M (2011) Forms of participatory modelling and its potential for Mukherjee S, Mishra A, Trenberth KE (2018) Climate change and widespread adoption in the water sector. Environ Policy Gov drought: a perspective on drought indices. Curr Clim Change Rep 21:386–402. https:// doi. org/ 10. 1002/ eet. 590 4:145–163. https:// doi. org/ 10. 1007/ s40641- 018- 0098-x Haslmayr H-P, Baumgarten A, Schwarz M, et al (2018) BEAT –Boden- Müller B, Bohn F, Dreßler G et al (2013) Describing human decisions bedarf für die Ernährungssicherung in Österreich in agent-based models – ODD + D, an extension of the ODD Herring SC, Hoerling MP, Peterson TC, Stott PA (2014) Explaining protocol. Environ Model Softw 48:37–48. https:// doi. org/ 10. extreme events of 2013 from a climate perspective. Bull Am 1016/j. envso ft. 2013. 06. 003 Meteorol Soc 95:S1–S104. https:// doi. org/ 10. 1175/ 1520- 0477- Natural Earth (2022). https:// www. natur alear thdata. com/. Accessed 95.9. S1.1 10 Oct 2022 Herring SC, Christidis N, Hoell A, Stott PA (2022) Explaining extreme Newell RG, Prest BC, Sexton SE (2021) The GDP-Temperature rela- events of 2020 from a climate perspective. Bull Am Meteorol tionship: implications for climate change damages. J Environ Soc 103:S1–S129. https:// doi. org/ 10. 1175/ BAMS- Expla ining Econ Manag 108:102445. https:// doi. org/ 10. 1016/j. jeem. 2021. Extre meEve nts20 20.1 102445 Hiebl J, Frei C (2016) Daily temperature grids for Austria since Osbahr H, Dorward P, Stern R, Cooper S (2011) Supporting agricul- 1961—concept, creation and applicability. Theor Appl Climatol tural innovation in Uganda to respond to climate risk: linking 124:161–178. https:// doi. org/ 10. 1007/ s00704- 015- 1411-4 climate change and variability with farmer perceptions. Exp Huber R, Bakker M, Balmann A et al (2018) Representation of deci- Agric 47:293–316. https://doi. or g/10. 1017/ S0014 47971 00007 85 sion-making in European agricultural agent-based models. Agric Otto FEL, van der Wiel K, van Oldenborgh GJ et al (2018) Climate Syst 167:143–160. https:// doi. org/ 10. 1016/j. agsy. 2018. 09. 007 change increases the probability of heavy rains in Northern Eng- Huber R, Xiong H, Keller K, Finger R (2022) Bridging behavioural land/Southern Scotland like those of storm Desmond—a real- factors and standard bio-economic modelling in an agent-based time event attribution revisited. Environ Res Lett 13:024006. modelling framework. J Agric Econ 73:35–63. https:// doi. org/ https:// doi. org/ 10. 1088/ 1748- 9326/ aa9663 10. 1111/ 1477- 9552. 12447 Pe’er G, Zinngrebe Y, Hauck J et al (2017) Adding some green to IPCC (2022) Climate change 2022 - impacts, adaptation and vulnerability. the greening: improving the EU’s Ecological Focus Areas for Intergovernmental Panel on Climate Change, Working Group II biodiversity and farmers. Conserv Lett 10:517–530. https:// doi. Janssen M, Ostrom E (2006) Empirically based, agent-based models. org/ 10. 1111/ conl. 12333 Ecol Soc 11:. https:// doi. org/ 10. 5751/ ES- 01861- 110237 Pe’er G, Zinngrebe Y, Moreira F et al (2019) A greener path for the Jurkėnaitė N, Baležentis T (2020) The ‘pure’ and structural contri- EU Common Agricultural Policy. Science 365:449–451. https:// butions to the average farm size growth in the EU: the index doi. org/ 10. 1126/ scien ce. aax31 46 decomposition approach. Ecol Indic 117:106614. https://doi. or g/ Pe’er G, Bonn A, Bruelheide H et al (2020) Action needed for the EU 10. 1016/j. ecoli nd. 2020. 106614 Common Agricultural Policy to address sustainability challenges. Koesling M, Ebbesvik M, Lien G et al (2004) Risk and risk manage- People Nat 2:305–316. https:// doi. org/ 10. 1002/ pan3. 10080 ment in organic and conventional cash crop farming in Norway. Peltonen-Sainio P, Jauhiainen L, Trnka M et al (2010) Coincidence of variation in yield and climate in Europe. Agric Ecosyst Environ Acta Agric Scand Sect C — Food Econ 1:195–206. https:// doi. 139:483–489. https:// doi. org/ 10. 1016/j. agee. 2010. 09. 006 org/ 10. 1080/ 16507 54041 00196 92 1 3 Effects of extreme events on land‑use‑related decisions of farmers in Eastern Austria: the… Page 17 of 17 39 Perzl M (2021) Wirkungsweisen sozialer, ökonomischer und ökolo- Trenberth KE (2018) Climate change caused by human activities is hap- gischer Faktoren auf Entscheidungen, Anpassungsfähigkeit und pening and it already has major consequences. J Energy Nat Resour Entwicklungen in der Fruchtfolgengestaltung : Entscheidungen und Law 36:463–481. https://doi. or g/10. 1080/ 02646 811. 2018. 145 0895 Praktiken sowie Einschätzung der Anpassungsfähigkeit der Frucht- Troost C, Berger T (2015) Dealing with uncertainty in agent-based sim- folgen, -auswahl, -bearbeitung und -wechsel oberösterreichischer ulation: farm-level modeling of adaptation to climate change in Landwirt*innen (Bezirke Kirchdorf und Steyr-Land). Alpen-Adria- Southwest Germany. Am J Agric Econ 97:833–854. https:// doi. Universität Klagenfurtorg/ 10. 1093/ ajae/ aau076 Petz SA (2021) Entscheidungsverhalten im Ackerbau von Landwirt*innen Tzouramani I, Alexopoulos G, Kostianis G, Kazakopoulos L (2014) aus den politischen Bezirken Oberpullendorf und Wiener Neustadt Exploring risk management strategies for organic farmers: a Greek Land. Alpen-Adria-Universität Klagenfurt, Klima- und Umwelts- case study. Renew Agric Food Syst 29:167–175. https://doi. or g/10. chutzmaßnahmen im Fokus1017/ S1742 17051 30000 57 Resare Sahlin K, Carolus J, von Greyerz K et al (2022) Delivering “less Ummenhofer CC, Meehl GA (2017) Extreme weather and climate events but better” meat in practice—a case study of a farm in agroecologi- with ecological relevance: a review. Philos Trans R Soc B Biol Sci cal transition. Agron Sustain Dev 42:24. https:// doi. org/ 10. 1007/ 372:20160135. https:// doi. org/ 10. 1098/ rstb. 2016. 0135 s13593- 021- 00737-5 Valbuena D, Verburg PH, Bregt AK (2008) A method to define a typology Schattman RE, Conner D, Méndez VE (2016) Farmer perceptions of for agent-based analysis in regional land-use research. Agric Ecosyst climate change risk and associated on-farm management strategies Environ 128:27–36. https:// doi. org/ 10. 1016/j. agee. 2008. 04. 015 in Vermont, northeastern United States. Elem Sci Anthr 4:000131. van Duinen R, Filatova T, Jager W, van der Veen A (2016) Going beyond https:// doi. org/ 10. 12952/ journ al. eleme nta. 000131 perfect rationality: drought risk, economic choices and the influ- Schmitzberger I, Wrbka Th, Steurer B et al (2005) How farming styles ence of social networks. Ann Reg Sci 57:335–369. https:// doi. org/ influence biodiversity maintenance in Austrian agricultural land-10. 1007/ s00168- 015- 0699-4 scapes. Agric Ecosyst Environ 108:274–290. https:// doi. org/10. van Winsen F, de Mey Y, Lauwers L et al (2016) Determinants of risk 1016/j. agee. 2005. 02. 009 behaviour: effects of perceived risks and risk attitude on farmer’s Schreinemachers P, Berger T (2011) An agent-based simulation model adoption of risk management strategies. J Risk Res 19:56–78. of human–environment interactions in agricultural systems. https:// doi. org/ 10. 1080/ 13669 877. 2014. 940597 Environ Model Softw 26:845–859. https:// doi. or g/ 10. 1016/j. van Zanten BT, Verburg PH, Espinosa M et al (2014) European agri- envso ft. 2011. 02. 004 cultural landscapes, common agricultural policy and ecosystem Scown MW, Brady MV, Nicholas KA (2020) Billions in misspent EU services: a review. Agron Sustain Dev 34:309–325. https://doi. or g/ agricultural subsidies could support the sustainable development 10. 1007/ s13593- 013- 0183-4 goals. One Earth 3:237–250. h t t p s : / / d o i . o rg / 1 0 . 1 0 1 6 / j . o n e e a r . Verburg PH, Dearing JA, Dyke JG et al (2016) Methods and approaches 2020. 07. 011 to modelling the Anthropocene. Glob Environ Change 39:328–340. Serra A, Duncan J (2016) European farmers and the “Greening” of the https:// doi. org/ 10. 1016/j. gloen vcha. 2015. 08. 007 CAP: a critical discourse analysis. Glob Governancepolitics Clim Verburg PH, Alexander P, Evans T et al (2019) Beyond land cover Justice Agrar Justice Link Chall. https://www .eur .nl/ sites/ cor por ate/ change: towards a new generation of land use models. Sustain Gov files/ 13- ICAS_ CP_ Serra_ and_ Duncan. pdf Transform 38:77–85. https://doi. or g/10. 1016/j. cosus t.2019. 05. 002 Severini S, Tantari A, Di Tommaso G (2016) Do CAP direct payments Vogel E, Donat MG, Alexander LV et al (2019) The effects of climate stabilise farm income? Empirical evidences from a constant sam- extremes on global agricultural yields. Environ Res Lett 14:054010. ple of Italian farms. Agric Food Econ 4:6. https://d oi.o rg/1 0.1 186/ https:// doi. org/ 10. 1088/ 1748- 9326/ ab154b s40100- 016- 0050-0 Wheeler S, Zuo A, Bjornlund H (2013) Farmers’ climate change beliefs Sheng Y, Zhao S, Nossal K, Zhang D (2015) Productivity and farm and adaptation strategies for a water scarce future in Australia. Glob size in Australian agriculture: reinvestigating the returns to scale. Environ Change 23:537–547. https:// doi. org/ 10. 1016/j. gloen vcha. Aust J Agric Resour Econ 59:16–38. https:// doi. org/ 10. 1111/ 2012. 11. 008 1467- 8489. 12063 WKO Statistik (2020) Jahresarbeitszeit 2018 im EU-Vergleich - Sollar- Shukla R, Agarwal A, Sachdeva K et al (2019) Climate change percep- beitszeit von Arbeitnehmern tion: an analysis of climate change and risk perceptions among Yang YCE, Son K, Hung F, Tidwell V (2020) Impact of climate change farmer types of Indian Western Himalayas. Clim Change 152:103– on adaptive management decisions in the face of water scarcity. J 119. https:// doi. org/ 10. 1007/ s10584- 018- 2314-z Hydrol 588:125015. https://d oi.o rg/1 0.1 016/j.j hydro l.2 020.1 25015 Singh A (2021) Soil salinization management for sustainable develop- Zagaria C, Schulp CJE, Zavalloni M et al (2021) Modelling transforma- ment: a review. J Environ Manage 277:111383. https:// doi. org/ 10. tional adaptation to climate change among crop farming systems in 1016/j. jenvm an. 2020. 111383 Romagna. Italy. Agric Syst 188:103024. https:// doi. org/ 10. 1016/j. Smajgl A, Brown DG, Valbuena D, Huigen MGA (2011) Empirical agsy. 2020. 103024 characterisation of agent behaviours in socio-ecological systems. Zimmermann A, Möhring A, Mack G et al (2015) Pathways to truth: com- Environ Model Softw 26:837–844. https://doi. or g/10. 1016/j. en vso paring different upscaling options for an agent-based sector model. ft. 2011. 02. 011 J Artif Soc Soc Simul 18:11. https:// doi. org/ 10. 18564/ jasss. 2862 Smetschka B, Gaube V (2020) Co-creating formalized models: participa- Zwiers FW, Alexander LV, Hegerl GC et al (2013) Climate extremes: tory modelling as method and process in transdisciplinary research challenges in estimating and understanding recent changes in the and its impact potentials. Environ Sci Policy 103:41–49. https://doi. frequency and intensity of extreme climate and weather events. In: org/ 10. 1016/j. envsci. 2019. 10. 005 Asrar GR, Hurrell JW (eds) Climate Science for Serving Society: Stoate C, Báldi A, Beja P et al (2009) Ecological impacts of early Research, Modeling and Prediction Priorities. Springer, Nether- 21st century agricultural change in Europe – a review. J Envi- lands, Dordrecht, pp 339–389 ron Manage 91:22–46. https://doi. or g/10. 1016/j. jen vman. 2009. 07. 005 Publisher's note Springer Nature remains neutral with regard to Stott P (2016) How climate change affects extreme weather events. Sci- jurisdictional claims in published maps and institutional affiliations. ence 352:1517–1518. https:// doi. org/ 10. 1126/ scien ce. aaf72 71 1 3 http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Agronomy for Sustainable Development Springer Journals http://www.deepdyve.com/lp/springer-journals/effects-of-extreme-events-on-land-use-related-decisions-of-farmers-in-82D72vZijm

Loading next page...

References (107)

(2014)
EXPLAINING EXTREME EVENTS OF 2013 FROM A CLIMATE PERSPECTIVE
Łukasz Kryszak, Marta Guth, B. Czyżewski (2021)
Determinants of farm profitability in the EU regions. Does farm size matter?
Agricultural Economics-zemedelska Ekonomika, 67
Š. Bojnec, I. Fertő (2019)
Do CAP subsidies stabilise farm income in Hungary and Slovenia?
Agricultural Economics (Zemědělská ekonomika)
E. Garmendia, A. Aldezabal, E. Galán, A. Andonegi, A. Prado, G. Gamboa, Oihana García, G. Pardo, N. Aldai, L. Barrón (2022)
Mountain sheep grazing systems provide multiple ecological, socio-economic, and food quality benefits
Agronomy for Sustainable Development, 42
B. Müller, F. Bohn, G. Dressler, J. Groeneveld, Christian Klassert, Romina Martin, Maja Schlüter, Jule Schulze, H. Weise, Nina Schwarz (2013)
Describing human decisions in agent-based models - ODD + D, an extension of the ODD protocol
Environ. Model. Softw., 48
E. Boserup (1966)
The conditions of agricultural growth: The economics of agrarian change under population pressure
DA Ahsan (2010)
309
Mar Resour Econ, 25
R. Duinen, T. Filatova, W. Jager, A. Veen (2016)
Going beyond perfect rationality: drought risk, economic choices and the influence of social networks
The Annals of Regional Science, 57
V. Grimm, U. Berger, D. DeAngelis, J. Polhill, J. Giske, S. Railsback (2010)
The ODD protocol: A review and first update
Ecological Modelling, 221
JG Arbuckle, LW Morton, J Hobbs (2015)
Understanding farmer perspectives on climate change adaptation and mitigation: the roles of trust in sources of climate information, climate change beliefs, and perceived risk
Environ Behav, 47
V. Gaube, C. Kaiser, M. Wildenberg, Heidi Adensam, P. Fleissner, J. Kobler, J. Lutz, A. Schaumberger, J. Schaumberger, Barbara Smetschka, Angelika Wolf, Andreas Richter, H. Haberl (2009)
Combining agent-based and stock-flow modelling approaches in a participative analysis of the integrated land system in Reichraming, Austria
Landscape Ecology, 24
(2021)
Wirkungsweisen sozialer, ökonomischer und ökologischer Faktoren auf Entscheidungen
A. Fanchone, Laetitia Nelson, Nastassja Dodet, L. Martín, N. Andrieu (2022)
How agro-environmental and climate measures are affecting farming system performances in Guadeloupe?: Lessons for the design of effective climate change policies
International Journal of Agricultural Sustainability, 20
C. Egger, C. Plutzar, Andreas Mayer, Iwona Dullinger, S. Dullinger, F. Essl, Andreas Gattringer, A. Bohner, H. Haberl, V. Gaube (2022)
Using the SECLAND model to project future land-use until 2050 under climate and socioeconomic change in the LTSER region Eisenwurzen (Austria)
Ecological Economics
Iwona Dullinger, Andreas Gattringer, J. Wessely, Dietmar Moser, C. Plutzar, W. Willner, C. Egger, V. Gaube, H. Haberl, Andreas Mayer, A. Bohner, Christian Gilli, K. Pascher, F. Essl, S. Dullinger (2020)
A socio‐ecological model for predicting impacts of land‐use and climate change on regional plant diversity in the Austrian Alps
Global Change Biology, 26
C Brown, P Alexander, S Holzhauer, MDA Rounsevell (2017)
Behavioral models of climate change adaptation and mitigation in land-based sectors
WIREs Clim Change, 8
G. Carmona, C. Varela-Ortega, J. Bromley (2013)
Participatory modelling to support decision making in water management under uncertainty: two comparative case studies in the Guadiana river basin, Spain.
Journal of environmental management, 128
J. Groeneveld, B. Müller, C. Buchmann, G. Dressler, C. Guo, N. Hase, Falk Hoffmann, F. John, Christian Klassert, T. Lauf, V. Liebelt, Henning Nolzen, N. Pannicke, Jule Schulze, H. Weise, Nina Schwarz (2017)
Theoretical foundations of human decision-making in agent-based land use models - A review
Environ. Model. Softw., 87
(2021)
at/ defau lt
A. Gobiet, S. Kotlarski (2020)
Future Climate Change in the European Alps
C Troost, T Berger (2015)
Dealing with uncertainty in agent-based simulation: farm-level modeling of adaptation to climate change in Southwest Germany
Am J Agric Econ, 97
Ingo Grass, P. Batáry, T. Tscharntke (2020)
Combining land-sparing and land-sharing in European landscapes
S. Wheeler, A. Zuo, H. Bjornlund (2013)
Farmers’ climate change beliefs and adaptation strategies for a water scarce future in Australia
Global Environmental Change-human and Policy Dimensions, 23
Roopam Shukla, A. Agarwal, K. Sachdeva, J. Kurths, P. Joshi (2018)
Climate change perception: an analysis of climate change and risk perceptions among farmer types of Indian Western Himalayas
Climatic Change, 152
J. Arbuckle, L. Morton, J. Hobbs (2015)
Understanding Farmer Perspectives on Climate Change Adaptation and Mitigation
Environment and Behavior, 47
JG Arbuckle (2015)
205
Environ Behav, 47
S. Mukherjee, A. Mishra, K. Trenberth (2018)
Climate Change and Drought: a Perspective on Drought Indices
Current Climate Change Reports, 4
ML Amadou (2018)
196
Agric Syst, 166
A. Zimmermann, A. Möhring, G. Mack, A. Ferjani, S. Mann (2015)
Pathways to Truth: Comparing Different Upscaling Options for an Agent-Based Sector Model
J. Artif. Soc. Soc. Simul., 18
T. Filatova, D. Parker, A. Veen
Agent-based Urban Land Markets: Agent's Pricing Behavior, Land Prices and Urban Land Use Change
G. Pe’er, A. Bonn, H. Bruelheide, Petra Dieker, N. Eisenhauer, P. Feindt, G. Hagedorn, B. Hansjürgens, I. Herzon, Â. Lomba, Elisabeth Marquard, F. Moreira, Heike Nitsch, R. Oppermann, A. Perino, N. Röder, C. Schleyer, Stefan Schindler, C. Wolf, Y. Zinngrebe, Sebastian Lakner (2020)
Action needed for the EU Common Agricultural Policy to address sustainability challenges
People and nature (Hoboken, N.J.), 2
E. Delay, C. Piou, H. Quénol (2015)
The mountain environment, a driver for adaptation to climate change
Land Use Policy, 48
Stephanie Herring, N. Christidis, A. Hoell, P. Stott (2022)
Explaining Extreme Events of 2020 from a Climate Perspective
Bulletin of the American Meteorological Society
S. Loarie, P. Duffy, H. Hamilton, G. Asner, C. Field, D. Ackerly (2009)
The velocity of climate change
Nature, 462
F. Fogelberg, J. Recknagel (2017)
Developing soy production in central and Northern Europe
A. Lurette, Fabien Stark, Léa Lecomte, J. Lasseur, C. Moulin (2020)
A model to explore which diversity is needed to design sustainable agricultural systems at the territorial level
Agronomy for Sustainable Development, 40
V. Grimm, U. Berger, F. Bastiansen, S. Eliassen, V. Ginot, J. Giske, J. Goss‐Custard, Tamara Grand, Simone Heinz, G. Huse, A. Huth, J. Jepsen, C. Jørgensen, W. Mooij, B. Müller, G. Pe’er, C. Piou, S. Railsback, A. Robbins, M. Robbins, E. Rossmanith, N. Rüger, E. Strand, S. Souissi, R. Stillman, Rune Vabø, U. Visser, D. DeAngelis (2006)
A standard protocol for describing individual-based and agent-based models
Ecological Modelling, 198
Yu Sheng, Shiji Zhao, K. Nossal, Dandan Zhang (2015)
Productivity and Farm Size in Australian Agriculture: Reinvestigating the Returns to Scale
SRPN: Other Food Industry (Topic)
O. Flaten, Gudbrand Lien, M. Koesling, P. Valle, M. Ebbesvik (2005)
Comparing risk perceptions and risk management in organic and conventional dairy farming: empirical results from Norway
Livestock Production Science, 95
Diego Valbuena, P. Verburg, A. Bregt (2008)
A method to define a typology for agent-based analysis in regional land-use research
Agriculture, Ecosystems & Environment, 128
F. Zwiers, L. Alexander, G. Hegerl, T. Knutson, J. Kossin, P. Naveau, N. Nicholls, C. Schär, S. Seneviratne, Xuebin Zhang (2013)
Climate Extremes: Challenges in Estimating and Understanding Recent Changes in the Frequency and Intensity of Extreme Climate and Weather Events
Andreas Mayer, C. Egger, A. Loyau, C. Plutzar, D. Schmeller, V. Gaube (2022)
Mountain pastures increase the resilience of livestock farming to extreme events in the Ariège department, France
Agronomy for Sustainable Development, 42
J. Box, W. Colgan, T. Christensen, N. Schmidt, M. Lund, F. Parmentier, Ross Brown, U. Bhatt, E. Euskirchen, V. Romanovsky, J. Walsh, J. Overland, Muyin Wang, R. Corell, W. Meier, B. Wouters, S. Mernild, J. Mård, J. Pawlak, M. Olsen (2019)
Key indicators of Arctic climate change: 1971–2017
Environmental Research Letters, 14
M. Amadou, G. Villamor, N. Kyei-Baffour (2018)
Simulating agricultural land-use adaptation decisions to climate change: An empirical agent-based modelling in northern Ghana
Agricultural Systems
P. Stott (2016)
How climate change affects extreme weather events
Science, 352
J. Houwer, D. Barnes-Holmes, A. Moors (2013)
What is learning? On the nature and merits of a functional definition of learning
Psychonomic Bulletin & Review, 20
C. Ummenhofer, G. Meehl (2017)
Extreme weather and climate events with ecological relevance: a review
Philosophical Transactions of the Royal Society B: Biological Sciences, 372
Y Sheng, S Zhao, K Nossal, D Zhang (2015)
Productivity and farm size in Australian agriculture: reinvestigating the returns to scale
Aust J Agric Resour Econ, 59
I. Tzouramani, George Alexopoulos, Giorgos Kostianis, L. Kazakopoulos (2013)
Exploring risk management strategies for organic farmers: A Greek case study
Renewable Agriculture and Food Systems, 29
Biogeographical regions in Europe -European Environoment Agency
H. Mitter, M. Larcher, M. Schönhart, Magdalena Stöttinger, E. Schmid (2019)
Exploring Farmers’ Climate Change Perceptions and Adaptation Intentions: Empirical Evidence from Austria
Environmental Management, 63
I. Schmitzberger, T. Wrbka, B. Steurer, G. Aschenbrenner, J. Peterseil, H. Zechmeister (2005)
How farming styles influence biodiversity maintenance in Austrian agricultural landscapes
Agriculture, Ecosystems & Environment, 108
PH Verburg, P Alexander, T Evans (2019)
Beyond land cover change: towards a new generation of land use models
Sustain Gov Transform, 38
M. Scown, M. Brady, K. Nicholas (2020)
Billions in Misspent EU Agricultural Subsidies Could Support the Sustainable Development Goals
One Earth, 3
P. Schreinemachers, T. Berger (2011)
An agent-based simulation model of human-environment interactions in agricultural systems
Environ. Model. Softw., 26
NS Diffenbaugh, D Singh, JS Mankin (2017)
Quantifying the influence of global warming on unprecedented extreme climate events
Proc Natl Acad Sci, 114
A. Serra, J. Duncan (2016)
European farmers and the “Greening” of the CAP: A Critical Discourse Analysis
A. Smajgl, Daniel Brown, Diego Valbuena, M. Huigen (2011)
Empirical characterisation of agent behaviours in socio-ecological systems
Environ. Model. Softw., 26
H. Osbahr, P. Dorward, Roger Stern, S. Cooper (2011)
SUPPORTING AGRICULTURAL INNOVATION IN UGANDA TO RESPOND TO CLIMATE RISK: LINKING CLIMATE CHANGE AND VARIABILITY WITH FARMER PERCEPTIONS
Experimental Agriculture, 47
Ajay Singh (2020)
Soil salinization management for sustainable development: A review.
Journal of environmental management, 277
S. Brunner, A. Grêt-Regamey (2016)
Policy strategies to foster the resilience of mountain social-ecological systems under uncertain global change
Environmental Science & Policy, 66
L Kryszak, M Guth, B Czyzewski (2021)
Determinants of farm profitability in the EU regions
Does farm size matter Agricultural Economics – Czech, 3
R. Newell, Brian Prest, Steven Sexton (2021)
The GDP-Temperature relationship: Implications for climate change damages
Journal of Environmental Economics and Management
Matthias *, M. Ebbesvik, Gudbrand Lien, O. Flaten, P. Valle, H. Arntzen (2004)
Risk and risk management in organic and conventional cash crop farming in Norway
Acta Agriculturae Scandinavica, Section C — Food Economics, 1
A Mayer, C Egger, A Loyau (2022)
Mountain pastures increase the resilience of livestock farming to extreme events in the Ari�ge department
France. Agron Sustain Dev, 42
E. Vogel, M. Donat, L. Alexander, M. Meinshausen, D. Ray, D. Karoly, N. Meinshausen, K. Frieler (2019)
The effects of climate extremes on global agricultural yields
Environmental Research Letters, 14
B. Zanten, P. Verburg, Maria Espinosa, S. Gomez-y-Paloma, G. Galimberti, J. Kantelhardt, M. Kapfer, M. Lefebvre, R. Manrique, A. Piorr, M. Raggi, L. Schaller, S. Targetti, Ingo Zasada, D. Viaggi (2014)
European agricultural landscapes, common agricultural policy and ecosystem services: a review
Agronomy for Sustainable Development, 34
IPCC (2022) Climate change 2022 -impacts, adaptation and vulnerability. Intergovernmental Panel on Climate Change
Y.C. Yang, K. Son, F. Hung, V. Tidwell (2020)
Impact of climate change on adaptive management decisions in the face of water scarcity
Journal of Hydrology, 588
C. Stoate, A. Báldi, P. Beja, N. Boatman, I. Herzon, A. Doorn, G. Snoo, G. Snoo, L. Rákosy, C. Ramwell (2009)
Ecological impacts of early 21st century agricultural change in Europe--a review.
Journal of environmental management, 91 1
C. Troost, T. Berger (2015)
Dealing with Uncertainty in Agent‐Based Simulation: Farm‐Level Modeling of Adaptation to Climate Change in Southwest Germany
Political Economy - Development: Environment eJournal
R. Huber, Hang Xiong, K. Keller, R. Finger (2021)
Bridging behavioural factors and standard bio‐economic modelling in an agent‐based modelling framework
Journal of Agricultural Economics
N. Diffenbaugh, Deepti Singh, J. Mankin, D. Horton, D. Swain, D. Touma, A. Charland, Yunjie Liu, Matz Haugen, Michael Tsiang, B. Rajaratnam (2017)
Quantifying the influence of global warming on unprecedented extreme climate events
Proceedings of the National Academy of Sciences, 114
F. Winsen, Y. Mey, L. Lauwers, S. Passel, M. Vancauteren, E. Wauters (2016)
Determinants of risk behaviour: effects of perceived risks and risk attitude on farmer’s adoption of risk management strategies
Journal of Risk Research, 19
A. Marvuglia, Alper Bayram, Paul Baustert, T. Gutiérrez, E. Igos (2021)
Agent-based modelling to simulate farmers’ sustainable decisions: Farmers’ interaction and resulting green consciousness evolution
Journal of Cleaner Production
M. Janssen, E. Ostrom (2006)
Empirically Based, Agent-based models
Ecology and Society, 11
Dewan Ahsan, E. Roth (2010)
Farmers' Perceived Risks and Risk Management Strategies in an Emerging Mussel Aquaculture Industry in Denmark
, 25
P Borrelli, DA Robinson, P Panagos (2020)
Land use and climate change impacts on global soil erosion by water (2015?2070)
Proc Natl Acad Sci, 117
S. Carpenter, K. Arrow, S. Barrett, R. Biggs, W. Brock, A. Crépin, Gustav Engström, C. Folke, T. Hughes, N. Kautsky, C. Li, G. McCarney, Kyle Meng, K. Mäler, S. Polasky, M. Scheffer, J. Shogren, T. Sterner, J. Vincent, B. Walker, A. Xepapadeas, A. Zeeuw (2012)
General Resilience to Cope with Extreme Events
Barbara Smetschka, V. Gaube (2020)
Co-creating formalized models: Participatory modelling as method and process in transdisciplinary research and its impact potentials
Environmental Science & Policy
D. Läpple, A. Renwick, J. Cullinan, F. Thorne (2016)
What drives innovation in the agricultural sector? A spatial analysis of knowledge spillovers
Land Use Policy, 56
(2020)
Daigneault A (2020) Climate change costs more than we think because people
Maria Bowman, D. Zilberman (2013)
Economic Factors Affecting Diversified Farming Systems
Ecology and Society, 18
(2008)
Klimamodelle, Klimaszenarien und ihre Bedeutung für Österreich
P. Verburg, P. Alexander, T. Evans, N. Magliocca, Ž. Malek, M. Rounsevell, Jasper Vliet (2019)
Beyond land cover change: towards a new generation of land use models
Current Opinion in Environmental Sustainability
Calum Brown, P. Alexander, S. Holzhauer, M. Rounsevell (2017)
Behavioral models of climate change adaptation and mitigation in land‐based sectors
Wiley Interdisciplinary Reviews: Climate Change, 8
C Zagaria, CJE Schulp, M Zavalloni (2021)
Modelling transformational adaptation to climate change among crop farming systems in Romagna
Italy. Agric Syst, 188
K. Trenberth (2018)
Climate change caused by human activities is happening and it already has major consequences
Journal of Energy & Natural Resources Law, 36
F. Otto, Karin Wiel, G. Oldenborgh, S. Philip, S. Kew, P. Uhe, H. Cullen (2018)
Climate change increases the probability of heavy rains in Northern England/Southern Scotland like those of storm Desmond—a real-time event attribution revisited
Environmental Research Letters, 13
P. Borrelli, D. Robinson, P. Panagos, E. Lugato, Jae-E. Yang, C. Alewell, David Wuepper, L. Montanarella, C. Ballabio (2020)
Land use and climate change impacts on global soil erosion by water (2015-2070)
Proceedings of the National Academy of Sciences of the United States of America, 117
Kajsa Sahlin, Johannes Carolus, Karin Greyerz, I. Ekqvist, E. Röös (2022)
Delivering “less but better” meat in practice—a case study of a farm in agroecological transition
Agronomy for Sustainable Development, 42
R. Huber, M. Bakker, A. Balmann, T. Berger, M. Bithell, Calum Brown, A. Grêt-Regamey, Hang Xiong, Q. Le, G. Mack, P. Meyfroidt, J. Millington, B. Müller, J. Polhill, Zhanli Sun, R. Seidl, C. Troost, R. Finger (2018)
Representation of decision-making in European agricultural agent-based models
Agricultural Systems
G. Pe’er, Y. Zinngrebe, J. Hauck, Stefan Schindler, A. Dittrich, S. Zingg, T. Tscharntke, R. Oppermann, L. Sutcliffe, Clélia Sirami, Jenny Schmidt, C. Hoyer, C. Schleyer, Sebastian Lakner (2017)
Adding Some Green to the Greening: Improving the EU's Ecological Focus Areas for Biodiversity and Farmers
Conservation Letters, 10
J. Hiebl, C. Frei (2015)
Daily temperature grids for Austria since 1961—concept, creation and applicability
Theoretical and Applied Climatology, 124
I Grass, P Batáry, T Tscharntke (2021)
Chapter six - combining land-sparing and land-sharing in European landscapes
Adv Ecol Res, 64
(2021)
Grüner Bericht 2021 -Die Situation der österreichischen Land-und Forstwirtschaft Gemäß §9 des Landwirtschaftgesetzes
R. Schattman, D. Conner, V. Méndez (2016)
Farmer perceptions of climate change risk and associated on-farm management strategies in Vermont, northeastern United States
, 4
Sharon Bard, P. Barry (2001)
ASSESSING FARMERS' ATTITUDES TOWARD RISK USING THE "CLOSING-IN" METHOD
Journal of Agricultural and Resource Economics, 26
N. Jurkėnaitė, T. Baležentis (2020)
The ‘pure’ and structural contributions to the average farm size growth in the EU: The index decomposition approach
Ecological Indicators, 117
P. Peltonen-Sainio, L. Jauhiainen, M. Trnka, J. Olesen, P. Calanca, H. Eckersten, J. Eitzinger, Anne Gobin, K. Kersebaum, J. Kozyra, Suresh Kumar, A. Marta, F. Micale, B. Schaap, B. Séguin, A. Skjelvåg, S. Orlandini (2010)
Coincidence of variation in yield and climate in Europe.
Agriculture, Ecosystems & Environment, 139
D. Gawith, I. Hodge, Fraser Morgan, A. Daigneault (2020)
Climate change costs more than we think because people adapt less than we assume
Ecological Economics, 173
C. Zagaria, C. Schulp, M. Zavalloni, D. Viaggi, P. Verburg (2021)
Modelling transformational adaptation to climate change among crop farming systems in Romagna, Italy
Agricultural Systems
G. Pe’er, Y. Zinngrebe, F. Moreira, Clélia Sirami, Stefan Schindler, R. Müller, V. Bontzorlos, Dagmar Clough, P. Bezák, A. Bonn, B. Hansjürgens, Â. Lomba, S. Möckel, G. Passoni, C. Schleyer, Jenny Schmidt, Sebastian Lakner (2019)
A greener path for the EU Common Agricultural Policy
Science, 365
M. Hare (2011)
Forms of Participatory Modelling and its Potential for Widespread Adoption in the Water Sector
Environmental Policy and Governance, 21
D. Davidson (2016)
Gaps in agricultural climate adaptation research
Nature Climate Change, 6
S. Severini, A. Tantari, G. Tommaso (2016)
Do CAP direct payments stabilise farm income? Empirical evidences from a constant sample of Italian farms
Agricultural and Food Economics, 4
P. Verburg, J. Dearing, J. Dyke, S. Leeuw, S. Seitzinger, W. Steffen, J. Syvitski (2016)
Methods and approaches to modelling the Anthropocene
Global Environmental Change-human and Policy Dimensions, 39

Publisher: Springer Journals
Copyright: Copyright © The Author(s) 2023
ISSN: 1774-0746
eISSN: 1773-0155
DOI: 10.1007/s13593-023-00890-z
Publisher site: See Article on Publisher Site

Abstract

European farm households will face increasingly challenging conditions in the coming decades due to climate change, as the frequency and severity of extreme weather events rise. This study assesses the complex interrelations between external framework conditions such as climate change or adjustments in the agricultural price and subsidy schemes with farmers’ decision-making. As social aspects remain understudied drivers for agricultural decisions, we also consider value-based characteristics of farmers as internal factors relevant for decision-making. We integrate individual learning as response to extreme weather events into an agent-based model that simulates farmers’ decision-making. We applied the model to a region in Eastern Austria that already experiences water scarcity and increasing drought risk from climate change and simulated three future scenarios to compare the effects of changes in socio-economic and climatic conditions. In a cross-comparison, we then investigated how farmers can navigate these changes through individual adaptation. The agricultural trajectories project a decline of active farms between −27 and −37% accompanied by a reduction of agricultural area between −20 and −30% until 2053. The results show that regardless of the scenario conditions, adaptation through learning moderates the decline in the number of active farms and farmland compared to scenarios without adaptive learning. However, adaptation increases the workload of farmers. This highlights the need for labor support for farms. Keywords Agent-based modeling · Learning · Scenario analysis · Adaptation · Austria · Agriculture 1 Introduction et al. 2017; IPCC 2022), such as droughts, heavy rainfall, or floods (Otto et al. 2018; Mukherjee et al. 2018; Trenberth Climate change threatens agricultural production not only 2018) that cause soil erosion (Borrelli et al. 2020) and yield through changing climatic conditions such as further increas- declines (Vogel et al. 2019). Human impacts on global ecosys- ing temperatures and changing rainfall patterns (Loarie et al. tems increased the frequency of severe extreme events world- 2009; Box et al. 2019) but also through severe extreme wide (Stott 2016; Ummenhofer and Meehl 2017; IPCC 2022), weather events (Zwiers et al. 2013; Stott 2016; Diffenbaugh which are also an emerging topic in science: While the first Bulletin of the American Meteorological Society, published in 2012, covered the occurrence of six extreme events (Her- Veronika Gaube was the supervisor of the project this paper was written for. ring et al. 2014), the most recent publication not only covers a * Claudine Egger multitude of extreme events but also focuses on their anthro- claudine.egger@boku.ac.at pogenic causes and effects (Herring et al. 2022). In contrast to the long-term impacts of climate change, of which the full Department of Economics and Social Sciences, Institute extent will only become apparent in the medium to long-term of Social Ecology, University of Natural Resources and Life Sciences, Schottenfeldgasse 29, 1070 Vienna, Austria future, extreme weather events have an immediate impact on regional agricultural and socio-economic systems (Gobiet Environment Agency Austria, Spittelauer Lände 5, 1090 Vienna, Austria and Truhetz 2008). This makes it essential to better under- stand the societal coping strategies of farmers to such events. Community Ecology and Conservation, Faculty of Environmental Sciences, Community Ecology Because occurrence and magnitude of extreme events rise and Conservation Research Group, Kamýcká 129, (Stott 2016; IPCC 2022), private and public decision-making CZ-165 00 Prague 6, Czech Republic Vol.:(0123456789) 1 3 39 Page 2 of 17 C. Egger et al. is increasingly challenged to develop suitable response strate- learning based on network experiences or beliefs but learn- gies. The damage potential of extreme events depends not ing from an individual perspective has rarely been studied only on their physical characteristics, but crucially also on (Brown et al. 2017). the response of the affected socio-ecological systems and the In order to investigate spatially explicit effects of indi- ability to adapt and transform through feedback mechanisms vidual decision-making, agent-based modeling represents an (Carpenter et al. 2012). The ability of actors (individuals and approach that is particularly suitable. While system-dynamic organizations) to overcome risks (e.g., food scarcity) imposed models follow a “top-down” approach in which changes of by changes in the natural environment through adaptation to a socio-ecological system emerge based on the overarch- new circumstances has been a key success factor for societal ing systemic relationships, agent-based models (ABMs) development ever since the hunter-gatherer age (Boserup simulate systemic transitions in a “bottom-up” process that 1965). starts with the individual agent. ABMs consider interac- Climate change poses long-time risks to agricultural tions and the feedback dynamics emerging from agent’s production through the gradual change in temperature and reaction to changes in their social and biophysical environ- precipitation and the risks of intermediate losses through ment when simulating the dynamics of a system (Verburg the rising frequency and severity of extreme events (IPCC et al. 2016). This predestines such models for the analysis of 2022). Depending on the governance level, there are dif- socio-ecological systems such as farming systems, as they ferent, complementary response strategies to address these can integrate the complex interrelations between land-use risks. (1) Reduce climate change in a collective societal decision-making and changes in external socio-economic, effort, e.g., through mitigation attempts that limit the tem- biophysical, and climatic conditions (Verburg et al. 2019). perature rise compared to the pre-industrial level to 1.5°C Recent agent-based applications that integrate climate- (Newell et al. 2021). (2) In addition, farmers can pursue related risks (van Duinen et al. 2016; Amadou et al. 2018; individual reaction strategies by adapting their decision- Zagaria et al. 2021) primarily focus on peer influence from making as response to short-term changes such as yield loss social networks. In combination with the agent’s goal orien- due to extreme events, and thereby anticipating changing tation towards profit maximization, these models consider agricultural conditions from a long-ranging changing cli- learning in a social and economic context but neglect other mate. This requires that farmers perceive climate change factors such as political or environmental impacts. While the as risk factor that needs to be considered in their decision- influence of stochastic extreme events on land-use manage- making, and several studies identify a relation of climate ment has been integrated in ABMs (Mayer et al. 2022; Egger change perception and related risk association (Osbahr et al. et al. 2022), the occurrence of extreme events as driver for 2011; Arbuckle et al. 2015; van Winsen et al. 2016; Schatt- individual learning has primarily been modeled for drought man et al. 2016; van Duinen et al. 2016; Shukla et al. 2019; perception and the subsequent transition to irrigated pro- Mitter et al. 2019; Zagaria et al. 2021). duction systems (van Duinen et al. 2016; Yang et al. 2020; Brown et al. (2017) stress that the decisions on adapta- Zagaria et al. 2021). With this study, we want to deepen tion to climate change are shaped by social, economic, the knowledge on learning from extreme events related to environmental, and political contexts. The influence of climate change. social factors such as gender or social relations in agri- As European farmers operate within boundaries set by cultural production systems remains understudied (David- socio-political guidelines such as CAP policies and their cli- son 2016), leaving significant gaps in related literature matic environment (Stoate et al. 2009; Peltonen-Sainio et al. contributions on farmers’ decision-making in relation to 2010; van Zanten et al. 2014; Pe’er et al. 2017), this paper climate change adaptation. The dominant assumptions investigates how farm households can navigate changes of economic rationality lack full apprehension of social coming from their socio-ecological framework conditions complexity and thereby limit the potential for integrating through individual adaptation investigating the following realistic forms of decision-making into land-use-based questions: adaptation models (Groeneveld et al. 2017; Brown et al. 2017). Many agricultural land-use models that focus on How do climatic and socio-economic scenarios impact climate change adaptation do not include any form of the agricultural development trajectories and how do learning (Brown et al. 2017), meaning that the integration farms respond to climate change impacts and changes in of learning in a functional way in which the experience of agricultural subsidy schemes? a changing environment results in adaptation (De Houwer How does the integration of “learning” triggered by et al. 2013). Similarly, Huber et al. (2018) identified learn- extreme events affect the development of farms? ing as underrepresented aspect in a review on European How can individual farms adapt to external framework land-use models. In cases where learning is integrated in changes? Which socio-economic effects does this have land-use models, the focus lies on the integration of social on farms? 1 3 Effects of extreme events on land‑use‑related decisions of farmers in Eastern Austria: the… Page 3 of 17 39 We use the SECLAND ABM (Mayer et al. 2022; Egger irrigation because water availability is generally limited, and et al. 2022) to explore the socio-ecological effects of adap- small-scale farming prevails profitable irrigation. Farmers tation within the existing agricultural production system reported that the recurring droughts and the lack of precipita- through the application of on-site farm improvement man- tion already had negative impacts on agricultural production agement actions. We apply the model to a study region in quantity (e.g., deficits in harvested grassland biomass, lower Eastern Austria and simulate agricultural trajectories for the crop yields) and quality (e.g., of summer grains) (Fessler years 2015–2053 in line with three distinct future scenarios 2021; Petz 2021). to evaluate the impact learning from extreme events has on agro-structural development. The study site, characterized 2.2 Conceptual modeling framework by cropland farming in the Eastern lowlands and livestock farming in the hilly areas of the Western parts (Fig. 1), The purpose of the SECLAND ABM (Egger et al. 2022) is belongs to a region in Austria predicted to experience notice- the study of systemic feedbacks between external changes able impacts of climate change in the upcoming decades in the socio-political, biophysical, and climatic framework (Haslmayr et al. 2018). conditions and the internal dynamics arising from the decision-making of farm agents and its effects on the land- use dynamics of the study region (Dullinger et al. 2020; 2 Methods Mayer et al. 2022). This application of the model features the integration of learning as model update that builds upon 2.1 Study region the interactions between the decision-making process and the occurrence of extreme weather events. Figure 3 depicts The study region consisted of the three districts Oberpul- an overview of the model’s structural relations between lendorf, Neunkirchen, and Wiener Neustadt Land located external and internal dynamics. A thorough description in Eastern Austria (Fig. 2). The area encompasses a total of the SECLAND model is available in Dullinger et al. area of about 2.818km , 107 municipalities, and a popula- (2020), Egger et al. (2022), and Mayer et al. (2022). The tion of about 203,000. Biogeographically, the study region ODD description (Müller et al. 2013) of the here presented is divided: the West situated on the outskirts of the Alps updated version of the model can be found in the supple- belonging to the Alpine and the Eastern parts being part of mentary material (Table SI2). the Continental region (EEA 2017). Elevation ranges from 169 (Northeast, Southeast) to 2056 m a.s.l (Northwest). Cor-2.3 Initialization respondingly, the annual average precipitation is between 400 and 2000 mm/year, and average annual temperatures 2.3.1 Farms and agricultural area range from +4° to +12° (Hiebl and Frei 2016). Farmers in the study region are facing rising risks of weather extremes, The ABM is initialized with 2990 farms that cultivate 76,220 especially droughts, that can hardly be compensated through patches (parcels of land) with a total area of 87,545 hectares Fig. 1 a View into the study region from the Mountain Semmering: showing the transition from the mountainous West towards the lowlands in the Eastern parts. b View over a rapeseed field in the Eastern lowlands (Oberpullendorf). 1 3 39 Page 4 of 17 C. Egger et al. Fig. 2 Location of the study region encompassing the districts Neunkirchen, Wiener Neustadt Land, and Oberpullendorf in the Eastern part of Austria (Natural Earth 2022). Fig. 3 Overview of the modeling process and functional relations between external settings (blue), internal dynamics (green), and the effects of extreme weather events (red) of the SECLAND ABM. 1 3 Effects of extreme events on land‑use‑related decisions of farmers in Eastern Austria: the… Page 5 of 17 39 (ha) agricultural land in 2015. Farm agents are distinguished We used qualitative data to understand farmers’ motiva- according to the following core characteristics: farming type, tions and relevant management actions, which is a common farming style, and production system. There are three farm- approach in agent-based modeling (Janssen and Ostrom ing types: cash crop farms, processing farms that engage 2006; Valbuena et al. 2008; Smajgl et al. 2011). We con- either in pig fattening or suckler production, and (cattle) ducted semi-structured interviews with 20 farmers (11 cash livestock farms focusing on meat or milk production (for crop and 9 livestock farms; Fessler 2021; Petz 2021) dur- an extensive description see Supplementary Tables SI2 and ing summer 2020 to identify and calibrate action sets with SI4). The farming style (Schmitzberger et al. 2005) represents probabilities. In addition, we held an online stakeholder the “value system” of farmers. The model incorporates five workshop with regional experts and decision-makers in July farming styles that are decisive for the decision-making of 2020, to discuss past and current agricultural trends, the role farmers: “innovative” (highly flexible and open to learning), of climate change-related extreme events (recurring droughts “yield optimizer” (prioritizes yield maximization), “sup- and heat waves) and assess the scenario storylines on future port optimizer” (tries to maximize income from subsidies), development of agriculture. The workshop was followed “idealist” (self-realization in farm work), and “traditionalist” up by two expert interviews, where important topics that (continuing the tradition). Without specific information on emerged during the workshop were discussed in more detail proportions, farming styles are randomly assigned among (see Supplementary Table SI6). the model’s farmer population. Farm management is either conventional or organic, which each includes five intensity 2.4 Internal processes levels that differ in labor demand, yields, prices and costs, respectively, gross margins, and subsidies. Farmland consists 2.4.1 Evaluation indicator and decision‑making of croplands and/or grasslands. Cropland is managed in three cropping cycles with sugar beet, rapeseed, and soy as lead Farms in the SECLAND ABM are setup as bounded rational crops. Grassland contains meadows, pastures, and mountain agents (Groeneveld et al. 2017) that pursue well-being rather pastures. Cattle livestock units (lu) are bound to grassland. than profit maximization. We defined well-being as satisfy - Livestock numbers are computed based on the forage provi- ing balance between (minimum) income and (maximum) sion (computed as area × intensity level) of meadows/pas- workload (per fulltime farm worker (full time equivalent, tures held by a farm. Porcine livestock numbers are assigned fte)). Farms invest labor to cultivate their patches (related to farms (in lu) (see Supplementary Table SI1). crops and livestock units) from which they generate agri- cultural income. Farm intensity levels are linked with spe- cific labor input demand and gross margin outputs. Farms 2.3.2 Input data use three satisfaction criteria to annually evaluate their well-being: (1) a minimum agricultural income (per fte) For the setup of the ABM, we relied on the complemen- that is based on reported averages per farm type (Grüner tary combination of quantitative and qualitative data. The Bericht NÖ 2017; SI2). (2) A workload maximum of 1800 main quantitative source was the Invekos (IACS) data h (per fte) that relates to the yearly workload of Austrian that we used for the initialization of farm structure. Farm employees (WKO Statistik 2020). (3) To account for income areas were based on spatially explicit land-use information comparison among peers, the hourly agricultural income on parcels provided by the Invekos GIS-data. The initial- (computed per farming type) needs to exceed a yearly com- ization year 2015 was the first year with comprehensive, puted minimum (average value - 1SD; see Supplementary parcel-specific land-use information. We chose the simula- Table SI2). Depending on the satisfaction (with either or tion period 2015–2053 as such to allow subsequent com- both), farms take a decision among ten possible actions that putations on crop cultures as moving average for the year affect their farm management to improve/maintain satisfac- 2050. Subsidies, farm, and livestock classification, num - tion by increasing/decreasing their workload and/or income bers, and densities (e.g., livestock units per hectare, lu/ha) (Table 1). Based on the synthesis of the qualitative data, all are drawn from the aggregated analysis of reported Inve- relevant actions were assigned with pre-defined probabilities kos numbers. We relied on production-region-related yield that depend on income-workload satisfaction, farming type, data from the bookkeeping farms (Buchführungsbetriebe). and farming style. For missing (either unreported or database secrets) values and to compute gross margins, we used price and cost and 2.4.2 Adaptive decision‑making labor requirement information on state/federal level (BAB 2021) and for socio-economic inputs (farmers age, off-farm Based on strategies from interviews with regional farmers income, etc.) data from Statistik Austria (see Supplementary (Fessler 2021; Petz 2021) as well as from previous pro- Tables SI3–SI5). jects in Austria (Freudenberg 2017; Perzl 2021) and France 1 3 39 Page 6 of 17 C. Egger et al. Table 1 Set of possible land-use decisions for individual farmers in SECLAND. Farms take land-use decisions based on their satisfaction with income and workload; probabilities depend on happiness, farm type, farming style and scenario. Actions Description Act1: Nothing Farms decide not to change their operations and postpone their decision into the next year Act2: Hire farm worker Farms hire an additional farm worker (10% of a full-time equivalent or 180 hours yearly) Act3: Intensification Farms with intensity level ≤ 4 increase their intensity level by +1 Act4: Extensification Farms with intensity level > 1 decrease their intensity level by−1 Act5: Organic production Farms switch their production style from conventional to organic, this is possible once in the 37 years simulation period Act6: Land-use change Farms decide between income gain (maximum income) or time saving (minimal time use) and switch their cropland between crop cycles accordingly Act7: Afforestation Farms plant forest on one of their grassland patches and decrease their suffer-counter Act8: Expansion Farms try to acquire 1 patch from the rental market to expand their farming size Act9: Reduction Farms dismiss additional workers if they are employed; Farms send 1 patch to the rental market to reduce their farming size Act10: Termination Farms pass their remaining areas to the rental market; patches change their patch unit to the one of the rental markets and set their intensity level to 0; Farms then track their “death” year and reason (termination) before switching their activity status to 0 (Mayer et al. 2022), we identified four actions relevant for we decided to link four adaptation types, based on the dis- adaptation: land-use change, organic production, extensifica- tinction made by Mitter et al. (2019), with the model’s five tion, and expansion. For example, farmers mentioned that farming styles. Schmitzberger et al. (2005) classify the farm- due to climate change, yield fluctuations are increasing and ing styles according to their agricultural decision-making, they need to compensate for this by expanding their farm ranking traditionalists as very static, idealists as static, sup- size. We decided against the introduction of new actions, as port and yield optimizers as equally dynamic, and innovative these would have had the potential to introduce new risks, as as very dynamic. Based on this assessment, we associated for example irrigation often presented as adaptation meas- the farming styles with adaptation types, for which we devel- ure to stabilize water supply for agricultural production oped risk-related adaptation functions: with a presumable (Wheeler et al. 2013; van Duinen et al. 2016; Zagaria et al. impact on the decision-making of innovative, lighter and 2021) induces risks such as soil salinization (Singh 2021). equal effects for both optimizer types but minor effects on A stakeholder workshop (Supplementary Table SI6) in the idealists and even less on traditionalists (Fig. 4a). This is study region and expert interviews confirmed that water represented via adaptation function: scarcity and decreasing ground water levels are already a (x) =(x∕37) problem for the region and would be amplified by irrigation projects. Therefore, we focused on the integration of adapta- where i denotes the four adaptation types 1–4 and α depends tion strategies considering on-site farm measures within the on the number of extreme events x. Each occurrence of x existing set of actions. The results of this study, however, increases α persistently. α reaches its maximum of 1 in the i i can give indication for the integration of new actions and (unlikely) case of 37 extreme events, meaning that one event decision-options in future model versions. occurred in each year of the modeling period. The root value In a recent study, Mitter et al. (2019) developed a typol- z symbolizes the inertia towards change; the higher its value ogy for farmers’ attitudes towards adaptation strategies the lower is the willingness to incorporate adaptation (see for a case study in Austria that builds upon their climate Fig. 4a). change perception and related climate risk association. They The α-functions affect the likelihood LAct of the afore- identified four types of farmers’ attitudes (climate change mentioned actions Act (k=4, 5, 6, 8) in the decision-making adaptors, integrative adaptors, cost-benefit calculators, and process, as their algorithm is altered by 1 + α (Fig. 4b). In climate change fatalists) that were determined by farmers’ the case of reaching its maximum value of 1 (37 extreme perception and appraisal of climate change as well as indi- events), the likelihood doubles. LAct (α ) reflects the new like- k i vidual and regional farm characteristics. Depending on their lihood of an action after the multiplication by (1+α ). respective attitudes, they show varying grades of inertia towards adopting climate change adaptation measures. To LAct = LAct ∗(1 + ) k i k i integrate adaptive decision-making in the SECLAND ABM, 1 3 Effects of extreme events on land‑use‑related decisions of farmers in Eastern Austria: the… Page 7 of 17 39 Fig. 4 A Functional relation- ship of for the adaptation types 1–4: the increasing value for the square root z reflects the increasing the resistance (inertia) towards adaptation; each occurrence of an extreme event x has a persisting effect on . b Illustrative visualization of the effects of on decision- making: increases the likeli- hood for the actions Act and Act with the rising number of extreme events x (for x 0–4). As a result of the stakeholder process, we have outlined 2.5 External settings three distinct scenarios to assess future agricultural develop- ment pathways for the study region (Table 2). As baseline, a 2.5.1 Scenarios business-as-usual (BAU) scenario depicted the continuation of current development trajectories with constant subsidies Farms are exposed to several risks such as environmental, and moderate impacts of climate change. In a high subsidy production, market, price, and political risks (Bard and Barry (HS) scenario, subsidies were used as a steering mechanism 2001; Huber et al. 2022). Subsidy schemes as well as mar- to target favorable socio-economic conditions for agricultural ket demand for agricultural products and prices are external production. It describes high societal market intervention to factors in the SECLAND model. While market demand and achieve climate goals and, correspondingly, low impacts from price play a subordinate role for this application, we assess climate change. In contrast, the free market (FM) scenario the variation of subsidy schemes in two distinct scenarios depicted the development of a society that forgoes controlling and focus on the model integration of production risk as intervention and abolishes all subsidies by 2052 to focus on result of climate change-induced extreme weather events. Table 2 Assumptions made to construct the scenarios driving the (business-as-usual), HS (sustainability driven high subsidy), and FM agent-based model: yields, prices, subsidies, preferences, and the (free-market & fast climate change) scenarios. Thresholds for work- probability of extreme events per annum (p.a.) distinguishing BAU load and agricultural income are assumed to remain constant. Scenarios BAU HS FM Yields Decrease: 5–25% Decrease: 0–10% Decrease: 15–50% Prices Constant conv: +15% Constant org: +25% Variable costs Constant Constant Constant Subsidies Constant Increase: conv. 50%/org. 75% in 5-year Decrease: conv./org. 100% in 5-year inter- intervals until 2030, thereafter constant vals until 2030, thereafter constant Workload Increase: 10% Increase: 5% Increase: 10% Extreme events Expected value: 10% p.a. (ran- Expected value: 7.5% p.a. (random nor- Expected value: 15% p.a. (random normal dom normal distribution, SD mal distribution, SD 2.5%) distribution, SD 2.5%) 2.5%) yields: decrease 30% yields: decrease 60% yields: decrease 40% Alterations in Constant Constant Constant, possibility to switch from org. to farmer’s decision conv. production matrices 1 3 39 Page 8 of 17 C. Egger et al. free market competition instead, which we combined with 3 Results more pronounced climate change. The effects of climate change were accounted for in two 3.1 Extreme events ways in the model and the scenarios (Mayer et al. 2022; Egger et al. 2022). For the effect of climate change on yield The 300 simulation runs led to a total of 1,080 extreme development, we used region-specific yield prognoses for events. The MC distribution of extreme events in Fig. 5a low (HS), moderate (BAU), and a large temperature increase shows overlaps between scenarios. On average, each (FM) from (Haslmayr et al. 2018) for the forecast of yields. 37-year run involved 3.73 (BAU), 2.81 (HS), and 4.26 (FM) Extreme weather events related with climate change that extreme events. In all scenarios, runs without extreme events lower the annual harvest also played a crucial role in the occurred. This is contrasted by a maximum of 11 events in modeling framework. While recent climate projections one run of the FM scenario (BAU, 8; HS, 7). The scenario foresee rising intensity of such events for the study region, comparison shows a broader variation with outliers (dia- substantial differences based on climate scenarios are not mond shaped points) for the FM scenario. The years most expected until the second half of the twenty-first century affected by extreme events vary greatly among scenarios (Gobiet and Kotlarski 2020). We therefore integrated sto- (Fig. 5b). While in the BAU scenario, the years 2032 and chastic weather extreme events with minor variations in 2052 were most affected by extreme events in 18 runs; this severity and occurrence among the scenarios (Table 2). number was lower for the HS, where the years 2025 and 2042 are hit in 12 and 13 runs. For the FM scenario, the extreme events occurred earlier, in 2021 and 2024, and in 2.6 Limitations and model evaluation higher number with 21 and 20 runs. For each year and run, the model tracked the occurrence of extreme events and we We relied on random distributions to bridge data gaps. We reused these modeling outputs from the non-adaptive sce- performed 100 Monte Carlo (MC) runs per scenario to take narios, as inputs for the adaptive scenarios. Having similar into account the stochasticity of these assumptions, but remain extreme events within (adaptive and non-adaptive) scenarios within the limits of our computer performance power. We eval- allowed us to study the effects of adaptation. uated the ABM calibration by comparing model results from the “Business-as-usual” (BAU) scenario with historic data for 3.2 Farms and areas key variables such as the number of active farms, agricultural area, or livestock numbers (Supplementary Figure SI7A and The mean across all runs and scenarios showed a decline B), in line with the validation by results approach proposed from the initial number of 2990 active farms in 2015 to 2059 by Troost and Berger (2015). To ensure transparency, we fol- in 2053. In the BAU scenario, the number of active farms lowed the ODD protocol (Grimm et al. 2006, 2010; Müller decreased by 31%, which was similar to the HS scenarios et al. 2013) to document model inputs and calibration. (2,085 farms, −30%). The FM scenario showed a stronger Fig. 5 Comparison of Monte- Carlo (MC) results of extreme events. a Box-plots showing the distribution of extreme events with mean numbers of occurrence per run varying between 3 and 4. b Statistical overview of years affected by extreme events compared for all MC runs and scenarios; the size of the circles is used as indicator for the occurrence of extreme events (small=low, medium=intermediate, large=high). Scenarios assessed: business-as-usual (BAU), a sustainability-driven high subsidy (HS) and low- subsidy free market (FM) scenario. 1 3 Effects of extreme events on land‑use‑related decisions of farmers in Eastern Austria: the… Page 9 of 17 39 decline to (1873 farms, −37%) in 2053 (Fig. 6a). The results The scenario outcomes reflected the range of results for agri- of the adaptive scenarios illustrated similar farm abandon- cultural area, as the difference between highest (HSA) and ment trends, but at lower rates, with 2141 (−28%) active lowest (FM) means of cultivated cropland in 2053 marks farms in 2053 in BAUA, and comparable lower, respectively, 11% of the total cropland area 2015. Grassland depicted higher numbers for HSA (2173; −27%) and FMA (1941; less scenario variation with consistent decreases of about −35%). In bivariate comparison for 2053, the adaptive −32% to −35% until 2053, again with lower rates (∆3%) of scenarios depicted significantly (p <0.01) higher values of abandonment in the adaptive scenarios (see Supplementary about +4% in MC means of active farms. The split of mean Table SI8). The highest area of grassland cultivated in 2053 active farms in 2053 by farm type (Fig. 6b) revealed higher was observed in the BAUA scenario. Compared to cropland, scenario sensitivity for farm types, with numbers varying the range of results for grasslands was less diverse, with dif- between −37% (HSA) and −61% (FM) for processing, −29% ferences of about 6% of 2015 grassland area. (HSA) and −39% (FM) for cash crop, and −25% (BAUA) and −33% (FM) livestock farms. The adaptation-related dif- 3.3 Eec ff ts from adaptive management ferences of mean active farms split by farm type varied from 3 (livestock farms) up to 6% (processing farms). We used four specific indicators (Fig. 7a–d) to assess the Corresponding to the decline of the numbers of farms, impact of the adaptation measures extensification, land-use also a decline of agricultural area was detected with change, organic farming, and expansion. The development means ranging from 69,848 (HSA) to 61,568 ha (FM) in trajectories for the shares of extensive areas and the shares of the year 2053 when compared to the initial 87,545 ha in organic farms in the adaptive scenarios highlight the influence 2015 (Fig. 6c). The mean decrease of agricultural areas and of extensification and organic farming (Fig. 7a, c). Extensive assumed subsequent forest transition of abandoned agricul- areas ranged from 30% to 40% in 2053, with noticeably higher tural areas (Fig. 6d) was considerably lower in the BAU shares for all adaptive scenarios (Fig. 7a). The corresponding (−25%) and HS (−23%) scenarios than in the FM (−30%). results of the FMA and BAU scenario underlined the effect of Again, the adaptive scenarios depicted differences of about adaptation. Across all scenarios, increasing shares of organic 4%, meaning fewer agricultural areas abandoned in 2053 and farms until 2053 was a dominant future development trend therefore also less forest transition. The decrease of cropland (Fig. 7c). Compared to 2015, the proportion of organic farms was smaller compared to grassland. Cropland declined by nearly doubled for the BAU and HS scenarios until 2053, −17% (HSA) to −28% (FM), with higher cropland cultiva- especially in the adaptation scenarios where this trend was tion in the adaptive scenarios. Trends were similar in all more pronounced. Despite low, absolute numbers in the FM scenarios, with stronger pronunciation for the FM scenarios. scenarios in 2053 (BAU, 545; BAUA, 590; see Supplementary Fig. 6 Comparison of the simulation results between scenario runs without adap- tation (BAU, HS, FM) and adaptation scenarios (indicated by the suffix A in the scenario code: BAUA, HSA, and FMA). Distribution of the Monte Carlo (MC) results 2053 for (a) active farms and (c) agricultural area; comparison of initial 2015 num- bers against 2053 means for (b) active farms split by farm type (proc=processing, crop=cash crop, ls= livestock) and (d) mean areas split by land-use type (forest, grass=grassland, crop=cropland). Scenarios assessed: Business-as-usual (BAU), a sustainability-driven high subsidy (HS), and low- subsidy free market (FM) scenario. 1 3 39 Page 10 of 17 C. Egger et al. Fig. 7 Cross comparison of four indicators related to the adapta- tion actions: (a) for extensifica- tion the mean development of shares of extensive (intensity level <3) areas; (b) for land- use change the mean cropland shares split by crop cycle in 2053; (c) for organic production the mean development of shares of organic farms; (d) for expan- sion the mean share of farms > 20ha in 2053 (with 20 ha being the Austrian average of agri- cultural area per farm in 2016). Scenarios assessed: business-as- usual (BAU), a sustainability- driven high subsidy (HS), and low-subsidy free market (FM) scenario; adaptation scenarios are indicated by the suffix A. Table SI8), nevertheless, the relative share of organic farms the scenario conditions were the decisive factor for the devel- increased even in these scenarios. opment of the mean share of farms with insufficient income In contrast, the comparison of the initial values of 2015 (Fig. 8a). While in the BAU settings the rate of farms with and with 2053 means for selected indicators showed marginal insufficient income remained stable over time, the favorable effects for the adaptation measures land-use change and expan- conditions of the HS scenarios slightly decreased this rate. In sion (Fig. 7b, d). The cropland shares among the crop cycles contrast, in the FM scenarios, the mean rate of farms unsatis- (cr1, cr2, cr3; see Supplementary Table SI1) revealed similar fied with income increased to nearly 50% by 2053. development for the BAU and HS scenarios with expanding The comparison of the development of the mean share of shares for crop cycles 2 and 3, contrary to the FM scenarios farms with excess workload showed similar scenario trends with higher shares of cr1 in 2053 (Fig. 7b). The similarity but consistently higher rates for the adaptive scenarios between the adaptive and non-adaptive scenarios showed that (Fig. 8b). Until about 2025 across scenarios, farms pursued the development of the crop cycles was primarily driven by the a reduction of workload with decreasing shares of farms scenario conditions. These favored the cropping of soy (lead that work more than 1800 h/fte/year. From then onwards, crop cr2) and rapeseed (lead crop cr3) in the BAU/HS scenar- the scenario conditions had a stronger effect, which was ios and upholded the production of root crops (lead crop cr1) in reflected in the continued declines under the favorable the FM scenarios. The shares of farms with a farm size of more conditions in the HS scenarios, decelerated and stagnant than 20ha remained merely stable among scenarios (Fig. 7d). development in the BAU scenarios, and a trend reversal While in 2015 about 44% of farms exceeded 20ha, this number with increasing numbers in the FM scenarios. In all adap- increased slightly towards 46%–47% in 2053. Interestingly, tive scenarios, the trajectories depicted higher workloads the results depicted the highest farm sizes in the HS scenarios, per full-time equivalent for the period between 2025 and compared to the lowest shares in the FM scenario, which indi- 2053; for the FM scenarios, this was already observed from cated that the high subsidy volumes and prices allowed more 2020 onwards. In both graphs, the impact of extreme events farms to increase their farm size. was visible in the punctual spikes of the trajectories. 3.4 Income and workload satisfaction 4 Discussion The impact of learning on farmer satisfaction was repre- sented by its impact on agricultural income and workload, This paper investigates the impact of learning from extreme with a positive effect for income (higher absolute numbers) weather events on land-use decision-making and subse- and a negative effect for workload (Fig. 8a–b). We found that quently agricultural development. For this purpose, we 1 3 Effects of extreme events on land‑use‑related decisions of farmers in Eastern Austria: the… Page 11 of 17 39 Fig. 8 Development of shares of farms 2015–2053 with insuf- ficient income (a), respectively, the share of farms excess workload (full-time workers (fte) working more than 1800 h/ year) (b). Scenarios assessed: business-as-usual (BAU), a sustainability-driven high sub- sidy (HS) and low-subsidy free market (FM) scenario; adapta- tion scenarios are indicated by the suffix A. integrated learning into the SECLAND ABM and simulated case study under climate change in New Zealand, Gawith future trajectories for three scenarios for a rural region in et al. (2020) identified that socio-economic constraints on eastern Austria. Despite the simulated, unilateral decline farmers’ decision-making resulted in considerably lower of active farms and agricultural area until 2053, the results adaptation rates compared to pure profit maximization, showed that adaptation made a difference by buffering this and concluded that economic models underestimated the evolution. However, its impact varied depending on the sce- realistic cost of adaptation. Although adaptation had a nario conditions and increasing labor demand emerged as positive effect on the number of active farms and agricul- limiting factor for adaptation. tural area under cultivation in 2053, the SECLAND ABM With the integration of learning in the SECLAND ABM, depicted a realistic picture on (social and economic) costs we build on the two essential factors of the decision-making and revealed that this did not translate to the well-being process: the farming style symbolizing the intrinsic motiva- of farms. Income satisfaction was predominantly driven tion of farmers, and well-being (a balance between income by socio-politic scenario conditions, and partially affected and workload). It is common in agent-based modeling to by the occurrence of extreme events but independent of address risk-related adaptation of an agent through imita- adaptation. This confirms empirical analyses on Italian, tion or orientation within its social network (van Duinen Hungarian, Slovakian, and Swiss farmers (Severini et al. et al. 2016; Amadou et al. 2018; Gawith et al. 2020; Zagaria 2016; Brunner and Grêt-Regamey 2016; Bojnec and Fertő et al. 2021; Marvuglia et al. 2022; Huber et al. 2022). How- 2019), demonstrating that direct payments from the first ever, this acknowledges only on the social aspect of learn- pillar of the Common Agricultural Policy (CAP) increase ing, while the SECLAND ABM integrates learning as socio- and stabilize farm income by partially offsetting produc- ecological process that builds on the farming style and the tion and market volatilities (Lurette et al. 2020). Further- occurrence of the extreme events. This model design brings more, our simulations highlighted workload as trade-off two advantages. First, the decision to model varying degrees factor for the decision-making of farms. On the one hand, of inertia towards adaptation depending on the farming style the already high initial burden presented a strong motive integrates heterogeneity among the farmer population. Sec- for farms to purse a reduction in their workload. Limita- ond, the increasing effect of extreme events on adaptation tion in the production factors capital and labor represent desire allows the consideration of gradual transition to adap- important constrains for diversification of farms, espe- tation but avoids conglomerated network effects. cially concerning family farms and the seasonal peaks While SECLANDs farm agents consider workload and during harvest season (Bowman and Zilberman 2013). On income as decisive factors for their actions, farmers are the other hand, did adaptation result in a higher workload commonly parameterized to seek profit maximization for farms, which is connected to the dominant adaptation (Filatova et al. 2009; Schreinemachers and Berger 2011; measures organic farming and extensification, as organic Zimmermann et al. 2015; van Duinen et al. 2016; Ama- and extensive farming (small-scale farming in general) are dou et al. 2018; Yang et al. 2020; Zagaria et al. 2021; associated with higher labor demand then conventional or Huber et al. 2022). Consequently, the consideration of highly mechanized industrial farming (BAB 2021). This non-financial aspects or limitations is underrepresented means that farms only managed to compensate environ- in the decision-making processes of farm agents. For a mental risks by increasing their individual workload. 1 3 39 Page 12 of 17 C. Egger et al. structure with scattered fields) and legal reasons (conflicts 4.1 Adaptation strategies with the water regulation framework, already existing water scarcity). As a result, this study offered an alternative We followed a participatory modeling approach that involved stakeholders into several phases of the project from approach towards climate change adaptation, compared to the generally in literature proposed introduction of irriga- data collection and scenario outline to the model definition (Hare 2011). On the one hand, participatory modeling can tion systems (Wheeler et al. 2013; van Duinen et al. 2016; Amadou et al. 2018; Yang et al. 2020; Zagaria et al. 2021). increase modeling performance and results (Gaube et al. 2009; Smetschka and Gaube 2020). On the other hand, it The simulation results showed varying degrees of effects from the adaptation measures. Future cropping patterns gives heterogenous stakeholders the opportunity to use the option space created by future scenarios as base for discus- depicted a shift from sugar beet towards rapeseed and soy cultivation, mainly influenced by scenario settings. In Europe, sions and to compare their future management options (Car- mona et al. 2013). Due to the COVID-19 pandemic, we had rapeseed is an essential crop for the production of biodiesel (van Duinen et al. 2016), which is as substitute for fossil fuels, to adapt the planned stakeholder activities (adopt one online workshop followed by expert interviews instead of recur- especially important in sustainability scenarios. Independent of the scenario, we observed an increase of soy cultivation in ring in person meetings), which unfortunately had an impact on the involvement of local stakeholder. Nevertheless, we 2053. The production of domestic, high-quality GMO-free soy has become a lucrative market for European farmers, managed to capture essential topics in our exchanges with regional stakeholders. During the interviews, farmers men- which competes with the cultivation of established crops in terms of cropland demand. Especially for organic production, tioned price pressure, the need to expand production, and subsequently rising workload of farm workers as well as soy is attractive due to its ability to fix atmospheric nitrogen in organic crop rotations; moreover, it generates good income concerns about the negative impacts from recurring droughts on agricultural production in the study region. Furthermore, due to its high price (Fogelberg and Recknagel 2017). Despite a trend towards farm enlargement within EU the discussion on future agricultural development during the stakeholder workshop revealed the desire for experiments countries (Jurkėnaitė and Baležentis 2020), the simula- tions suggested only a marginal increase for the share of and alternative support approaches as part of substantial reforms for the current agro-environmental framework to farms exceeding the 2016 Austrian average farm size of 20 ha (Grüner Bericht 2021). However, there are other factors promote a fundamental transformation of agriculture. We deducted four adaptation strategies organic produc- to consider for farms in terms of productivity and profit- ability than just possible economies of scale from a larger tion, extensification, expansion, and land-use change based on farm management strategies from the 20 interviews in farm size. An empirical analysis from Australia depicts that, especially for smaller farms, access to advanced pro- the study region supplemented with additional 40 inter- views with farmers in Austria and France. With the shift to duction technology plays a decisive role in increasing farm productivity (Sheng et al. 2015). For European countries, organic and extensive production, farms can decrease the pressure on production quantity; additionally, higher prices the farming type also plays a decisive role, as diverse and mixed farms oppose farm size growth, while specialized can increase their gross margins (Resare Sahlin et al. 2022; Garmendia et al. 2022). As farms in this application of crop and livestock farms showed the highest growth rates in the last decade (Jurkėnaitė and Baležentis 2020). On the the SECLAND ABM only consider the agricultural income as satisfaction criteria, they cannot substitute farm income other hand, the consideration of workload threshold in the decision-making is a limiting factor in the expansion of farm with off-farm work (Brunner and Grêt-Regamey 2016). Instead, they can decrease their external input costs (e.g., size. The analysis of EU Farm Accountancy Data Network (FADN) highlights the ability to rely on family labor capac- fertilizer, machinery) through an extensification strategy. Additionally, through organic production, extensification, ity as key determinant of farm profitability for small and medium farms (Kryszak et al. 2021). and by expansion of their farm size, farms can increase the inflow of subsidies from both pillar 1 (direct payments) and The results revealed the shift to organic production and decrease of production intensity as most decisive actions pillar 2 (rural and structural development) of the CAP frame- work. Land-use change gives farms a chance to change their for land-use and farm management. The dominant trend of increasing organic farms, which was even more pronounced agricultural products, and switch between crops and crops cycles, to adapt to a changing climatic and economic condi- in the adaptive versions, aligned well with historic devel- opments. Since Austria’s accession into the EU in 1995, tions (Wheeler et al. 2013; Marvuglia et al. 2022). We did not consider the strategic reduction of farm area (Wheeler the share of organic farms has been rising strongly. Austria reached the EU goal of 25% of organic agricultural areas et al. 2013). The exchange with local stakeholders revealed early on that irrigation systems are not a viable solution for in 2030, already in 2020 with a share of 26.4%, placing it among the EU countries with the highest shares of organic the region mainly for economic (small-scale agricultural 1 3 Effects of extreme events on land‑use‑related decisions of farmers in Eastern Austria: the… Page 13 of 17 39 farming (Kummer et al. 2021). Additionally, several stud- The earlier an extreme event occurs the sooner it triggers ies identified organic farmers as young, educated, better adaptive decisions of farmers, and the longer it influences informed, and less risk averse then conventional famers their decision-making. On the other hand, every extreme (Koesling et al. 2004; Flaten et al. 2005; Tzouramani et al. event has a negative impact on farm income and farmers’ 2014; Schattman et al. 2016; Läpple et al. 2016; Mitter et al. satisfaction, as farmers have to keep investing in damage 2019), meaning that they have higher sensibility for climate control limiting their options. We deliberately decided to change and are more willing to adapt their production; there- apply slight variations in the stochastic extreme event func- fore, ultimately more of them will exist in 2053. tions to capture these offsetting effects, which can be seen The comparison of the results for agricultural area showed in the compilation of the scenario results. In the HS sce- that losses of extensive land occur in all scenarios, although nario, there was less incentive for adaptation due to the low the extent of the decline differed between the scenarios. We frequency of extreme events. In the FM scenario, the high observed varying land sharing and land sparing strategies rate and severity of extreme events in combination with (Grass et al. 2021), as the reduction of subsidies in the FM declining subsidies decreased the efficiency of adaptation scenarios reduced total farmed area, but the remainder was actions. The apparent difference between the results with cultivated at higher intensity. By contrast, higher subsidies in and without adaptation in the BAU scenarios highlighted the HS scenarios kept more agricultural area under cultiva- efficient adaptation based on the interplay between subsidy tion, but this land was farmed with lower intensity. Irrespec- volume and extreme event frequency. Therefore, if adapta- tive of scenario narrative, adaptation led to higher shares of tion is implemented as reaction to a negative event, any extensive areas and generally more agricultural area in 2053. circumstances that lower farmers’ distress (e.g., compen- This has two implications for the sustainability of farm man- sation payments, “mild” climate change) also reduce their agement. First, the reduction of external inputs such as fer- perceived risk and consequently their desire for adapta- tilizers and pesticides serves as a risk management strategy tion. In addition, there is the temporal aspect of adaptation, (Ahsan and Roth 2010; Tzouramani et al. 2014; Schattman which have a greater effect at lower costs, the earlier they et al. 2016) that reduces a farmers’ exposure to fluctuating are implemented (Brunner and Grêt-Regamey 2016). production costs (Serra and Duncan 2016; Bojnec and Fertő For a shift towards ex-ante adaptation, the negative stimu- 2019). Second, organic and extensive farming reduces these lus must be substituted by an alternative driver. One way input costs but increases the revenues (prices, subsidies) to overcome this dilemma provides the focus on positive simultaneously. In addition, organic farmers consider envi- incentives as motivator for the implementation of adapta- ronmentally friendly agricultural practices to be effective tion measures ex-ante. Such a change in perspective would measures for climate change adaptation (Schattman et al. dissolve the constraint of climate-related risk perception and 2016; Mitter et al. 2019), which was confirmed by regional broaden the group of potentially adopting farmers. The sce- farmers during our interviews (Fessler 2021; Petz 2021). nario comparison provides valuable implications for socio- political measures that could guide such as result-based 4.2 From ex‑post to ex‑ante adaptation payments approach. The success and efficiency of the CAP measures and its reforms have been broadly discussed and Our results projected the decline of about one-third of the especially criticized for the shortcoming in support for envi- initially active farms accompanied by the loss of one-quar- ronment- and climate-friendly practices, despite its major ter of the initial agricultural area up until the year 2053. financial volume (Serra and Duncan 2016; Pe’er et al. 2019, Although a decline in agricultural production seems inevi- 2020; Scown et al. 2020). How effectiveness of the CAP table, the scenario comparison showed that the setting of can be increased in a target-oriented manner has yet to be the external framework conditions can slow down or accel- defined (Scown et al. 2020). To add to this discussion, we erate this trend. Independent of the scenario conditions, our investigate the potential impact of socio-political measures results highlighted the positive effect of adaptation on the on adaptation decision-making and agricultural develop- number of active farms as well as agricultural area. This ment in the “high subsidy” and the “free market” scenarios. corresponds with findings of Zagaria et al. (2021) and van The comparison of our results indicates that financial incen- Duinen et al. (2016), who based the adaptation mechanism tives alone are insufficient: despite the significant increase on farmers’ perception of drought risk for adaptation, for in subsidies in the HS scenarios, the superior results from case studies in Italy and the Netherlands. Whether adap- the adaptive BAU scenario depict comparable outcomes in tation measures incorporate the transition of an agricul- active farms and agricultural area and illustrate the potential tural production system or not, modeling frameworks that positive effect of adaptation on agro-structural development. build learning and adaptation on climate change-related On the other hand, synergies to existing farm manage- extreme events, first need a negative impact to create an ment practices play a role in the adoption of agro-environ- ex-post response that changes farmers’ decision-making. mental and climate measures of farmers (Fanchone et al. 1 3 39 Page 14 of 17 C. Egger et al. 2022). The findings of this study are in line with the EU for farmers. Furthermore, the comparison of the results farm-to-fork strategy that builds on organic and low-input against the socio-politic framework conditions revealed farming with the aim to strengthen regional production and the limited scope of financial incentives. consumption (Kummer et al. 2021). Our analysis also high- We identified the need for agricultural subsidy schemes lights workload as limiting factor for the decision-making of that increase the scope of action for farms, by supporting farms as their already high workload increased further with them in workload reduction in the long run. To increase successful adaptation. This crystallizes workload reduc- the resilience of farms and achieve successful long-term tion as an essential factor to incentivize management shifts. shifts towards sustainable development paths in agricul- Socio-political measures that provide temporal and seasonal ture, individual and societal efforts must complement each farm labor support have the potential to increase the sus- other. Future directives of the CAP should therefore focus tainability of farms, not only farm profitability but also the not only on economic and ecological but equally on social social well-being of farm households. objectives in order to create sustainable framework condi- For this first application of learning in the SECLAND tions for European farmers. ABM, we concentrated solely on the integration of learn- Supplementary Information The online version contains supplemen- ing and on aggregated dynamics on study region level. The tary material available at https://doi. or g/10. 1007/ s13593- 023- 00890-z . analysis of spatial heterogeneity as driver for adaptation (Delay et al. 2015) was beyond the scope of this paper, as Acknowledgements We thank all the local farmers and regional we focused on the comparison of results with and without experts for their participation in interviews and the stakeholder work- shop. Furthermore, we want to acknowledge the work of Martina adaptation. Nevertheless, we plan to address the implications Perzl, Bernadette Fessler, and Sabine Petz for conducting the farmer of learning in a heterogenous study region in future research. interviews. Last but not the least, we also thank the two anonymous reviewers for their excellent feedback that really helped to improve the manuscript. 5 Conclusion Authors' contributions Conceptualization: C.E., A.M., V.G.; model design: C.E.; analysis: C.E., A.M., V.G.; writing—original draft: European farmers are embedded in complex socio-ecologi- C.E.; writing—review and editing: all authors; funding acquisition: cal systems. Their decision-making is influenced not only by C.P, S.S., V.G. restrictions set by farm production capacity, but they also have Funding Open access funding provided by University of Natural to comply with the guidelines lined out by the national and Resources and Life Sciences Vienna (BOKU). This study was funded international subsidy schemes. Additionally, in the upcoming by the Climate and Energy Fund Austria as part of the project CHESS decades, farmers will face the challenge to adapt to chang- - Managing Climate cHange impacts on land use and EcoSystem Ser- vices, ACRP11 - CHESS - KR18AC0K14615. ing climatic conditions that have long-term effects as well as abrupt extreme events that affect agricultural production. Data availability The agent-based module (ABM) is written in Net- For this study, we integrated learning through climate logo 6.0.2. The open-source software is accessible on https://ccl. nor th change adaptation into the SECLAND ABM. In a novel wester n.edu/ ne tlogo/ inde x.shtml . The model code is not publicly avail- able but a thoroughly description can be found in the supplementary approach, we linked the effects of extreme events (envi- material. ronmental) and value-based characteristics (social and eco- A request for use for the Austrian IACS data can be made with nomic) on farmers’ decision-making to integrate learning the ministry for agriculture, regions, and tourism via https:// dafne. at/. as socio-ecological process into the model. This allowed The socio-economic data are publicly available from Statistik Aus- tria https://www .s tatis tik.at/ and Bundesanstalt für Agrarwirtschaft und us to use the particular advantage of agent-based modeling Bergbauernfragen BAB - Deckungsbeiträge und Kalkulationsdaten and simulate the effects of learning on socio-economic farm (agrarforschung.at). structure development as well as its spatial impacts on land- use dynamics across diverse future scenarios. Declarations From the scenario analysis, we conclude that adapta- Research involving human participants The study was conducted tion has a positive impact on both the number of active according to the guidelines laid down in the 1964 Helsinki Declara- farms and the agricultural area in 2053. However, suc- tion and its amendments. cessful adaptation does not necessarily mean a shift to an irrigation system, which is an infeasible solution in Consent to participate (include appropriate statements) All study par- ticipants gave informed consent to participate in the study. small-scaled, or grassland-dominated farming systems. We identified organic and extensive farming as successful and Consent for publication The authors affirm that human research par - well-established adaptation strategies to climate change. ticipants provided informed consent for this publication. Our results also depicted that adaptation was accompanied Competing interests The authors declare no competing interests. by an additional increase of the already high workload 1 3 Effects of extreme events on land‑use‑related decisions of farmers in Eastern Austria: the… Page 15 of 17 39 Open Access This article is licensed under a Creative Commons Attri- basin, Spain. J Environ Manage 128:400–412. https:// doi. org/ bution 4.0 International License, which permits use, sharing, adapta-10. 1016/j. jenvm an. 2013. 05. 019 tion, distribution and reproduction in any medium or format, as long Carpenter SR, Arrow KJ, Barrett S et al (2012) General resilience to as you give appropriate credit to the original author(s) and the source, cope with extreme events. Sustainability 4:3248–3259. https:// provide a link to the Creative Commons licence, and indicate if changes doi. org/ 10. 3390/ su412 3248 were made. The images or other third party material in this article are Davidson D (2016) Gaps in agricultural climate adaptation research. included in the article's Creative Commons licence, unless indicated Nat Clim Change 6:433–435. https://doi. or g/10. 1038/ nclim ate30 otherwise in a credit line to the material. If material is not included in 07 the article's Creative Commons licence and your intended use is not De Houwer J, Barnes-Holmes D, Moors A (2013) What is learning? permitted by statutory regulation or exceeds the permitted use, you will On the nature and merits of a functional definition of learn- need to obtain permission directly from the copyright holder. To view a ing. Psychon Bull Rev 20:631–642. https:// doi. or g/ 10. 3758/ copy of this licence, visit http://cr eativ ecommons. or g/licen ses/ b y/4.0/ .s13423- 013- 0386-3 Delay E, Piou C, Quenol H (2015) The mountain environment, a driver for adaptation to climate change. Land Use Policy 48:51–62. https:// doi. org/ 10. 1016/j. landu sepol. 2015. 05. 008 Diffenbaugh NS, Singh D, Mankin JS et al (2017) Quantifying the References influence of global warming on unprecedented extreme climate events. Proc Natl Acad Sci 114:4881–4886. https:// doi. org/ 10. 1073/ pnas. 16180 82114 Ahsan DA, Roth E (2010) Farmers’ perceived risks and risk manage- Dullinger I, Gattringer A, Wessely J et al (2020) A socio-ecological ment strategies in an emerging mussel aquaculture industry in model for predicting impacts of land-use and climate change on Denmark. Mar Resour Econ 25:309–323. https:// doi. org/ 10. regional plant diversity in the Austrian Alps. Glob Change Biol 5950/ 0738- 1360- 25.3. 309 26:2336–2352. https:// doi. org/ 10. 1111/ gcb. 14977 Amadou ML, Villamor GB, Kyei-Baffour N (2018) Simulating agri- EEA (2017) Biogeographical regions in Europe - European Environo- cultural land-use adaptation decisions to climate change: an ment Agency. https:// www. eea. europa. eu/ data- and- maps/ figur empirical agent-based modelling in northern Ghana. Agric es/b iogeo graph ical-r egion s-i n-e urope-2. Accessed 20 May 2021 Syst 166:196–209. https://d oi.o rg/1 0.1 016/j.a gsy.2 017.1 0.0 15 Egger C, Plutzar C, Mayer A et al (2022) Using the SECLAND model Arbuckle JG, Morton LW, Hobbs J (2015) Understanding farmer to project future land-use until 2050 under climate and socio- perspectives on climate change adaptation and mitigation: the economic change in the LTSER region Eisenwurzen (Austria). roles of trust in sources of climate information, climate change Ecol Econ 201:107559. https://d oi.o rg/1 0.1 016/j.e colec on.2 022. beliefs, and perceived risk. Environ Behav 47:205–234. https:// doi. org/ 10. 1177/ 00139 16513 503832 Fanchone A, Nelson L, Dodet N et al (2022) How agro-environmental BAB (2021) IDB Deckungsbeiträge und Kalkulationsdaten - Bunde- and climate measures are affecting farming system performances sanstalt für Agrarwirtschaft und Bergbauernfragen. https://idb. in Guadeloupe?: lessons for the design of effective climate agrar forsc hung. at/ defau lt. html. Accessed 27 Jan 2021 change policies. Int J Agric Sustain 20:1348–1359. https:// doi. Bard SK, Barry PJ (2001) Assessing farmers’ attitudes toward risk org/ 10. 1080/ 14735 903. 2022. 21368 36 using the “closing-in” method. J Agric Resour Econ 26:248– Fessler BM (2021) Entscheidungsverhalten in der Grünlandbe- 260. https:// doi. org/ 10. 22004/ ag. econ. 31153 wirtschaftung von Landwirten und Landwirt*innen aus den Bojnec Š, Fertő I (2019) Do CAP subsidies stabilise farm income in politischen Bezirken Neunkirchen und Wiener Neustadt Land. Hungary and Slovenia? Agric Econ 103–111. https:// doi. org/ Alpen-Adria-Universität Klagenfurt, Klima- und Umweltschutz- 10. 17221/ 190/ 2018- AGRIC ECON maßnahmen im Fokus Borrelli P, Robinson DA, Panagos P et al (2020) Land use and cli- Filatova T, Parker D, van der Veen A (2009) Agent-based urban land mate change impacts on global soil erosion by water (2015– markets: agent’s pricing behavior, land prices and urban land use 2070). Proc Natl Acad Sci 117:21994–22001. https:// doi. org/ change. Journal of Artificial Societies and Social Simulation 3:. 10. 1073/ pnas. 20014 03117 https:// www. jasss. org/ 12/1/ 3. html Boserup E (1965) The conditions of agricultural growth - the eco- Flaten O, Lien G, Koesling M et al (2005) Comparing risk perceptions nomics of agrarian change under population pressure. Georg and risk management in organic and conventional dairy farming: Allen and Unwin Ltd. empirical results from Norway. Livest Prod Sci 95:11–25. https:// Bowman M, Zilberman D (2013) Economic factors affecting diver - doi. org/ 10. 1016/j. livpr odsci. 2004. 10. 014 sified farming systems. Ecol Soc 18:. https:// doi. org/ 10. 5751/ Fogelberg F, Recknagel J (2017) Developing soy production in central ES- 05574- 180133 and northern Europe. Legum Crop Syst 109–124. https://doi. or g/ Box JE, Colgan WT, Christensen TR et al (2019) Key indica- 10. 1079/ 97817 80644 981. 0109 tors of Arctic climate change: 1971–2017. Environ Res Lett Freudenberg A (2017) Wahrnehmung und Stellenwert des Klimawan- 14:045010. https:// doi. org/ 10. 1088/ 1748- 9326/ aafc1b dels bei Landnutzungsentscheidungen von LandwirtInnen im Brown C, Alexander P, Holzhauer S, Rounsevell MDA (2017) Ennstal. Alpen-Adria-Universität Klagenfurt Behavioral models of climate change adaptation and mitiga- Garmendia E, Aldezabal A, Galan E et al (2022) Mountain sheep graz- tion in land-based sectors. WIREs Clim Change 8:e448. https:// ing systems provide multiple ecological, socio-economic, and doi. org/ 10. 1002/ wcc. 448 food quality benefits. Agron Sustain Dev 42:47. https:// doi. org/ Brunner SH, Grêt-Regamey A (2016) Policy strategies to foster the 10. 1007/ s13593- 021- 00751-7 resilience of mountain social-ecological systems under uncer- Gaube V, Kaiser C, Wildenberg M et al (2009) Combining agent-based tain global change. Environ Sci Policy 66:129–139. https://doi. and stock-flow modelling approaches in a participative analysis org/ 10. 1016/j. envsci. 2016. 09. 003 of the integrated land system in Reichraming, Austria. Landsc Carmona G, Varela-Ortega C, Bromley J (2013) Participatory model- Ecol 24:1149–1165. https://doi. or g/10. 1007/ s10980- 009- 9356-6 ling to support decision making in water management under Gawith D, Hodge I, Morgan F, Daigneault A (2020) Climate change uncertainty: two comparative case studies in the Guadiana river costs more than we think because people adapt less than we 1 3 39 Page 16 of 17 C. Egger et al. assume. Ecol Econ 173:106636. https:// doi. org/ 10. 1016/j. ecole Kryszak L, Guth M, Czyzewski B (2021) Determinants of farm profit- con. 2020. 106636 ability in the EU regions. Does farm size matter Agricultural Gobiet A, Truhetz H (2008) Klimamodelle, Klimaszenarien und ihre Economics – Czech 3:90–100. https:// doi. or g/ 10. 17221/ 415/ Bedeutung für Österreich. In: Böhm R, Godina R, Nachtnebel 2020- AGRIC ECON HP, Pirker O (eds) Auswirkungen des Klimawandels auf die Kummer S, Klingbacher E, Petrasek R et al (2021) Stärkung der Österreichische Wasserwirtschaft. Bundesministerium für Land- biologischen Landwirtschaft in Österreich bis 2030 Studie zu und Forstwirtschaft, Umwelt und Wasserwirtschaft und Österrei- Erfolgsfaktoren und Handlungsoptionen. Forschungsinstitut für chischer Wasser- und Abfallwirtschaftsverband (ÖWAV), Wien biologischen Landbau FiBL, Wien Gobiet A, Kotlarski S (2020) Future climate change in the European Läpple D, Renwick A, Cullinan J, Thorne F (2016) What drives inno- Alps. Oxf Res Encycl Clim Sci. https://doi. or g/10. 1093/ acr efor e/ vation in the agricultural sector? A spatial analysis of knowl- 97801 90228 620. 013. 767 edge spillovers. Land Use Policy 56:238–250. https:// doi. org/ Grass I, Batáry P, Tscharntke T (2021) Chapter six - combining land-10. 1016/j. landu sepol. 2016. 04. 032 sparing and land-sharing in European landscapes. Adv Ecol Res Loarie SR, Duffy PB, Hamilton H et al (2009) The velocity of climate 64:251–303. https:// doi. org/ 10. 1016/ bs. aecr. 2020. 09. 002 change. Nature 462:1052–1055. https:// doi. org/ 10. 1038/ natur Grimm V, Berger U, Bastiansen F et al (2006) A standard protocol for e08649 describing individual-based and agent-based models. Ecol Model Lurette A, Stark F, Lecomte L et al (2020) A model to explore which 198:115–126. https:// doi. org/ 10. 1016/j. ecolm odel. 2006. 04. 023 diversity is needed to design sustainable agricultural systems at Grimm V, Berger U, DeAngelis DL et al (2010) The ODD protocol: a the territorial level. Agron Sustain Dev 40:32. https://doi. or g/10. review and first update. Ecol Model 221:2760–2768. https://doi. 1007/ s13593- 020- 00634-3 org/ 10. 1016/j. ecolm odel. 2010. 08. 019 Marvuglia A, Bayram A, Baustert P et al (2022) Agent-based model- Groeneveld J, Müller B, Buchmann CM et al (2017) Theoretical foun- ling to simulate farmers’ sustainable decisions: farmers’ interac- dations of human decision-making in agent-based land use mod- tion and resulting green consciousness evolution. J Clean Prod els – a review. Environ Model Softw 87:39–48. https:// doi. org/ 332:129847. https:// doi. org/ 10. 1016/j. jclep ro. 2021. 129847 10. 1016/j. envso ft. 2016. 10. 008 Mayer A, Egger C, Loyau A et al (2022) Mountain pastures increase Grüner Bericht (2021) Grüner Bericht 2021 - Die Situation der öster- the resilience of livestock farming to extreme events in the reichischen Land- und Forstwirtschaft Gemäß §9 des Land- Ariège department. France. Agron Sustain Dev 42:49. https:// wirtschaftgesetzes. Bundesministerium für Landwirtschaft, doi. org/ 10. 1007/ s13593- 022- 00779-3 Regionen und Tourismus, Wien Mitter H, Larcher M, Schönhart M et al (2019) Exploring farmers’ Grüner Bericht NÖ (2017) DER GRÜNE BERICHT 2017 - Ber- climate change perceptions and adaptation intentions: empirical icht über die wirtschaftliche und soziale Lage der Land- und evidence from Austria. Environ Manage 63:804–821. https://doi. Forstwirtschaft 2017org/ 10. 1007/ s00267- 019- 01158-7 Hare M (2011) Forms of participatory modelling and its potential for Mukherjee S, Mishra A, Trenberth KE (2018) Climate change and widespread adoption in the water sector. Environ Policy Gov drought: a perspective on drought indices. Curr Clim Change Rep 21:386–402. https:// doi. org/ 10. 1002/ eet. 590 4:145–163. https:// doi. org/ 10. 1007/ s40641- 018- 0098-x Haslmayr H-P, Baumgarten A, Schwarz M, et al (2018) BEAT –Boden- Müller B, Bohn F, Dreßler G et al (2013) Describing human decisions bedarf für die Ernährungssicherung in Österreich in agent-based models – ODD + D, an extension of the ODD Herring SC, Hoerling MP, Peterson TC, Stott PA (2014) Explaining protocol. Environ Model Softw 48:37–48. https:// doi. org/ 10. extreme events of 2013 from a climate perspective. Bull Am 1016/j. envso ft. 2013. 06. 003 Meteorol Soc 95:S1–S104. https:// doi. org/ 10. 1175/ 1520- 0477- Natural Earth (2022). https:// www. natur alear thdata. com/. Accessed 95.9. S1.1 10 Oct 2022 Herring SC, Christidis N, Hoell A, Stott PA (2022) Explaining extreme Newell RG, Prest BC, Sexton SE (2021) The GDP-Temperature rela- events of 2020 from a climate perspective. Bull Am Meteorol tionship: implications for climate change damages. J Environ Soc 103:S1–S129. https:// doi. org/ 10. 1175/ BAMS- Expla ining Econ Manag 108:102445. https:// doi. org/ 10. 1016/j. jeem. 2021. Extre meEve nts20 20.1 102445 Hiebl J, Frei C (2016) Daily temperature grids for Austria since Osbahr H, Dorward P, Stern R, Cooper S (2011) Supporting agricul- 1961—concept, creation and applicability. Theor Appl Climatol tural innovation in Uganda to respond to climate risk: linking 124:161–178. https:// doi. org/ 10. 1007/ s00704- 015- 1411-4 climate change and variability with farmer perceptions. Exp Huber R, Bakker M, Balmann A et al (2018) Representation of deci- Agric 47:293–316. https://doi. or g/10. 1017/ S0014 47971 00007 85 sion-making in European agricultural agent-based models. Agric Otto FEL, van der Wiel K, van Oldenborgh GJ et al (2018) Climate Syst 167:143–160. https:// doi. org/ 10. 1016/j. agsy. 2018. 09. 007 change increases the probability of heavy rains in Northern Eng- Huber R, Xiong H, Keller K, Finger R (2022) Bridging behavioural land/Southern Scotland like those of storm Desmond—a real- factors and standard bio-economic modelling in an agent-based time event attribution revisited. Environ Res Lett 13:024006. modelling framework. J Agric Econ 73:35–63. https:// doi. org/ https:// doi. org/ 10. 1088/ 1748- 9326/ aa9663 10. 1111/ 1477- 9552. 12447 Pe’er G, Zinngrebe Y, Hauck J et al (2017) Adding some green to IPCC (2022) Climate change 2022 - impacts, adaptation and vulnerability. the greening: improving the EU’s Ecological Focus Areas for Intergovernmental Panel on Climate Change, Working Group II biodiversity and farmers. Conserv Lett 10:517–530. https:// doi. Janssen M, Ostrom E (2006) Empirically based, agent-based models. org/ 10. 1111/ conl. 12333 Ecol Soc 11:. https:// doi. org/ 10. 5751/ ES- 01861- 110237 Pe’er G, Zinngrebe Y, Moreira F et al (2019) A greener path for the Jurkėnaitė N, Baležentis T (2020) The ‘pure’ and structural contri- EU Common Agricultural Policy. Science 365:449–451. https:// butions to the average farm size growth in the EU: the index doi. org/ 10. 1126/ scien ce. aax31 46 decomposition approach. Ecol Indic 117:106614. https://doi. or g/ Pe’er G, Bonn A, Bruelheide H et al (2020) Action needed for the EU 10. 1016/j. ecoli nd. 2020. 106614 Common Agricultural Policy to address sustainability challenges. Koesling M, Ebbesvik M, Lien G et al (2004) Risk and risk manage- People Nat 2:305–316. https:// doi. org/ 10. 1002/ pan3. 10080 ment in organic and conventional cash crop farming in Norway. Peltonen-Sainio P, Jauhiainen L, Trnka M et al (2010) Coincidence of variation in yield and climate in Europe. Agric Ecosyst Environ Acta Agric Scand Sect C — Food Econ 1:195–206. https:// doi. 139:483–489. https:// doi. org/ 10. 1016/j. agee. 2010. 09. 006 org/ 10. 1080/ 16507 54041 00196 92 1 3 Effects of extreme events on land‑use‑related decisions of farmers in Eastern Austria: the… Page 17 of 17 39 Perzl M (2021) Wirkungsweisen sozialer, ökonomischer und ökolo- Trenberth KE (2018) Climate change caused by human activities is hap- gischer Faktoren auf Entscheidungen, Anpassungsfähigkeit und pening and it already has major consequences. J Energy Nat Resour Entwicklungen in der Fruchtfolgengestaltung : Entscheidungen und Law 36:463–481. https://doi. or g/10. 1080/ 02646 811. 2018. 145 0895 Praktiken sowie Einschätzung der Anpassungsfähigkeit der Frucht- Troost C, Berger T (2015) Dealing with uncertainty in agent-based sim- folgen, -auswahl, -bearbeitung und -wechsel oberösterreichischer ulation: farm-level modeling of adaptation to climate change in Landwirt*innen (Bezirke Kirchdorf und Steyr-Land). Alpen-Adria- Southwest Germany. Am J Agric Econ 97:833–854. https:// doi. Universität Klagenfurtorg/ 10. 1093/ ajae/ aau076 Petz SA (2021) Entscheidungsverhalten im Ackerbau von Landwirt*innen Tzouramani I, Alexopoulos G, Kostianis G, Kazakopoulos L (2014) aus den politischen Bezirken Oberpullendorf und Wiener Neustadt Exploring risk management strategies for organic farmers: a Greek Land. Alpen-Adria-Universität Klagenfurt, Klima- und Umwelts- case study. Renew Agric Food Syst 29:167–175. https://doi. or g/10. chutzmaßnahmen im Fokus1017/ S1742 17051 30000 57 Resare Sahlin K, Carolus J, von Greyerz K et al (2022) Delivering “less Ummenhofer CC, Meehl GA (2017) Extreme weather and climate events but better” meat in practice—a case study of a farm in agroecologi- with ecological relevance: a review. Philos Trans R Soc B Biol Sci cal transition. Agron Sustain Dev 42:24. https:// doi. org/ 10. 1007/ 372:20160135. https:// doi. org/ 10. 1098/ rstb. 2016. 0135 s13593- 021- 00737-5 Valbuena D, Verburg PH, Bregt AK (2008) A method to define a typology Schattman RE, Conner D, Méndez VE (2016) Farmer perceptions of for agent-based analysis in regional land-use research. Agric Ecosyst climate change risk and associated on-farm management strategies Environ 128:27–36. https:// doi. org/ 10. 1016/j. agee. 2008. 04. 015 in Vermont, northeastern United States. Elem Sci Anthr 4:000131. van Duinen R, Filatova T, Jager W, van der Veen A (2016) Going beyond https:// doi. org/ 10. 12952/ journ al. eleme nta. 000131 perfect rationality: drought risk, economic choices and the influ- Schmitzberger I, Wrbka Th, Steurer B et al (2005) How farming styles ence of social networks. Ann Reg Sci 57:335–369. https:// doi. org/ influence biodiversity maintenance in Austrian agricultural land-10. 1007/ s00168- 015- 0699-4 scapes. Agric Ecosyst Environ 108:274–290. https:// doi. org/10. van Winsen F, de Mey Y, Lauwers L et al (2016) Determinants of risk 1016/j. agee. 2005. 02. 009 behaviour: effects of perceived risks and risk attitude on farmer’s Schreinemachers P, Berger T (2011) An agent-based simulation model adoption of risk management strategies. J Risk Res 19:56–78. of human–environment interactions in agricultural systems. https:// doi. org/ 10. 1080/ 13669 877. 2014. 940597 Environ Model Softw 26:845–859. https:// doi. or g/ 10. 1016/j. van Zanten BT, Verburg PH, Espinosa M et al (2014) European agri- envso ft. 2011. 02. 004 cultural landscapes, common agricultural policy and ecosystem Scown MW, Brady MV, Nicholas KA (2020) Billions in misspent EU services: a review. Agron Sustain Dev 34:309–325. https://doi. or g/ agricultural subsidies could support the sustainable development 10. 1007/ s13593- 013- 0183-4 goals. One Earth 3:237–250. h t t p s : / / d o i . o rg / 1 0 . 1 0 1 6 / j . o n e e a r . Verburg PH, Dearing JA, Dyke JG et al (2016) Methods and approaches 2020. 07. 011 to modelling the Anthropocene. Glob Environ Change 39:328–340. Serra A, Duncan J (2016) European farmers and the “Greening” of the https:// doi. org/ 10. 1016/j. gloen vcha. 2015. 08. 007 CAP: a critical discourse analysis. Glob Governancepolitics Clim Verburg PH, Alexander P, Evans T et al (2019) Beyond land cover Justice Agrar Justice Link Chall. https://www .eur .nl/ sites/ cor por ate/ change: towards a new generation of land use models. Sustain Gov files/ 13- ICAS_ CP_ Serra_ and_ Duncan. pdf Transform 38:77–85. https://doi. or g/10. 1016/j. cosus t.2019. 05. 002 Severini S, Tantari A, Di Tommaso G (2016) Do CAP direct payments Vogel E, Donat MG, Alexander LV et al (2019) The effects of climate stabilise farm income? Empirical evidences from a constant sam- extremes on global agricultural yields. Environ Res Lett 14:054010. ple of Italian farms. Agric Food Econ 4:6. https://d oi.o rg/1 0.1 186/ https:// doi. org/ 10. 1088/ 1748- 9326/ ab154b s40100- 016- 0050-0 Wheeler S, Zuo A, Bjornlund H (2013) Farmers’ climate change beliefs Sheng Y, Zhao S, Nossal K, Zhang D (2015) Productivity and farm and adaptation strategies for a water scarce future in Australia. Glob size in Australian agriculture: reinvestigating the returns to scale. Environ Change 23:537–547. https:// doi. org/ 10. 1016/j. gloen vcha. Aust J Agric Resour Econ 59:16–38. https:// doi. org/ 10. 1111/ 2012. 11. 008 1467- 8489. 12063 WKO Statistik (2020) Jahresarbeitszeit 2018 im EU-Vergleich - Sollar- Shukla R, Agarwal A, Sachdeva K et al (2019) Climate change percep- beitszeit von Arbeitnehmern tion: an analysis of climate change and risk perceptions among Yang YCE, Son K, Hung F, Tidwell V (2020) Impact of climate change farmer types of Indian Western Himalayas. Clim Change 152:103– on adaptive management decisions in the face of water scarcity. J 119. https:// doi. org/ 10. 1007/ s10584- 018- 2314-z Hydrol 588:125015. https://d oi.o rg/1 0.1 016/j.j hydro l.2 020.1 25015 Singh A (2021) Soil salinization management for sustainable develop- Zagaria C, Schulp CJE, Zavalloni M et al (2021) Modelling transforma- ment: a review. J Environ Manage 277:111383. https:// doi. org/ 10. tional adaptation to climate change among crop farming systems in 1016/j. jenvm an. 2020. 111383 Romagna. Italy. Agric Syst 188:103024. https:// doi. org/ 10. 1016/j. Smajgl A, Brown DG, Valbuena D, Huigen MGA (2011) Empirical agsy. 2020. 103024 characterisation of agent behaviours in socio-ecological systems. Zimmermann A, Möhring A, Mack G et al (2015) Pathways to truth: com- Environ Model Softw 26:837–844. https://doi. or g/10. 1016/j. en vso paring different upscaling options for an agent-based sector model. ft. 2011. 02. 011 J Artif Soc Soc Simul 18:11. https:// doi. org/ 10. 18564/ jasss. 2862 Smetschka B, Gaube V (2020) Co-creating formalized models: participa- Zwiers FW, Alexander LV, Hegerl GC et al (2013) Climate extremes: tory modelling as method and process in transdisciplinary research challenges in estimating and understanding recent changes in the and its impact potentials. Environ Sci Policy 103:41–49. https://doi. frequency and intensity of extreme climate and weather events. In: org/ 10. 1016/j. envsci. 2019. 10. 005 Asrar GR, Hurrell JW (eds) Climate Science for Serving Society: Stoate C, Báldi A, Beja P et al (2009) Ecological impacts of early Research, Modeling and Prediction Priorities. Springer, Nether- 21st century agricultural change in Europe – a review. J Envi- lands, Dordrecht, pp 339–389 ron Manage 91:22–46. https://doi. or g/10. 1016/j. jen vman. 2009. 07. 005 Publisher's note Springer Nature remains neutral with regard to Stott P (2016) How climate change affects extreme weather events. Sci- jurisdictional claims in published maps and institutional affiliations. ence 352:1517–1518. https:// doi. org/ 10. 1126/ scien ce. aaf72 71 1 3

Journal

Agronomy for Sustainable Development – Springer Journals

Published: Jun 1, 2023

Keywords: Agent-based modeling; Learning; Scenario analysis; Adaptation; Austria; Agriculture

Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Effects of extreme events on land-use-related decisions of farmers in Eastern Austria: the role of learning

Effects of extreme events on land-use-related decisions of farmers in Eastern Austria: the role of learning

Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Effects of extreme events on land-use-related decisions of farmers in Eastern Austria: the role of learning

Effects of extreme events on land-use-related decisions of farmers in Eastern Austria: the role of learning

References (107)

Abstract

Journal

Recommended Articles

There are no references for this article.

Our policy towards the use of cookies