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

IF 6.4 1区 农林科学 Q1 AGRONOMY
Claudine Egger, Andreas Mayer, Bastian Bertsch-Hörmann, Christoph Plutzar, Stefan Schindler, Peter Tramberend, Helmut Haberl, Veronika Gaube
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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.

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极端事件对奥地利东部农民土地利用相关决策的影响:学习的作用。
随着极端天气事件的频率和严重程度的上升,由于气候变化,欧洲农场家庭在未来几十年将面临越来越具有挑战性的条件。这项研究评估了气候变化或农业价格和补贴计划调整等外部框架条件与农民决策之间的复杂相互关系。由于社会方面仍然是农业决策的驱动因素,我们也认为农民基于价值的特征是决策的内部因素。我们将个人学习作为对极端天气事件的反应集成到一个基于代理的模型中,该模型模拟农民的决策。我们将该模型应用于奥地利东部的一个地区,该地区已经经历了气候变化带来的缺水和日益增加的干旱风险,并模拟了未来的三种情景,以比较社会经济和气候条件变化的影响。在交叉比较中,我们调查了农民如何通过个人适应来应对这些变化。农业轨迹预测,在2053年之前,活跃农场将减少-27%至-37%,农业面积将减少-20%至-30%。结果表明,与没有自适应学习的情景相比,无论情景条件如何,通过学习的适应都会减缓活跃农场和农田数量的下降。然而,适应增加了农民的工作量。这突出了对农场劳动力支持的必要性。补充信息:在线版本包含补充材料,网址为10.1007/s13593-023-00890-z。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Agronomy for Sustainable Development
Agronomy for Sustainable Development 农林科学-农艺学
CiteScore
10.70
自引率
8.20%
发文量
108
审稿时长
3 months
期刊介绍: Agronomy for Sustainable Development (ASD) is a peer-reviewed scientific journal of international scope, dedicated to publishing original research articles, review articles, and meta-analyses aimed at improving sustainability in agricultural and food systems. The journal serves as a bridge between agronomy, cropping, and farming system research and various other disciplines including ecology, genetics, economics, and social sciences. ASD encourages studies in agroecology, participatory research, and interdisciplinary approaches, with a focus on systems thinking applied at different scales from field to global levels. Research articles published in ASD should present significant scientific advancements compared to existing knowledge, within an international context. Review articles should critically evaluate emerging topics, and opinion papers may also be submitted as reviews. Meta-analysis articles should provide clear contributions to resolving widely debated scientific questions.
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