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

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60460709%3A41330%2F23%3A97535" target="_blank" >RIV/60460709:41330/23:97535 - isvavai.cz</a>

Result on the web

<a href="http://dx.doi.org/10.1007/s13593-023-00890-z" target="_blank" >http://dx.doi.org/10.1007/s13593-023-00890-z</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s13593-023-00890-z" target="_blank" >10.1007/s13593-023-00890-z</a>

Result language

angličtina

Original language name

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

Original language description

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.

Czech name

—

Czech description

—

Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

40106 - Agronomy, plant breeding and plant protection; (Agricultural biotechnology to be 4.4)

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2023

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Agronomy for Sustainable Development

ISSN

1774-0746

e-ISSN

1774-0746

Volume of the periodical

43

Issue of the periodical within the volume

3

Country of publishing house

CZ - CZECH REPUBLIC

Number of pages

17

Pages from-to

1-17

UT code for WoS article

000985753200001

EID of the result in the Scopus database

2-s2.0-85159171884