Evidence map›Paper›PMID 37200584›Full record

ArticleAgronomy for sustainable development2023

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

Abstract read
In one paragraph

Article in Agronomy for sustainable development, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Claudine EggerDepartment of Economics and Social Sciences, Institute of Social Ecology, University of Natural Resources and Life Sciences, Schottenfeldgasse 29, 1070 Vienna, Austria.ORCID 0000-0003-2259-4543
Andreas MayerDepartment of Economics and Social Sciences, Institute of Social Ecology, University of Natural Resources and Life Sciences, Schottenfeldgasse 29, 1070 Vienna, Austria.
Bastian Bertsch-HörmannDepartment of Economics and Social Sciences, Institute of Social Ecology, University of Natural Resources and Life Sciences, Schottenfeldgasse 29, 1070 Vienna, Austria.
Christoph PlutzarEnvironment Agency Austria, Spittelauer Lände 5, 1090 Vienna, Austria.
Stefan SchindlerEnvironment Agency Austria, Spittelauer Lände 5, 1090 Vienna, Austria.
Peter TramberendEnvironment Agency Austria, Spittelauer Lände 5, 1090 Vienna, Austria.
Helmut HaberlDepartment of Economics and Social Sciences, Institute of Social Ecology, University of Natural Resources and Life Sciences, Schottenfeldgasse 29, 1070 Vienna, Austria.
Veronika GaubeDepartment of Economics and Social Sciences, Institute of Social Ecology, University of Natural Resources and Life Sciences, Schottenfeldgasse 29, 1070 Vienna, Austria.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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. Supplementary Information: The online version contains supplementary material available at 10.1007/s13593-023-00890-z.

Indexed as

AdaptationAgent-based modelingAgricultureAustriaLearningScenario analysis

Identifiers

PMID37200584
PMCPMC10176289

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.