Mostviertel
General information
Name of region | Mostviertel, Austria |
Global Environmental Zone(s) (Metzger) | H. Cool temperate and dry/G. Cold and mesic/J/F |
Population density (persons per km2) | |
Contact (general) | Martin Schönhart |
Contact (ag. scenarios) | Martin Schönhart |
Location (NUTS code) | AT121 |
Dominant regional farming system(s) (SEAMLESS nomenclature) |
Arable/cereal and mixed farming |
The three most important farming systems in region (SEAMLESS nomenclature) |
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Main crop species |
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Main livestock species |
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Regional development goal in rural spatial planning
Specific issues the region deals with/will deal with
We focus on two case study landscapes in the Mostviertel region either dominated by cropland or grassland. We specifically focus on climate change impacts on landscape development and biodiversity.
Regional challenges with regard to climate change
According to previous studies, we expect moderate yield gains from climate change. This can lead to intensification and deterioration of environmental quality. Extreme events play a certain role, particularly soil erosion.
Proposed solutions to overcome the challenges
Contribution to answering the focus question
Our Regional Pilot Study should provide insights into the question whether and how climate change impacts landscape development, land abandonment/intensification, and the biotic environment. These results also allow statements on the development of agricultural productivity (food, resource production) under climate change. Furthermore, we are focusing on the trade-offs between mitigation and adaptation. Diverse adaptation strategies are modelled including soil management (e.g. cover crops), changes in crop rotations and fertilization intensity levels.
Important adaptation measures that are or will be considered in the study
Water management | |
Irrigation | |
Drainage | |
Species/varietal choice | is important to this region AND is/will be included in the modelling exercise. |
Plant breeding | |
Changed planting/sowing days | is important to this region AND is/will be included in the modelling exercise. |
Crop rotations | is important to this region AND is/will be included in the modelling exercise. |
Alternative tillage methods | is important to this region AND is/will be included in the modelling exercise. |
Pest/weed management | is important to this region. |
Housing of livestock | |
Land consolidation | is important to this region AND is/will be included in the modelling exercise. |
Management of feeding and reproduction of livestock | is important to this region AND is/will be included in the modelling exercise. |
Structure and scale of production adjustment | is important to this region |
Crop insurance | is important to this region AND is/will be included in the modelling exercise. |
Exit from agriculture | is important to this region AND is/will be included in the modelling exercise. |
Climate alertness | is important to this region AND is/will be included in the modelling exercise. |
Political regulations at various administrative levels | is important to this region AND is/will be included in the modelling exercise. |
Others | |
Models, stakeholders, advancement of knowledge
Socio-economy | Crops | Grassland | Livestock |
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FAMOS[space], information is needed on socio-economic pathways (developed by the modelling team) | EPIC, CropRota (crop rotation generator), climate change data from statistical model is already available | EPIC is applied to model changes in grassland yields, SpatialGRAM will be applied as well | Contained within socio-economic model. |
How are results of of crop and livestock models assimilated in socio-economic models? | How is technological progress in arable agriculture taken into account? | How is technological progress in livestock farming taken into account? | |
We improved the interface between our model components. Furthermore, we plan to integrate a second model on grassland yield changes. | We assume productivity changes based on observations in the economic model but not so in the crop model. | We assume productivity changes based on observations in the economic model. |
Participating stakeholders
Agro-business or agro-food chain | Administrative bodies or regional or national governments |
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The group of stakeholders includes agricultural experts such as teachers from agricultural schools, staff from extension services and administration, as well as farmers. | |
Approaches for involving stakeholders | |
We organized one stakeholder workshop in the region. Further stakeholder activities will follow. |
Improvement of the modelling capability by involving stakeholders
How did the modelling capability improve by involving stakeholders? | Effect of the involvement of stakeholders on the questions asked, on the assessment, or on the solutions suggested |
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Stakeholders contributed to model validation. However, we did not change the models based on this. | We revised the interpretation of our results but did not change the methods. |
Points that researchers learned from stakeholders | Points that stakeholders learned from researchers |
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Heterogeneity plays a role as suggested by our models; some (crop model) results are questioned and need further analysis; economic model results are more difficult to discuss with stakeholders. | climate change impacts may be positive; adaptation is required; uncertainty on climate change is considerable; |