Overview Modelling the risk of Western Corn Rootworm infestation on Austrian cropland

(1)

University of Natural Resources and Life Sciences, Vienna Department of Economics and Social Sciences

ÖGA Conference 2018 I Katharina Falkner, Elena Moltchanova, Hermine Mitter, Erwin Schmid

16.05.2017 1

Modelling the risk of Western Corn Rootworm infestation on Austrian cropland

ÖGA Conference 2018

Katharina Falkner, Elena Moltchanova, Hermine Mitter, Erwin Schmid

Overview

 Research background

 Research objectives

 Material & Methods

 Integrated modelling framework

 Assumptions and scenarios

 Results

 Economic effects

 Western Corn Rootworm (WCR) abundance

 Conclusions

27.09.2018 2

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Research background

27.09.2018 3

Source: Own illustration based on GeDaBa 2017.

Research background

 2002: 1 ^st WCR detection in Austria

 Hotspots of maize production = hotspots for WCR infestation

 Economic losses

 WCR monitoring with pheromone traps

 Factors influencing WCR infestation

 Maize cultivation intensity (monocultures)

 Climatic conditons (life cycle development)

27.09.2018 4

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Research objectives

 We aim at

a) analyzing the effect of crop rotation regulations with upper limits for maize shares and the effect of climate change on WCR infestation.

b) identify effective and efficient management strategies to control WCR spreading.

 Model design

 Development and calibration of a WCR abundance model.

 Application of the WCR model within an integrated land use modelling framework.

27.09.2018 5

Integrated modelling framework

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Integrated modelling framework

27.09.2018 7

O

ACLiReM

I

Historical Climate Data

(1975 – 2007)

Climate Scenarios (2010-2040)

1 km pixel

Integrated modelling framework

27.09.2018 8

O

ACLiReM

I

(1975 – 2007)

1 km pixel

O

CropRota

I

Observed Land Use

(2012-2014) Expert knowledge

Typical Crop Rotations Municipality level

WCR Policy (maize restriction) Crop Rotation Systems Reference crop rotation system ● Alternative crop

rotation systems

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Integrated modelling framework

27.09.2018 9

O

ACLiReM

I

(1975 – 2007)

1 km pixel

O

CropRota

I

Observed Land Use

rotation systems

I

O

EPIC ^I

Biophysical Data (soil, site attributes,

climate) Management intensities

I I

Crop yields ● Environmental

parameters 1 km pixel

O

EPIC ^I

Gross Margin Calculation

I

Integrated modelling framework

10

O

ACLiReM

I

Variable production costs ● Prices and

policy premiums

I O

CropRota

I

Observed Land Use

rotation systems

I

Historical Climate Data (1975 – 2007)

1 km pixel

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O

EPIC ^I

Gross Margin Calculation

I

Integrated modelling framework

11 Variable production

costs ● Prices and policy premiums

I

O

ACLiReM

I

(1975 – 2007)

1 km pixel

O

CropRota

I

Observed Land Use

rotation systems

I

O

BiomAT

Land Use Scenarios optimization using a PMP

approach

I

Integrated modelling framework

12

O

CropRota

I

WCR Policy (maize restriction)

I

O

EPIC ^I

Gross Margin Calculation

I I

I

policy premiums

I

O

ACLiReM

I

(1975 – 2007)

1 km pixel

O

BiomAT

approach

I

WCR Model

WCR Scenarios Presence-absence maps

● Species abundance maps 1 km pixel

O

Crop Rotation Systems Reference crop rotation system ● Alternative crop

rotation systems Observed Land Use (2012-2014) Expert knowledge

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Integrated modelling framework

13

I

O

BiomAT

I

WCR Model calibration

WCR monitoring data ● Climate data ● Land

use data I

policy premiums

I

O

ACLiReM

I

(1975 – 2007)

1 km pixel

O

CropRota

I

Observed Land Use

rotation systems

I

O

EPIC ^I

Gross Margin Calculation

I

WCR Model

WCR Scenarios Presence-absence maps

● Species abundance maps 1 km pixel

O

approach

Assumptions and scenarios

27.09.2018 14

 Crop rotation systems

 Climate change scenarios

Scenario Upper limit for maize in crop rotations

REF unrestricted

MS50 50%

MS25 25%

MS10 10%

Scenario Temperature trend Precipitation sums SIMILAR + 0.05°C/year resemble the past

WET + 0.05°C/year increase (+20%)

DRY + 0.05°C/year decrease (-20%)

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Results

Economic effects of maize restrictions

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Source: Own illustration based on model results. Note: outliers not shown.

Results

Changes in net-returns by maize restriction and climate change scenarios.

27.09.2018 16

Source: Own illustration based on model results. Note: outliers not shown.

Crop rotation scenario

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Results

Economic effects of maize restrictions:

Compared to the REF, net-returns

 show a decreasing trend if we limit maize production to MS50, MS25 or MS10.

 are highest under WET and lowest under DRY climatic conditions.

 decrease most under most restrictive maize limits in crop rotations.

 show a higher variability.

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Results

WCR abundance

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Source: Own illustration and calculation based on model results.

SIMILAR WET DRY

Crop rotation system

High abundance [ha cropland]

Change in high abundance [%]

High abundance [ha cropland]

Change in high abundance[%]

High abundance [ha cropland]

Change in high abundance[%]

REF 88,406

MS50 MS25 MS10 REF

SIMILAR WET DRY

Crop rotation system

High abundance [ha cropland]

Change in high abundance [%]

High abundance [ha cropland]

Change in high abundance[%]

High abundance [ha cropland]

Change in high abundance[%]

REF 88,406 100,401 +13.6% 69,389 -21.5%

MS50 MS25 MS10

WET DRY

REF

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SIMILAR WET DRY

Crop rotation system

High abundance [ha cropland]

Change in high abundance [%]

High abundance [ha cropland]

Change in high abundance[%]

High abundance [ha cropland]

Change in high abundance[%]

REF 88,406 100,401 +13.6% 69,389 -21.5%

MS50 68,092 -23.0%

MS25 MS10

REF SIMILAR

SIMILAR WET DRY

Crop rotation system

High abundance [ha cropland]

Change in high abundance [%]

High abundance [ha cropland]

Change in high abundance[%]

High abundance [ha cropland]

Change in high abundance[%]

REF 88,406 100,401 +13.6% 69,389 -21.5%

MS50 68,092 -23.0% 85,514 +/-0.0% 49,279 -44.3%

MS25 MS10 REF

WET

SIMILAR

DRY

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SIMILAR WET DRY

Crop rotation system

High abundance [ha cropland]

Change in high abundance [%]

High abundance [ha cropland]

Change in high abundance[%]

High abundance [ha cropland]

Change in high abundance[%]

REF 88,406 100,401 +13.6% 69,389 -21.5%

MS50 68,092 -23.0% 85,514 +/-0.0% 49,279 -44.3%

MS25 5,286 -94.0%

MS10

REF SIMILAR

SIMILAR WET DRY

Crop rotation system

High abundance [ha cropland]

Change in high abundance [%]

High abundance [ha cropland]

Change in high abundance[%]

High abundance [ha cropland]

Change in high abundance[%]

REF 88,406 100,401 +13.6% 69,389 -21.5%

MS50 68,092 -23.0% 85,514 +/-0.0% 49,279 -44.3%

MS25 5,286 -94.0% 13,053 -85.2% 812 -99.1%

MS10

REF SIMILAR

WET DRY

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SIMILAR WET DRY

Crop rotation system

High abundance [ha cropland]

Change in high abundance [%]

High abundance [ha cropland]

Change in high abundance[%]

High abundance [ha cropland]

Change in high abundance[%]

REF 88,406 100,401 +13.6% 69,389 -21.5%

MS50 68,092 -23.0% 85,514 +/-0.0% 49,279 -44.3%

MS25 5,286 -94.0% 13,053 -85.2% 812 -99.1%

MS10 111 -99.9%

REF SIMILAR

SIMILAR WET DRY

Crop rotation system

High abundance [ha cropland]

Change in high abundance [%]

High abundance [ha cropland]

Change in high abundance[%]

High abundance [ha cropland]

Change in high abundance[%]

REF 88,406 100,401 +13.6% 69,389 -21.5%

MS50 68,092 -23.0% 85,514 +/-0.0% 49,279 -44.3%

MS25 5,286 -94.0% 13,053 -85.2% 812 -99.1%

MS10 111 -99.9% 213 -99.8% 2 -100%

REF SIMILAR

WET DRY

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Conclusions

 Farmers are increasingly aware of risks resulting from pests and climate change.

 Important to develop robust cropping systems and adequate policies to slow down pest dispersal rates.

 Analysis allows us to analyze the effect of

I. management strategies (i.e. crop rotation decisions) and II. climate change

on the risk of WCR infestation.

 Crop rotation regulations with upper limits for maize can help to reduce WCR pressure.

27.09.2018 27

Conclusions

 Net-returns of crop production with maize restrictions.

 WCR regulations should consider regional production characteristics.

 Farm and regional specific analysis of the effects are important.

 Livestock farms and biogas plants highly dependent on maize.

 Evaluating the trade-offs between crop rotation regulations, economic effects and the risk of WCR infestation.

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16.05.2017 29

University of Natural Resources and Life Sciences, Vienna Department of Economics and Social Sciences

Thank you for your attention!

Katharina Falkner Elena Moltchanova Hermine Mitter Erwin Schmid

Gregor Mendel-Straße 33, A-1180 Wien Tel.: +43 1 47654-4416, Fax: +43 1 47654-1005 katharina.falkner@boku.ac.at , www.boku.ac.at

Acknowledgements:

The presented research and results are derived from the project ‘COMBIned weather related RISK assessment monitor for tailoring climate change adaptation in Austrian crop production’ (COMBIRISK, KR15AC8K12614). The project is funded within the Austrian Climate Research Program (ACRP) of the Climate and Energy Fund.

This work is also funded by „Innobrotics - Lösung der Maiswurzelbohrerproblematik in den Ackerbau- und Veredelungsgebieten Österreichs“. Innobrotics is part ofEIP-Agriwithin the Austrian Rural Development Programme and supported by the Austrian government, federal states and the European Union.

Overview Modelling the risk of Western Corn Rootworm infestation on Austrian cropland

Modelling the risk of Western Corn Rootworm infestation on Austrian cropland

ÖGA Conference 2018

Katharina Falkner, Elena Moltchanova, Hermine Mitter, Erwin Schmid

Overview

 Research background

 Research objectives

 Material & Methods

 Integrated modelling framework

 Assumptions and scenarios

 Results

 Economic effects

 Western Corn Rootworm (WCR) abundance

 Conclusions

Research background

Research background

 2002: 1 st WCR detection in Austria

 Hotspots of maize production = hotspots for WCR infestation

 Economic losses

 WCR monitoring with pheromone traps

 Factors influencing WCR infestation

 Maize cultivation intensity (monocultures)

 Climatic conditons (life cycle development)

Research objectives

 We aim at

a) analyzing the effect of crop rotation regulations with upper limits for maize shares and the effect of climate change on WCR infestation.

b) identify effective and efficient management strategies to control WCR spreading.

 Model design

 Development and calibration of a WCR abundance model.

 Application of the WCR model within an integrated land use modelling framework.

Integrated modelling framework

Integrated modelling framework

O

ACLiReM

I

Integrated modelling framework

O

ACLiReM

I

O

CropRota

I

Integrated modelling framework

O

ACLiReM

I

O

CropRota

I

I

O

EPIC I

I I

O

EPIC I

Gross Margin Calculation

I

Integrated modelling framework

O

ACLiReM

I

I O

CropRota

I

I

I

O

EPIC I

Gross Margin Calculation

I

Integrated modelling framework

I

O

ACLiReM

I

O

CropRota

I

I

I

 2002: 1 ^st WCR detection in Austria

EPIC ^I

EPIC ^I

EPIC ^I

EPIC ^I

EPIC ^I