Flevoland

General information

Name of region	Flevoland
Global Environmental Zone(s) (Metzger)	J. Cool temperate and moist
Population density (persons per km²)	284
Contact (general)	Pytrik Reidsma
Contact (scenarios)
Location (NUTS code)	NL23
Dominant regional farming system(s) (SEAMLESS nomenclature)	permanent crops and arable/specialised crops
The three most important farming systems in region (SEAMLESS nomenclature)	arable-specialised crops arable-others horticulture
Main crop species	Potato Sugar beet Onion
Main livestock species	Dairy cows Horses Sheep

Regional development goal in rural spatial planning

Flevoland was reclaimed from the sea for agriculture. Agriculture is intensive and export oriented. Currebtly, an national ecological network is implemented, but this is stagnating. The city of Almere is expanding.

Specific issues the region deals with/will deal with

soil compaction, increasing sea water levels, national ecological network

Regional challenges with regard to climate change

Heat waves inducing second growth in potatoes,
Warm and wet conditions increasinf fungi development in onions,
Warm winters causing early sprouting of poatatoes,
Changes in rainfall patterns in combination with soil compaction

Proposed solutions to overcome the challenges

For heat waves: resistant cultivar, drip irrigation, wider ridges, optimal crop cover, earlier sowing and harvesting, For fungi development: chemical or UV-light protection,
For early sprouting: sprouting control or air conditioning,
For soil compaction: rotations with less frequent potato, light machinery, more wheat and grass

Contribution to answering the focus question

An integrated assessment was performed, using a market model, crop model and bio-economic farm model to assess the impacts of climate and socio-economic change on arable farming in 2050 (Reidsma et al. 2015). In addition, farm structural change was assessed. Also, a semi-quantitative and participatory approach was used to assess impacts of extreme events on crops (yields and quality). This also included pests and diseases.

Important adaptation measures that are or will be considered in the study

Water management	is important to this region AND will be included in the modelling exercise
Irrigation	is important to this region AND will be included in the modelling exercise
Drainage	is important to this region
Species/varietal choice	is important to this region AND will be included in the modelling exercise
Plant breeding	is important to this region AND will be included in the modelling exercise
Changed planting/sowing days	is important to this region AND will be included in the modelling exercise
Crop rotations	is important to this region AND will be included in the modelling exercise
Alternative tillage methods
Pest/weed management	is important to this region AND will be included in the modelling exercise
Housing of livestock
Land consolidation
Management of feeding and reproduction of livestock
Structure and scale of production adjustment
Crop insurance
Exit from agriculture	is important to this region AND will be included in the modelling exercise
Climate alertness
Political regulations at various administrative levels
Others	is important to this region AND will be included in the modelling exercise
	farm structural change (epxansion, intensification, exit), specific adaptation measurtes to extreme events including pests and diseases, institutional adaptation (water management, resistant cultivars)

Models, stakeholders, advancement of knowledge

Models used in the study
Socio-economy	Crops	Grassland
FSSIM (CAPRI as input for prices)	WOFOST and SIMPLACE	LINGRA in another region, but livestock was not assessed in Flevoland
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?
Crop yield changes due to technological development similar to Ewert et al. (2005). Rijk et al. (2013) analysed genetic improvements of main crops in the past. Reidsma et al. (2015) showed projections based on two approaches.	In another region, grass and maize yields similar as arable crops. Milk yield has increased linearly in the past, and was extrapolated in an A1 scenario, and 1/3 of this trend for a B2 scenario.	Two studies was performed using FSSIM. Kanellopoulos et al. (2014) used WOFOST as input, Wolf et al. (2015) used SIMPLACE. Tsutstumi (2015) assessed the influence of crop yield simulations on farm plans and gross margins in FSSIM. Activities and constraints included in FSSIM appeared to have more influence on farm plans than crop yield changes. For gross margin, yield changes were important. Technological development is more important than climate change however. And price changes also have a large influence. In Mandryk (2016) we used FarmDesign, and inlcuded the impacts of extreme events as estimated by the semi-quantitative and participatory approach of Schaap et al. (2013). Impacts of extreme events, including pests and diseases, are more important than impacts of gradual climate chnage as simulated by crop models. Adaptation measures can largely reduce the impact however. For farmers, assessing impacts of extreme events and a portfolio of adaptation measures is more relevant than crop model simulations, as these are more transparent and provide clear measures.

Participating stakeholders

Agro-business or agro-food chain	Administrative bodies or regional or national governments
LTO Noord (farmers' AssociationFarmers	Water board ZuiderzeelandMinistry of Infrastructure and Environment
Approaches for involving stakeholders
Stakeholder workshopsInterviews Consultation with farmers

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
Less focus on modelling, and more on complementary approaches, such as the AgroClimateCalender assessing impacts of extreme events (Schaap et al., 2013), assessing farm structural change (Mandryk et al., 2012), and assessing insitutional feasibility of adaptation (Mandryk et al., 2015). Mandryk et al. (2014) interviewed farmers on preferred adaptation measures (changes in crop rotations). As we wanted discussion, we did not provide one optimal solution, but provided a range of close to optimal solutions, that respected their main objectives. The focus in the modelling with FarmDesign was not on gross margin only, but also other objectives, and improving soil quality appeared to be another important objetive.	When is an adaptation measure cost-effective? Instead of using an optimization model like FSSIM, adaptation measures were first assessed using a cost-benefit analysis by Schaap et al. (2013). How can I improve gross margin and soil quality simultaneously? By understanding main objectives, we searched for adapted cropping patterns with FarmDesign that corresponded to farmers' objectives (Mandryk et al., 2014). Adaptation will be different depending on the objective (Mandryk, 2016).

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

Less focus on modelling, and more on complementary approaches, such as the AgroClimateCalender assessing impacts of extreme events (Schaap et al., 2013), assessing farm structural change (Mandryk et al., 2012), and assessing insitutional feasibility of adaptation (Mandryk et al., 2015). Mandryk et al. (2014) interviewed farmers on preferred adaptation measures (changes in crop rotations). As we wanted discussion, we did not provide one optimal solution, but provided a range of close to optimal solutions, that respected their main objectives. The focus in the modelling with FarmDesign was not on gross margin only, but also other objectives, and improving soil quality appeared to be another important objetive.

When is an adaptation measure cost-effective? Instead of using an optimization model like FSSIM, adaptation measures were first assessed using a cost-benefit analysis by Schaap et al. (2013). How can I improve gross margin and soil quality simultaneously? By understanding main objectives, we searched for adapted cropping patterns with FarmDesign that corresponded to farmers' objectives (Mandryk et al., 2014). Adaptation will be different depending on the objective (Mandryk, 2016).

Points that researchers learned from stakeholders	Points that stakeholders learned from researchers
Estimated effect of extreme events (Schaap et al. 2013) Main adaptation measures farmers consider (Schaap et al., 2013) Important objectives for farmers (Mandryk et al., 2014) Institutional feasibility of regional adaptation measures (Mandryk etal., 2015).	Change in frequency of extreme events (Schaap et al., 2013). Cost-effectiveness of adaptation measures in different scenarios (Schaap et al., 2013, Kanellopoulos et al., 2014). Impacts of climate change on farm plans and gross margin of farmers (Kanellopoulos et al., 2014; Wolf et al., 2015l Mandryk, 2016). Possitbilities for improved crop rotations and management, respecting farmers' objectives (Mandryk et al., 2014, 2016).

Points that researchers learned from stakeholders

Points that stakeholders learned from researchers

Estimated effect of extreme events (Schaap et al. 2013)
Main adaptation measures farmers consider (Schaap et al., 2013)
Important objectives for farmers (Mandryk et al., 2014)
Institutional feasibility of regional adaptation measures (Mandryk etal., 2015).

Change in frequency of extreme events (Schaap et al., 2013).
Cost-effectiveness of adaptation measures in different scenarios (Schaap et al., 2013, Kanellopoulos et al., 2014).
Impacts of climate change on farm plans and gross margin of farmers (Kanellopoulos et al., 2014; Wolf et al., 2015l Mandryk, 2016).
Possitbilities for improved crop rotations and management, respecting farmers' objectives (Mandryk et al., 2014, 2016).