A presentation made at the WCCA 2011 event in Brisbane, Australia.

1. A FRAMEWORK FOR EXPLORING RURAL
FUTURES THROUGH COLLECTIVE LEARNING
M.E. Wedderburn, T.T. Kingi, A.D. Mackay, M.
Brown, O. Montes de Oca, K. Maani,
R. Burton, H. Campbell, S. Peoples, J Manhire, R.
Dynes, B. Kaye-Blake
AgResearch
University of Otago
University of Queensland
Lincoln University
NZER

2. COUPLING OF HUMAN CAPABILITY AND NATURAL CAPITAL
(SOCIAL-ECOLOGICAL SYSTEM) IS NEW ZEALAND’S COMPETITIVE
ADVANTAGE

3. GLOBAL INTERCONNECTION
World Production NZ% of World Production World Trade NZ% of World Trade
Million tonne Million tonne
Beef 61 1% 6 7%
Game Meat 2 3% 6 42%
Sheep Meat 9 6% 1 38%
Wool 2 10% 0.9 17%
Whole Milk 550 3% 7 1%
Casein 0.2 21%
Butter 7 6% 0.8 48%
Cheese 14 3% 1.2 22%
Milk Powder 7 5% 2.5 35%
Source: FAOSTAT & USDA
Production figures at http://faostat.fao.org/site/569/DesktopDefault.aspx?PageID=569
Export figures are at http://faostat.fao.org/site/535/DesktopDefault.aspx?PageID=535

4. LAND USE CHANGE AND FLEXIBILITY
A KEY CHARACTERISTIC FOR
SUCCESS
Dairy Number Milking Effective Cows/ Total Area of
of Farms Cows/Farm Area (ha) Ha Pasture in Dairying
1990 13,357 160 67 2.4 894,919
2007 11,630 337 121 2.81 1,407,230
Sheep & Number Total Stock Effective Stock Total Area of
Beef of Farms Units per Area (ha) Units/ Pasture in Sheep and
Farm Ha Beef farming
1990 21,300 3,155 516 6.5 10,990,800
2007 13,600 4,268 645 6.2 8,772,000
Source: Meat and Wool NZ, Livestock Improvement

5. RURAL FUTURES OBJECTIVES
• Build capacity to explore, test and develop strategies, policies and
decisions to address future issues
• The future of systems dynamics research in agriculture lies in the
integration of biophysical and social elements
• To facilitate the use of quantitative and qualitative information
produced in the programme in the processes involving stakeholder
interaction
• To explore participatory modelling and processes during this
interaction (i.e. systems dynamics, bayesian networks, influence
diagrams) to stimulate collective learning

6. Framework for exploring Futures Drivers obj 2
Stakeholder workshops 4
Agent Based model 3
System dynamics 4 Reflect
6
Issues
1 Identification
Test
Strategies 5 Collective SH workshops 4
Policies learning Future Scenarios
2
Decisions
4
3
Evaluation of system Farm system Farmer behaviour 1
performance representation
Biological Libraries 2
and behaviour
System workshops 4
Models
Agent Based model 3
Stakeholder experience 4

7. Testing the Framework
Manawatu Study Group

8. Framework for exploring Futures Drivers obj 2
Stakeholder workshops 4
Issues
1 Identification
Collective
learning

9. DRIVERS:INTERNAL MEGA THEMES
•Production efficiency, optimising productivity
• Efficiency - energy use, inputs e.g. fertiliser, chemicals,
precision agriculture, organic agriculture
• New technologies impact – infomatics,
nanotechnologies, genetic engineering
•Product quality, market signals
• Production to specification, new markets/products
• Product – quality, attributes, safety, health
• From Quality assurance to Environmental Management
Systems
•Natural resources quality, availability, production impact
• Decrease negative impacts, enhance resource use
efficiency, climate change risks
• Reporting production impacts – traceability

10. DRIVERS
External Mega Themes
•Biosecurity
•Market Access
Others
•Farmer capacity development
•Industry development and evolution – power and
relationships: farmers/processors/retailers/consumer

11. OWNERSHIP SCALE SUCCESSION LABOUR SUPPLY
•Farm amalgamation •Aging farmers •Skilled labour/expertise
•Offshore investment •Farm succession planning •Skilled labour & management
•Maori ownership •[ wish to treat children equally either imposing •Staff
•Ownership high debt on those farming or fragmenting •Labour
•Form of ownership of farming business family farms] •Lack of incentive for people to get into the
industry
ANIMAL HEALTH BIOSECURITY
•Biosecurity issues
WELFARE •Biosecurity incursions such as current clover SKILLS &
•Changing animal welfare expectations from root weevil
community or market
•Animal health
•Disease outbreak (issues) animal EDUCATION
•Education x 2
•Animal welfare •Skills & education
•Education system
REGULATION URBAN INFLUENCE •People skills – relevance, availability
•Farm regulatory intervention •Urban influence •Increasing difficulty of suitable training for „farm
•“One Plan” •Urban housing cadets‟ and their ilk
•Regulatory hindrances •“reverse sensitivity” i.e. lifestyle blocks with
•Understanding of decision makers
different expectations of rural environment
•Resource consents, consented activities
•Landscape protection, expectations esp in iconic
areas
COST OF CAPITAL
•N-loss •Availability of finance
•Limits on physical production due to emissions to •Lack of capital
•Interest charges
water & air
• Lack of certainty around private property rights CLIMATE CHANGE •Interest rates x 2
•Environmental constraints eg nitrogen loss •Climate change & international rules •Do gooders (environmentalists)
•Statute •Climate changes (weather)
•Govt legislation •Climate change
•Reduced or restricted fertiliser usage and fall off in
•Climatic conditions
production
•Weather
•RMA
•Stable planning environment - political •Changing climate LAND USE BASE
•Land soil type
•Land location
•Soils – sustainability
•Geography
•Hill country erosion
What are the drivers that influence future farm systems?

12. R&D funding
S Resulting causal loop diagram
S
Rural/Urban
Climate Change Efficiency and O community awareness
Production
S S Environment water
quality and quantity
S Environmental S
policy
Regulation
Management
Labour S
On farm S
S
response Attitude Farmer
Profitability Values succession
Capital cost of S
land
Land Use Input costs
Economic Farm structure
outcomes Off farm income
signals
S S S X rate
S
Alternative Industry O
industry organisation Trade Family and
S S
S community
Consumer trends Local community
cultural obligations

13. INSIGHTS
•Stimulated discussion about the interconnectedness
of the system
•Revealed the different world views of stakeholders
•Not all stakeholders found the building of a
conceptual map intuitive
•Guided the prioritisation of drivers to form
scenarios

14. Framework for exploring Futures Drivers obj 2
Stakeholder workshops 4
Issues
1 Identification
Collective SH workshops 4
learning Future Scenarios
2

15. DRIVERS THAT GUIDED DEVELOPMENT
OF 2020 FARM SYSTEMS
•productivity and profitability,
•labour and staff skills,
•regulation, environmental constraints/limits and
continued well being (survivability).

16. Current and future 2020 () attributes of dairy and sheep and beef
base model farms in the Horizons region
Attribute Dairy Sheep and Beef
Ownership Owner operated Owner operated
Effective area 250ha 800ha
Fertiliser N kg/ha 150 (200) 25 (75)
Imported feed KgDM/cow 450 (2000)
Stocking Rate 2.8 cows/ha (3.16) 10.3 (11.4) SU/ha
Productivity KgMS/cow 950 (1230) Lambing 125% (138%)
Beef yearling 320kg (350)
Lacked Stretch

17. Framework for exploring Futures Drivers obj 2
Stakeholder workshops 4
Issues
1 Identification
Collective SH workshops 4
learning Future Scenarios
2
4
3
Evaluation of system Farm system Farmer behaviour 1
performance representation
Biological Libraries 2
and behaviour
System workshops 4
Models
Agent Based model 3
Stakeholder experience 4

18. Micro Macro
Farmers Rural community Supply chain Society
- Farmax and Farm Catchment- Region National International
Overseer Weekly Season Multi-year intergenerational

19. OUTCOMES
•Many of the farm parameters, e.g., stocking rate,
MS per cow and per hectare, were not significantly
pushed beyond the current top performing farms in
the region.
•Agreement that in 10 years’ time the “average”
farmer would continue down a business-as usual-
pathway, shifting to a position that reflected the
current top 10% of the industry.

20. OK AS FAR AS IT GOES BUT .........
The next generation of tools will require the linking of
human behaviour with economic and environmental
objectives
and the building of stakeholder understanding of the
emergent properties, behaviours and unintended
consequences of farm systems experiencing multiple
drivers required in Steps 4 and 5 of the framework

21. Variables influencing farmer ability to make changes
1
1
100% = Maximum influence
13
100
2
1=farm size
90
80 2=land class
70 3=debt levels
60
12 3
4=labour avail
50
40 5=gender
30 6=knowledge/exp
20
11 4 7=farm goals
10
0 8=sense of place
9=networks
10=biophysical/cli
10 5 mate
11=local economy
12=international
9 6 Series1 13=lifestage
Farmer 1
Series2
Farmer 2
Series3
8 7 Farmer 3

22. Transit/
Birth and F/Time Busin/s T/over of
Gen C n of
socialis/n on farm expans farm
respons
Transit/n Transit/
Birth and F/Time Busin/s T/over of Busin/s
Gen B of Consol/n n of Retire/t
socialis/n on farm expans farm expans
respons respons
Transit/n
T/over of Busin/s
Gen A Consol/n of Retire/t
farm expans
respons
MODERATE LIMITED HIGH LIMITED MODERATE LIMITED HIGH LIMITED MODERATE
Change CHANGE CHANGE CHANGE CHANGE CHANGE CHANGE CHANGE CHANGE CHANGE
Farmer life cycle: traditional succession and impacts on
change

23. AGENT BASED MODEL FARM DESCRIPTIONS

24. AGENT BASED MODEL FARMER TYPES

25. INSIGHTS ON FRAMEWORK
•Need a diversity of world views
•Participants expanding their perceptions and the
knowledge they will need to take into consideration
when strategic planning.
•Allows the exploration of multiple pressures
simultaneously
• It is generic but is anchored in context and place.

26. REFLECTIONS BY THE RESEARCH TEAM
•Ability to apply models to systems
•Building interdisciplinarity
•Developing the ability to have conversations across
social and biophysical
•Joined up view
•Tackling complexity and uncertainty

27. CHARACTERISTICS OF THE TEAM
MEMBERS
•Abundance mentality (no hoarding)
•Connectors
•Good discipline science
•Confident enough to simplify and bring into a
context
•Translator
•Leadership
•Shared goal

28. Thanks to the funder
FRST