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
AgResearchUniversity of OtagoUniversity of QueenslandLincoln UniversityNZER
COUPLING OF HUMAN CAPABILITY AND NATURAL CAPITAL (SOCIAL-ECOLOGICAL SYSTEM) IS NEW ZEALAND’S COMPETITIVE
ADVANTAGE
GLOBAL INTERCONNECTIONWorld
Production
Million tonne
NZ% of World Production World Trade
Million tonne
NZ% of World Trade
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
LAND USE CHANGE AND FLEXIBILITY A KEY CHARACTERISTIC FOR
SUCCESS
Dairy Number of Farms
Milking Cows/Farm
Effective Area (ha)
Cows/Ha
Total Area of Pasture in Dairying
1990 13,357 160 67 2.4 894,919
2007 11,630 337 121 2.81 1,407,230
Sheep & Beef
Number of Farms
Total Stock Units per Farm
Effective Area (ha)
Stock Units/Ha
Total Area of Pasture in Sheep and 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
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
Issues Identification
Future Scenarios
Farm system representation and behaviour
Evaluation of system performance
Test StrategiesPolicies Decisions
Reflect
Collective learning
Farmer behaviour 1
Biological Libraries 2
System workshops 4ModelsAgent Based model 3Stakeholder experience 4
Agent Based model 3System dynamics 4
Drivers obj 2Stakeholder workshops 4
SH workshops 4
1
2
34
5
6
Framework for exploring Futures
Testing the FrameworkManawatu Study Group
Issues Identification
Collective learning
Drivers obj 2Stakeholder workshops 4
1
Framework for exploring Futures
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
DRIVERS
External Mega Themes•Biosecurity
•Market Access
Others•Farmer capacity development
•Industry development and evolution – power and relationships: farmers/processors/retailers/consumer
OWNERSHIP SCALE• Farm amalgamation• Offshore investment• Maori ownership• Ownership• Form of ownership of farming business
ANIMAL HEALTH WELFARE• Changing animal welfare expectations from
community or market• Animal health• Animal welfare
SUCCESSION• Aging farmers• Farm succession planning• [ wish to treat children equally either
imposing high debt on those farming or fragmenting family farms]
LABOUR SUPPLY• Skilled labour/expertise• Skilled labour & management• Staff• Labour• Lack of incentive for people to get into the
industry
BIOSECURITY• Biosecurity issues• Biosecurity incursions such as current
clover root weevil• Disease outbreak (issues) animal
URBAN INFLUENCE• Urban influence• Urban housing• “reverse sensitivity” i.e. lifestyle blocks with
different expectations of rural environment
SKILLS & EDUCATION• Education x 2• Skills & education• Education system• People skills – relevance, availability • Increasing difficulty of suitable training for
‘farm cadets’ and their ilk
COST OF CAPITAL• Availability of finance• Lack of capital• Interest charges• Interest rates x 2• Do gooders (environmentalists)
REGULATION• Farm regulatory intervention• “One Plan”• Regulatory hindrances• Understanding of decision makers• Resource consents, consented activities• Landscape protection, expectations esp in iconic
areas• N-loss• Limits on physical production due to emissions to
water & air• Lack of certainty around private property rights• Environmental constraints eg nitrogen loss• Statute• Govt legislation• Reduced or restricted fertiliser usage and fall off
in production• RMA• Stable planning environment - political
CLIMATE CHANGE• Climate change & international rules• Climate changes (weather)• Climate change• Climatic conditions• Weather• 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?
Efficiency andProduction
Environment waterquality and quantity
Rural/Urbancommunity awareness
Regulation
Attitude FarmerValues
Management
Profitability
R&D funding
Labour
Capital cost ofland
Land Useoutcomes
Alternativeindustry
Consumer trends
Industryorganisation Trade
X rate
Input costsFarm structure
succession
SS
SS
S
S S
SS
O
S
S
cultural obligations
Local community
Off farm income
S
S
Environmentalpolicy
S
O
S
Climate Change
S
S
On farmresponse
Family andcommunity
Economicsignals
Resulting causal loop diagram
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
Issues Identification
Future Scenarios
Collective learning
Drivers obj 2Stakeholder workshops 4
SH workshops 4
1
2
Framework for exploring Futures
DRIVERS THAT GUIDED DEVELOPMENT OF 2020 FARM SYSTEMS
•productivity and profitability, •labour and staff skills, •regulation, environmental constraints/limits and continued well being (survivability).
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)
Current and future 2020 () attributes of dairy and sheep and beef base model farms in the Horizons region
Lacked Stretch
Issues Identification
Future Scenarios
Farm system representation and behaviour
Evaluation of system performance
Collective learning
Farmer behaviour 1
Biological Libraries 2
System workshops 4ModelsAgent Based model 3Stakeholder experience 4
Drivers obj 2Stakeholder workshops 4
SH workshops 4
1
2
34
Framework for exploring Futures
- Farmax and Overseer
Micro Macro
Farmers Rural community Supply chain Society
Farm Catchment- Region National International
Weekly Season Multi-year intergenerational
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.
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
1
2
3
4
5
6
78
9
10
11
12
13
0
50
100
Series1
Series2
Series3
1
Farmer 1Farmer 2Farmer 3
1=farm size2=land class3=debt levels4=labour avail5=gender6=knowledge/exp7=farm goals8=sense of place9=networks10=biophysical/climate11=local economy12=international13=lifestage
100% = Maximum influence
Variables influencing farmer ability to make changes
Gen C Birth and socialis/n
F/Time on farm
Busin/s expans
Transit/n of respons
T/over of farm
Gen B Birth and socialis/n
F/Time on farm
Busin/s expans
Transit/n of respons
T/over of farm Consol/n Busin/s
expans
Transit/n of respons
Retire/t
Gen A T/over of farm Consol/n Busin/s
expans
Transit/n of respons
Retire/t
Change MODERATE CHANGE
LIMITED CHANGE
HIGH CHANGE
LIMITED CHANGE
MODERATE CHANGE
LIMITED CHANGE
HIGH CHANGE
LIMITED CHANGE
MODERATE CHANGE
Farmer life cycle: traditional succession and impacts on change
AGENT BASED MODEL FARM DESCRIPTIONS
AGENT BASED MODEL FARMER TYPES
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.
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
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
Thanks to the funderFRST