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