exploring future scenarios of rural land use change daniel rutledge environmental defence society...

Exploring Future Scenarios of Rural Land Use Change

Daniel Rutledge

Environmental Defence Society

Conflict in Paradise Conference

11-12 June 2008

Acknowledgements• Environmental Defence Society

• Landcare Research– Alison Collins, Allan Hewitt, Anne-Gaelle Ausseil, Andrew Fenemor, Bob Frame,

Bruce Burns, Craig Briggs, Craig Trotter, Graham Sparling, Jeremy Gabe, John Dymond, John Innes, John Scott, Maureen Mara, Mike Krausse, Niels Hoffmann, Penny Nelson, Richard Gordon, Robert Gibb, Susan Walker

– Robbie “Combinatorial” Price

• University of Waikato: Louis Schipper, Myk Cameron, Jacques Poot

• NIWA: Graham McBride, Sandy Elliott, Andrew Tait, Ross Woods

• Environment Waikato: Beat Huser, Derek Phyn

• AgResearch: Liz Wedderburn, Bruce Small

• Market Economics: Garry McDonald

• Alchemists Ltd: Tony Fenton

• Homefront: Susanna Rutledge, Bugs, Daffy

Objectives

1. Introduce how we explore the future

2. Simple statistics on rural land use trends

3. Present highlights from several projects using scenarios to explore differentaspects of rural land use change

New ZealandLand CoverLCDB2(2001/2)

Step 1: Characterizethe present

Exploring the Future: Process

Step 3: Understand

past changes& trends

New ZealandLand CoverPre-humanEstimate(LENZ)

Step 2: Understand

the past

Step 4:Identify

key drivers & trends and

“model”possible

futurescenarios

Step 5: Explore

possible futures

Rural Land Use:A Simple Model

SOME IMPORTANT QUESTIONS

How big and where are land use stocks? What are their associated practices?

RURAL PERI-URBAN

URBANLANDSCAPE

CONSERVATION PRODUCTION

RESIDENTIALCOMMERCIALINDUSTRIAL…

RURALRESIDENTIAL

LANDUSE

LANDPRACTICE CONTROL PESTS FERTILIZE MOW GARDEN

How is land use changing & where, i.e. flows? What drives various changes?

What are the cultural, economic, environmental & social consequences?

How reversible are decisions? Do they reduce future options? Raise red flags?

How good is our collective knowledge of land use, practice, and change?

NATIONAL REGIONAL LOCAL

LAND USE

(Stocks)

No official land use classification or database

CLUES Project: Best attempt to date Production focus

Patchy but generally very good fundamental data

Very Good Zoning Infrastructure Valuations

LAND

PRACTICE

Agribase

Stats Agricultural Census

Consent MonitoringOthers?

Consent MonitoringOthers?

LAND USE CHANGE

(Flows)

Best information on land use change comes from the land cover database

Patchy but generally very good fundamental data

Not sure…

Current Land UseStock Estimate

CONSERVATION31%

PRODUCTION67.5%

RESIDENTIALCOMMERCIALINDUSTRIAL

1%

RURALRESIDENTIAL

0.5%

Stock estimates based on: Agribase Land Cover Database v2 (LCDB2) Protected Areas Network – NZ (PAN-NZ)

“In play.”Available for current &

future primary production.

Underestimate 1-2%.Does not include local council data.

Production

Conservation

Urban

Rural Residential

Land Use Class

Land Use Flow Estimates

CONSERVATION31%

PRODUCTION67.5%

RESIDENTIALCOMMERCIALINDUSTRIAL

1%

RURALRESIDENTIAL

0.5%

*Courtesy of Susan Walker, Landcare Research

Private Covenants~25,000 ha/yr

Conversions~140,000 ha/yr

Urbanisation~550 – 4,500 ha/yr

?

Tenure Review*To Freehold

~12,600 ha/yr

Tenure Review*To Crown Estate

~11,500 ha/yr

THREATENED ENVIRONMENT % GAINAcutely Threatened 2

Chronically Threatened 5

At Risk 17

Critically Underprotected 14

Underprotected 48

Less reduced & better protected 75

Most in Needof Protection

Least in Needof Protection

Tenure ReviewConservation Outcomes

NZ Land Use:Consequences of Urbanisation

Land Use Class

% ofOriginal NZ

Stock

UrbanisationRate*

%/yr

SupplyRemaining

(Years)

1 1 0.11 880

2 5 0.08 1,224

3 9 0.05 2,079

4 11 0.03 2,975

5 1 0.02 5,486

6 29 0.01 9,317

7 22 0.01 18,713

8 22 <0.01 82,779

*Assumes 20 years

Best

Worst

AllBlacks

Super14

• Production– Net outflow of land to urban and conservation– Urbanisation seems to disproportionately affect our best lands & soils

• Conservation– Net inflows of land from production– Conservation outcomes may not be as good as they could be (controversial…)

• Land use practices– Not addressed – too hard!– Lots of good things happening but hard to get our heads around it.

• Data not very good!

• Take Home Message:increasing populationincreasing needs, wants, expectationsdecreasing production baseleading to…

Rural Land Use Trends Summary

CONFLICT IN PARADISE!

Now we’re readyto talk about the future…

Key Drivers to 2100

Driver Implications for Rural Land Use

Culture Differing values, beliefs, and worldviews

Population Increasing, Ageing, More Culturally Diverse,More Urban, Loss of Production Land

Climate Change Shifting Production, Changing Practices or Costs for Mitigation, Impacts on Infrastructure

Energy Everything! Production Costs, Transport Costs, Tourism, Search for Renewable Energy

Markets Increasing demand (see Population)

Consumers Demand for sustainable practices, Eco-verification, Preference for regional/local food production

Technology Increasing Efficiencies (e.g., precision farming), Potential for Greener Practices, R&D Investment

Exploring Coastal Environments• DOC & LCR project to support review of

National Coastal Policy Statement

• Develop scenarios to evaluate condition of terrestrial coastal environments

• Results– Condition (Remaining Native Land Cover)

• National: 48%• Scenario 1: 54% (better)• Scenarios 2-5: 31-43% (worse)

– Remaining Native Land Cover Protected• National: 62%• Scenarios: 34-44% (all worse)

• Conclusions:– Coastal environments in worse condition– More vulnerable to future development– More susceptible to future biodiversity loss

Exploring Climate Change Mitigation

• Context– Manawatu Region: Sustainable Land

Use Initiative following 2004 storm

– Prepare whole farm plans to identify and properly manage highly erodible lands (HEL)

Slides courtesy ofAnne-Gaelle Ausseil,Landcare Research

• Scenario– Convert HEL on first 500 priority

farms to plantation forestry

• Estimate co-benefits– Sedimentation– GHG emissions– C storage from plantation forestry

Farms with HEL

HEL Farms Co-Benefits

0

0.2

0.4

0.6

0.8

1

1.2

CH4 emission N2O emission fromgrazing

N2O emission fromfertiliser

Mt

of C

O2

equi

vale

nt

Current land use

HEL converted intoforestry-36%

0

0.5

1

1.5

2

2.5

3

3.5

4

Sediment Load

Mt

Sed

imen

t /

Yea

r

Greenhouse Gas EmissionsErosion

- 47%

-27%

-50%

HEL Farms Carbon Sequestration

CO2 stock (Mt)

0

2

4

6

8

10

12

14

0 2 4 6 8 10 12 14 16 18 20

Time (years)

Co

2 st

ock

(M

t)

Catchment Land Use for Environmental Sustainability

CLUES

Harris Consulting

Slides courtesy of Graham McBride, NIWA

CLUES: Exploring Land Use Impacts on Water Quality & Economics

• Explores impacts of land use & land use change on nutrient loads (P & N)

• Integrates several biophysical models +an economic model

• Estimates nutrient loadings & economics/employment on selected (sub)catchments based on land use

CLUES Process

MultipleReaches

SingleTerminalReach

1) Select Catchment 2) Create scenarios

5) Compare scenarios3) Modify land use

4) Display results

e.g., Yield Map (load/area)

150 tons/year N75 tons/year N

Example of Outputs

IDEASIntegrated Dynamic Environmental

Assessment System

Slides courtesy of John Dymond and Tim Davie, Landcare Research

IDEAS: Exploring IntegratedCatchment Management

• Part of Motueka ICM Programme

• Integrated Modeling– Land-Freshwater-Marine-Economic-Social

• Triple Bottom Line IndicatorsEconomic-Environmental-Social

• Embedded in a collaborative learning framework– Strong research networks– Strong council networks– Strong community networks

Natural

IDEAS SCENARIOS

Present + BMP

Intensive + BMP

agricultural job numbers (FTE)

0

500

1,000

1,500

2,000

2,500

3,000

historic present bmp_present intensive bmp_intensive

Gross output - land and marine ($/yr)

0

20,000,000

40,000,000

60,000,000

80,000,000

100,000,000

120,000,000

140,000,000

160,000,000

180,000,000

historic present bmp_present intensive bmp_intensive

lowflow - max. water take (m3/s)

-15.0

-10.0

-5.0

0.0

5.0

10.0

historic present bmp_present intensive bmp_intensive

net nitrogen yield to marine (kg/yr)

0

500,000

1,000,000

1,500,000

2,000,000

2,500,000

3,000,000

3,500,000

4,000,000

historic present bmp_present intensive bmp_intensive

Series1

Agricultural Job Numbers Gross Economic Output - $/yr

Low Flow Rate – Max Water Take (m3/s) Net N Yield to Marine (kg / yr)

But wait…!

Potential Effect on Aquacultureof Increased N Yields

0 500 1000 1500 2000 2500 3000 3500 40000.9

0.95

1

1.05

1.1

1.15

1.2

1.25

1.3

1.35

1.4

y = 0.00013685x + 0.9002

r2 = 0.99776

N load (Ton/yr)

Ave

rage

Chl

orop

hyll

Con

c. (

g/l)

Historical

Current

FutureLinear Fit Data

Current production capacity.

Estimatedfuture

productioncapacity.Good news?

OBJECTIVE 1:Improved communication

& deliberation tools

OBJECTIVE 2:Spatial decision support

system development

Choosing Regional Futures

Developing and applyingplanning tools to make informed choices for the future

NZ & World

Waikato Region Dynamic Economy-Environment Model

NZCEE

External DriversExternal Sources

Water QualityNIWA

DemographyUoW-PSC

ZoningDistrict Councils

BiodiversityLCR Spatial Indicators

Climate Change ScenariosNIWA

DairyingUoW-SM

Land UseRIKS/LCR/EW

SUITABILITY

ACCESSIBILITY

LOCAL INFLUENCE

HydrologyNIWA

SDSSSystem Design

Region

District

Local

GEONAMICA - RIKSINTEGRATION - LCR LEAD

3 Scenarios for Waikato’s Future2001-2050 based on SDSS Prototype

Land Use

Abandoned

Bare Ground

Broad-Acre

Forestry

Infrastructure

Mine

Indigenous Vegetation

Pastoral - Dairy

Pastoral - Other

Other Primary

Residential

Water

Wetland

Utilities

Services

Manufacturing

Construction

Dairy Expansion Diversification Village LifeLand for dairying

increases ~4% annuallyDemand for non-dairy primary

production land increasesResidential landincreases 7-fold

Good news?

Good news?

Good news?

Summary• Futures research and scenarios help us think more constructively

about the futureThey help us make decisions – they do not provide solutions.

• Understanding and (spatially-explicit) modelling of land use &land use change and its consequences for rural landscapesis now gaining momentum

• Rural Landscapes– Conflicts arising from competing demands will only intensify over time– How to produce more with less land? Technology to the rescue?– How best to decide amongst those competing uses? Who decides?

• Better futures requires better information!– We need better information about land use– “Better” includes quantity (targeted), quality, and accessibility– But – balance between public good & private opportunity? Confidentiality?– Ultimately hidden/inaccessible data is the same as no data.

“The best way to predict the future is to create it.”

Peter Drucker