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 different
aspects of rural land use change
Exploring the Future: Process
Step 2:
Understand
the past
Step 1:
Characterize
the present
Step 5:
Explore
possible futures
Step 3:
Understand
past changes
& trends
New Zealand
Land Cover
Pre-human
Estimate
(LENZ)
New Zealand
Land Cover
LCDB2
(2001/2)
Step 4:
Identify
key drivers &
trends and
“model”
possible
future
scenarios
Rural Land Use:
A Simple Model
LANDSCAPE
RURAL
PERIURBAN
URBAN
LAND
USE
CONSERVATION
PRODUCTION
RURAL
RESIDENTIAL
RESIDENTIAL
COMMERCIAL
INDUSTRIAL
…
LAND
PRACTICE
CONTROL PESTS
FERTILIZE
MOW
GARDEN
SOME IMPORTANT QUESTIONS
How big and where are land use stocks? What are their associated practices?
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?
LAND USE
(Stocks)
NATIONAL
REGIONAL
No official land use
classification or database
Patchy but generally
very good
fundamental data
Very Good
Zoning
Infrastructure
Valuations
Consent Monitoring
Others?
Consent Monitoring
Others?
Patchy but generally
very good
fundamental data
Not sure…
CLUES Project:
Best attempt to date
Production focus
LAND
PRACTICE
Agribase
LOCAL
Stats Agricultural Census
LAND USE
CHANGE
(Flows)
Best information on land
use change comes from
the land cover database
Current Land Use
Stock Estimate
PRODUCTION
67.5%
CONSERVATION
31%
“In play.”
Available for current &
future primary production.
Underestimate 1-2%.
Does not include local
council data.
RESIDENTIAL
COMMERCIAL
INDUSTRIAL
1%
RURAL
RESIDENTIAL
0.5%
Land Use Class
Production
Stock estimates based on:
Agribase
Land Cover Database v2 (LCDB2)
Protected Areas Network – NZ (PAN-NZ)
Conservation
Urban
Rural Residential
Land Use Flow Estimates
Tenure Review*
To Crown Estate
~11,500 ha/yr
Urbanisation
~550 – 4,500 ha/yr
Private Covenants
~25,000 ha/yr
?
CONSERVATION
31%
PRODUCTION
67.5%
Tenure Review*
To Freehold
~12,600 ha/yr
*Courtesy of Susan Walker, Landcare Research
RURAL
RESIDENTIAL
0.5%
Conversions
~140,000 ha/yr
RESIDENTIAL
COMMERCIAL
INDUSTRIAL
1%
Tenure Review
Conservation Outcomes
THREATENED ENVIRONMENT
Most in Need
of Protection
Least in Need
of Protection
% GAIN
Acutely Threatened
2
Chronically Threatened
5
At Risk
17
Critically Underprotected
14
Underprotected
48
Less reduced & better protected
75
NZ Land Use:
Consequences of Urbanisation
All
Blacks
Best
Super
14
Worst
Land Use
Class
% of
Original NZ
Stock
Urbanisation
Rate*
%/yr
Supply
Remaining
(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
Rural Land Use Trends Summary
•
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 population
increasing needs, wants, expectations
decreasingIN
production
base
CONFLICT
PARADISE!
leading to…
Now we’re ready
to 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)
• 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
Slides courtesy of
Anne-Gaelle Ausseil,
Landcare Research
4
1.2
3.5
1
Current land use
3
- 47%
2.5
2
1.5
1
Mt of CO2 equivalent
Mt Sediment / Year
HEL Farms Co-Benefits
HEL converted into
forestry
-36%
0.8
0.6
0.4
-27%
0.2
-50%
0.5
0
0
CH4 emission
Sediment Load
Erosion
N2O emission from
grazing
N2O emission from
fertiliser
Greenhouse Gas Emissions
HEL Farms Carbon Sequestration
CO2 stock (Mt)
14
Co2 stock (Mt)
12
10
8
6
4
2
0
0
2
4
6
8
10
12
Time (years)
14
16
18
20
CLUES
Catchment Land Use
for Environmental Sustainability
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
1) Select Catchment
Single
Terminal
Reach
2) Create scenarios
Multiple
Reaches
3) Modify land use
4) Display results
e.g., Yield Map (load/area)
5) Compare scenarios
75 tons/year N
150 tons/year N
Example of Outputs
IDEAS
Integrated Dynamic Environmental
Assessment System
Slides courtesy of John Dymond and Tim Davie, Landcare Research
IDEAS: Exploring Integrated
Catchment Management
• Part of Motueka ICM Programme
• Integrated Modeling
– Land-Freshwater-Marine-Economic-Social
• Triple Bottom Line Indicators
Economic-Environmental-Social
• Embedded in a collaborative learning framework
– Strong research networks
– Strong council networks
– Strong community networks
IDEAS SCENARIOS
Natural
Present
+ BMP
Intensive
+ BMP
agricultural job num bers (FTE)
Gross output - land and m arine ($/yr)
Agricultural Job Numbers
Gross Economic Output - $/yr
3,000
180,000,000
160,000,000
2,500
140,000,000
120,000,000
2,000
100,000,000
1,500
80,000,000
60,000,000
1,000
40,000,000
500
20,000,000
0
0
historic
present
bmp_present
intensive
historic
bmp_intensive
Low Flow Rate – Max Water Take (m3/s)
low flow - m ax. w ater take (m 3/s)
10.0
present
bmp_present
intensive
bmp_intensive
net nitrogen yield to m arine (kg/yr)
Net N Yield to Marine (kg / yr)
4,000,000
3,500,000
5.0
3,000,000
But wait…!
2,500,000
0.0
historic
present
bmp_present
intensive
bmp_intensive
2,000,000
-5.0
Series1
1,500,000
1,000,000
-10.0
500,000
0
-15.0
historic
present
bmp_present
intensive
bmp_intensive
Potential Effect on Aquaculture
of Increased N Yields
1.4
Future
Linear Fit Data
Average Chlorophyll Conc. ( g/l)
1.35
y = 0.00013685x + 0.9002
r2 = 0.99776
1.3
1.25
1.2
Estimated
future
production
capacity.
1.15
1.1
1.05
1
Current production
capacity.
Current
0.95
Historical
0.9
0
500
1000
1500
2000
N load (Ton/yr)
2500
3000
3500
4000
Choosing Regional Futures
Developing and applying
planning tools to make
informed choices for the future
OBJECTIVE 1:
Improved communication
& deliberation tools
OBJECTIVE 2:
Spatial decision support
system development
SDSS
System Design
Climate Change Scenarios
External Drivers
NIWA
External Sources
NZ &
World
Region
Hydrology
NIWA
Waikato Region Dynamic
Economy-Environment Model
NZCEE
Water Quality
NIWA
District
Zoning
Demography
Dairying
District Councils
UoW-PSC
UoW-SM
Local
Land Use
RIKS/LCR/EW
SUITABILITY
Biodiversity
LCR
ACCESSIBILITY
Spatial Indicators
LOCAL INFLUENCE
INTEGRATION - LCR LEAD
GEONAMICA - RIKS
3 Scenarios for Waikato’s Future
2001-2050 based on SDSS Prototype
Dairy Expansion
Land for dairying
increases ~4% annually
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
Diversification
Demand for non-dairy primary
production land increases
Village Life
Residential land
increases 7-fold
Summary
• Futures research and scenarios help us think more constructively
about the future
They 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 landscapes
is 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