Exploring Future Scenarios of Rural Land Use Change Daniel Rutledge Environmental Defence Society Conflict in Paradise Conference 11-12 June 2008
Dec 27, 2015
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