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Exploring Future Scenarios of Rural Land Use Change

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

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Exploring Future Scenarios of Rural Land Use Change

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  1. Exploring Future Scenarios of Rural Land Use Change Daniel Rutledge Environmental Defence Society Conflict in Paradise Conference 11-12 June 2008

  2. 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

  3. Objectives • Introduce how we explore the future • Simple statistics on rural land use trends • Present highlights from several projects using scenarios to explore differentaspects of rural land use change

  4. Step 1: Characterize the present Step 2: Understand the past New Zealand Land Cover Pre-humanEstimate(LENZ) New Zealand Land Cover LCDB2 (2001/2) Exploring the Future: Process Step 5: Explorepossible futures Step 3: Understand past changes & trends Step 4:Identify key drivers & trends and “model” possible future scenarios

  5. LANDSCAPE RURAL PERI-URBAN URBAN RESIDENTIAL COMMERCIAL INDUSTRIAL … LAND USE RURALRESIDENTIAL PRODUCTION CONSERVATION LAND PRACTICE GARDEN MOW CONTROL PESTS FERTILIZE How reversible are decisions? Do they reduce future options? Raise red flags? Rural Land Use:A Simple Model 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?

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

  7. Current Land UseStock Estimate “In play.”Available for current & future primary production. PRODUCTION67.5% Underestimate 1-2%. Does not include local council data. CONSERVATION 31% RESIDENTIAL COMMERCIAL INDUSTRIAL 1% RURALRESIDENTIAL 0.5% Land Use Class Production Conservation Stock estimates based on: Agribase Land Cover Database v2 (LCDB2) Protected Areas Network – NZ (PAN-NZ) Urban Rural Residential

  8. 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 ? RESIDENTIAL COMMERCIAL INDUSTRIAL 1% RURALRESIDENTIAL 0.5% PRODUCTION 67.5% CONSERVATION 31% Conversions ~140,000 ha/yr Tenure Review* To Freehold ~12,600 ha/yr *Courtesy of Susan Walker, Landcare Research

  9. Tenure ReviewConservation Outcomes Most in Needof Protection Least in Needof Protection

  10. NZ Land Use:Consequences of Urbanisation All Blacks Best Super 14 Worst *Assumes 20 years

  11. CONFLICT IN PARADISE! 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 populationincreasing needs, wants, expectationsdecreasing production base leading to…

  12. Now we’re readyto talk about the future…

  13. Key Drivers to 2100

  14. 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

  15. Farms with HEL 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 Slides courtesy ofAnne-Gaelle Ausseil, Landcare Research

  16. HEL Farms Co-Benefits -36% - 47% -27% -50% Erosion Greenhouse Gas Emissions

  17. HEL Farms Carbon Sequestration

  18. Catchment Land Use for Environmental Sustainability CLUES Harris Consulting Slides courtesy of Graham McBride, NIWA

  19. 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

  20. CLUES Process 1) Select Catchment 2) Create scenarios SingleTerminalReach Multiple Reaches 3) Modify land use 5) Compare scenarios 4) Display results e.g., Yield Map (load/area)

  21. 75 tons/year N 150 tons/year N Example of Outputs

  22. IDEASIntegrated Dynamic Environmental Assessment System Slides courtesy of John Dymond and Tim Davie, Landcare Research

  23. 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

  24. IDEAS SCENARIOS Natural Intensive + BMP Present + BMP

  25. Agricultural Job Numbers Gross Economic Output - $/yr Low Flow Rate – Max Water Take (m3/s) Net N Yield to Marine (kg / yr) But wait…!

  26. Good news? Potential Effect on Aquacultureof Increased N Yields Estimatedfutureproductioncapacity. Current production capacity.

  27. Choosing Regional Futures Developing and applyingplanning tools to make informed choices for the future OBJECTIVE 2: Spatial decision support system development OBJECTIVE 1: Improved communication & deliberation tools

  28. NZ & World Climate Change ScenariosNIWA Region District Local Water QualityNIWA HydrologyNIWA Land UseRIKS/LCR/EW External DriversExternal Sources SUITABILITY DemographyUoW-PSC ZoningDistrict Councils ACCESSIBILITY LOCAL INFLUENCE INTEGRATION - LCR LEAD GEONAMICA - RIKS DairyingUoW-SM BiodiversityLCR Waikato Region Dynamic Economy-Environment ModelNZCEE Spatial Indicators SDSSSystem Design

  29. Land Use Abandoned Bare Ground Broad-Acre Forestry Infrastructure Mine Indigenous Vegetation Pastoral - Dairy Good news? Good news? Good news? Pastoral - Other Other Primary Residential Water Wetland Utilities Services Manufacturing Construction 3 Scenarios for Waikato’s Future2001-2050 based on SDSS Prototype Village Life Dairy Expansion Diversification Land for dairying increases ~4% annually Demand for non-dairy primaryproduction land increases Residential land increases 7-fold

  30. 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 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.

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

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