using soft-systems methods to evaluate the outputs from multi-objective land use planning tools...
TRANSCRIPT
Using soft-systems methods to evaluate the outputs from
multi-objective land use planning tools
Keith Matthews, The Macaulay Institute,Aberdeen, U.K.
iEMSs 2002 – Integrated Assessment and Decision Support MODSS - Special Session
Land-use Planning (LADSS)
Strategic
Farm-scale: individual land-management units
Spatially explicit
Multi-objective: financial, social and environmental
Exploiting systems-based research
Assisting by finding and evaluating alternativepatterns of land-use.
Search and optimisation tools to define the trade-off between objectives
Utopian Solution
Obj
ectiv
e 2
Opt
imum
Objective 1 Optimum
RegionInfeasible
RegionFeasible
Pareto-optimal range Objective1
Pare
to-o
ptim
al r
ange
Obj
ectiv
e2
Objective1Pareto-optimal solutions
Obj
ectiv
e2
Decision Support
Soft Systems
Multiple perspective appraisal (rapid rural appraisal)
Exercises - best if real world
Stakeholders
Convenor - independent
Facilitator - reporter
Introduction - exercises - plenary session(s)
Delegates
Task - individual and group
Materials
Bank Advisor, Systems Analyst, Agri-Science (2), Biologist, Conservationist, Estate Managers (2), Farm Managers (2)
Workable compromise - between financial and diversity goals -pattern of allocation
Maps, photos, tables - soil, climate, topography. Some interpretation - land capability, conservation value.
Option of map or table output
Test Application
Research station: 90 blocks, 10 uses - typical size/options.
Financial returns and diversity/evenness of land use (Shannon index)
Assumptions/Rules of the game
Ours Theirs10 land uses 5 land use - trees classified
Diversification possible
Existing boundaries Accepted (but limiting)
No bought or sold Renting out possible
Existing land use does Fix all existing woodlandnot preclude a new one
Capital and infrastruc- Accepted (but a real worldture not limiting problem of lock-in)
Delegate Maps
Individual
Current
LB mGA
P&P mGA
Financial Returns - NPV(£M)
1 2 3 4 5
1.5
1.0
Div
ersi
ty -
Sha
nnon
-Wei
ner
Inde
x
0.0
0.5
2.0
6
E1-2
E1-1SA2 AG1
E2
B1 F2C2 AG2
BA1
Individual Allocations - 1
Individual
Current
Proximity-P&P mGA
P&P mGA
Financial Returns - NPV(£M)
1 2 3 4 5
1.5
1.0
Div
ersi
ty -
Sha
nnon
-Wei
ner
Inde
x
0.5
2.0
6
E1-2
BA1
E1-1SA2 AG1
E2
B1F2
C2AG2
0.0
Individual Allocations - 2
Individual Group Centre of Gravity
LB mGA P&P mGA
G1
Financial Returns - NPV(£M)
1 2 3 4 5
1.5
1.0
Div
ersi
ty -
Shan
non-
Wei
ner I
ndex
Sub-group 1
B1
E1-2
BA-1
E1-2
AG1
Group Allocations - 1
Sub-group 2
Financial Returns - NPV(£M)
G2
1 2 3 4 5
Individual Group Centre of Gravity
LB mGA P&P mGA
1.5
1.0
Div
ersi
ty -
Shan
non-
Wei
ner I
ndex F2
C2AG2
SA2
Group Allocations - 2
Delegate Influence-1
Find the weights that would need to applied to the C-o-G calculation result in a C-o-G located at the group allocation.
Individual Group Centre of Gravity
LB mGA P&P mGA
G1
Financial Returns - NPV(£M)
1 2 3 4 5
1.5
1.0
Div
ersi
ty -
Shan
non-
Wei
ner I
ndex
Sub-group 1
B1
E1-2
BA-1
E1-2
AG1
Delegate PMI Mean WTBA1 0.20 0.22AG1 0.16 0.21B1 0.0 0.04E1-1 0.0 0.12E1-2 0.64 0.48
Sub-group 2
Financial Returns - NPV(£M)
G2
1 2 3 4 5
Individual Group Centre of Gravity
LB mGA P&P mGA
1.5
1.0
Div
ersi
ty -
Shan
non-
Wei
ner I
ndex F2
C2AG2
SA2
Delegate Influence-2
Delegate PMI Mean WTSA2 0.26 0.19AG2 0.22 0.17C2 0.18 0.19E2 0.34 0.29F2 0.0 0.14
Conclusions
Practitioners operate within social constraints on management
Solutions proposed across the trade-off
Practical management concerns can be incorporatedinto the optimisation algorithms to make the solutions found more realistic.
Finding range of best compromise solutions is useful -compromise between individual practitioners can result inpoorer solutions
http://www.macaulay.ac.uk/LADSS
Individual
Current
LB mGA
P&P mGA
Financial Returns - NPV(£M)
1 2 3 4 5
1.5
1.0
Div
ersi
ty -
Sha
nnon
-Wei
ner
Inde
x
0.0
0.5
2.0
6
E1-2
E1-1SA2 AG1
E2
B1 F2C2 AG2
BA1
Individual Allocations - 1
Individual Allocations - 1