Transcript Slide 1

Measures of Short and Long Term
Viability of an Agricultural Region
Researchers: Xianfeng Su, Geoff Carlin
Project leaders: Senthold Asseng, Freeman Cook, Peter
Campbell, Michael Poole
Main collaborators: Steven Schilizzi, Henry Brockman,
Blair Nancarrow, Mescal Stephens, Atakelty Hailu, Angela
Wardell-Johnson, Shams Bhuiyan, Scott Heckbert, Mick
Harcher, Tom McShane, Art Langston
University of
Western
Australia
Simulating Farmers and Land-Use Change
OUTLINE

Project Aims & Background

Biophysical, Economic and Social
Components Analysis

Model Design

Model Framework

Simulation
Improve the long-term viability of
agricultural regions ?
Long-term viability characterised by outcomes from:
Yield/profit, economic
sustainability region
Natural Resource
Condition and Trend
Stable & resilient local communities
Objectives, Scenarios and Case Study Areas
Objectives
Burdekin Delta
Katanning Region
in Blackwood Catchment
• To understand the complex interactions
between human and landscape change
processes
• To study emergent behaviours in
human-landscape systems
• To improve the long-term viability of an
agricultural region
Scenarios
•
•
•
•
•
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Climate change
Environmental risk
perception/management
New technology
Policies
Market
Social values
Biophysical, Economic and Social Changes

-
Trajectory
Individual farmers information
Shires record
Regional data

-
Drivers found
Farmer interviews
Consultants
Literature reviews
Historical land-use change analysis
Digital Elevation Map
risk
Natural/planted
vegetation
30 km
Historical Land-use Changes
Natural/planted vegetation cover
Salinity & Waterlogging risk
30 km
Resource: CMIS CSIRO
Change in crop/pasture ratio, Katanning/Kent shire
& Auction Price of Greasy Wool
% of total area
% of total area
Wool price (cent/kg)
80
crop
70
pasture
800
700
60
600
50
500
40
400
30
300
20
200
10
100
0
0
90
90
92
92
94
94
96
96
Years
Year
98
98
00
0
02
2
1
90
2
3
92
4
5
94
6
7
96
8
Years
9
98
10
11
00
12
13
02
14
Years
Data: Hailu, UWA
Other changes:
Population & age pattern Katanning town
% of Population
Population
50
2400
Male
Female
2300
40
30
8 - 32 Yrs
33 - 57 Yrs
58 - 72 Yrs
2200
20
2100
10
2000
0
1983
1991
1996
Year
2001
1983
1991
1996
Year
Data: ABS
2001
Summary 1990 - 2004
 Population decreased, old age trends
 cropped area increased & pasture area
decreased
 Land value increased
 Costs of farm operating increased
 Market price changed
 New technology: canola has become a major
income for some farmers
 Land degraded
Model design
Integrating biophysical, economic and social models
Landscape
Output
Soil (erosion, degradation)
Surface and ground water
Atmosphere
Land cover (crops/crop
trees/pasture/natural vegetation,
infrastructure, town)
Hydrology model
Land cover and livestock dynamics
% degraded land
Cropping model
Nutrient cycling
Livestock
Society
Output
Farmer: attributes and behaviours.
Household: structure, status, management
strategies
Agent’s Courses of Actions;
Social Network Model
Demographics pattern;
Community: relations & structure, functions
Communities structure and changes
Demography dynamics
Information diffusion
Policies
Economics
International and National market
Bank: interest
Household: productions, investment and
consumption
Output
Economical Model
Household cash flow
Loan
Model design
Concept Behind the Decision Making Process
Capacities & Constrains of
biophysical, economic and social components
Abstraction of Main Objects
Biophysical
Model
Biophysical
Capacities
& Constraints
Financial
Model & Constraints
Financial
Capacities
Decide
Off-farm
income
Climate
Attitude
Model& Constraints
Social
Capacities
Market
Goes to
Produce
&influence
Impact
Consumption
Production
Used in
Contains
Impact
Investment
Land
cover
Change/
impact
Farmer Attitude
& intended
Behaviours
Attitude Model
Decide
Change
New Tech.
Decide
Adopted
Impact
Impact
: External input
Policies (past,
current, future)
Interact
Model design
Decision Making Process

Agent Beliefs + Situation-action rules

behaviour
(Doran 1999)
(reactive agents)

Agent Beliefs + Goals + “Rational” Planning  behaviour
(deliberative agents)
(Doran 1999)

Person needs and value
Opportunity


Ability/capability 
Uncertainty

Behaviour
Model design
Decision making drivers - land use
 Market
 Economic scale and margin
 Rotation
 Time
 Profit
 Habits
 Do the same as last year
-- from Farmer Interviews
Model design
Constraints for running a farm:
 Money
 Time
 Successful plan
 Family
 Skilled casual workers
 Farm size
 Risk management
-- from Farmer Interviews
Top Level COA VIEW
Farmer – farm diary driven COA
Adopt new farming
technology or new
crop COA
Evaluate
environmental
perceptions COA
Evaluate Lifestyle
Factors COA
Abbatoirs
Lumped Farm
Wholesale
Employment COA
Evaluate Regional
Amentities/Services
COA
Major employer viability
COA
Tree
Nursery
Non-farmer low resolution
collection of COA’s
Sheep
Saleyards
Lumped
Retail
Government & other
organization regional services
viability COA
Health Services
Sports Clubs
Education Services
Local
Government
Services
Other Recreation &
Service Clubs, etc.
Model design
Simulation For Farmer Action
Trigger
Action
Individual
/household
consequence
Action A
community
Information
Action B
Information
organization
Gov.
Market
Finance
flow
Behaviour-oriented
A set of Actions
A
C
D
Start End
Feb
Mar.
Dec
Jan
Year
Information transfer ->Make a decision-> take a action ->Behaviour Change
Model design
An Example of COA in Programming
Machine
Farmer
Parameter:
Resource Manager
Atmosphere
Time, Soil
Crop
Seed available
Bank Balance
Resource Pool: FarmerRole
List of machines
Resource Manager
Resource Manager
Resource Pool:
Machine
MachineRole
Aspects:
Sow
COA
Site
preparation
Participant: farmer
Duration: 1week
Timeout: before
sowing time
Atmosphere
(Rainfall)
Start sow
Participant: person,
machine,rainfall,
Duration: effectArea/(ha/d)
Timeout: not late than the
common sow time for
different species
Resource Pool:
atmosphere
A simple Time Template used in the program
Time
Entity (&aspects)
Atmosphere
Landscape
Process
Feb
Mar
Template
Apr
May
Jun
last Week
*
first 2
July
Aug
Sep
Oct
Nov
Dec
Jan
last2
bf Xmas
2weeks*
rainfall(change)
soil (no change)
crop (growth)
canola
wheat
barley
lupin
pasture (growth)
Farmer
(coa)
check Agenda
sow
harvest
sell crops
banking
on loan
pay loan
household
calculate bankBalance
pay monthly bill
one day per month
a day or few days in a week, depending on the task
1 week, from 25th for canola, if rainfall >20mm
2 weeks
3 weeks
whole month
1day
Framework – DIAS/FACET/JEOVIEWER
 Domain model -- DIAS framework
Connect:
-
Hydrology Model
-
Economic Model
-
Social network model
 Social network & COAs -- FACET
 Output -- JEOVIEWER
Simulation