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Measures of Short and Long Term Viability of an Agricultural Region Researchers: Xianfeng Su, Geoff Carlin Project leaders: Senthold Asseng, Freeman Cook,

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Presentation on theme: "Measures of Short and Long Term Viability of an Agricultural Region Researchers: Xianfeng Su, Geoff Carlin Project leaders: Senthold Asseng, Freeman Cook,"— Presentation transcript:

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

2  Project Aims & Background  Biophysical, Economic and Social Components Analysis  Model Design  Model Framework  Simulation OUTLINE Simulating Farmers and Land-Use Change

3 Improve the long-term viability of agricultural regions ? Natural Resource Condition and Trend Stable & resilient local communities Long-term viability characterised by outcomes from: Yield/profit, economic sustainability region

4 Objectives, Scenarios and Case Study Areas Objectives 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 Climate change Environmental risk perception/management New technology Policies Market Social values Katanning Region in Blackwood Catchment Burdekin Delta

5 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

6 30 km risk Natural/planted vegetation Digital Elevation Map

7 Historical Land-use Changes Resource: CMIS CSIRO Salinity & Waterlogging riskNatural/planted vegetation cover 30 km

8 Change in crop/pasture ratio, Katanning/Kent shire & Auction Price of Greasy Wool Data: Hailu, UWA Wool price (cent/kg) Years % of total area Years

9 Other changes: Population & age pattern Katanning town Data: ABS

10  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 Summary

11 Model design Integrating biophysical, economic and social models Cropping model Social Network Model Economical Model Policies Landscape Soil (erosion, degradation) Surface and ground water Land cover (crops/crop trees/pasture/natural vegetation, infrastructure, town) Livestock Hydrology model Output Land cover and livestock dynamics % degraded land Nutrient cycling Society Farmer: attributes and behaviours. Household: structure, status, management strategies Community: relations & structure, functions Demography dynamics Economics International and National market Bank: interest Household: productions, investment and consumption Output Household cash flow Loan Atmosphere Output Agent’s Courses of Actions; Demographics pattern; Communities structure and changes Information diffusion

12 Concept Behind the Decision Making Process Capacities & Constrains of biophysical, economic and social components Model design

13 Abstraction of Main Objects Biophysical Capacities & ConstraintsFinancial Capacities & ConstraintsSocial Capacities & Constraints

14 Decision Making Process Model design  Agent Beliefs + Situation-action rules behaviour (reactive agents) (Doran 1999)  Agent Beliefs + Goals + “Rational” Planning behaviour (deliberative agents) (Doran 1999)  Person needs and value Ability/capability Opportunity Uncertainty Behaviour    

15 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

16  Money  Time  Successful plan  Family  Skilled casual workers  Farm size  Risk management -- from Farmer Interviews Constraints for running a farm: Model design

17 Evaluate Lifestyle Factors COA Farmer – farm diary driven COA Adopt new farming technology or new crop COA Evaluate environmental perceptions COA Non-farmer low resolution collection of COA’s Employment COA Evaluate Regional Amentities/Services COA Major employer viability COA Abbatoirs Tree Nursery Sheep Saleyards Lumped Retail Lumped Farm Wholesale Government & other organization regional services viability COA Health Services Education Services Local Government Services Sports Clubs Other Recreation & Service Clubs, etc. Top Level COA VIEW

18 Simulation For Farmer Action A set of Actions A C Behaviour-oriented Finance flow Trigger Action Information organization Individual /household consequence Gov. Market community D JanFebDecMar. Action AAction B Start End Year Information Information transfer -> Make a decision-> take a action ->Behaviour Change Model design

19 An Example of COA in Programming Model design

20 A simple Time Template used in the program

21 Framework – DIAS/FACET/JEOVIEWER  Domain model -- DIAS framework Connect: -Hydrology Model -Economic Model -Social network model  Social network & COAs -- FACET  Output -- JEOVIEWER

22 Simulation


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