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Agent-based modelling approach to simulate the impact of irrigation expansion on the region and support effective decision-making

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Shahpari, S and Allison, J, Agent-based modelling approach to simulate the impact of irrigation expansion on the region and support effective decision-making, MODSIM 2017 Book of Abstracts, 3-8 December 2017, Hobart, Tasmania, pp. 74. ISBN 978-0-9872143-6-2 (2017) [Conference Extract]


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Abstract

Investment in irrigation infrastructure in Tasmania prompts the need for highest and best returns in the use of land and water resources. The complexity of decision-making on irrigation expansion to optimise the benefit of regional agricultural land points to a modelling approach that includes not only spatial and agronomic considerations but also captures human judgment and the role of agents in regards to decision making. This research proposes Spatial Agent-Based Modelling (ABM) as a powerful modelling approach to simulate the farmers’ behavioural rules under different agricultural scenarios and assesses their influence on the regional agricultural development and economic return from irrigation. It also outlines the steps by which the farmer behavioural rules were captured and incorporated into Geographic Information Systems (GIS). This spatial ABM simulates the regional land use and crops pattern based on a set of macro-level (regional scale), and micro-level (farm scale) rules via GIS layers. The individual farmer is the subject of the model who is a decision maker, and the agricultural land parcel is the object of the model that includes the geographic information and interactively changes over time based on farmers’ decision.

Crop GIS-ABM is a spatial ABM developed in Agent Analyst toolbox (developed by ESRIArcGIS) that simulates a vector polygon (Block) agent interactions with a generic human agent (Farmer) to determine the changes in patterns of alternative crops over time. A Block agent includes available resources of the spatial and environmental dataset, and a Farmer agent contains human decisions criteria and social networks. The spatial interactions among farmers and the region depend on the place of the land farm and its relation to the irrigation districts. Farmers (generic agents) interact with each other and with their environment in each time step and the make choices to produce a different type of crops (e.g. ryegrass, poppy and hemp). The Block (vector polygon agents) are changed upon the farmers’ decision based on the parameters and interaction with the neighbours land. As a result, the region’s crop pattern emerges based on farmers’ decisions and interaction. Using this approach, the behaviour of farmer agents and their interaction with Block agents and their environment can be simulated under different scenarios such as (proximity to the processing plant, irrigation availability, and neighbour’s decision to adopt alternative crops).

The model presents complex dynamic interdependencies between farmers’ decisions and regional land-use and crop pattern change. The strength of Crop GIS-ABM (by utilising both ABM and GIS) lies in the opportunity to visualise the land parcels’ interactions and represent the changing pattern of alternative crops over time. The Crop GIS-ABM facilitates the human-environment interaction visualisation in GIS as well as flexible crossscale simulation of farmers’ micro behaviours to the macro emergent pattern. The validity of the model is ascertained by comparing the simulation output with different stakeholders’ responses about the future of agriculture in the region. This Spatial ABM could be applied with appropriate modifications on similar regions to simulate the stakeholders’ decision-making processes on agricultural land and water use.

Item Details

Item Type:Conference Extract
Keywords:Spatial ABM, polygon agent, Agent Analyst
Research Division:Built Environment and Design
Research Group:Urban and Regional Planning
Research Field:Urban and Regional Planning not elsewhere classified
Objective Division:Construction
Objective Group:Construction Planning
Objective Field:Regional Planning
UTAS Author:Shahpari, S (Mrs Sahar Shahpari)
UTAS Author:Allison, J (Professor Janelle Allison)
ID Code:123261
Year Published:2017
Deposited By:Institute for Regional Development
Deposited On:2018-01-02
Last Modified:2018-01-08
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