ABM - Rangitaiki Catchment

An Agent-Based model ARLUNZ, was used as the modelling platform for the BEST assessment  undertaken in the Rangitāiki catchment in the Bay of Plenty, New Zealand.

 As part of a participatory process scenarios were defined by the working group and assessed using the Agent-Based ARLUNZ model.

The Biodiversity Ecosystem Services decision making assessmenT (BEST) framework is designed to enable land managers and communities to compare the impact of policy, land manager behaviour, and market drivers on land-use choices and the subsequent flow of ecosystems service. This is to facilitate the incorporation of biodiversity outcomes and relevant ecosystem services into land management decisions. The BEST framework uses participatory approaches and spatial modelling tools (i.e. ARLUNZ) to inform and influence resources management decisions. The framework is being piloted in the Rangitāiki catchment.

The model can also assess the resulting land-use effects caused by changes in farming demographics, social networks, and decision making. It was designed to examine and resolve complex environmental issues within the rural environment, provide information about how farmers will adapt (both economically and socially) to global change, and reduce vulnerability to resource scarcity.


The model consists of three layers – a landscape on which the agents make decisions; the agents themselves and the associated decision-making framework; and the economic information associated with both the landscape and the agents. The model:

  • contains a landscape that holds a range of spatial information about the area (e.g. catchment) being modelled. This includes cadastral boundaries, an initial land-use map to define the current land use for each parcel of land, and productivity zones (e.g. land use capability)
  • uses the cadastral boundaries to generate an agent at the centroid of each parcel. This ‘farm agent’ does not have any decision-making ability but represents the farm as a whole. The area of the farm is also defined on a cellular landscape. This ‘farm agent’ queries the vector datasets for the predominant land use (using the initial land-use map) and productivity zone within the farm boundary. The model simulates the land-use conversion between the types of land uses in the area being modelled
  • creates an agent that represents the farmer at the same location as the ‘farm agent’. This ‘farmer agent’ encompasses the decision making framework for the model. The agent holds a range of social and economic attributes about the farmer, such as the size of their social networks, age, potential revenues, and net revenue from the last step of the model
  • uses the information from the farm and farmer agents to determine the optimised use of the farm parcel based on yields, input costs, output prices, and environmental constraints to generate the expected net revenue for the possible land use enterprises available in the model. The land use that generates the highest net revenue for the farm is defined as the land use that each farmer agent assesses for conversion.

The three components (landscape, agent, economic) are integrated through the development of dependencies and feedback loops between each layer, specifically the decision-making framework built around the farmer agent, which takes into account farm, farmers, and economic information when making a land use decision.


- Reason for using DSS (the underlying science/policy question being investigated)

- Overview of implementation of the DSS

- Outputs and findings

- Any recommendations or lessons learnt

Associated Models

Agent Based Modelling (ABM)

An agent-based model (ABM) (also sometimes related to the term multi-agent system or multi-agent simulation) is a class of computational models for simulating the actions and interactions of autonomous agents (both individual or collective entities such as organizations or groups) with a view to assessing their effects on the system as a whole.


Te Ara Whanui o Rangitaiki - Pathways of the Rangitaiki - Rangitaiki River Forum 2014

Fraser J. Morgan, Philip Brown, and Adam J. Daigneault (2015): Simulation vs. Definition: Differing Approaches to Setting Probabilities for Agent Behaviour. Land 2015, 4, 914-937