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.
ARLUNZ is being used as the modelling platform for the BEST assessment being undertaken in the Rangitāiki catchment in the Bay of Plenty, New Zealand.
