Agricultural land-use change is a dynamic process that varies as a function of social, economic and environmental factors spanning from the local to the global scale. The cumulative regional impacts of these factors on land use adoption decisions by farmers are neither well accounted for nor reflected in agricultural land use planning. We present an innovative spatially explicit agent-based modelling approach (Crop GIS-ABM) that accounts for factors involved in farmer decision making on new irrigation adoption to enable land-use predictions and exploration. The model was designed using a participatory approach, capturing stakeholder insights in a conceptual model of farmer decisions. We demonstrate a case study of the factors influencing the uptake of new irrigation infrastructure and land use in Tasmania, Australia. The model demonstrates how irrigated land-use expansion promotes the diffusion of alternative crops in the region, as well as how coupled social, biophysical and environmental conditions play an important role in crop selection. Our study shows that agricultural land use reflected the evolution of multiple simultaneous interacting biophysical and socio-economic drivers, including soil and climate type, crop and commodity prices, and the accumulated effects of interactive decisions of farmers.
Agricultural land use is influenced not only by multiple aspects of biophysical and socio-economic processes, but also the cumulative impacts of individual farmer decisions. Farmers’ activities and decisions at farm scale shape land use and water utilisation at regional scale, yet land use planning processes do not take into account farmers’ knowledge and decision-making processes as they respond to, and in turn shape, change. Farmers’ voices are missing in the planning system. In this paper, we address the complexity of agricultural land use planning and examine the possibility of agricultural land use planning from the bottom-up via simulation to integrate environmental, economic and human factors that influence land use change. We present an innovative approach to model the interactions between government policy, market signals, and farmers’ land use decisions, and how the accumulated effects of these individual decisions change agricultural land use patterns at regional scale, using spatial and temporal agent-based modeling. A multi-stage mixed method spatial agent-based modeling (ABM) approach, aligned with the Geodesign framework, can incorporate local knowledge and decision-making into models of regional land use change. To illustrate the new approach, we examine the impact of milk market price on changes in land use in Tasmania, Australia. This approach brings together local knowledge with scientific, planning, and policy knowledge to generate dynamic scenarios for informed agricultural land-use planning decisions.
Decision-making on agricultural land-use in Tasmania is a very complex process that involves consideration of opportunities generated from the expansion of irrigation to meet agricultural water demands and mitigate climatic risks to crop yields. Tasmania Irrigation (a State Government owned company) is interested in attracting farmer investment to new water schemes. Tasmanian farmers are concerned about production choices that might maximize investment returns when buying into irrigation schemes. In this context of increased water security and changing crop values, farmers and other decision makers need a framework to guide investment decisions. Geo-spatial Agent-Based modelling (ABM) has potential for representing the dynamic processes in decisionmaking and agricultural systems. It allows for a flexible use of tools and modelling techniques particularly in relation to geospatial modelling. Agent Analyst is a free and open source extension recently developed by ESRIArcGIS to be implemented as a new model-tool type of ABM to analyze the spatial relationships of agents. It has the potential to create agents 1 from GIS layers and execute the agent behavior rules with display of the result of the simulation within the ArcGIS environment. Advances in Agent Analyst allow creating, editing, and running Repast models from within the ArcGIS 10 Geoprocessing framework. As a result, Agent Analyst is a useful tool for analyzing the decision making process and simulating actions of farmers and measuring the resulting system behaviors and outcomes. This paper covers the development of an agent based model using Agent Analyst software to study the consequences of changes in patterns of both land use and water use over time in the Dorset region of Northern Tasmania. Dorset has been a region undergoing significant change from commodity based production to higher value added production with access to guaranteed water and opportunities to consider new and alternative crops. Agent Analyst offers a way of taking into account decision-making on agricultural land use at different levels by taking advantages of ABM within ArcGIS modelling environment. The paper illustrates the types of information that can be generated in order to support farmers' decision with respect to irrigation expansion.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.