Abstract
Agent-based models provide detailed, bottom-up approaches to investigate complex socio-ecological systems. This study presents a first step towards a modular agent-based simulation that is based upon empirical data, as well as environmental suitability maps and an assessment of livestock units. To illustrate the capabilities of our simulation, we use a geographically explicit approach to simulate a component of the production of animal products of a rural settlement in the lower Bakırçay catchment, western Turkey. The model structurally couples various agent types representing several elements and processes of the animal husbandry and food production value chain, such as sedentary herders—practising daily, short-distance pastoralism—and their flocks of goats and sheep, as well as milking and slaughtering. The modelling tool captures the fundamental socio-ecological dynamics of animal husbandry and food production in rural settlements. Therefore, the tool is valuable as a basis to discuss hypotheses regarding the number of animals that are needed to cover the requirements of different growing populations.
Highlights
The transparent modelling of complex interactions between interrelated components, such as herders and their flocks, is a big challenge for simulating socio-ecological dynamics [1]
This study presents a first step towards a modular agent-based simulation that is based upon empirical data, as well as environmental suitability maps and an assessment of livestock units
Due to the open and flexible structure of our agent-based modelling approach, the simulation tool can be adapted to a wide range of research questions about animal husbandry and food production
Summary
The transparent modelling of complex interactions between interrelated components, such as herders and their flocks, is a big challenge for simulating socio-ecological dynamics [1] For this purpose, different simulation approaches have evolved over the years [2,3]. In contrast to cellular automata and GIS-based approaches, the agent-based representation of the decision-making and feedback between agents and their environment enables bottom-up approaches for studying complex socio-ecological systems [11]. This method allows for the simulation of an individual’s specific behaviour, as well as its actions and interactions with the environment. Due to the flexibility of ABMs, a wide range of modelling approaches has evolved that allows for the study of socio-ecological systems and human–environment interactions, e.g., [1,13], as well as animal husbandry and pastoralist land use practises, e.g., [14,15,16]
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