scholarly journals Modelling Farm Growth and Its Impact on Agricultural Land Use: A Country Scale Application of an Agent-Based Model

Land ◽  
2018 ◽  
Vol 7 (3) ◽  
pp. 109 ◽  
Author(s):  
Veronique Beckers ◽  
Jeroen Beckers ◽  
Matthias Vanmaercke ◽  
Etienne Van Hecke ◽  
Anton Van Rompaey ◽  
...  
Keyword(s):  
Land Use ◽  
Agricultural Land ◽  
Farm Size ◽  
Agent Based Models ◽  
Agent Based ◽  
Agent Based Modelling ◽  
Country Level ◽  
Agricultural Employment ◽  
Policy Measures ◽  
Agricultural Trends

The ongoing economic pressure on farmers has resulted in lower gross margins, lower income, and a continuous decrease in the number of farmers in large parts of the world. Most remaining farmers upscale their activities by taking over the land of their former competitors, resulting in a decrease in agricultural employment and an increase in average farm size, accompanied by specialisation and new management techniques. Understanding these significant trends and their impact on the land use and environment requires a deeper knowledge of the mechanisms involved and the impacts of different policy measures. These processes are ideally represented through agent-based modelling. Currently, agent-based models are rarely for larger regions. This paper presents ADAM (Agricultural Dynamics through Agent-based Modelling), using it for the case study of Belgium. ADAM was created to obtain insights in past and current agricultural trends and to explore possible effects of policy measures. ADAM simulates the evolution of a farmer population and their farms at a fine scale on the country level. It produces yearly outputs on the number of farms, their size, and the type of farming activity on every parcel. Results show that ADAM is capable of adequately modelling a farmer population according to past trends and that it can be used to explore the results of a business-as-usual scenario, therefore showing the possibility of creating agent-based models for larger scale real-world applications.

An open framework for agent based modelling of agricultural land use change

2014 ◽  
Vol 61 ◽  
pp. 19-38 ◽  
Author(s):  
Dave Murray-Rust ◽  
Derek T. Robinson ◽  
Eleonore Guillem ◽  
Eleni Karali ◽  
Mark Rounsevell
Keyword(s):  
Land Use ◽  
Land Use Change ◽  
Agricultural Land ◽  
Agricultural Land Use ◽  
Agent Based ◽  
Open Framework ◽  
Agent Based Modelling

Modeling Farmer’s Decision-Making to integrate climate, land use and ecosystem functions

2020 ◽  
Author(s):  
Veronica Gaube ◽  
Claudine Egger ◽  
Christoph Plutzar ◽  
Andreas Mayer ◽  
Helmut Haberl
Keyword(s):  
Decision Making ◽  
Land Use ◽  
Agricultural Land ◽  
Land Use Changes ◽  
Ecosystem Functions ◽  
Land Use Patterns ◽  
Study Region ◽  
Agent Based ◽  
Agent Based Modelling ◽  
Biogeochemical Models

<p>Land use and climate change are important drivers of environmental change and pose a major threat to ecosystems. Although systemic feedbacks between climate and land use changes are expected to have important impacts, research has rarely focused on the interaction between the two drivers. One reason for this could be that forecasts of land use are hardly available on suitable spatial and thematic scales. Agent-based models (ABMs) represent a potentially powerful tool for creating thematic and spatially fine-grained land use scenarios. In order to derive such scenarios, the complex interaction between land users (e.g. farmers) and the broader socio-economic context in which they operate must be taken into account. On landscape to regional scales, agent-based modelling (ABM) is one way to adequately consider these intricacies. ABMs simulate human decisions, and with individual land owners/users as agents, they can simulate usage paths for individual plots of land in thematically fine resolution. Ideally, these simulations are based on an understanding of how farmers make decisions, including anticipated strategies, adaptive behavior and social interactions. In order to develop such an understanding, participatory approaches are useful because they incorporate stakeholders' perspectives into the model calibration, thereby taking into account culture and traditions that often play an important role in land use decisions. A greater proximity to stakeholder perspectives also increases the political relevance of such land use models. Here we present an example where we developed an ABM (SECLAND) parameterised for 1,329 stakeholders, mostly farmers, in the LTSER region Eisenwurzen (Austria) and simulate the changes in land use patterns resulting from their response to three scenarios of changing socio-economic conditions. Summarized in broad categories, the study region currently consists of 67% deciduous and coniferous forests (including logging), 19% grassland, 9% agricultural land and 6% alpine areas. SECLAND simulated small to moderate changes in these percentages until 2050, with little difference between the scenarios. In general, an increase in forests is predicted at the expense of grasslands. The size of agricultural land remains approximately constant. At the level of the 22 land use classes, the trends between the land use change scenarios differ more strongly. This ABM at the individual or farm level is combined with biodiversity and biogeochemical models that analyse how landowners' decision-making affects various ecosystem parameters. We conclude that agent-based modelling is a powerful tool for integrating land use and climate effects into ecosystem projections, especially at regional level.</p>

scholarly journals Comparison of empirical methods for building agent-based models in land use science

2007 ◽  
Vol 2 (1) ◽  
pp. 31-55 ◽  
Author(s):  
Derek T. Robinson ◽  
Daniel G. Brown ◽  
Dawn C. Parker ◽  
Pepijn Schreinemachers ◽  
Marco A. Janssen ◽  
...  
Keyword(s):  
Land Use ◽  
Empirical Methods ◽  
Agent Based Models ◽  
Agent Based

Improving the Efficiency of Large-Scale Agent-Based Models Using Compression Techniques

2012 ◽  
pp. 301-326
Author(s):  
Mitchell Welch ◽  
Paul Kwan ◽  
A.S.M. Sajeev ◽  
Graeme Garner
Keyword(s):  
Large Scale ◽  
Agent Based Models ◽  
Agent Based ◽  
Agent Based Modelling ◽  
Livestock Disease ◽  
Interacting Agents ◽  
National Model ◽  
Complex Phenomena ◽  
Modelling System

Agent-based modelling is becoming a widely used approach for simulating complex phenomena. By making use of emergent behaviour, agent based models can simulate systems right down to the most minute interactions that affect a system’s behaviour. In order to capture the level of detail desired by users, many agent based models now contain hundreds of thousands and even millions of interacting agents. The scale of these models makes them computationally expensive to operate in terms of memory and CPU time, limiting their practicality and use. This chapter details the techniques for applying Dynamic Hierarchical Agent Compression to agent based modelling systems, with the aim of reducing the amount of memory and number of CPU cycles required to manage a set of agents within a model. The scheme outlined extracts the state data stored within a model’s agents and takes advantage of redundancy in this data to reduce the memory required to represent this information. The techniques show how a hierarchical data structure can be used to achieve compression of this data and the techniques for implementing this type of structure within an existing modelling system. The chapter includes a case study that outlines the practical considerations related to the application of this scheme to Australia’s National Model for Emerging Livestock Disease Threats that is currently being developed.

scholarly journals Using Agent-Based Models to Generate Transformation Knowledge for the German Energiewende—Potentials and Challenges Derived from Four Case Studies

Energies ◽  
2020 ◽  
Vol 13 (22) ◽  
pp. 6133
Author(s):  
Georg Holtz ◽  
Christian Schnülle ◽  
Malcolm Yadack ◽  
Jonas Friege ◽  
Thorben Jensen ◽  
...  
Keyword(s):  
Case Studies ◽  
Model Development ◽  
Stakeholder Involvement ◽  
Energy System ◽  
Agent Based Models ◽  
Agent Based ◽  
Industrial Economy ◽  
Agent Based Modelling ◽  
Model Studies ◽  
Phase Out

The German Energiewende is a deliberate transformation of an established industrial economy towards a nearly CO2-free energy system accompanied by a phase out of nuclear energy. Its governance requires knowledge on how to steer the transition from the existing status quo to the target situation (transformation knowledge). The energy system is, however, a complex socio-technical system whose dynamics are influenced by behavioural and institutional aspects, which are badly represented by the dominant techno-economic scenario studies. In this paper, we therefore investigate and identify characteristics of model studies that make agent-based modelling supportive for the generation of transformation knowledge for the Energiewende. This is done by reflecting on the experiences gained from four different applications of agent-based models. In particular, we analyse whether the studies have improved our understanding of policies’ impacts on the energy system, whether the knowledge derived is useful for practitioners, how valid understanding derived by the studies is, and whether the insights can be used beyond the initial case-studies. We conclude that agent-based modelling has a high potential to generate transformation knowledge, but that the design of projects in which the models are developed and used is of major importance to reap this potential. Well-informed and goal-oriented stakeholder involvement and a strong collaboration between data collection and model development are crucial.

An Application of Linear Programming and Geographic Information Systems: Cropland Allocation in Antigua

1992 ◽  
Vol 24 (4) ◽  
pp. 535-549 ◽  
Author(s):  
J C Campbell ◽  
J Radke ◽  
J T Gless ◽  
R M Wirtshafter
Keyword(s):  
Land Use ◽  
Linear Programming ◽  
Natural Resources ◽  
Agricultural Sector ◽  
Agricultural Land ◽  
Geographic Information ◽  
Farm Size ◽  
Agricultural Land Use ◽  
Cost Information ◽  
Policy Application

This paper is focused on the application of linear programming (LP) in combination with a geographic information system (GIS) in planning agricultural land-use strategies. One of the essential inputs for planning any agricultural land-use strategy is a knowledge of the natural resources. This is even more critical in small countries such as those in the Eastern Caribbean, where land-area limitations dictate a greater need for careful assessment and management of these resources. The first step of the proposed methodology is to obtain an assessment of the natural resources available to agriculture. The GIS is used to delineate land-use conflicts and provide reliable information on the natural-resource database. This is followed by combining the data on natural resources with other quantifiable information on available labour, market forecasts, technology, and cost information in order to estimate the economic potential of the agricultural sector. LP is used in this step. Finally, the GIS is applied again to map the crop and land-allocation patterns generated by the LP model. The results are concrete suggestions for resource allocation, farm-size mix, policy application, and implementation projects.

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