Agent-based spatial models applied to agriculture: A simulation tool

By Thomas Berger. 2001

758 citations recorded by the source corpus.

This paper presents a spatial multi-agent programming model, which has been developed for assessing policy options in the diffusion of innovations and resource use changes. Unlike conventional simulation tools used in agricultural economics, the model class described here applies a multi-agent/cellular automata (CA) approach by using heterogeneous farm-household models and capturing their social and spatial interactions explicitly. The individual choice of the farm-household among available production, consumption, investment and marketing alternatives is represented in recursive linear programming models. Adoption constraints are introduced in form of network-threshold values that reflect the cumulative effects of experience and observation of peers' experiences. The model's economic and hydrologic components are tightly connected into a spatial framework. The integration of economic and hydrologic processes facilitates the consideration of feedback effects in the use of water for inigation. The simulation runs of the model are carried out with an empirical data set, which has been derived from various data sources on an agricultural region in Chile. Simulation results show that agent-based spatial modelling constitutes a powerful approach to better understanding processes of innovation and resource use change. © 2001 Elsevier Science B.V. All rights reserved.

Subfields: Global and Planetary Change, Strategy and Management

View Paper: https://www.semanticscholar.org/paper/e749145c868f75cec5ea2767b655e262d01c3631

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Citations in the corpus

Modelling land use change and environmental impact. (2004, background): “Recently land use change models have been developed to simulate the behaviour of individuals and the up scaling of this behaviour, in order to relate it to changes in the land pattern (Berger, 2001; Parker et al., 2003).”
Multi-Agent Systems for the Simulation of Land-Use and Land-Cover Change: A Review (2003, methodology): “Several agricultural economists have performed studies on how new agricultural practices are adopted by a population of farmers in an agricultural region (Balmann 1997; Berger 2001; Polhill, Gotts, and Law 2001; Balmann et al. 2002).”
An agent-based simulation model of human-environment interactions in agricultural systems (2011, methodology): “Modeling features are demonstrated with empirical applications to study sites in Chile, Germany, Ghana, Thailand, Uganda, and Vietnam.”
Artificial Intelligence techniques: An introduction to their use for modelling environmental systems (2008, background): “• MAS and CA: simulating land-use and land-cover change [124,116], policy analysis [17], modelling urban sprawl [104].”
Social capital, risk preference and adoption of improved farm land management practices in Ethiopia (2015, background): “, 2009), as well as heterogeneity among farm households in terms of adoption costs (Berger, 2001; Schreinemachers et al., 2009; Schreinemachers et al., 2010; Suri, 2011).”
DEALINGWITHUNCERTAINTY INAGENT-BASED SIMULATION:FARM-LEVELMODELING OFADAPTATION TOCLIMATECHANGE INSOUTHWESTGERMANY (2015, methodology): “Such a model is akin to a nonconnected agent-based model (Berger, Schreinemachers, and Woelcke 2006) and also represents the first step in developing a connected agricultural economic agent–based model that explicitly captures interactions between farms (Nolan et al. 2009; for examples, see Balmann ”
Assessing farm innovations and responses to policies: A review of bio-economic farm models (2007, background): “, 1998) and farm families living in the neighbourhood (Berger, 2001).”
The Perception of and Adaptation to Climate Change in Africa (2007, methodology): “Agent based spatial modeling used as a simulation tool for technology diffusion and policy analysis may yield important insights (see for example Berger, 2001).”
Does structure matter? The impact of switching the agricultural policy regime on farm structures (2008, background): “Page 3 of 23 Ac ce pt ed M an us cr ip t 3 systems and land use problems (Balmann 1997, Barreteau and Bousquet 2000, Berger 2001, Bousquet et al. 1998, Evans and Kelley 2004, Deffuant et al. 2002, Parker and Meretsky 2004).”
A review of Agent Based Modeling for agricultural policy evaluation (2018, methodology): “Balmann (1997) used a cellular automata approach for modeling structural change of agricultural production systems; and Berger (2001) used a spatial multi-agent programming model to assess policy options in the diffusion of innovations and resource use changes.”