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Spatially explicit modelling of agricultural dynamics in semi-arid environments

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  • Chen, Assaf

Abstract

This paper describes a software package called Spatially Explicit Agricultural Dynamics (SEAD), which investigates agricultural spatial and temporal land-use changes. SEAD is an agent-based model whose rules are set to mimic the human decision making processes regarding agricultural land expansion. Model calibration is based on remotely sensed data. Detection of agricultural lands was executed using intra- and inter-annual Landsat images, with an overall accuracy of 86%. An analysis regarding the correlation of agricultural spread with water availability, irrigation infrastructure, road infrastructure, soil type, and geographical variables was conducted. These data, along with interviews with agricultural experts examining farmers' decision-making processes, were translated into rules that were used to model agricultural expansion. The SEAD results show that agricultural expansion in the north-western Negev (Israel) is highly dependent on water availability, which in turn is sensitive to forecasted climate change scenarios. While the spatial expansion patterns are mostly influenced by irrigation and road infrastructure positioning and placement, they vary in their infrastructural costs and agricultural settlement clustering and connectivity, thus affecting landscape fragmentation, travel time, and social/ecological connectivity. SEAD, being highly encapsulated and object-oriented in nature, can be reused in different settings.

Suggested Citation

  • Chen, Assaf, 2017. "Spatially explicit modelling of agricultural dynamics in semi-arid environments," Ecological Modelling, Elsevier, vol. 363(C), pages 31-47.
    • Handle: RePEc:eee:ecomod:v:363:y:2017:i:c:p:31-47
    DOI: 10.1016/j.ecolmodel.2017.08.025
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    1. K C Clarke & S Hoppen & L Gaydos, 1997. "A Self-Modifying Cellular Automaton Model of Historical Urbanization in the San Francisco Bay Area," Environment and Planning B, , vol. 24(2), pages 247-261, April.
    2. Happe, Kathrin & Kellermann, Konrad & Balmann, Alfons, 2006. "Agent-based analysis of agricultural policies: An illustration of the agricultural policy simulator AgriPoliS, its adaptation and behavior," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 11(1).
    3. Pepijn Schreinemachers & Thomas Berger & Aer Sirijinda & Suwanna Praneetvatakul, 2009. "The Diffusion of Greenhouse Agriculture in Northern Thailand: Combining Econometrics and Agent‐Based Modeling," Canadian Journal of Agricultural Economics/Revue canadienne d'agroeconomie, Canadian Agricultural Economics Society/Societe canadienne d'agroeconomie, vol. 57(4), pages 513-536, December.
    4. Bert, Federico E. & Podestá, Guillermo P. & Rovere, Santiago L. & Menéndez, Ángel N. & North, Michael & Tatara, Eric & Laciana, Carlos E. & Weber, Elke & Toranzo, Fernando Ruiz, 2011. "An agent based model to simulate structural and land use changes in agricultural systems of the argentine pampas," Ecological Modelling, Elsevier, vol. 222(19), pages 3486-3499.
    5. Berger, Thomas, 2001. "Agent-based spatial models applied to agriculture: a simulation tool for technology diffusion, resource use changes and policy analysis," Agricultural Economics, Blackwell, vol. 25(2-3), pages 245-260, September.
    6. Grimm, Volker & Berger, Uta & DeAngelis, Donald L. & Polhill, J. Gary & Giske, Jarl & Railsback, Steven F., 2010. "The ODD protocol: A review and first update," Ecological Modelling, Elsevier, vol. 221(23), pages 2760-2768.
    7. Paul M. Torrens, 2003. "Cellular Automata and Multi-agent Systems as Planning Support Tools," Advances in Spatial Science, in: Stan Geertman & John Stillwell (ed.), Planning Support Systems in Practice, chapter 12, pages 205-222, Springer.
    8. L Sanders & D Pumain & H Mathian & F Guérin-Pace & S Bura, 1997. "SIMPOP: A Multiagent System for the Study of Urbanism," Environment and Planning B, , vol. 24(2), pages 287-305, April.
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