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Modelling future land use scenarios based on farmers’ intentions and a cellular automata approach

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  • Gomes, Eduardo
  • Abrantes, Patrícia
  • Banos, Arnaud
  • Rocha, Jorge
Abstract
Different mechanisms drive land use and land cover changes (LUCC). This paper presents an exploratory analysis aimed at understanding the complex dynamics of LUCC based on farmers’ intentions when they are faced with four scenarios with the time horizon of 2025: (1) A0 – current social and economic trend; (2) A1 – intensified agricultural production; (3) A2 – reduced agricultural production; and (4) B0 - increasing demand for urban development. LUCC models are applied to a Torres Vedras (Portugal) case study. This territory is located in a peri-urban area near Lisbon dominated by forest and agricultural land, which has been suffering considerable urban pressure in the last decades. Farmers — major agents of agricultural land use change — were interviewed to obtain their LUCC intentions according to the scenarios studied. To model LUCC a Cellular automata-Markov chain approach was applied. Our results suggest that significant LUCC will occur depending on their intentions in the different scenarios. The highlights are: (1) the highest growth in permanently irrigated land in the A1 scenario; (2) the biggest drop in non-irrigated arable land, and the highest growth in forest in the A2 scenario; and (3) the greatest urban growth was recognized in the B0 scenario. To verify if the fitting simulations performed well, techniques to measure agreement and quantity-allocation disagreements were applied.These outcomes could provide decision-makers with the capacity to observe different possible futures in ‘what if’ scenarios, allowing them to anticipate future uncertainties, and consequently allowing them the possibility to choose the more desirable future.

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  • Gomes, Eduardo & Abrantes, Patrícia & Banos, Arnaud & Rocha, Jorge, 2019. "Modelling future land use scenarios based on farmers’ intentions and a cellular automata approach," Land Use Policy, Elsevier, vol. 85(C), pages 142-154.
    • Handle: RePEc:eee:lauspo:v:85:y:2019:i:c:p:142-154
    DOI: 10.1016/j.landusepol.2019.03.027
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    2. Ustaoglu, E. & Sisman, S. & Aydınoglu, A.C., 2021. "Determining agricultural suitable land in peri-urban geography using GIS and Multi Criteria Decision Analysis (MCDA) techniques," Ecological Modelling, Elsevier, vol. 455(C).
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    5. Cláudia M. Viana & Jorge Rocha, 2020. "Evaluating Dominant Land Use/Land Cover Changes and Predicting Future Scenario in a Rural Region Using a Memoryless Stochastic Method," Sustainability, MDPI, vol. 12(10), pages 1-28, May.
    6. Zhenzhi Jiao & Shaoying Li & Zhangping Lin & Zhipeng Lai & Zhuo Wu & Lin Liu, 2023. "Incorporating High-Speed Rail Development Scenario for Tourism Land Use Simulation: A Case Study of Xinxing County, China," Land, MDPI, vol. 12(6), pages 1-18, June.
    7. Eduardo Gomes, 2020. "Sustainable Population Growth in Low-Density Areas in a New Technological Era: Prospective Thinking on How to Support Planning Policies Using Complex Spatial Models," Land, MDPI, vol. 9(7), pages 1-14, July.
    8. Cunha, Elias Rodrigues da & Santos, Celso Augusto Guimarães & Silva, Richarde Marques da & Bacani, Vitor Matheus & Pott, Arnildo, 2021. "Future scenarios based on a CA-Markov land use and land cover simulation model for a tropical humid basin in the Cerrado/Atlantic forest ecotone of Brazil," Land Use Policy, Elsevier, vol. 101(C).
    9. Geng, Xin & Hu, Shi & Wang, Xunming & Cai, Diwen & Gong, Yuan, 2023. "Adaptive change of land use to nature and society in China’s agro-pastoral ecotone," Land Use Policy, Elsevier, vol. 126(C).
    10. Xueru Zhang & Jie Zhou & Wei Song, 2020. "Simulating Urban Sprawl in China Based on the Artificial Neural Network-Cellular Automata-Markov Model," Sustainability, MDPI, vol. 12(11), pages 1-13, May.
    11. Sadooghi, Seyed Ehsan & Taleai, Mohammad & Abolhasani, Somaie, 2022. "Simulation of urban growth scenarios using integration of multi-criteria analysis and game theory," Land Use Policy, Elsevier, vol. 120(C).

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