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Agricultural landscape generators for simulation models: A review of existing solutions and an outline of future directions

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  • Langhammer, Maria
  • Thober, Jule
  • Lange, Martin
  • Frank, Karin
  • Grimm, Volker

Abstract

There is an increasing need for an assessment of the impacts of land use and land use change (LUCC). In this context, simulation models are valuable tools for investigating the impacts of stakeholder actions or policy decisions. Agricultural landscape generators (ALGs), which systematically and automatically generate realistic but simplified representations of land cover in agricultural landscapes, can provide the input for LUCC models. We reviewed existing ALGs in terms of their objectives, design and scope. We found eight ALGs that met our definition. They were based either on generic mathematical algorithms (pattern-based) or on representations of ecological or land use processes (process-based). Most ALGs integrate only a few landscape metrics, which limits the design of the landscape pattern and thus the range of applications. For example, only a few specific farming systems have been implemented. We conclude that existing ALGs contain useful approaches that can be used for specific purposes, but ideally generic modular ALGs are developed that can be used for a wide range of scenarios, regions and model types. We have compiled features of such generic ALGs and propose a possible software architecture. Considerable joint efforts are required to develop such generic ALGs, but the benefits in terms of a better understanding and development of more efficient agricultural policies would be high.

Suggested Citation

  • Langhammer, Maria & Thober, Jule & Lange, Martin & Frank, Karin & Grimm, Volker, 2019. "Agricultural landscape generators for simulation models: A review of existing solutions and an outline of future directions," Ecological Modelling, Elsevier, vol. 393(C), pages 135-151.
  • Handle: RePEc:eee:ecomod:v:393:y:2019:i:c:p:135-151
    DOI: 10.1016/j.ecolmodel.2018.12.010
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    Cited by:

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    2. Henniger, Hans & Huth, Andreas & Frank, Karin & Bohn, Friedrich J., 2023. "Creating virtual forests around the globe and analysing their state space," Ecological Modelling, Elsevier, vol. 483(C).
    3. Salecker, Jan & Dislich, Claudia & Wiegand, Kerstin & Meyer, Katrin M. & Pe'er, Guy, 2019. "EFForTS-LGraf: A landscape generator for creating smallholder-driven land-use mosaics," EFForTS Discussion Paper Series 29, University of Goettingen, Collaborative Research Centre 990 "EFForTS, Ecological and Socioeconomic Functions of Tropical Lowland Rainforest Transformation Systems (Sumatra, Indonesia)".
    4. Drechsler, Martin & Wätzold, Frank & Grimm, Volker, 2022. "The hitchhiker's guide to generic ecological-economic modelling of land-use-based biodiversity conservation policies," Ecological Modelling, Elsevier, vol. 465(C).
    5. Evan B Brooks & John W Coulston & Kurt H Riitters & David N Wear, 2020. "Using a hybrid demand-allocation algorithm to enable distributional analysis of land use change patterns," PLOS ONE, Public Library of Science, vol. 15(10), pages 1-21, October.
    6. Langhammer, Maria & Grimm, Volker, 2020. "Mitigating bioenergy-driven biodiversity decline: A modelling approach with the European brown hare," Ecological Modelling, Elsevier, vol. 416(C).
    7. Behrendt, Karl & Brown, Colin & Qiao, Guanghua & Zhang, Bao, 2022. "Assessing the opportunity costs of Chinese herder compliance with a payment for environmental services scheme," Ecological Economics, Elsevier, vol. 193(C).

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