How to get started

You can:

  • try out the package using the provided example data in the Data-Tab taken from Gosling et al. (2020).
    It is a study that took place in a forest frontier region in Eastern Panama and used data from interviews with local farmers. The farmers ranked the performance of different conventional land-cover types and two agroforestry land-cover types against various socio-economic and ecological indicators. The data table contains the necessary expectations and uncertainties.

  • upload your own dataset with which strictly follows the format specifications of the example data:
    • Indicators for different land cover types
    • with their average expectations and uncertainties
    • and a further column with the direction for each indicator to indicate whether more or less of the indicator is desirable

  • go to Tab Data to upload the example data or your own data
  • go to Tab Model to perform the optimization and evaluate the results.

Example of needed xlsx-file structure:

Package Info

This is a graphical shiny application for the package optimLanduse to get a quick idea of the functionalities of the package. optimLanduse (version 1.2.0) has been released on CRAN and can be accessed via the project page.

Short summary

  • How to simultaneously combat biodiversity loss and maintain ecosystem functioning while increasing human welfare remains an open question. Multiobjective optimization approaches have proven helpful in revealing the trade-offs between multiple functions and goals provided by land-cover configurations. The R package optimLanduse provides tools for easy and systematic applications of the robust multiobjective land-cover composition optimization approach of Knoke et al. (2016).

  • The package includes tools to determine the land-cover composition that best balances the multiple functions a landscape can provide, and tools for understanding and visualizing how these compromises are reasoned. A tutorial on the basis of a published data set guides users through the application and highlights possible use-cases.

  • Illustrating the consequences of alternative ecosystem functions on the theoretically optimal landscape composition provides easily interpretable information for landscape modeling and decision making.

  • The package opens the approach of Knoke et al. (2016) to the community of landscape and planners and provides opportunities for straightforward systematic or batch applications.

  • References

    Knoke, T., Paul, C., Hildebrandt, P. et al. Compositional diversity of rehabilitated tropical lands supports multiple ecosystem services and buffers uncertainties. Nat Commun 7, 11877 (2016).
    Gosling, E., Reith, E., Knoke, T. et al. Exploring farmer perceptions of agroforestry via multi-objective optimisation: a test application in Eastern Panama. Agroforest Syst 94, 2003-2020 (2020).
    A data example is preloaded.
    The file used for upload must be of type xlsx. This file must also correspond to a certain structure for further processing. You can download the example data named exampleGosling.xlsx by clicking the following button:
    Download Example
    Info: Please use the following Options only if you are sure what they do. Values are limited to <= 10!


    Kai Husmann[1], Volker von Gross[1], Kai Boedeker[2], Jasper M. Fuchs[1], Carola Paul[1], Thomas Knoke[2]
    Volker von Gross[1], Carola Paul[1]

    [1]Department of Forest Economics and Sustainable Land-use Planning, Georg-August University Goettingen
    [2]Institute of Forest Management, TUM School of Life Sciences Weihenstephan, Department of Life Science Systems, Technical University of Munich





    V. v. G. was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)
    Project number 192626868 – SFB 990 in the framework of the collaborative German – Indonesian research project CRC 990.

    Data and Code

    optimLanduse (version 1.2.0) has been released on CRAN and can be accessed via the project page