CRAFTY applications

Competition for Resources between Agent Functional Types (CRAFTY)

CRAFTY is a large-scale agent-based modelling (ABM) framework for the simulation of land use change. It is designed to allow efficient but powerful simulation of a wide range of land uses across large geographical extents, based on the decision-making of simulated land managers who generate a variety of ecosystem services. It is fully open-source and can be used without the need for any programming.

 

The basic framework is described in Murray-Rust et al. 2014, and the following variations and applications have also been produced:

- Experimental applications: Brown et al. (2014, 2018)

- Application to rubber plantation expansion in Yunnan Province, China: Synes et al. (2016)

- Applications to pollinator-agriculture interactions: Synes et al. (2018), Urban et al. (2021)

- CRAFTY-Sweden: Blanco et al. (2017)

- CRAFTY-EU: Brown et al. (2019, 2021); online interface OSF item

- CRAFTY-Brazil: Millington et al. (2021)

- CRAFTY-Scotland: Burton et al. (in review)

- CRAFTY-GB: Brown et al. (2022); online interface OSF item

- Institutional modelling: Holzhauer et al. (2019)

CRAFTY also forms part of the LandSyMM sub-national to global scale model of the land system (https://landsymm.earth), and has been coupled with the eco-evolutionary modelling platform ‘RangeShifter’ (through the R package RangeShiftR)

 

Github repositories: https://github.com/CRAFTY-ABM 

Documentation: https://www.wiki.ed.ac.uk/display/CRAFTY (legacy)

                          https://github.com/CRAFTY-ABM/CRAFTY_Documentation (under construction)

 

Publications:

Alexander, P., Prestele, R., Verburg, P. H., Arneth, A., Baranzelli, C., Batista e Silva, F., … Rounsevell, M. D. A. A. (2017). Assessing uncertainties in land cover projections. Global Change Biology, 23(2), 767–781. https://doi.org/10.1111/gcb.13447

Blanco, V., Holzhauer, S., Brown, C., Lagergren, F., Vulturius, G., Lindeskog, M., & Rounsevell, M. D. A. (2017). The effect of forest owner decision-making, climatic change and societal demands on land-use change and ecosystem service provision in Sweden. Ecosystem Services, 23(December 2016), 174–208. https://doi.org/10.1016/j.ecoser.2016.12.003

Brown, C., Murray-Rust, D., Van Vliet, J., Alam, S. J., Verburg, P. H., & Rounsevell, M. D. (2014). Experiments in globalisation, food security and land use decision making. PLoS ONE, 9(12), 1–24. https://doi.org/10.1371/journal.pone.0114213

Brown, C., Holzhauer, S., Metzger, M. J., Paterson, J. S., & Rounsevell, M. (2018). Land managers’ behaviours modulate pathways to visions of future land systems. Regional Environmental Change, 1–15. https://doi.org/10.1007/s10113-016-0999-y

Brown, C., Seo, B., & Rounsevell, M. (2019). Societal breakdown as an emergent property of large-scale behavioural models of land use change. Earth System Dynamics, 10, 809-845. https://doi.org/10.5194/esd-10-809-2019

Brown, C.; Holman, I.; Rounsevell, M. How modelling paradigms affect simulated future land use change. 2021. Earth System Dynamics, 12 (1), 211–231. doi:10.5194/esd-12-211-2021

Brown, C.; Seo, B.; Alexander, P.; Burton, V.; Chacón-Montalván, E. A.; Dunford, R.; Merkle, M.; Harrison, P. A.; Prestele, R.; Robinson, E. L.; Rounsevell, M. Agent‐Based Modeling of Alternative Futures in the British Land Use System 2022. Earth’s Future, 10 (11). doi:10.1029/2022EF002905

Holzhauer, S., Brown, C., & Rounsevell, M. (2019). Modelling dynamic effects of multi-scale institutions on land use change. Regional Environmental Change, 19(3), 733–746. https://doi.org/10.1007/s10113-018-1424-5

Millington, James D. A., Valeri Katerinchuk, Ramon Felipe Bicudo da Silva, Daniel de Castro Victoria, and Mateus Batistella. 2021. “Modelling Drivers of Brazilian Agricultural Change in a Telecoupled World.” Environmental Modelling & Software, February, 105024. https://doi.org/10.1016/j.envsoft.2021.105024

Murray-Rust, D., Brown, C., van Vliet, J., Alam, S. J., Robinson, D. T., Verburg, P. H., & Rounsevell, M. (2014). Combining agent functional types, capitals and services to model land use dynamics. Environmental Modelling and Software, 59, 187–201. https://doi.org/10.1016/j.envsoft.2014.05.019

Seo, B., Brown, C., & Rounsevell, M. D. A. (2018). Evaluation and calibration of an agent based land use model using remotely sensed land cover and primary productivity data. In IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium(pp. 7476–7479). 10.1109/IGARSS.2018.8518023

Staccione, A., Brown, C., Arneth, A., Rounsevell, M., Essenfelder, A.H., Seo, B., Mysiak, J. (2023). Exploring the effects of Protected Area Networks on the European land system. Journal of Environmental Management, 337, 117741 https://doi.org/10.1016/j.jenvman.2023.117741

Synes, N. W.; Brown, C.; Watts, K.; White, S. M.; Gilbert, M. A.; Travis, J. M. J. Emerging Opportunities for Landscape Ecological Modelling. 2016. Current Landscape Ecology Reports, 1 (4), 146–167. doi:10.1007/s40823-016-0016-7

Synes, N., Brown, C., Palmer, S., Bocedi, G., Osborne, P. E., Watts, K., … Travis, J. (2018). Coupled land use and ecological models reveal emergence and feedbacks in socio-ecological systems. Ecography. https://doi.org/10.1111/ecog.04039

Urban, M. C.; Travis, J. M. J.; Zurell, D.; Thompson, P. L.; Synes, N. W.; Scarpa, A.; Peres-Neto, P. R.; Malchow, A.-K.; James, P. M. A.; Gravel, D.; De Meester, L.; Brown, C.; Bocedi, G.; Albert, C. H.; Gonzalez, A.; Hendry, A. P.
Coding for Life: Designing a Platform for Projecting and Protecting Global Biodiversity.
2021. BioScience. doi:10.1093/biosci/biab099