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 (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:

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

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

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

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