LPJmL is a dynamic global vegetation model and is developed at the Potsdam Institute for Climate Impact Research. The model simulates, among other things, natural vegetation and managed land, water, energy and carbon fluxes, photosynthesis, plant physiology, fire disturbance and agricultural management practices. The spatial simulation unit of the model is a raster grid cell.
Simulation results of the model have been published in about 250 research articles in the last 15 years (as of December 2021). LPJmL is one of the best evaluated and tested global vegetation models. The model source code is available at GitHub. I started using LPJmL for my PhD research and continue to use it.
Spatial representation of the agricultural landscape in a global vegetation model.
Find the model description and the model evaluation papers here:
Schaphoff et al. (2018) LPJmL4 – A dynamic global vegetation model with managed land – Part 1: Model description, Geoscientific Model Development, 11(4). doi: 10.5194/gmd-11-1343-2018. Available online: https://gmd.copernicus.org/articles/11/1343/2018/
Schaphoff et al. (2018) LPJmL4 – a dynamic global vegetation model with managed land: Part 2: Model evaluation, Geoscientific Model Development, 11, pp. 1377–1403. doi: 10.5194/gmd-2017-146. Available online: https://www.geosci-model-dev-discuss.net/gmd-2017-146/
Bondeau et al. (2007) Modelling the role of agriculture for the 20th century global terrestrial carbon balance, Global Change Biology, 13(3), pp. 679–706. doi: 10.1111/j.1365-2486.2006.01305.x. Available online: https://onlinelibrary.wiley.com/doi/10.1111/j.1365-2486.2006.01305.x
Find descriptions of selected modeled processes here:
Tillage: Lutz et al. (2019) Simulating the effect of tillage practices with the global ecosystem model LPJmL (version 5.0-tillage), Geoscientific Model Development, 12(6), pp. 2419–2440. doi: 10.5194/gmd-12-2419-2019. Available online: https://gmd.copernicus.org/articles/12/2419/2019/
Nitrogen implementation: von Bloh et al. (2018) Implementing the nitrogen cycle into the dynamic global vegetation, hydrology, and crop growth model LPJmL (version 5.0)’, Geoscientific Model Development, 11(7), pp. 2789–2812. doi: 10.5194/gmd-11-2789-2018. Available online: https://www.geosci-model-dev.net/11/2789/2018/
Double cropping: Waha et al. (2013) Adaptation to climate change through the choice of cropping system and sowing date in sub-Saharan Africa, Global Environmental Change, 23(1), pp. 130–143. doi: 10.1016/j.gloenvcha.2012.11.001. Available online: http://www.sciencedirect.com/science/article/pii/S095937801200132X
Crop phenology and sowing dates: Waha et al. (2012) Climate-driven simulation of global crop sowing dates, Global Ecology and Biogeography, 21(2), pp. 247–259. doi: 10.1111/j.1466-8238.2011.00678.x. Available online: https://doi.org/10.1111/J.1466-8238.2011.00678.X