MAgPIE - An Open Source land-use modeling framework

The Model of Agricultural Production and its Impact on the Environment (MAgPIE) is developed and used to assess the competition for land and water and the associated consequences for sustainable development under future scenarios of rising food, energy and material demand as well as production, climate change impacts and greenhouse gas mitigation and different land related policies (Dietrich et al. (2019)).

MAgPIE is a global partial equilibrium model of the land-use sector that operates in a recursive dynamic mode and incorporates spatially explicit information on biophysical constraints into an economic decision making process (Lotze-Campen et al. (2008)). It takes regional economic conditions such as elastic demand for agricultural commodities, technological development and production costs as well as spatially explicit data on biophysical constraints into account. Geographically explicit data on biophysical conditions are provided by the Lund-Potsdam-Jena managed land model (LPJmL) (Bondeau et al. (2007), Müller and Robertson (2014)) on a 0.5 degree resolution and include e.g. carbon densities of different vegetation types, agricultural productivity such as crop yields and water availability for irrigation. Based on the strong interaction with LPJmL, MAgPIE can also help to assess the consequences of climate change and increased CO2 in the atmosphere on the land-use sector (Stevanovic et al. (2016))

Available land types in MAgPIE are cropland, pasture area, forest, other land (including non-forest natural vegetation, abandoned agricultural land and deserts) and settlements. Cropland (rainfed and irrigated), pasture, forest and other land are endogenously determined, while settlement areas are assumed to be constant over time. The cropland covers cultivation of different crop types (e.g. temperate and tropical cereals, maize, rice, oilseeds, roots), both rainfed and irrigated systems, and two 2nd generation bioenergy crop types (grassy and woody).

Taking into account international trade based on historical trade patterns and economic competitiveness (21_trade), global production has to meet demand for food, feed, seed, processing, bioenergy and material demand (16_demand). Food demand is derived based on population growth (09_drivers) and dietary transitions, accounting for changes in intake and food waste, the shift in the share of animal calories, processed products, fruits and vegetables as well as staples (15_food). Primary products can be processed to secondary products such as sugar, oil or ethanol (20_processing). The quantity of livestock production in combination with dynamic regional and livestock-specific feed baskets determines the demand for feed (70_livestock). The supply of animal-based food commodities is divided into five livestock production activities (ruminant meat, pig meat, poultry meat, eggs and milk) (Weindl et al. (2017)). The spatial distribution of crops (30_crop), livestock (71_disagg_lvst) and pasture (31_past) in MAgPIE is guided by geographically explicit information on vegetation growth and the balance between crop water demand and water availability, by initial land cover distribution maps as well as by economic conditions like trade barriers (21_trade), management intensity (13_tc) and transport costs (40_transport). It therefore integrates information about market access into the model’s optimization process that determines where cropping activities and livestock production are allocated to. Parts of forests and other natural land area can be excluded from conversion into agricultural land if designated for wood production or located in protected areas (32_forestry, 35_natveg) (Kreidenweis et al. (2018)).

Due to computational constraints, all model inputs in 0.5 degree resolution are aggregated to simulation units for the optimization process (80_optimization) based on a clustering algorithm (Dietrich, Popp, and Lotze-Campen (2013)).

MAgPIE estimates flows of different land-based greenhouse gases (GHGs). CO2 emissions are computed from land-use change dynamics, i.e. from conversion of different biomes into agricultural land and consequent loss of terrestrial carbon stocks (Popp et al. (2014)), also including the depletion of organic matter in soils (59_som). The land also serves as a sink for atmospheric carbon when agricultural land is set aside from production and the associated regrowth of natural vegetation generates negative emissions from land-use change. Nitrogen emissions (51_nitrogen) are estimated based on nitrogen budgets for croplands, pastures ([50_nr_budgets]) and the livestock sector (55_awms) (Bodirsky et al. (2014)). CH4 emissions are based on livestock feed and rice cultivation areas (Popp, Lotze-Campen, and Bodirsky (2010)). In the case of mitigation policies for the land sector, the model can reduce CO2 emissions by restraining land-use conversion and consequent carbon release as well as CH4 and N emissions by applying improved agricultural management (such as anaerobic digesters for CH4 capture from animal waste, or use of fertilizer spreaders) (Popp et al. (2014), Stevanovic et al. (2017)). In addition, the model covers land-based carbon removal technologies such as bioenergy with carbon capture and sequestration (CCS) and afforestation (Humpenöder et al. (2014), Humpenöder et al. (2018), Kreidenweis et al. (2016)).

In response to all involved demand for agricultural commodities, costs of production, biophysical constraints and land-related policies, MAgPIE simulates major dynamics of the land-use sector like investments in research and development (R&D) (13_tc) (Dietrich et al. (2014)) and associated increases in both crop yields (14_yields) and biomass removal through grazing on pastures (31_past), land use change (39_landconversion), interregional trade flows (21_trade), and irrigation (41_area_equipped_for_irrigation).

Sets are treated slightly different: Instead of adding a prefix sets should get a 2-digit number suffix giving the number of the module in which the set is exclusively used. If the set is used in more than one module no suffix should be given.

Besides prefixes also suffixes should be used. Suffixes should indicate the level of aggregation of an object:

Authors

How to cite

Dietrich J, Bodirsky B, Weindl I, Humpenöder F, Stevanovic M, Kreidenweis U, Wang X, Karstens K, Mishra A, Beier F, Molina Bacca E, Klein D, Ambrósio G, Araujo E, Biewald A, Lotze-Campen H, Popp A (2021). “MAgPIE - An Open Source land-use modeling framework - Version 4.3.2.” doi: 10.5281/zenodo.1418752 (URL: https://doi.org/10.5281/zenodo.1418752), <URL: https://github.com/magpiemodel/magpie>.

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References

Bodirsky, Benjamin Leon, Alexander Popp, Hermann Lotze-Campen, Jan Philipp Dietrich, Susanne Rolinski, Isabelle Weindl, Christoph Schmitz, et al. 2014. “Reactive Nitrogen Requirements to Feed the World in 2050 and Potential to Mitigate Nitrogen Pollution.” Nature Communications 5 (May). https://doi.org/10.1038/ncomms4858.

Bondeau, Alberte, Pascalle C. Smith, Sönke Zaehle And Sibyll Schaphoff, Wolfgang Lucht, Wolfgang Cramer, Dieter Gerten, Hermann Lotze-Campen, Christoph Müller, Markus Reichstein, and Benjamin Smith. 2007. “Modelling the Role of Agriculture for the 20th Century Global Terrestrial Carbon Balance.” Global Change Biology 13 (3): 679–706. https://doi.org/10.1111/j.1365-2486.2006.01305.x.

Dietrich, Jan Philipp, Alexander Popp, and Hermann Lotze-Campen. 2013. “Reducing the Loss of Information and Gaining Accuracy with Clustering Methods in a Global Land-Use Model.” Ecological Modelling 263 (August): 233–43. https://doi.org/10.1016/j.ecolmodel.2013.05.009.

Dietrich, Jan Philipp, Christoph Schmitz, Hermann Lotze-Campen, Alexander Popp, and Christoph Müller. 2014. “Forecasting Technological Change in Agriculture—an Endogenous Implementation in a Global Land Use Model.” Technological Forecasting and Social Change 81: 236–49. https://doi.org/10.1016/j.techfore.2013.02.003.

Dietrich, J. P., B. L. Bodirsky, F. Humpenöder, I. Weindl, M. Stevanović, K. Karstens, U. Kreidenweis, et al. 2019. “MAgPIE 4 – a Modular Open-Source Framework for Modeling Global Land Systems.” Geoscientific Model Development 12 (4): 1299–1317. https://doi.org/10.5194/gmd-12-1299-2019.

Humpenöder, Florian, Alexander Popp, Benjamin Leon Bodirsky, Isabelle Weindl, Anne Biewald, Hermann Lotze-Campen, Jan Philipp Dietrich, et al. 2018. “Large-Scale Bioenergy Production: How to Resolve Sustainability Trade-Offs?” Environmental Research Letters 13 (2): 024011. https://doi.org/10.1088/1748-9326/aa9e3b.

Humpenöder, Florian, Alexander Popp, Jan Philip Dietrich, David Klein, Hermann Lotze-Campen, Markus Bonsch, Benjamin Leon Bodirsky, Isabelle Weindl, Miodrag Stevanovic, and Christoph Müller. 2014. “Investigating Afforestation and Bioenergy CCS as Climate Change Mitigation Strategies.” Environmental Research Letters 9 (6): 064029. https://doi.org/10.1088/1748-9326/9/6/064029.

Kreidenweis, Ulrich, Florian Humpenöder, Laura Kehoe, Tobias Kuemmerle, Benjamin Leon Bodirsky, Hermann Lotze-Campen, and Alexander Popp. 2018. “Pasture Intensification Is Insufficient to Relieve Pressure on Conservation Priority Areas in Open Agricultural Markets.” Global Change Biology 0 (0). https://doi.org/10.1111/gcb.14272.

Kreidenweis, Ulrich, Florian Humpenöder, Miodrag Stevanovic, Benjamin Leon Bodirsky, Elmar Kriegler, Hermann Lotze-Campen, and Alexander Popp. 2016. “Afforestation to Mitigate Climate Change: Impacts on Food Prices Under Consideration of Albedo Effects.” Environmental Research Letters 11 (8): 085001. https://doi.org/10.1088/1748-9326/11/8/085001.

Lotze-Campen, Hermann, Christoph Müller, A. Bondeau, S. Rost, Alexander Popp, and W. Lucht. 2008. “Global Food Demand, Productivity Growth, and the Scarcity of Land and Water Resources: A Spatially Explicit Mathematical Programming Approach.” Agricultural Economics 39 (3): 325–38.

Müller, C., and R. D. Robertson. 2014. “Projecting Future Crop Productivity for Global Economic Modeling.” Agricultural Economics 45 (1): 37–50.

Popp, Alexander, Florian Humpenöder, Isabelle Weindl, Benjamin Leon Bodirsky, Markus Bonsch, Hermann Lotze-Campen, Christoph Müller, et al. 2014. “Land-Use Protection for Climate Change Mitigation.” Nature Climate Change 4 (November): 1095–8. https://doi.org/10.1038/nclimate2444.

Popp, Alexander, Hermann Lotze-Campen, and Benjamin Bodirsky. 2010. “Food Consumption, Diet Shifts and Associated Non-CO2 Greenhouse Gases from Agricultural Production.” Global Environmental Change 20 (3): 451–62. https://doi.org/10.1016/j.gloenvcha.2010.02.001.

Stevanovic, Miodrag, Alexander Popp, Benjamin Leon Bodirsky, Florian Humpenöder, Christoph Müller, Isabelle Weindl, Jan Philipp Dietrich, et al. 2017. “Mitigation Strategies for Greenhouse Gas Emissions from Agriculture and Land-Use Change: Consequences for Food Prices.” Environmental Science & Technology 51 (1): 365–74. https://doi.org/10.1021/acs.est.6b04291.

Stevanovic, Miodrag, Alexander Popp, Hermann Lotze-Campen, Jan Philipp Dietrich, Christoph Müller, Markus Bonsch, Christoph Schmitz, Benjamin Leon Bodirsky, Florian Humpenöder, and Isabelle Weindl. 2016. “The Impact of High-End Climate Change on Agricultural Welfare.” Science Advances 2 (8): e1501452. https://doi.org/10.1126/sciadv.1501452.

Weindl, Isabelle, Alexander Popp, Benjamin Leon Bodirsky, Susanne Rolinski, Hermann Lotze-Campen, Anne Biewald, Florian Humpenöder, Jan Philipp Dietrich, and Miodrag Stevanovic. 2017. “Livestock and Human Use of Land: Productivity Trends and Dietary Choices as Drivers of Future Land and Carbon Dynamics.” Global and Planetary Change 159 (Supplement C): 1–10. https://doi.org/10.1016/j.gloplacha.2017.10.002.