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Simplified International Model of agricultural Prices, Land use, and the Environment

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SIMPLE is an acronym standing for the Simplified International Model of agricultural Prices, Land use, and the Environment. SIMPLE models were emerged in 2010s for understanding the linkage between food security and environmental sustainability[1]. It is used in learning about how land and water uses around the world are linked to global food demand. This framework is based on microeconomics and international trade theories but it is simplified for non-economists[2].

The SIMPLE models have been used in education and research. They were used in K-12 education as well as advanced college-level courses. In addition, they are used by economists and non-economists in research about questions about land, water, and food. A set of open-access, open-source tools have been developed based on this framework. The SIMPLE models are criticized for being partial equilibrium and aggregating all crops in the model[3].

History[edit]

SIMPLE is one of the global models that are designed to study food security and sustainability. In complex global models such as GLOBIOM, MAGNET, MAgPIE, GCAM, and IMPACT, multiple systems interact and many details are covered [4][5][6]. SIMPLE is developed based on GTAP. While GTAP is a global model for all economic sectors, SIMPLE is focused only on agricultural sector. In the global models, agriculture is studied at the grid cell level with details about climate, soil, and water thanks to geospatial data revolution. Each model may include thousands or millions of agricultural production unit. However, solving a gridded model is difficult due to computational challenges[7][8]. The growth in high-performance computation has facilitated the computation of SIMPLE models for large-scale sustainability questions[9].

Emergence in academic curriculum[edit]

During early 2010s, economists at Purdue University introduced the SIMPLE model in an interdisciplinary graduate course in food sustainability and environmental security[2]. Thomas Hertel and Uris Baldos are credited with creating the first version of SIMPLE for use in the classroom[10]. The goal was to help non-economists to perform economic analysis and to interpret results and provide analysis of challenges facing the global food and environment systems. Major topics of particular relevance covered were the tradeoffs between food security and environmental sustainability, and the role of international trade in mediating the impact of particular sustainability solutions[11][12].

Adoption in research in sustainability[edit]

The SIMPLE models were adopted by both economists and non-economists for interdisciplinary research. This framework was also the foundation of other models developed for understanding of sustainability and food security in a changing world such as IMLAP and GTAP-BIO-WBM [13].

Overview[edit]

This diagram shows the critical components of SIMPLE-G model. Here, changes in income and population will cause changes in demand for food and dietary shifts. While the changes in hydroclimatic conditions or investment patterns affect the production and supply of food.

The original SIMPLE framework was developed based on demand and supply for food commodities and agricultural production factors. The major elements of this framework are regional food consumption, regional production, global trade, and nutrition[14].

Food consumption and trade[edit]

The food consumption of SIMPLE shows how demand for crops, livestock products, and processed food evolves when population increases and income grows. SIMPLE model includes exports and imports of food. It assumes that consumers do not treat the imported food commodities as a perfect substitute for domestic food. The degree of substitutability shows the consumer preferences[2].  

Food Production and spatial resolution[edit]

SIMPLE determines the regional production of food as well as the local demand for agricultural inputs (land, and non-land). In the regional versions of SIMPLE, there is one regional production structure for crops that depends on total amount of land and non-land inputs. This takes into account differences in crop yields and production cost structures. Early versions of SIMPLE were criticized for the regional level and ignoring spatial details[3]. The gridded version of SIMPLE was introduced in 2017[15]. The high-resolution version of the SIMPLE family of models is dubbed "SIMPLE-G" in which the size of agricultural production units can vary from 50 to 10,000 hectares. In gridded versions of SIMPLE-G, agricultural production has been modeled at the level of individual grid cells[16]. The number of grid-cells ranges from 10 thousand to 10 million production units. For example, the SIMPLE-G for the United States that models the crop production to the level of 5 arc minutes -- or roughly 50 square km. This results in around 75 thousand production units in the US. In SIMPLE-G-Global there are 1.3 million grid cells[17].

Computational tools[edit]

There are four platforms that have been utilized to run the SIMPLE model family including:

  • GEMPACK[18]
  • GAMS
  • R
  • cloud computing on myGeoHub[19]

Among these. GEMPACK provides condensation for large scale models. However, GEMPACK and GAMS require a license except for solving small models. In R and MyGeoHub, the users do not need a license even for large models.

Applications[edit]

Food security[edit]

Food security has been introduced in some versions of SIMPLE model. Some outputs of the SIMPLE nutrition include malnutrition and obesity metrics such as the proportion of the population that is experiencing undernourishment, or excessive caloric intake, as well as the intensity (in average caloric terms) of this deficit or excess. In the case of obesity, the excessive intake has been translated into increases in body mass index (BMI) which, in turn can have serious health consequences[20].

Land use[edit]

SIMPLE models were used in land use modeling[21] where land use is linked to growth in population, income, biofuel demand, and technological growth. Land use change has been translated into terrestrial carbon fluxes to inform studies of future greenhouse gas emissions [22]. In the deforestation context, the expansion of cropland is linked also to intensive use of non-land inputs [23].

Water resources[edit]

This model has been used for evaluating policies about groundwater and surface water. SIMPLE assumes the supply and demand for agricultural commodities affect the decision on groundwater and surface water withdrawals. This model is used to show how local changes in water scarcity or groundwater conservation transmit their impact to consumers through local supply in agricultural markets and global trade[24].

Climate change, productivity, and other global changes[edit]

This approach was also used for studying the sustainability and food security implications of global changes including research and development (R&D) investments[25], climate change, and pandemics.

Validation[edit]

The SIMPLE model is validated at the regional scale. The gridded SIMPLE-G model was also validated for the United States.

Limitations[edit]

The SIMPLE model has been criticized for some limitations. First, the aggregated all-crops commodity that does not consider the changes in crop mix explicitly. Second, focusing on long-run equilibrium and being comparative static may reduce the ability to connect to the biophysical models that follow a specific temporal time span.[3]

See also[edit]

References[edit]

  1. Cisneros-Pineda, Alfredo; Dukes, Jeffrey S; Johnson, Justin; Brouder, Sylvie; Ramankutty, Navin; Corong, Erwin; Chaudhary, Abhishek (2023-04-01). "The missing markets link in global-to-local-to-global analyses of biodiversity and ecosystem services". Environmental Research Letters. 18 (4): 041003. Bibcode:2023ERL....18d1003C. doi:10.1088/1748-9326/acc473. ISSN 1748-9326. Unknown parameter |s2cid= ignored (help)
  2. 2.0 2.1 2.2 Norton, George W. (2016). "Review of Global Change and the Challenges of Sustainably Feeding a Growing Planet". American Journal of Agricultural Economics. 98 (5): 1558–1560. doi:10.1093/ajae/aaw013. ISSN 0002-9092. JSTOR 44132458.
  3. 3.0 3.1 3.2 Engström, Kerstin; Rounsevell, Mark D. A.; Murray-Rust, Dave; Hardacre, Catherine; Alexander, Peter; Cui, Xufeng; Palmer, Paul I.; Arneth, Almut (2016-01-01). "Applying Occam's razor to global agricultural land use change". Environmental Modelling & Software. 75: 212–229. Bibcode:2016EnvMS..75..212E. doi:10.1016/j.envsoft.2015.10.015. ISSN 1364-8152.
  4. Schmitz, Christoph; van Meijl, Hans; Kyle, Page; Nelson, Gerald C.; Fujimori, Shinichiro; Gurgel, Angelo; Havlik, Petr; Heyhoe, Edwina; d'Croz, Daniel Mason; Popp, Alexander; Sands, Ron; Tabeau, Andrzej; van der Mensbrugghe, Dominique; von Lampe, Martin; Wise, Marshall (2014). "Land-use change trajectories up to 2050: insights from a global agro-economic model comparison". Agricultural Economics. 45 (1): 69–84. doi:10.1111/agec.12090.
  5. Robertson, Simon (2021). "Transparency, trust, and integrated assessment models: An ethical consideration for the Intergovernmental Panel on Climate Change". WIREs Climate Change. 12 (1). Bibcode:2021WIRCC..12E.679R. doi:10.1002/wcc.679. ISSN 1757-7780. Unknown parameter |s2cid= ignored (help)
  6. Schwanitz, Valeria Jana (2013-12-01). "Evaluating integrated assessment models of global climate change". Environmental Modelling & Software. 50: 120–131. Bibcode:2013EnvMS..50..120S. doi:10.1016/j.envsoft.2013.09.005. ISSN 1364-8152.
  7. Skea, Jim; Shukla, Priyadarshi; Al Khourdajie, Alaa; McCollum, David (2021). "Intergovernmental Panel on Climate Change: Transparency and integrated assessment modeling". WIREs Climate Change. 12 (5). Bibcode:2021WIRCC..12E.727S. doi:10.1002/wcc.727. hdl:10044/1/90388. ISSN 1757-7780. Unknown parameter |s2cid= ignored (help)
  8. Cultice, Brian; Irwin, Elena; Jones, Mackenzie (2023-03-01). "Accounting for spatial economic interactions at local and meso scales in integrated assessment model (IAM) frameworks: challenges and recent progress". Environmental Research Letters. 18 (3): 035009. Bibcode:2023ERL....18c5009C. doi:10.1088/1748-9326/acbce6. ISSN 1748-9326. Unknown parameter |s2cid= ignored (help)
  9. Song, Carol X; Merwade, Venkatesh; Wang, Shaowen; Witt, Michael; Kumar, Vipin; Irwin, Elena; Zhao, Lan; Walton, Amy (2023-07-01). "Cyberinfrastructure for sustainability sciences". Environmental Research Letters. 18 (7): 075002. Bibcode:2023ERL....18g5002S. doi:10.1088/1748-9326/acd9dd. ISSN 1748-9326. Unknown parameter |s2cid= ignored (help)
  10. Seifert, Jenny (2021-09-22). "Two Things to Know about SIMPLE-G, An Economic Model Designed to Pair with Ecological Models". FEWscapes. Retrieved 2023-11-19.
  11. Hertel, Thomas W. (2021). "Educating the Next Generation of Interdisciplinary Researchers to Tackle Global Sustainability Challenges: A Graduate Course". Applied Economics Teaching Resources (AETR). doi:10.22004/AG.ECON.308494.
  12. MyGeohub. "MyGeohub - Courses: Understanding Food Waste Through the Trade-off and Opportunity Cost Concepts". mygeohub.org. Retrieved 2023-11-12.
  13. Taheripour, Farzad; Tyner, Wallace E.; Sajedinia, Ehsanreza; Aguiar, Angel; Chepeliev, Maksym; Corong, Erwin; de Lima, Cicero Z.; Haqiqi, Iman (2020-09-28). Water in the Balance. World Bank, Washington, DC. p. 34. doi:10.1596/34498. hdl:10986/34498. Unknown parameter |s2cid= ignored (help) Search this book on
  14. Nelson, Gerald C.; van der Mensbrugghe, Dominique; Ahammad, Helal; Blanc, Elodie; Calvin, Katherine; Hasegawa, Tomoko; Havlik, Petr; Heyhoe, Edwina; Kyle, Page; Lotze-Campen, Hermann; von Lampe, Martin; Mason d'Croz, Daniel; van Meijl, Hans; Müller, Christoph; Reilly, John (2014). "Agriculture and climate change in global scenarios: why don't the models agree". Agricultural Economics. 45 (1): 85–101. doi:10.1111/agec.12091.
  15. Liu, Jing; Hertel, Thomas W; Lammers, Richard B; Prusevich, Alexander; Baldos, Uris Lantz C; Grogan, Danielle S; Frolking, Steve (2017-10-01). "Achieving sustainable irrigation water withdrawals: global impacts on food security and land use". Environmental Research Letters. 12 (10): 104009. Bibcode:2017ERL....12j4009L. doi:10.1088/1748-9326/aa88db. ISSN 1748-9326. Unknown parameter |s2cid= ignored (help)
  16. Johnson, Justin Andrew; Brown, Molly E; Corong, Erwin; Dietrich, Jan Philipp; C Henry, Roslyn; von Jeetze, Patrick José; Leclère, David; Popp, Alexander; Thakrar, Sumil K; Williams, David R (2023-02-01). "The meso scale as a frontier in interdisciplinary modeling of sustainability from local to global scales". Environmental Research Letters. 18 (2): 025007. Bibcode:2023ERL....18b5007J. doi:10.1088/1748-9326/acb503. ISSN 1748-9326. Unknown parameter |s2cid= ignored (help)
  17. Baldos, U. L. C.; Haqiqi, I.; Hertel, T. W.; Horridge, M.; Liu, J. (2020-11-01). "SIMPLE-G: A multiscale framework for integration of economic and biophysical determinants of sustainability". Environmental Modelling & Software. 133: 104805. Bibcode:2020EnvMS.13304805B. doi:10.1016/j.envsoft.2020.104805. ISSN 1364-8152. Unknown parameter |s2cid= ignored (help)
  18. Horridge, Mark; Meeraus, Alex; Pearson, Ken; Rutherford, Thomas F. (2013-01-01), Dixon, Peter B.; Jorgenson, Dale W., eds., "Chapter 20 - Solution Software for Computable General Equilibrium Modeling", Handbook of Computable General Equilibrium Modeling, Handbook of Computable General Equilibrium Modeling SET, Vols. 1A and 1B, Elsevier, 1, pp. 1331–1381, doi:10.1016/b978-0-444-59568-3.00020-1, ISBN 9780444595683, retrieved 2023-11-08
  19. Sachdev, Gaurav; Woo, Jungha; Zhao, Lan (2023), SIMPLE-G US Jupyter Notebook, MyGeoHUB, doi:10.21981/5c2h-p780, retrieved 2023-11-08
  20. Lopez Barrera, Emiliano; Shively, Gerald (2022-05-01). "Excess calorie availability and adult BMI: A cohort analysis of patterns and trends for 156 countries from 1890 to 2015". Food Policy. 109: 102271. doi:10.1016/j.foodpol.2022.102271. ISSN 0306-9192. Unknown parameter |s2cid= ignored (help)
  21. van Asselen, Sanneke; Verburg, Peter H. (2013). "Land cover change or land-use intensification: simulating land system change with a global-scale land change model". Global Change Biology. 19 (12): 3648–3667. Bibcode:2013GCBio..19.3648V. doi:10.1111/gcb.12331. PMID 23893426. Unknown parameter |s2cid= ignored (help)
  22. Stehfest, Elke; van Zeist, Willem-Jan; Valin, Hugo; Havlik, Petr; Popp, Alexander; Kyle, Page; Tabeau, Andrzej; Mason-D’Croz, Daniel; Hasegawa, Tomoko; Bodirsky, Benjamin L.; Calvin, Katherine; Doelman, Jonathan C.; Fujimori, Shinichiro; Humpenöder, Florian; Lotze-Campen, Hermann (2019-05-15). "Key determinants of global land-use projections". Nature Communications. 10 (1): 2166. Bibcode:2019NatCo..10.2166S. doi:10.1038/s41467-019-09945-w. ISSN 2041-1723. PMC 6520344 Check |pmc= value (help). PMID 31092816.
  23. Villoria, Nelson B.; Byerlee, Derek; Stevenson, James (2014). "The Effects of Agricultural Technological Progress on Deforestation: What Do We Really Know?". Applied Economic Perspectives and Policy. 36 (2): 211–237. doi:10.1093/aepp/ppu005. ISSN 2040-5790.
  24. Troy, Tara J; Bowling, Laura C; Jame, Sadia A; Lee, Charlotte I; Liu, Jing; Perry, Chris; Richter, Brian (2023-08-01). "Envisioning a sustainable agricultural water future across spatial scales". Environmental Research Letters. 18 (8): 085003. Bibcode:2023ERL....18h5003T. doi:10.1088/1748-9326/ace206. ISSN 1748-9326. Unknown parameter |s2cid= ignored (help)
  25. Beckman, Jayson; Ivanic, Maros; Jelliffe, Jeremy (December 2022). "Market impacts of Farm to Fork: Reducing agricultural input usage". Applied Economic Perspectives and Policy. 44 (4): 1995–2013. doi:10.1002/aepp.13176. ISSN 2040-5790. Unknown parameter |s2cid= ignored (help)


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