Understanding the Complexity of the Food System: Differences and Commonalities between Two Optimization Models †
by
, , , ,
Samantha N. Heerschop
1,*
,
Renee P. M. Cardinaals
2
,
Sander Biesbroek
1,
Argyris Kanellopoulos
3,
Johanna M. Geleijnse
1,
Pieter van ‘t Veer
1 and
Hannah H. E. van Zanten
2,4 1
Division of Human Nutrition and Health, Wageningen University & Research, 6708 PB Wageningen, The Netherlands
2
Farming Systems Ecology Group, Department of Plant Sciences, Wageningen University & Research, 6708 PB Wageningen, The Netherlands
3
Operations Research and Logistics Group, Wageningen University & Research, 6708 PB Wageningen, The Netherlands
4
Department of Global Development, Cornell University, Ithaca, NY 14853, USA
*
Author to whom correspondence should be addressed.
†
Presented at the 14th European Nutrition Conference FENS 2023, Belgrade, Serbia, 14–17 November 2023.
Proceedings 2023, 91(1), 17; https://doi.org/10.3390/proceedings2023091017
Published: 14 November 2023
(This article belongs to the Proceedings of The 14th European Nutrition Conference FENS 2023)
Abstract
:Background and objectives: There is a compelling need for a more sustainable food system because of climate change and contemporary Western diets, which pose a threat to human and planetary health. The food system is a social–ecological system, consisting of both biophysical and social sub-systems which are interlinked. This implies that changes in one sub-system can lead to synergies and trade-offs elsewhere. To identify such synergies and tradeoffs, researchers are integrating work from a range of disciplines in optimization models. This has resulted in models that are unique but have a similar overarching aim: ‘to create a sustainable food system by understanding the implications of food system choices’. However, the results of these models may differ. Therefore, the aim of this paper was to understand the differences and complementarity of two optimization models to grasp the complexity of the food system. Methods: we compared the Circular Food System (CiFoS) model with the Sustainable, Healthy, Acceptable, Realistic, and Preferable diets (SHARP) model. CiFoS is a biophysical optimization model that aims to produce a healthy diet for a growing population within planetary boundaries. SHARP is a benchmarking model that optimizes current diets for health and sustainability for consumers. Both models propose a healthy and sustainable diet. While CiFoS is detailed on how environmental impacts are calculated, SHARP has a finer grid on the consumption aspects. Results: based on previously modelled scenarios that showed different results in diet composition, we identified that these differences could be explained by fundamental characteristics of the model (e.g., environmental impact calculations or the consideration of distance to the current diet), data input and scenario settings. Besides, the models work complementary regarding the time scale (i.e., solutions for the upcoming years versus upcoming decades), geographic scale and an individual versus population approach. Conclusion: Optimization models may be used for the same goal, e.g., finding an optimal diet, but the nuance chosen will lead to different outcomes. The outcomes of such models are complementary and can therefore be used in conjunction to inform policy or other food system stakeholders.
Author Contributions
Conceptualization, S.N.H. and R.P.M.C.; methodology, S.N.H. and R.P.M.C.; formal analysis, S.N.H., R.P.M.C. and H.H.E.v.Z.; investigation, S.N.H. and R.P.M.C.; resources, H.H.E.v.Z. and S.B.; data curation, S.B. and H.H.E.v.Z.; writing—original draft preparation, S.N.H. and R.P.M.C.; writing—review and editing, H.H.E.v.Z., P.v.’t.V., S.B., A.K. and J.M.G.; visualization, S.N.H. and R.P.M.C.; supervision, H.H.E.v.Z., P.v.’t.V., S.B., A.K. and J.M.G.; funding acquisition, P.v.’t.V., J.M.G. and H.H.E.v.Z. All authors have read and agreed to the published version of the manuscript.
Funding
This research received no external funding.
Institutional Review Board Statement
Not applicable.
Informed Consent Statement
Not applicable.
Data Availability Statement
Data of the Dutch National Food Consumption Survey 2012–2016 can be requested for at https://www.rivm.nl/en/dutch-national-food-consumption-survey/data-on-request (accessed on 16 October 2019). Primary environmental data of 250 food products can be found at https://www.rivm.nl/voedsel-en-voeding/duurzaam-voedsel/databasemilieubelasting-voedingsmiddelen (accessed on 16 October 2019). Raw data of the CiFoS model have been deposited in the GIT repository and are available on request under a license similar to Creative Commons Attribution-NonCommercial-Share A like 4.0 International Public License. A dashboard is available on www.circularfoodsystems.org providing detailed data related to the results of this publication.
Conflicts of Interest
The authors declare no conflict of interest.
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© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
MDPI and ACS Style
Heerschop, S.N.; Cardinaals, R.P.M.; Biesbroek, S.; Kanellopoulos, A.; Geleijnse, J.M.; van ‘t Veer, P.; van Zanten, H.H.E. Understanding the Complexity of the Food System: Differences and Commonalities between Two Optimization Models. Proceedings 2023, 91, 17. https://doi.org/10.3390/proceedings2023091017
AMA Style
Heerschop SN, Cardinaals RPM, Biesbroek S, Kanellopoulos A, Geleijnse JM, van ‘t Veer P, van Zanten HHE. Understanding the Complexity of the Food System: Differences and Commonalities between Two Optimization Models. Proceedings. 2023; 91(1):17. https://doi.org/10.3390/proceedings2023091017
Chicago/Turabian StyleHeerschop, Samantha N., Renee P. M. Cardinaals, Sander Biesbroek, Argyris Kanellopoulos, Johanna M. Geleijnse, Pieter van ‘t Veer, and Hannah H. E. van Zanten. 2023. "Understanding the Complexity of the Food System: Differences and Commonalities between Two Optimization Models" Proceedings 91, no. 1: 17. https://doi.org/10.3390/proceedings2023091017
