Abstract
As the complexity of our food systems increases, they also become susceptible to unanticipated natural and human-initiated events. Commodity trade networks are a critical component of our food systems in ensuring food availability. We develop a generic data-driven framework to construct realistic agricultural commodity trade networks. Our work is motivated by the need to study food flows in the context of biological invasions. These networks are derived by fusing gridded, administrative-level, and survey datasets on production, trade, and consumption. Further, they are periodic temporal networks reflecting seasonal variations in production and trade of the crop. We apply this approach to create networks of tomato flow for two regions – Senegal and Nepal. Using statistical methods and network analysis, we gain insights into spatiotemporal dynamics of production and trade. Our results suggest that agricultural systems are increasingly vulnerable to attacks through trade of commodities due to their vicinity to regions of high demand and seasonal variations in production and flows.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Barrett, C., et al.: Planning and response in the aftermath of a large crisis: an agent-based informatics framework. In: Proceedings of the 2013 Winter Simulation Conference, pp. 1515–1526 (2013). https://informs-sim.org/wsc13papers/includes/files/132.pdf
Biondi, A., Guedes, R.N.C., Wan, F.H., Desneux, N.: Ecology, worldwide spread, and management of the invasive South American tomato pinworm, Tuta absoluta: past, present, and future. Annu. Rev. Entomol. 63, 239–258 (2018)
Breiman, L.: Classification and Regression Trees. Routledge, Milton Park (2017)
Carrasco, L., et al.: Unveiling human-assisted dispersal mechanisms in invasive alien insects: integration of spatial stochastic simulation and phenology models. Ecol. Model. 221(17), 2068–2075 (2010)
ComTrade: Import and export (2021). http://comtrade.un.org/db/
Ercsey-Ravasz, M., Toroczkai, Z., Lakner, Z., Baranyi, J.: Complexity of the international agro-food trade network and its impact on food safety. PLoS ONE 7(5), e37810 (2012)
Eubank, S., et al.: Modelling disease outbreaks in realistic urban social networks. Nature 429(6988), 180–184 (2004)
FAO: Production and trade (2019). http://www.fao.org/faostat/en/#data
Google: Distance Matrix API (2017). https://developers.google.com/maps/documentation/distance-matrix/
Guimapi, R.Y., Mohamed, S.A., Okeyo, G.O., Ndjomatchoua, F.T., Ekesi, S., Tonnang, H.E.: Modeling the risk of invasion and spread of Tuta absoluta in Africa. Ecol. Complex. 28, 77–93 (2016)
Hulme, P.E.: Trade, transport and trouble: managing invasive species pathways in an era of globalization. J. Appl. Ecol. 46(1), 10–18 (2009)
Hwang, H.L., et al.: The freight analysis framework version 4 (FAF4)-building the FAF4 regional database: data sources and estimation methodologies. Technical report, Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States) (2016)
Kaluza, P., Kölzsch, A., Gastner, M.T., Blasius, B.: The complex network of global cargo ship movements. J. R. Soc. Interface 7(48), 1093–1103 (2010)
Manning, L., Baines, R.N., Chadd, S.A.: Deliberate contamination of the food supply chain. Br. Food J. 107(4), 225–245 (2005). https://doi.org/10.1108/00070700510589512
McNitt, J., et al.: Assessing the multi-pathway threat from an invasive agricultural pest: Tuta absoluta in Asia. Proc. R. Soc. B 286(1913), 20191159 (2019)
Mistry, D., et al.: Inferring high-resolution human mixing patterns for disease modeling. Nat. Commun. 12(1), 323 (2021). https://doi.org/10.1038/s41467-020-20544-y
Mohamed, I.A.: Cartographie des flux commerciaux de la tomate au Sénégal: reconstitution des filières, modélisation des flux et r\(\hat{0}\)le dans la propagation du ravageur Tuta absoluta (2017)
Nath, M., et al.: Using network reliability to understand international food trade dynamics. In: Aiello, L.M., Cherifi, C., Cherifi, H., Lambiotte, R., Lió, P., Rocha, L.M. (eds.) COMPLEX NETWORKS 2018. SCI, vol. 812, pp. 524–535. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-05411-3_43
Nopsa, J.F.H., et al.: Ecological networks in stored grain: key postharvest nodes for emerging pests, pathogens, and mycotoxins. BioScience 65(10), 985–1002 (2015)
Pimentel, D., Zuniga, R., Morrison, D.: Update on the environmental and economic costs associated with alien-invasive species in the united states. Ecol. Econ. 52(3), 273–288 (2005)
Robinson, C., Shirazi, A., Liu, M., Dilkina, B.: Network optimization of food flows in the US. In: 2016 IEEE International Conference on Big Data (Big Data), pp. 2190–2198. IEEE (2016)
Rosenzweig, C., Iglesius, A., Yang, X.B., Epstein, P.R., Chivian, E.: Climate change and extreme weather events-implications for food production, plant diseases, and pests (2001)
Singh, S., Kumar, R., Panchal, R., Tiwari, M.K.: Impact of COVID-19 on logistics systems and disruptions in food supply chain. Int. J. Prod. Res. 59(7), 1993–2008 (2021)
Suweis, S., Carr, J.A., Maritan, A., Rinaldo, A., D’Odorico, P.: Resilience and reactivity of global food security. Proc. Natl. Acad. Sci. 112(22), 6902–6907 (2015)
USDA: Farms and Land in Farms 2017 Summary (2018). https://www.nass.usda.gov/Publications/Todays_Reports/reports/fnlo0218.pdf
Venkatramanan, S., et al.: Modeling commodity flow in the context of invasive species spread: study of Tuta Absoluta in Nepal. Crop Prot. 135, 104736 (2020)
Voigt, A., et al.: Containing pandemics through targeted testing of households. BMC Infect. Dis. 21, 548 (2021). https://bmcinfectdis.biomedcentral.com/articles/10.1186/s12879-021-06256-8
You, L., Wood-Sichra, U., Fritz, S., Guo, Z., See, L., Koo., J.: Spatial Production Allocation Model (SPAM) 2005 v3.2 (2017). http://mapspam.info
Acknowledgments
This work has been partially supported by USAID under the Cooperative Agreement No. AID-OAA-L-15-00001, USDA NIFA FACT 2019-67021-29933, DTRA (Contract HDTRA1-19-D-0007), University of Virginia Strategic Investment Fund award number SIF160, NSF grants IIS-1633028 (BIG DATA), CMMI-1745207 (EAGER), OAC-1916805 (CINES), CCF-1918656 (Expeditions), OAC-2027541 (RAPID) and IIS-1908530, The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright annotation thereon.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Adiga, A. et al. (2022). Realistic Commodity Flow Networks to Assess Vulnerability of Food Systems. In: Benito, R.M., Cherifi, C., Cherifi, H., Moro, E., Rocha, L.M., Sales-Pardo, M. (eds) Complex Networks & Their Applications X. COMPLEX NETWORKS 2021. Studies in Computational Intelligence, vol 1072. Springer, Cham. https://doi.org/10.1007/978-3-030-93409-5_15
Download citation
DOI: https://doi.org/10.1007/978-3-030-93409-5_15
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-93408-8
Online ISBN: 978-3-030-93409-5
eBook Packages: EngineeringEngineering (R0)