Optimization of the aflatoxin monitoring costs along the maize supply chain in Risk Analysis

M. Focker, H.J. van der Fels*, A.G.J.M. Oude Lansink

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

An optimization model was used to gain insight into cost-effective monitoring plans for aflatoxins along the maize supply chain. The model was based on a typical Dutch maize chain, with maize grown in the Black Sea region, and transported by ship to the Netherlands for use as an ingredient in compound feed for dairy cattle. Six different scenarios, with different aflatoxin concentrations at harvest and possible aflatoxin production during transport, were used. By minimizing the costs and using parameters such as the concentration, the variance of the sampling plan, and the monitoring and replacement costs, the model optimized the control points (CPs; e.g., after harvest, before or after transport by sea ship), the number of batches sampled at the CP, and the number of samples per batch. This optimization approach led to an end-of-chain aflatoxin concentration below the predetermined limit. The model showed that, when postharvest aflatoxin production was not possible, it was most cost-effective to collect samples from all batches and replace contaminated batches directly after the harvest, since the replacement costs were the lowest at the origin of the chain. When there was aflatoxin production during storage, it was most cost-effective to collect samples and replace contaminated batches after storage and transport to avoid the duplicate before and after monitoring and replacement costs. Further along the chain a contaminated batch is detected, the more stakeholders are involved, the more expensive the replacement costs and possible recall costs become.
Original languageEnglish
Pages (from-to)2227-2236
JournalRisk Analysis
Volume39
Issue number10
Early online date27 Jun 2019
DOIs
Publication statusPublished - Oct 2019

Fingerprint

Aflatoxins
Risk analysis
Supply chains
Zea mays
Costs and Cost Analysis
Monitoring
Costs
Ships
Black Sea
Dairies
Oceans and Seas
Netherlands
Sampling

Keywords

  • Monitoring; mycotoxins; optimization

Cite this

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title = "Optimization of the aflatoxin monitoring costs along the maize supply chain in Risk Analysis",
abstract = "An optimization model was used to gain insight into cost-effective monitoring plans for aflatoxins along the maize supply chain. The model was based on a typical Dutch maize chain, with maize grown in the Black Sea region, and transported by ship to the Netherlands for use as an ingredient in compound feed for dairy cattle. Six different scenarios, with different aflatoxin concentrations at harvest and possible aflatoxin production during transport, were used. By minimizing the costs and using parameters such as the concentration, the variance of the sampling plan, and the monitoring and replacement costs, the model optimized the control points (CPs; e.g., after harvest, before or after transport by sea ship), the number of batches sampled at the CP, and the number of samples per batch. This optimization approach led to an end-of-chain aflatoxin concentration below the predetermined limit. The model showed that, when postharvest aflatoxin production was not possible, it was most cost-effective to collect samples from all batches and replace contaminated batches directly after the harvest, since the replacement costs were the lowest at the origin of the chain. When there was aflatoxin production during storage, it was most cost-effective to collect samples and replace contaminated batches after storage and transport to avoid the duplicate before and after monitoring and replacement costs. Further along the chain a contaminated batch is detected, the more stakeholders are involved, the more expensive the replacement costs and possible recall costs become.",
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Optimization of the aflatoxin monitoring costs along the maize supply chain in Risk Analysis. / Focker, M.; van der Fels, H.J.; Oude Lansink, A.G.J.M.

In: Risk Analysis, Vol. 39, No. 10, 10.2019, p. 2227-2236.

Research output: Contribution to journalArticleAcademicpeer-review

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