An integrated model for pre- and post-harvest aflatoxin contamination in maize

Area of Science:

• Agricultural Science
• Food Safety
• Mycology

Background:

• Aflatoxin contamination from Aspergillus flavus in maize is a critical food safety issue affecting human and livestock health.
• Existing risk models primarily focus on pre-harvest conditions, neglecting post-harvest contamination, especially in regions with storage limitations.

Purpose of the Study:

• To develop and validate an integrated, meteorology-driven epidemiological model for predicting aflatoxin contamination.
• To cover the entire maize supply chain, from planting through delivery, including post-harvest stages.

Main Methods:

• Utilized approximate Bayesian computation to parameterize the model using six years of monthly time-series data on aflatoxin levels.
• Incorporated daily shipment data from multiple sourcing regions at a maize processing plant in South Central India.

Main Results:

• The parameterized model accurately replicated the historical aflatoxin data profiles, scale, and variance for both fitting and validation datasets.
• The model successfully illustrated the dynamics of Aspergillus flavus growth and aflatoxin production across pre- and post-harvest phases.

Conclusions:

• The integrated model provides a robust tool for understanding and predicting aflatoxin contamination dynamics.
• The model can inform decision-making for sourcing strategies and evaluate interventions to mitigate aflatoxin risks in the food supply chain.