Supporting information for the paper entitled 'Climate change impacts on aflatoxin B1 in maize and aflatoxin M1 in milk' in the PLOS ONE journal.
HJ Van der Fels-Klerx1*, LC Vermeulen1, AK Gavai1, C Liu1 1RIKILT Wageningen University and Research, Wageningen, the Netherlands
This study aimed to investigate the impacts of climate change on aflatoxin B1 production in maize and its consequences on aflatoxin M1 contamination in dairy cow’s milk, using a full chain modelling approach.
Crop phenology model, Forecasting model, Carryover model, Ensemble modelling, Data infrastructure, Systems modelling, Aflatoxin B1, Aflatoxin M1, Milk, Maize
Various models and datasets related to aflatoxins in the maize and dairy production chain have been developed and used but they have not yet been linked with each other. This study aimed to investigate the impacts of climate change on aflatoxin B1 production in maize and its consequences on aflatoxin M1 contamination in dairy cow’s milk, using a full chain modelling approach. To this end, available models and input data were chained together in a modelling framework. As a case study, we focused on maize grown in Eastern Europe and imported to the Netherlands to be fed – as part of dairy cows’ compound feed – to dairy cows in the Netherlands. Three different climate models, one aflatoxin B1 prediction model and five different carryover models were used. For this particular case study of East European maize, most of the calculations suggest an increase (up to 50%) of maximum mean AfM1 in milk by 2030, except for one climate (DMI) model suggesting a decrease. All calculations suggest a stable, with a slight increase (up to 0.6%), chance of finding AfM1 in milk above the EC limit of 0.05 µg/kg by 2030. Results varied mainly with the climate model data and carryover model considered. The model framework infrastructure is flexible so that forecasting models for other mycotoxins or other food safety hazards as well as other production chains, together with necessary input databases, can easily be included as well. This modelling framework for the first time links datasets and models related to aflatoxin B1 in maize and related aflatoxin M1 the dairy production chain to obtain a unique predictive methodology based on Monte Carlo simulation. Such an integrated approach with scenario analysis provides possibilities for policy makers and risk managers to study the effects of changes in the beginning of the chain on the end product.
Structure of the project with the Git Repo. Each of the models consists of a data folder (input/output) with associated metadata attached to it. The source code associated with each of the models are in the same folder.
Following folders exist:
- carry-overModels
- cropPhenologyModels
- forecastingModels
- workflows
Set up of the data and model flow is illustrated in Fig 1.pdf. Box 1-2 show the two models that are linked. Box A-D provide the input for the forecasting model, and box E-H are the input for the Carryover model. Box F and I are predicted model outcomes.
The authors acknowledge the Joint Research Centre of the European Commission for providing the calibrated temperature sums for maize from the MARS Crop Growth Monitoring System and its related databases. This study is supported by the Ministry of Economic Affairs, the Netherlands, through the KB programme.