Mycotoxins in animal feed
and effects on animal production
Key Objective
The objective of this use case is to analyze the presence of various mycotoxins in animal feed and to identify potential correlations among them. Additionally, it aims to explore the relationship between mycotoxin contamination and climate change. By understanding these correlations, the use case seeks to inform the development of more effective strategies for managing mycotoxin contamination, ultimately improving animal health and productivity.
Despite significant advancements in agricultural technology and analytical methods, tackling the issue of mycotoxins in animal feed remains a complicated task. Nowadays, analyzing the volume of data generated across global feed supply chains presents an overwhelming task. This data is diverse, incomplete, and scattered across various formats, making it difficult to leverage. Failing to do so leads to serious consequences: animals continue to suffer from mycotoxin exposure, resulting in decreased productivity, increased veterinary costs, and severe financial losses for producers due to contaminated feed batches.
This Use Case will leverage advanced machine learning techniques and a wide array of data sources, both public and private, to develop AI models capable of predicting and mitigating the risks associated with mycotoxins in animal feed, ultimately safeguarding animal health and ensuring the economic viability of the livestock industry. This will be done in close collaboration with an extra-consortium organization who specializes in the field, called Food Foretress.
The dataset used for this use case was sourced from Food Fortress, which included laboratory test results for mycotoxin analysis in animal feed. Each record in the dataset contains details on the date of analysis, the type of matrix (feed type), and the concentration of detected mycotoxins, expressed in micrograms per kilogram (ug/kg). In addition, Food Fortress provided data on the typical composition of feed rations for different types of livestock, enabling a comprehensive understanding of feed content and its potential contamination risks.
We initiated a Federated Learning use-case between Food Fortress and Moy Park to analyze the impact of mycotoxins on poultry production. This approach ensures data privacy and security, supporting the evaluation and refinement of the EFRA Platform.
We analyzed 10 years of historical data using the EFRA Timeseries Engine to extract insights on the evolution and prevalence of mycotoxins. This use-case supports a Federated Learning deployment between Food Fortress and Moy Park, enhancing our understanding of mycotoxin effects on poultry production and reinforcing scientific research in this area.
Additionally, by combining this use-case with use-case #1, we will support a Federated Learning deployment between Food Fortress and Moy Park. This is a very meaningful and promising use-case that will be further explored in the second period of the project, since Food Fortress tests a lot of the animal feed used by Moy Park in its poultry farms. The effects of mycotoxins in feed on animal production are a recent area of scientific research, with promising initial results, which we can further reinforce with this novel approach.
The expected outcome of this use case includes identifying long-term trends of various mycotoxins in animal feed, with findings already submitted for publication in the IAFP (International Association for Food Protection) special issue “. Additionally, this use case aims to develop an initial AI predictive model that leverages additional data sources to predict future mycotoxin contamination in animal feed. This will support more effective strategies for managing feed safety and protecting animal health.
Latest news about the Use Case
Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or European Commission-EU. Neither the European Union nor the granting authority can be held responsible for them.
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