BIG DATA ANALYTICS AND PRECISION ANIMAL AGRICULTURE SYMPOSIUM: Data to decisions.

Area of Science:

• Agricultural Science
• Data Science
• Animal Science

Background:

• Big data is increasingly utilized in business and agronomy to improve operational decision-making.
• Livestock operations generate substantial data suitable for advanced analytical techniques.
• Predictive analytics offers a powerful methodology for forecasting specific outcomes.

Purpose of the Study:

• To outline the process of applying big data and predictive analytics in livestock management.
• To develop tools that support informed decisions in livestock production, health, and welfare.
• To demonstrate the application of a systematic predictive analytics framework for animal agriculture.

Main Methods:

• The predictive analytic process includes target variable selection, data management, and data partitioning.
• Algorithm development, refinement, and accuracy comparison are key steps.
• Dataset partitioning enables model building and refinement before testing on unseen data.

Main Results:

• A systematic approach to predictive analytics can be applied to existing or collectable livestock data.
• The process facilitates the creation of accurate predictive models for livestock management.
• Multiple classification algorithms can be tested to achieve optimal predictive performance.

Conclusions:

• Implementing a structured big data and predictive analytics framework is crucial for livestock operations.
• This approach leads to enhanced livestock operational decisions and improved animal management.
• The methodology supports the advancement of precision animal management through data-driven insights.