Unveiling livestock trade trends: A beginner's guide to generative AI-powered visualization

Area of Science:

• Agricultural Economics
• Data Science
• Animal Science

Background:

• Optimizing livestock production requires understanding export trends.
• Analyzing international livestock trade data can be complex for non-programmers.
• Existing literature lacks accessible tools for livestock export data visualization.

Purpose of the Study:

• To provide a guide for visualizing US-Japan livestock export trends.
• To empower novice researchers with Python and AI for data analysis.
• To address the need for accessible tools in livestock data interpretation.

Main Methods:

• Utilizing federal datasets for data preparation.
• Employing Python programming for data analysis and visualization.
• Leveraging generative AI (Microsoft Copilot, Google Gemini) for code generation.
• Addressing common data visualization challenges like overlapping points.

Main Results:

• A simplified workflow for preparing and visualizing livestock export data.
• Generated Python code provided in appendices for practical application.
• Demonstrated methods for extracting insights and making predictions from data.

Conclusions:

• The tutorial effectively bridges the gap for non-programmers in livestock data analysis.
• This approach enhances researchers' ability to interpret and utilize livestock trade data.
• The methodology offers a significant contribution to the field of livestock research and data science.