AI Assisted Assessment of Türkiye Breed Diversity and In Situ Conservation in Sheep and Goats Using FAO Data (1983 2024)

Özet

Understanding and conserving livestock biodiversity is essential for resilient agricultural systems and food security. This study presents an AI assisted analysis of global sheep and goat breed diversity using FAOs Domestic Animal Diversity Information System (DAD IS) data spanning 1983 to 2024. Breed richness (number of distinct breeds), population dynamics and risk status were evaluated using ecological diversity indices including Shannon-Wiener Index, Simpson Index, Berger-Parker Index, Menhinick Index and Hill numbers. Results showed that sheep populations maintain higher diversity and more structured conservation coverage than goats. A clear positive correlation was observed between in situ conservation programs and population stability. Machine learning and Python-based libraries facilitated dynamic visualizations and pattern recognition. The findings highlight the importance of combining biodiversity metrics with artificial intelligence to improve livestock monitoring systems and inform future conservation strategies. Strengthening data continuity and expanding in situ programs remain crucial to securing genetic resources for future generations.