Computational Approaches In Livestock Breeding: A Review

The integration of computational and genomic methodologies has instigated a transformation in the realm of livestock breeding, enabling the utilization of sophisticated data-driven techniques for the analysis of traits, prediction of breeding values, design of programs, and assessment of agro-environmental impacts. This paper provides an overview of seminal methodologies and applications across various domains that drive innovation. Genomic selection employs markers distributed throughout the genome and statistical algorithms to derive genomic estimated breeding values, thereby expediting genetic progress. Utilising sensors and AI for real-time monitoring can effectively prevent diseases and facilitate progress. The utilization of biosensors and artificial intelligence for real-time monitorinPreserving endangered species is facilitated by the utilization of high-throughput sequencing techniques. The utilization of machine learning and advanced algorithms in predictive modelling has demonstrated significant promise in the estimation of phenotypes, hence providing valuable guidance for breeding decisions. Additionally, data mining tools evaluate the impact of policies on livelihoods and food security. Existing challenges in implementing integrated computational pipelines in the field and the uncertainty surrounding long-term implications at multiple levels underscore the need for further research. In conclusion, the fusion of biotechnology, advanced statistical genomics, and computational analytics has ushered in an era of precision livestock improvement driven by data, enabling a detailed analysis of the genetic factors that underlie complex traits. This presents unprecedented opportunities to achieve a balance between productivity gains, environmental adaptability, and genetic conservation, thereby offering valuable scientific insights for sustainable agricultural intensification in the face of increasing protein demands and climatic uncertainty.