Best Researcher Award
Yhan Carlos Rojas De La Cruz
Federal University of Lavras, Brazil
Yhan Carlos Rojas De La Cruz is a researcher affiliated with the Federal University of Lavras whose scholarly activities focus on genetics, genomics, livestock improvement, and computational approaches for animal production. His published work integrates quantitative genetics, statistical modeling, and machine learning techniques to address practical challenges in animal breeding and agricultural science. Through contributions involving cattle, sheep, and genetic identification of animal products, his research demonstrates an interdisciplinary perspective that combines biological sciences with data-driven methodologies.[1]
Abstract
The research portfolio of Yhan Carlos Rojas De La Cruz reflects continuing work in genetics and genomics applied to livestock production systems. His publications emphasize predictive analytics, genetic evaluation, molecular identification, and growth modeling in economically important animal species. By integrating machine learning algorithms with traditional quantitative genetic methods, his studies contribute to more accurate breeding decisions and improved productivity while supporting evidence-based agricultural management.[2]
Keywords
Genetics, Genomics, Animal Breeding, Machine Learning, Livestock Production, Growth Curves, Quantitative Genetics, Precision Agriculture.
Introduction
Modern livestock science increasingly depends upon computational analysis, genomic technologies, and predictive statistical models. Within this context, the research undertaken by Yhan Carlos Rojas De La Cruz explores practical applications of data analysis to improve breeding efficiency, animal performance, and product traceability. His publications demonstrate collaboration across veterinary science, genetics, and agricultural technology while addressing challenges relevant to sustainable livestock systems.[3]
Research Profile
According to the available Scopus author profile, the researcher has produced eight indexed documents with four citations and an h-index of two. His scholarly activities focus primarily on genetics and genomics, with complementary interests in statistical modeling, livestock production, and artificial intelligence applications in agriculture. These publications collectively demonstrate a consistent emphasis on analytical methodologies supporting biological research.[1]
Research Contributions
- Applied machine learning methods for predicting body weight in Peruvian sheep populations.
- Developed statistical approaches for genetic evaluation of Brahman cattle growth curves.
- Investigated molecular identification techniques for cattle, pigs, and horses in animal-derived products.
- Contributed to predictive livestock management through quantitative genetic analysis and agricultural data science.
Publications
- Genetic analysis of Brahman cattle growth curves using two-stage and joint analysis methods (2025).
- Prediction models for live body weight and body compactness of Criollo sheep (2024).
- Machine learning approaches for body weight prediction in Peruvian Corriedale sheep (2024).
- Genetic identification of cattle, pigs and horses in products of animal origin (2022).
- Effects of Saccharomyces cerevisiae on silage composition (2021).
Research Impact
Although the publication profile represents an emerging stage of academic development, the available work demonstrates interdisciplinary integration between genetics, computational analysis, and agricultural sciences. The application of predictive models and machine learning contributes to modern precision livestock management and supports reproducible scientific methodologies suitable for future research expansion.[4]
Award Suitability
The research profile demonstrates measurable scholarly productivity within genetics and genomics, supported by peer-reviewed publications addressing computational methods in animal science. The combination of quantitative genetics, artificial intelligence, and agricultural innovation aligns with the interdisciplinary objectives recognized by the Computer Scientists Awards, particularly where computational techniques advance biological research and applied scientific knowledge.[5]
Conclusion
Yhan Carlos Rojas De La Cruz has established a focused research trajectory combining genetics, genomics, machine learning, and quantitative analysis within livestock science. His publications illustrate the value of computational methods for solving biological and agricultural problems while supporting evidence-based breeding and production strategies. Continued research in these interdisciplinary areas is expected to strengthen scientific understanding and practical agricultural applications.
External Links
References
- Elsevier. (n.d.). Scopus author details: Yhan Carlos Rojas De La Cruz, Author ID 57220588566.
https://www.scopus.com/authid/detail.uri?authorId=57220588566
- Rojas De La Cruz, Y.C. (2025). Análisis genético de curvas de crecimiento de bovinos de raza Brahman. Revista de Investigaciones Veterinarias del Perú. DOI:
https://doi.org/10.15381/rivep.v36i3.29053
- Prediction models for live body weight and body compactness of Criollo sheep. The Indian Journal of Animal Sciences (2024).
https://doi.org/10.56093/ijans.v94i7.148186
- Use of machine learning approaches for body weight prediction in Peruvian Corriedale Sheep. Smart Agricultural Technology (2024).
https://doi.org/10.1016/j.atech.2024.100419
- Genetic Identification of Cattle, Pigs and Horses in Products of Animal Origin. REBIOL (2022).
https://doi.org/10.17268/rebiol.2022.42.02.01