References
- Chafai N., Hayah I., Houaga I., Badaoui B. (2023): A review of machine learning models applied to genomic prediction in animal breeding. Frontiers in Genetics, 14: 1150596. https://doi.org/10.3389/fgene.2023.1150596
- Chollet F. (2017): Deep Learning with Python. Manning Publications Co.
- Dekkers J.C.M. (2004): Commercial application of marker- and gene-assisted selection in livestock: strategies and lessons. Journal of Animal Science, 82 E-Suppl: E313-328. https://doi.org/10.2527/2004.8213_supplE313x
- FAO (2018): The state of Food and Agriculture. In: The State of the World. https://www.fao.org/3/i9549en/I9549EN.pdf
- Foley J.A., Ramankutty N., Brauman K.A., Cassidy E.S., Gerber J.S., Johnston M., Mueller N.D., O’Connell C., Ray D.K., West P.C., Balzer C., Bennett E.M., Carpenter S.R., Hill J., Monfreda C., Polasky S., Rockström J., Sheehan J., Siebert S., Tilman D., Zaks D.P.M. (2011): Solutions for a cultivated planet. Nature, 478(7369): 337-342. https://doi.org/10.1038/nature10452
- González-Recio O., Rosa G.J.M., Gianola D. (2014): Machine learning methods and predictive ability metrics for genome-wide prediction of complex traits. Livestock Science, 166: 217-231. https://doi.org/https://doi.org/10.1016/j.livsci.2014.05.036
- Hayes B.J., Bowman P.J., Chamberlain A.J., Goddard M.E. (2009): Invited review: Genomic selection in dairy cattle: Progress and challenges. Journal of Dairy Science, 92(2): 433-443. https://doi.org/https://doi.org/10.3168/jds.2008-1646
- ICAR (2023): The Global Standard for Livestock Data. Statistics 2023. Available at: https://my.icar.org/stats/list (accessed on 17 January 2023).
- Meyer K. (2007): WOMBAT - A tool for mixed model analyses in quantitative genetics by restricted maximum likelihood (REML). Journal of Zhejiang University SCIENCE B, 8(11): 815-821. https://doi.org/10.1631/jzus.2007.B0815
- Morota G., Ventura R.V, Silva F.F., Koyama M., Fernando S.C. (2018): Big Data Analytics and Precision Animal Agriculture Symposium: Machine learning and data mining advance predictive big data analysis in precision animal agriculture 1. Journal of Animal Science, 96(4): 1540-1550. https://doi.org/10.1093/jas/sky014
- Nayeri S., Sargolzaei M., Tulpan D. (2019): A review of traditional and machine learning methods applied to animal breeding. Animal Health Research Reviews, 20(1): 31-46. https://doi.org/10.1017/S1466252319000148
- Shahinfar S., Mehrabani-Yeganeh H., Lucas C., Kalhor A., Kazemian M., Weigel K.A. (2012): Prediction of Breeding Values for Dairy Cattle Using Artificial Neural Networks and Neuro-Fuzzy Systems. Computational and Mathematical Methods in Medicine, 127130. https://doi.org/10.1155/2012/127130
- Stanojević D., Đedović R., Bogdanović V., Popovac M., Perišić P., Beskorovajni R., Lazarević M. (2015): The potentials of using selection index in the assessment of breeding values of Holstein breeds in Serbia. Biotechnology in Animal Husbandry, 31(4): 523–532. https://doi.org/10.2298/bah1504523s
- Štrbac L., Pracner D., Šaran M., Janković D., Trivunović S., Ivković M., Tarjan L., Dedović N. (2023): Mathematical Modeling and Software Tools for Breeding Value Estimation Based on Phenotypic, Pedigree and Genomic Information of Holstein Friesian Cattle in Serbia. Animals, 13(4): 597. https://doi.org/10.3390/ani13040597
- Tarjan L., Šenk I., Pracner D., Rajković D., Štrbac L. (2021): Possibilities for applying machine learning in dairy cattle breeding. 20th International Symposium INFOTEH - Jahorina (INFOTEH), 1-6. https://doi.org/10.1109/INFOTEH51037.2021.9400672
- UN (2019): World Population Prospects 2019. Available at: https://reliefweb.int/attachments/f46d3fce-97bb-327f-a065-a813f9969af7/WPP2019_Highlights.pdf
- Walstra P., Walstra P., Wouters J.T.M., Geurts T.J. (2005): Dairy Science and Technology (2nd ed.). CRC Press, Taylor & Francis. https://doi.org/10.1201/9781420028010
