A machine learning approach to predict the most and the least feed–efficient groups in beef cattle

Author:

Shirzadifar Alimohammad,Miar Younes,Plastow Graham,Basarab John,Li Changxi,Fitzsimmons Carolyn,Riazi Mohammad,Manafiazar Ghader

Publisher

Elsevier BV

Subject

Artificial Intelligence,General Agricultural and Biological Sciences,Computer Science (miscellaneous)

Reference38 articles.

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2. Digital technology adoption in livestock production with a special focus on ruminant farming;Groher;Animal,2020

3. Intelligent perception for cattle monitoring: a review for cattle identification, body condition score evaluation, and weight estimation;Qiao;Comput. Electron. Agricult.,2021

4. Optimizing feed intake recording and feed efficiency estimation to increase the rate of genetic gain for feed efficiency in beef cattle;Manafiazar;Can. J. Anim. Sci.,2017

5. Genomic prediction ability for feed efficiency traits using different models and pseudo-phenotypes under several validation strategies in Nelore cattle;Brunes;Animal,2021

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