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Inferring Relationship of Blood Metabolic Changes and Average Daily Gain With Feed Conversion Efficiency in Murrah Heifers: Machine Learning Approach

  • Sikka, Poonam1
  • Nath, Abhigyan2
  • Paul, Shyam Sundar3
  • Andonissamy, Jerome1
  • Mishra, Dwijesh Chandra4
  • Rao, Atmakuri Ramakrishna4
  • Balhara, Ashok Kumar1
  • Chaturvedi, Krishna Kumar4
  • Yadav, Keerti Kumar5
  • Balhara, Sunesh1
  • 1 Animal Biochemistry, Division of Genetics and Breeding, Central Institute for Research on Buffaloes (ICAR), Hisar , (India)
  • 2 Department of Biochemistry, Pt. Jawahar Lal Nehru Memorial Medical College, Pt. Deendayal Upadhyay Memorial Health Sciences and Ayush University of Chhatisgarh, Raipur , (India)
  • 3 Poultry Nutrition, Directorate of Poultry Research (DPR), ICAR, Hyderabad , (India)
  • 4 Indian Agricultural Statistics Research Institute, Indian Council of Agricultural Research, New Delhi , (India)
  • 5 Department of Bioinfromatics, School of Earth, Biological and Environmental Sciences, Central University of South Bihar, Patna , (India)
Published Article
Frontiers in Veterinary Science
Frontiers Media S.A.
Publication Date
Sep 02, 2020
DOI: 10.3389/fvets.2020.00518
PMID: 32984408
PMCID: PMC7492607
PubMed Central


Machine learning algorithms were employed for predicting the feed conversion efficiency (FCE), using the blood parameters and average daily gain (ADG) as predictor variables in buffalo heifers. It was observed that isotonic regression outperformed other machine learning algorithms used in study. Further, we also achieved the best performance evaluation metrics model with additive regression as the meta learner and isotonic regression as the base learner on 10-fold cross-validation and leaving-one-out cross-validation tests. Further, we created three separate partial least square regression (PLSR) models using all 14 parameters of blood and ADG as independent (explanatory) variables and FCE as the dependent variable, to understand the interactions of blood parameters, ADG with FCE each by inclusion of all FCE values (i), only higher FCE values (negative RFI) (ii), and inclusion of only lower FCE (positive RFI) values (iii). The PLSR model including only the higher FCE values was concluded the best, based on performance evaluation metrics as compared to PLSR models developed by inclusion of the lower FCE values and all types of FCE values. IGF1 and its interactions with the other blood parameters were found highly influential for higher FCE measures. The strength of the estimated interaction effects of the blood parameter in relation to FCE may facilitate understanding of intricate dynamics of blood parameters for growth.

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