Inferring Relationship of Blood Metabolic Changes and Average Daily Gain With Feed Conversion Efficiency in Murrah Heifers: Machine Learning Approach
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. Fur...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2020-09-01
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| Series: | Frontiers in Veterinary Science |
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| Online Access: | https://www.frontiersin.org/article/10.3389/fvets.2020.00518/full |
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| author | Poonam Sikka Abhigyan Nath Shyam Sundar Paul Jerome Andonissamy Dwijesh Chandra Mishra Atmakuri Ramakrishna Rao Ashok Kumar Balhara Krishna Kumar Chaturvedi Keerti Kumar Yadav Sunesh Balhara |
| author_facet | Poonam Sikka Abhigyan Nath Shyam Sundar Paul Jerome Andonissamy Dwijesh Chandra Mishra Atmakuri Ramakrishna Rao Ashok Kumar Balhara Krishna Kumar Chaturvedi Keerti Kumar Yadav Sunesh Balhara |
| author_sort | Poonam Sikka |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-11T06:54:38Z |
| format | Article |
| id | doaj.art-90f7386d1ad746ccb35e5ef5f970f589 |
| institution | Directory Open Access Journal |
| issn | 2297-1769 |
| language | English |
| last_indexed | 2024-12-11T06:54:38Z |
| publishDate | 2020-09-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Veterinary Science |
| spelling | doaj.art-90f7386d1ad746ccb35e5ef5f970f5892022-12-22T01:16:48ZengFrontiers Media S.A.Frontiers in Veterinary Science2297-17692020-09-01710.3389/fvets.2020.00518545908Inferring Relationship of Blood Metabolic Changes and Average Daily Gain With Feed Conversion Efficiency in Murrah Heifers: Machine Learning ApproachPoonam Sikka0Abhigyan Nath1Shyam Sundar Paul2Jerome Andonissamy3Dwijesh Chandra Mishra4Atmakuri Ramakrishna Rao5Ashok Kumar Balhara6Krishna Kumar Chaturvedi7Keerti Kumar Yadav8Sunesh Balhara9Animal Biochemistry, Division of Genetics and Breeding, Central Institute for Research on Buffaloes (ICAR), Hisar, IndiaDepartment of Biochemistry, Pt. Jawahar Lal Nehru Memorial Medical College, Pt. Deendayal Upadhyay Memorial Health Sciences and Ayush University of Chhatisgarh, Raipur, IndiaPoultry Nutrition, Directorate of Poultry Research (DPR), ICAR, Hyderabad, IndiaAnimal Biochemistry, Division of Genetics and Breeding, Central Institute for Research on Buffaloes (ICAR), Hisar, IndiaIndian Agricultural Statistics Research Institute, Indian Council of Agricultural Research, New Delhi, IndiaIndian Agricultural Statistics Research Institute, Indian Council of Agricultural Research, New Delhi, IndiaAnimal Biochemistry, Division of Genetics and Breeding, Central Institute for Research on Buffaloes (ICAR), Hisar, IndiaIndian Agricultural Statistics Research Institute, Indian Council of Agricultural Research, New Delhi, IndiaDepartment of Bioinfromatics, School of Earth, Biological and Environmental Sciences, Central University of South Bihar, Patna, IndiaAnimal Biochemistry, Division of Genetics and Breeding, Central Institute for Research on Buffaloes (ICAR), Hisar, IndiaMachine 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.https://www.frontiersin.org/article/10.3389/fvets.2020.00518/fullbuffalobloodfeed conversion efficiencypartial least square regressionprediction models |
| spellingShingle | Poonam Sikka Abhigyan Nath Shyam Sundar Paul Jerome Andonissamy Dwijesh Chandra Mishra Atmakuri Ramakrishna Rao Ashok Kumar Balhara Krishna Kumar Chaturvedi Keerti Kumar Yadav Sunesh Balhara Inferring Relationship of Blood Metabolic Changes and Average Daily Gain With Feed Conversion Efficiency in Murrah Heifers: Machine Learning Approach Frontiers in Veterinary Science buffalo blood feed conversion efficiency partial least square regression prediction models |
| title | Inferring Relationship of Blood Metabolic Changes and Average Daily Gain With Feed Conversion Efficiency in Murrah Heifers: Machine Learning Approach |
| title_full | Inferring Relationship of Blood Metabolic Changes and Average Daily Gain With Feed Conversion Efficiency in Murrah Heifers: Machine Learning Approach |
| title_fullStr | Inferring Relationship of Blood Metabolic Changes and Average Daily Gain With Feed Conversion Efficiency in Murrah Heifers: Machine Learning Approach |
| title_full_unstemmed | Inferring Relationship of Blood Metabolic Changes and Average Daily Gain With Feed Conversion Efficiency in Murrah Heifers: Machine Learning Approach |
| title_short | Inferring Relationship of Blood Metabolic Changes and Average Daily Gain With Feed Conversion Efficiency in Murrah Heifers: Machine Learning Approach |
| title_sort | inferring relationship of blood metabolic changes and average daily gain with feed conversion efficiency in murrah heifers machine learning approach |
| topic | buffalo blood feed conversion efficiency partial least square regression prediction models |
| url | https://www.frontiersin.org/article/10.3389/fvets.2020.00518/full |
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