Follow
International Journal of Current Microbiology and Applied Sciences (IJCMAS)
IJCMAS is now DOI (CrossRef) registered Research Journal. The DOIs are assigned to all published IJCMAS Articles.
Index Copernicus ICI Journals Master List 2018 - IJCMAS--ICV 2018: 95.39 For more details click here
National Academy of Agricultural Sciences (NAAS) : NAAS Score: *5.38 (2020) [Effective from January 1, 2020]For more details click here

Login as a Reviewer

Indexed in



National Academy of Agricultural Sciences (NAAS)
NAAS Score: *5.38 (2020)
[Effective from January 1, 2020]
For more details click here

ICV 2018: 95.39
Index Copernicus ICI Journals Master List 2018 - IJCMAS--ICV 2018: 95.39
For more details click here

See Guidelines to Authors
Current Issues

Original Research Articles

PRINT ISSN : 2319-7692
Online ISSN : 2319-7706
Issues : 12 per year
Publisher : Excellent Publishers
Email : editorijcmas@gmail.com / submit@ijcmas.com
Editor-in-chief: Dr.M.Prakash
Index Copernicus ICV 2018: 95.39
NAAS RATING 2020: 5.38

Int.J.Curr.Microbiol.App.Sci.2020.9(4): 1867-1873
DOI: https://doi.org/10.20546/ijcmas.2020.904.220


Prediction of Lifetime Performance in Sahiwal Cattle by Artificial Intelligence Based Machine Learning Models
Namith Chandrashekar1*, Archana Verma1, Ashok Kumar Gupta1, Adesh Kumar Sharma2, C. G. Shashank3, Saleem Yousuf1 and Ravinder Malhotra2
1Department of Animal Genetics and Breeding
3Department of Animal Physiology,
ICAR-National Dairy Research Institute, Karnal, Haryana, India
2Department of Dairy Economics, Statistics & Management, ICAR-National Dairy Research Institute, Karnal, Haryana, India
*Corresponding author
Abstract:

Selecting animals for lifetime traits by predicting them based on first lactation traits is a useful method for economic dairy farming. This study was conducted to analyze the prediction performance of various models for prediction of lifetime performance in Sahiwal cattle. The first lactation and lifetime traits were generated from the data on milk production and fertility traits of Sahiwal cattle maintained at ICAR-National Dairy Research Institute, Karnal, India. The adjusted data was used for analysis and using two ML algorithms and also conventional MLR models; lifetime traits were predicted using first lactation traits. Results of this study revealed that most of the lifetime traits were best predicted by SVR model and also RF model. Thus, it is concluded that for prediction of lifetime traits in Sahiwal cattle, one can explore emerging ML models as an alternative to conventional MLR models.


Keywords: Lifetime performance, Machine learning algorithms, Prediction, Sahiwal cattle
Download this article as Download

How to cite this article:

Namith Chandrashekar, Archana Verma, Ashok Kumar Gupta, Adesh Kumar Sharma, C. G. Shashank, Saleem Yousuf and Ravinder Malhotra. 2020. Prediction of Lifetime Performance in Sahiwal Cattle by Artificial Intelligence Based Machine Learning Models.Int.J.Curr.Microbiol.App.Sci. 9(4): 1867-1873. doi: https://doi.org/10.20546/ijcmas.2020.904.220