STATISTICAL MODELS FOR CLASSIFICATION OF GENOTYPES FOR YIELD OF LITTLE MILLET

M.S. NAGARAJA¹*, ABHISHEK SINGH^2
1Section of Agricultural Statistics, Department of Farm Engineering, Institute of Agricultural Sciences, Banaras Hindu University, Varanasi, 221005, Uttar Pradesh
²Section of Agricultural Statistics, Department of Farm Engineering, Institute of Agricultural Sciences, Banaras Hindu University, Varanasi, 221005, Uttar Pradesh
* Corresponding Author : msn8129@gmail.com

Received : 13-03-2018     Accepted : 28-03-2018     Published : 30-03-2018
Volume : 10     Issue : 6       Pages : 5593 - 5597
Int J Agr Sci 10.6 (2018):5593-5597

Keywords : Ordinal Logistic Regression Model, Ordinal Logistic Regression Model, Attributing, Classification and Significant
Academic Editor : T Kumareswari
Conflict of Interest : None declared
Acknowledgements/Funding : Author thankful to Institute of Agricultural Sciences, Banaras Hindu University, Varanasi, 221005, Uttar Pradesh
Author Contribution : All author equally contributed

Use of statistical models such as Ordinal Logistic Regression Model and Multiclass Discriminant Model for classification of genotypes or creation of genetic variability is undergoing an outpouring in interest among research workers. These models were fitted to data recorded on yield and yield attributing characters of 722 genotypes of little millet and the data has been collected from Project coordination cell, All India Coordinated Small Millets Improvement Project (AICSMIP), ICAR, Bengaluru. Classification ability measures such as Accuracy Rate, Kappa Statistics, Avgprecision, and Avgrecall were used for testing samples. Days to fifty percent flowering, Plant height, Number of basel tillers, Flag leaf length, Flag leaf width were considered to be important attributing characters for classification and Ordinal Logistic Regression Model (56.55%) was performed better than Multiclass Discriminant model (53.79%) for classification of genotypes for different classes of yield of little millets.

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