Decision support system for selection of dairy buffalo based on peak yield
16
Authors
-
Sunesh Balhara
Central Institute for Research on Buffaloes
-
Ekta Hooda
-
Naresh Dahiya
-
Ashok Kumar Balhara
-
Ashok Boora
-
Sarita Yadav
-
SK Phulia
Keywords:
animal selection, ANN, linear traits, machine learningAbstract
Efficient animal selection is vital for optimizing dairy farming. Traditionally, human reliance introduces errors. In the present study, a user-friendly Decision Support System (DSS) using Artificial Neural Networks (ANNs) was developed to predict peak milk yield of buffalo (Murrah breed) based on linear traits - Udder Depth (UD), Naval Udder Distance (NUD), Rear teat distance (RTD), Fore rear teat distance (FRTD), Teat length (TL), Rump width (RW), Rear udder width (RUW), Rear udder height (RUH), and Lactation number (LN). A dataset of 259 buffaloes was used for model training and validation, with ANN outperforming Support Vector machine, Random Forest and Multiple Linear Regression. The performance of ANN was assessed with the correlation coefficient (CC), root mean squared error (RMSE) and R2 metrics, revealing maximum CC of 0.86, minimal RMSE of 2.03 and a maximum R2 of 0.748, highlighting its accuracy. These findings provide valuable insights for dairy farmers, enabling informed decisions in selecting elite buffaloes. Implementation of this tool has the potential to revolutionize breeding, boost milk production, and enhance overall efficiency and profitability in buffalo based dairy farming.
Downloads
References
Angeles-Hernandez J C, Castro-Espinoza F A, Peláez-Acero A, Salinas-Martinez J A, Chay-Canul A J and Vargas-Bello-Pérez E. 2022. Estimation of milk yield based on udder measures of Pelibuey sheep using artificial neural networks. Scientific Reports 12(1): 9009.
Aruna T S, Deb S M, Ruhil A P, Malhotra R, and Yousaf S. 2022. Recent prediction tools for estimation of first lactation 305 days milk yield using monthly test days for genetic evaluation in crossbred cattle. The Indian Journal of Animal Sciences, 92(3), 374-377.
Balhara A K, Nayan V, Dey A, Singh K P, Dahiya S S and Singh I. 2017. Climate change and buffalo farming in major milk-producing states of India: present status and need for addressing concerns. Indian Journal of Animal Sciences 87(4): 403-411.
Balhara A K, Jan M H, Hooda E, Kumar K, Ghanghas A, Sangwan S, Balhara S, Phulia SK, Yadav S, Boora A and Kumar S. 2024. Prediction of core body temperature using infra-red thermography in buffaloes. Italian Journal of Animal Science 23(1): 834-841.
Balhara S, Balhara A K, Dahiya N, Singh R P and Ruhil A P. 2022. An algorithm-based approach for identification of most relevant linear traits for selecting high-producing Murrah buffaloes. Indian Journal of Animal Sciences 92(5): 618–623.
Dahiya S, Kumar S and Kumar M. 2020. Current status of research on linear type traits in Indian cattle and future strategies. Tropical Animal Health and Production 52: 2221–2232.
Fernández C, Soria E, Sanchez-Seiquer P, Gómez-Chova L, Magdalena R, Martín-Guerrero J D, Navarro M J and Serrano A J 2007. Weekly milk prediction on dairy goats using neural networks. Neural Computing and Applications 16: 373-381.
Grzesiak W, Zaborski D, Pluciński M, Jędrzejczak-Silicka M, Pilarczyk R and Sablik P. 2025. The use of selected machine learning methods in dairy cattle farming: A review. Animals 15(14): 2033.
Kaygisiz F and Sezgin F H. 2017. Forecasting goat milk production in turkey using artificial neural networks and Box-Jenkins models. Animal Review 4(3): 45-52.
Kumar V, Chakravarty A K, Magotra A, Patil C S and Shivahre P R. 2019. Comparative study of ANN and conventional methods in forecasting first lactation milk yield in Murrah buffalo. Indian Journal of Animal Sciences 89(11): 1262-1268.
Lawal A I. 2020. An artificial neural network-based mathematical model for the prediction of blast-induced ground vibration in granite quarries in Ibadan, Oyo State, Nigeria. Scientific African 8: e00413.
Murphy M D, O’Mahony M J, Shalloo L, French P and Upton J. 2014. Comparison of modelling techniques for milk-production forecasting. Journal of Dairy Science 97(6): 3352-3363.
NDDB. 2017. Guidelines for type classification of cattle and buffalo. National Dairy Development Board, Anand, Gujarat. Available at: https://www.dairyknowledge.in/sites/default/files/animal_type_classification_guidelines_version_ii.pdf.
Python Software Foundation. 2021. Tkinter - Python Interface to Tcl/Tk (8.6.10) [Software]. Available at: https://docs.python.org/3/library/tkinter.html. Accessed on 15th Aug., 2023.
Rana E, Gupta A K, Singh A, Ruhil A P, Malhotra R, Yousuf S and Ete G. 2021. Prediction of first lactation 305-Day milk yield based on bimonthly test day milk yield records in Murrah buffaloes. Indian Journal of Animal Research 55(4): 486-490.
Sahu S, Arya A, Kar D and Sarangi A. 2024. Correlation of Morphological Traits with Milk Production in Murrah Buffaloes. Archives of Current Research International 24(5): 98-102.
Sangwan S, Jan MH, Hooda E, Choudhary R, Balhara S, Rautela R, Yadav S, Phulia S K, Sharma R K, Paul V and Pal Y, 2025. Urinary metabolite signatures as predictive biomarkers for estrus detection in water buffaloes: a proton-NMR based study. Veterinary Quarterly 45(1): 2593361.
Sharma R, Dahiya S, Bangar Y C, Hada R and Yadav DC. 2024. Genetic and Phenotypic Trends for Production and Reproduction Traits in Murrah Buffaloes. Reproduction in Domestic Animals 59(9): 14712.
Singh, N. P., Usman, S. M., Maurya, V., Dutt, T., Bhatt, N.and Kumar, A. 2020. Comparative analysis of artificial neural network algorithms for prediction of FL305DMY in Murrah buffalo. International Journal of Livestock Research 10(9): 205-209.
Slob N, Catal C and Kassahun A. 2021. Application of machine learning to improve dairy farm management: a systematic literature review. Preventive Veterinary Medicine 187:105237.
Usman S M, Singh N P, Dutt T, Tiwari R and Kumar A. 2020. Comparative study of artificial neural network algorithms' performance for prediction of FL305DMY in crossbred cattle. Journal of Entomology and Zoology Studies 8: 516–520.
Waikato environment for knowledge analysis (WEKA). Available at: https://www.cs.waikato.ac.nz/ml/weka. Accessed on 18th Aug., 2023.
Submitted
Published
Issue
Section
License
Copyright (c) 2026 The Indian Journal of Animal Sciences
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
The copyright of the articles published in The Indian Journal of Animal Sciences is vested with the Indian Council of Agricultural Research, which reserves the right to enter into any agreement with any organization in India or abroad, for reprography, photocopying, storage and dissemination of information. The Council has no objection to using the material, provided the information is not being utilized for commercial purposes and wherever the information is being used, proper credit is given to ICAR.
How to Cite
