Prediction of first lactation 305 days milk yield using artificial neural network in Murrah buffalo


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Authors

  • NRIPENDRA PRATAP SINGH ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly, Uttar Pradesh 243 122 India
  • TRIVENI DUTT ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly, Uttar Pradesh 243 122 India
  • SHEIKH MOHD USMAN ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly, Uttar Pradesh 243 122 India
  • MOHD BAQIR ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly, Uttar Pradesh 243 122 India
  • RUPASI TIWARI ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly, Uttar Pradesh 243 122 India
  • AMIT KUMAR ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly, Uttar Pradesh 243 122 India

https://doi.org/10.56093/ijans.v92i9.117570

Keywords:

Artificial neural network, FL305DMY, Murrah buffalo, Test day milk yields

Abstract

In the present study, first lactation test day and monthly milk records of 301 Murrah buffaloes were used for prediction of first lactation 305-day milk yield (FL305DMY) using artificial neural network (ANN) and was compared with multiple linear regression (MLR). Models were evaluated on the basis of coefficient of determination and root mean square error (RMSE). Two different input sets (Input set-1 and Input set-2) were used in the study. In input set-1, four test day milk yields (6th, 36th, 66th and 96th day of lactation) along with age at first calving (AFC) and peak yield (PY) were taken together and in input set-2, four monthly milk yields record (1st, 2nd, 3rd and 4th month yield) along with AFC and PY were taken together. The ANN was trained using back propagation (BP) algorithm which is also known as Bayesian regularization (BR). ANN achieved highest accuracy of 82% with lowest RMSE value of 16.46% for input set-1 while MLRs accuracy was 80.53% with RMSE value of 17.48%. Higher accuracy and lower RMSE value for ANN clearly showed its better performance than MLR model. Hence, ANN could be alternatively used as a tool for prediction of FL305DMY in Murrah buffaloes using input set-1 with more than 80% accuracy. So, 96th day test day yield (TD4) can be used for prediction of FL305DMY and as a trait for early genetic evaluation of sires.

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References

Bhosale M D and Singh T P. 2015. Comparative study of feed-forward neuro-computing with multiple linear regression model for milk yield prediction in dairy cattle. Current Science 108(12): 2257–60.

Chaturvedi S, Gupta A, Yadav R and Sharma A K. 2013. Life time milk amount prediction in dairy cows using artificial neural networks. International Journal of Recent Research and Review 5: 1–6.

Dongre V B, Gandhi R S, Singh A and Ruhil A P. 2012. Comparative efficiency of artificial neural networks and multiple linear regression analysis for prediction of first lactation 305-day milk yield in Sahiwal cattle. Livestock Science 147: 192–97. DOI: https://doi.org/10.1016/j.livsci.2012.04.002

Fang Q, Hanna M A, Haque E and Spillman C K. 2000. Neural network modeling of energy requirements for size reduction of wheat. American Society of Agriculture and Biological Engineers 43: 947–52. DOI: https://doi.org/10.13031/2013.2991

Fausett L V. 1994. Fundamentals of Neural Networks: Architectures, Algorithms, and applications. Prentice Hall, Englewood Cliffs, New Jersey, USA.

Foresee F D and Hagan M T. 1997. Gauss–Newton approximation to Bayesian regularization. Proceedings of the IEEE International Joint Conference on Neural Networks, Houston, TX, USA, June 12.

Gandhi R S, Raja T V, Ruhil A P and Kumar A. 2010. Artificial neural network versus multiple regression analysis for prediction of lifetime milk production in Sahiwal cattle. Journal of Applied Animal Research 38: 233–37. DOI: https://doi.org/10.1080/09712119.2010.10539517

Gandhi R S, Raja Thiruvothur, Ruhil A P and Kumar Amit. 2009. Prediction of lifetime milk production using artificial neural network in Sahiwal cattle. Indian Journal of Animal Sciences 79: 1038–40.

Ghedira H and Bernier M. 2004. The effect of some internal neural network parameters on SAR texture classification performance. Proceedings of the IEEE International Geosciences and Remote Sensing Symposium, 6, Anchorage, Alaska, USA, September 20-24.

Gorgulu O. 2012. Prediction of 305-day milk yield in Brown Swiss cattle using artificial neural networks. South African Journal of Animal Science 42(3): 280–87. DOI: https://doi.org/10.4314/sajas.v42i3.10

Kokate L S. 2009. ‘Genetic evaluation Karan Fries sires based on test day milk yield records.’ MVSc Thesis, Deemed University, NDRI, Karnal, India.

Kominakis A P, Abas Z, Maltaris I and Rogdakis E. 2002. A preliminary study of the application of artificial neural networks to prediction of milk yield in dairy sheep. Computer and Electronics in Agriculture 35: 35–48. DOI: https://doi.org/10.1016/S0168-1699(02)00051-0

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): 1262–68.

Mundhe U T, Gandhi R S, Das D N, Dongre V B and Gupta A. 2015. Genetic and non-genetic factors affecting monthly part lactation milk yields in Sahiwal cattle. Indian Journal of Animal Sciences 85(5): 517–18.

Njubi D M, Wakhungu J W and Badamana M S. 2010. Use of test-day records to predict first lactation 305-day milk yield using artificial neural network in Kenyan Holstein–Friesian dairy cows. Tropical Animal Health Production 42: 639–44. DOI: https://doi.org/10.1007/s11250-009-9468-7

Sharma A K, Sharma R K and Kasana H S. 2007. Prediction of first lactation 305-day milk yield in Karan Fries dairy cattle using ANN modeling. Applied Soft Computing 7: 1112–20. DOI: https://doi.org/10.1016/j.asoc.2006.07.002

Sharma R K and Sharma A K. 2004. Neuro-computing paradigms with application to dairy production, Lecture Compendium, National Training Programme on Information Resources on Genetics and Documentation Techniques for Livestock Improvement, Centre for Advanced Studies AG&B, Dairy Cattle Breeding Division, NDRI, Karnal, India. 173–178.

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Submitted

2021-11-02

Published

2022-09-09

How to Cite

SINGH, N. P., DUTT, T., USMAN, S. M., BAQIR, M., TIWARI, R., & KUMAR, A. (2022). Prediction of first lactation 305 days milk yield using artificial neural network in Murrah buffalo. The Indian Journal of Animal Sciences, 92(9), 1116–1120. https://doi.org/10.56093/ijans.v92i9.117570

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