Abstract: Japanese Dairy Cattle Productivity Analysis is carried out based on Bayesian Network Model (BNM). Through the experiment with 280 Japanese anestrus Holstein dairy cow, it is found that the estimation for finding out the presence of estrous cycle using BNM represents almost 55% accuracy while considering all samples. On the contrary, almost 73% accurate estimation could be achieved while using suspended likelihood in sample datasets. Moreover, while the proposed BNM model have more confidence then the estimation accuracy is lies in between 93 to 100%. In addition, this research also reveals the optimum factors to find out the presence of estrous cycle among the 270 individual dairy cows. The objective estimation methods using BNM definitely lead a unique idea to overcome the error of subjective estimation of having estrous cycle among these Japanese dairy cattle.

Iqbal Ahmed, Kenji Endo, Osamu Fukuda, Kohei Arai, Hiroshi Okumura and Kenichi Yamashita, “Japanese Dairy Cattle Productivity Analysis using Bayesian Network Model (BNM)” International Journal of Advanced Computer Science and Applications(IJACSA), 7(11), 2016. http://dx.doi.org/10.14569/IJACSA.2016.071105

@article{Ahmed2016,
title = {Japanese Dairy Cattle Productivity Analysis using Bayesian Network Model (BNM)},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2016.071105},
url = {http://dx.doi.org/10.14569/IJACSA.2016.071105},
year = {2016},
publisher = {The Science and Information Organization},
volume = {7},
number = {11},
author = {Iqbal Ahmed and Kenji Endo and Osamu Fukuda and Kohei Arai and Hiroshi Okumura and Kenichi Yamashita}
}