Predicting heat stress index in Sasso hens using automatic linear modeling and artificial neural network

Abstract

There is an increasing use of robust analytical algorithms in the prediction of heat stress. The present investigation therefore, was carried out to forecast heat stress index (HSI) in Sasso laying hens. One hundred and sixty seven records on the thermo-physiological parameters of the birds were utilized. They were reared on deep litter and battery cage systems. Data were collected when the birds were 42- and 52-week of age. The independent variables fitted were housing system, age of birds, rectal temperature (RT), pulse rate (PR), and respiratory rate (RR). The response variable was HSI. Data were analyzed using automatic linear modeling (ALM) and artificial neural network (ANN) procedures. The ALM model building method involved Forward Stepwise using the F Statistic criterion. As regards ANN, multilayer perceptron (MLP) with back-propagation network was used. The ANN network was trained with 90% of the data set while 10% were dedicated to testing for model validation. RR and PR were the two parameters of utmost importance in the prediction of HSI. However, the fractional importance of RR was higher than that of PR in both ALM (0.947 versus 0.053) and ANN (0.677 versus 0.274) models. The two models also predicted HSI effectively with high degree of accuracy [r = 0.980, R2 = 0.961, adjusted R2 = 0.961, and RMSE = 0.05168 (ALM); r = 0.983, R2 = 0.966; adjusted R2 = 0.966, and RMSE = 0.04806 (ANN)]. The present information may be exploited in the development of a heat stress chart based largely on RR. This may aid detection of thermal discomfort in a poultry house under tropical and subtropical conditions.

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Acknowledgements

The Senate research grant of Nasarawa State University, Keffi to carry out this research is gratefully acknowledged. African Chicken Genetic Gains-Nigeria (ACGG-Ng) project donated the Sasso birds. Many thanks to the ACGG-Nigeria Principal Investigator (PI), Prof. E.B. Sonaiya, the Co-PI, Prof. Mrs. O.A. Adebambo, and the National Project Coordinator, Dr. Oladeji Bamidele.

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Correspondence to A. Yakubu.

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Yakubu, A., Oluremi, O. & Ekpo, E. Predicting heat stress index in Sasso hens using automatic linear modeling and artificial neural network. Int J Biometeorol 62, 1181–1186 (2018). https://doi.org/10.1007/s00484-018-1521-7

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Keywords

  • Sasso birds
  • Heat stress
  • Tropics
  • Neural network
  • Regression