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


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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  1. Abdul Niyas PA, Chaidanya K, Shaji S, Sejian V, Bhatta R, Bagath M, Rao GSLHVP, Kurien EK, Girish V (2015) Adaptation of livestock to environmental challenges. J Vet Sci Med Diagn 4:3

    Article  Google Scholar 

  2. Adedeji TA, Amao SR, Ogundairo OM, Fasoyin OA (2015) Heat tolerance attributes of Nigerian locally adapted chickens as affected by strain and some qualitative traits. J Biol Agric Healthcare 5:50–55

    Google Scholar 

  3. Aggarwal Y, Karan BM, Das BN, Sinha RK (2008) An unsupervised neural network to predict the level of heat stress. J Clin Monitor Comp 22:425–430

    Article  Google Scholar 

  4. Akbarian A, Michiels J, Degroote J, Majdeddin M, Golian A, De Smet S (2016) Association between heat stress and oxidative stress in poultry; mitochondrial dysfunction and dietary interventions with phytochemicals. J Anim Sci Biotech 7(37):37.

  5. Hornik K, Stinchcombe M, White H (1989) Multilayer feedforward networks are universal approximators. Elsevier Sci 2(5):359–366

    Google Scholar 

  6. Hurvich CM, Tsai CL (1989) Regression and time series model selection in small samples. Biometrika 76:297–307

    Article  Google Scholar 

  7. Ilori BM, Peters SO, Yakubu A, Imumorin IG, Adeleke MA, Ozoje MO, Ikeobi CON, Adebambo OA (2011) Physiological adaptation of local, exotic and crossbred turkeys to the hot and humid tropical environment of Nigeria. Acta Agric Scand A- Anim Sci 61:204–209

    Google Scholar 

  8. Krüger E, Rossi F, Drach P (2017) Calibration of the physiological equivalent temperature index for three different climatic regions. Int J Biometeorol 61:1323–1336

    Article  Google Scholar 

  9. Kumari KNR, Nath DN (2017) Ameliorative measures to counter heat stress in poultry. World’s Poult Sci J (in press) 74:117–130.

    Article  Google Scholar 

  10. Laaboudi A, Mouhouche B, Draou B (2012) Neural network approach to reference evapotranspiration modeling from limited climatic data in arid regions. Int J Biometeorol 56:831–841

    Article  Google Scholar 

  11. LaFaro RJ, Pothula S, Kubal KP, Inchiosa ME, Pothula VM, Yuan SC, Maerz DAML, Oleszkiewicz SM, Yusupov A, Perline R, Inchiosa MA Jr (2015) Neural network prediction of ICU length of stay following cardiac surgery based on pre-incision variables. PLoS One 10(12):e0145395.

    Article  CAS  Google Scholar 

  12. Lemerle C, Goddard ME (1986) Assessment of heat stress in dairy cattle in Papua New Guinea. Trop Anim Health Prod 18:232–242

    Article  CAS  Google Scholar 

  13. Lopes AZ, Yanagi T Jr, Lacerda WS, Rabelo G (2014) Predicting rectal temperature of broiler chickens with artificial neural network. Int J Eng Tech 14(5):29–34

    Google Scholar 

  14. Mignon-Grasteau S, Moreri U, Narcy A, Rousseau X, Rodenburg TB, Tixier-Boichard M, Zerjal T (2015) Robustness to chronic heat stress in laying hens: a meta-analysis. Poult Sci 94:586–600

    Article  CAS  Google Scholar 

  15. Mishra N, Soni HK, Sharma S, Upadhyay AK (2018) Development and analysis of artificial neural network models for rainfall prediction by using time-series data. Int J Intel Syst Appl (1):16–23

  16. Nazareno AC, Da Silva IJO, Fernandes DPB (2016) Prediction of mean surface temperature of broiler chicks and load microclimate during transport. Eng Agríc 36:593–603

    Article  Google Scholar 

  17. Nidamanuri AL, Murugesan S, Mahapatra RK (2017) Effect of heat stress on physiological parameters of layers-a review. Int J Livest Res 7:1–17

    Article  Google Scholar 

  18. Osakwe I, Igwe R (2015) Physiological responses of laying birds fed honey and vitamin C in drinking water. Tropentag: Conference on international research on food security, natural resource management and rural development organised by the Humboldt-Universität zu Berlin and the Leibniz Centre for Agricultural Landscape Research (ZALF). September 16–18, 2015, Berlin, Germany

  19. Osti R, Bhattarai D, Zhou D (2017) Climatic variation: effects on stress levels, feed intake, and bodyweight of broilers. Braz J Poult Sci 19:489–496

    Article  Google Scholar 

  20. Rojas-Downing MM, Nejadhashemi AP, Harrigan T, Woznicki SA (2017) Climate change and livestock: impacts, adaptation, and mitigation. Clim Risk Management 16:145–163

    Article  Google Scholar 

  21. Roush W, Cravener TL, Kirby YK, Wideman RF Jr (1997) Probabilistic neural network prediction of ascites in broilers based on minimally invasive physiological factors. Poult Sci 76(11):1513–1516

    Article  CAS  Google Scholar 

  22. Ryder AA, Feddes JJR, Zuidhof MJ (2004) Field study to relate heat stress index to broiler performance. J Appl Poult Res 13:493–499

    Article  Google Scholar 

  23. Sassi NB, Averós X, Estevez I (2016) Technology and poultry welfare. Animals 6.

  24. Sinkalu VO, Ayo JO (2018) Combined effects of retinol, ascorbic acid and α-tocopherol on diurnal variations in rectal temperature of Black Harco pullets subjected to heat stress. Int J Biometeorol 62:9–15

    Article  Google Scholar 

  25. Sirajuddin SN, Lestari VS, Saleh IM, Sara U, Kasim J (2017) Effect of climate change on laying hen farms. Int J Sci Basic Appl Res 32:206–214

  26. Šleger V, Neuberger P (2006) Using meteorological data to determine the risk of heat stress. Res Adr Eng 52:39–47

    Google Scholar 

  27. SPSS (2015) Statistical Package for Social Sciences Version 23. SPSS Inc., 444 Michigan Avenue, Chicago, IL60611, 2015

  28. Tseliou A, Tsiros IX, Nikolopoulou M (2017) Seasonal differences in thermal sensation in the outdoor urban environment of Mediterranean climates—the example of Athens, Greece. Int J Biometeorol 61:1191–1208

    Article  Google Scholar 

  29. Xie J, Tang L, Lu L, Zhang L, Xi L, Liu H-C, Odle J, Luo X (2014) Differential expression of heat shock transcription factors and heat shock proteins after acute and chronic heat stress in laying chickens (Gallus gallus). PLoS One 9(7):e102204.

    Article  CAS  Google Scholar 

  30. Yang H (2013) The case for being automatic: introducing the automatic LINEAR modeling (LINEAR) procedure in SPSS statistics. Multiple linear regression. Viewpoints 39(2):27–37

    Google Scholar 

  31. Zahoor I, de Koning D-J, Hocking PM (2017) Transcriptional profile of breast muscle in heat stressed layers is similar to that of broiler chickens at control temperature. Genet Sel Evol 49:69.

    Article  CAS  Google Scholar 

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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).

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