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, R = 0.961, adjusted R = 0.961, and RMSE = 0.05168 (ALM); r = 0.983, R = 0.966; adjusted R = 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.

Modelling hatchability and mortality in muscovy ducks using automatic linear modelling and artificial neural network

“…Heat stress made birds to pant and could result in heat stroke and mortality. It has been reported that extremes temperatures could be experienced in the hot-dry season in North Central Nigeria (Yakubu et al, 2018a), thereby making the birds uncomfortable. Such heat stressed birds could experience high rate of mortality and morbidity (Nidamanuri et al, 2017).…”

“…ANN is a non-linear parametric model that mimics the processing mechanism of the human brain. There is increasing use of this algorithm to predict hatchability (Bolzan et al, 2008), growth (Yakubu et al, 2018a) and egg production (Ahmad, 2011). It has also been used to model disease occurrence (Akil and Ahmad, 2016).…”

Evidence of immunometabolic dysregulation and airway dysbiosis in athletes susceptible to respiratory illness

“…An Automatic Linear Modelling (ALM) using SPSS 22 was further used to explore the effects of the peripheral immune cell compartments and the top 50 OTUs in the total athlete cohort ( n = 117) on RTI frequency. No significant associations were observed with any OTUs and number of RTIs, however, memory Treg % of CD4 + T cells were found to be associated with increased RTI susceptibility.…”