Prediction of mean surface temperature of broiler chicks and load microclimate during transport

Nazareno, Aérica Cirqueira; Silva, Iran J. O. da; Fernandes, Danielle Priscila Bueno

doi:10.1590/1809-4430-eng.agric.v36n4p593-603/2016

Cited by 3 publications

(1 citation statement)

References 32 publications

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“…The coefficient of variation is a way of expressing the variability of the data, so when the value of the coefficient of variation was smaller the dispersion around the mean was smaller. The superficial and 0.15m temperatures before and after the bed was turned had low coefficients of variation (CV ≤ 15%), indicating data homogeneity and low dispersion (Nazareno et al, 2016).…”

Section: Resultsmentioning

confidence: 99%

Bed Temperature in Compost Barns Turned With Rotary Hoe and Offset Disc Harrow

2019

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The success of confinement for dairy cattle in the compost barn model depends mainly on the management of the bed and consists of its turning. This paper characterises the spatial variability of the bed temperature in the compost barn confinement model, as well as verifying whether there was an effect on efficiency from turning the bed with different agricultural implements. The experiment was conducted during the summer and winter of 2016, with 8 days of collection in each period. Data were analysed with descriptive statistics methods and geostatistical modelling with semivariograms and kriging maps. The results of the t-test at a5% significance level indicated that, after turning the bed with the plough or rotary hoe, the temperature values were significantly lower. There was an efficient on bed turning based on different agricultural implements. Spatial dependence was observed on the data, with a better adjustment given by a Gaussian model. Kriging maps allowed the characterization of the spatial variability of bed temperature and the visualization across the compost barn bed at the superficial layer and 0.15 m in depth.

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Section: Resultsmentioning

confidence: 99%

Bed Temperature in Compost Barns Turned With Rotary Hoe and Offset Disc Harrow

2019

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

2018

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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.

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Effect of environmental conditions during transport on chick weight loss and mortality

2021

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The present study had 2 objectives: the first was to analyze the possible impact of transport on weight loss and mortality during transport, and first-week mortality. The second was to monitor the environmental condition (i.e., temperature, humidity, and so on) variability during transport with an effect on day-old chicks. Probe equipment was installed in a truck of a poultry company from Spain, including a total of 66 journeys made in commercial conditions between May and November 2017. Animal-based measures collected included BW (before and after transport), mortality during transport, mortality during the first week of life, which were contrasted against a series of environmental variables including air temperature, RH, and carbon dioxide (CO 2 ) atmospheric concentration for every journey, number of day-old chicks (%) per journey, transport duration (h), zones inside the loading area (zone 1, near to the cabin; zone 2, in the central point; and zone 3, close to the back doors), height (1, top; 2, medium; and 3, bottom), mo (May to November), number of stops, type of stop during journey (farm stops and driver stops), time to start the journey, as well as other intrinsic factors of chicks (gender, breed [Ross and Cobb], breeder flock age [wk] and egg storage day). Because the database included random factors, longitudinal data, and repeated measures, a multivariate model was used to analyze the data. The results showed that chick weight loss was positively associated with journey duration and RH. No effect of environmental variables was found on mortality during transport. However, chick mortality during the first week of life was related with the percentage of day-old chicks loaded per journey and chick gender. In conclusion, owing to the environmental heterogeneity during transport and the effect of the environment on chick weight during transport and mortality at first week of life, there is an urgent need to refine the air-conditioning and ventilation systems of day-old chick transport toward a greater environmental homogeneity.

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Prediction of mean surface temperature of broiler chicks and load microclimate during transport

Cited by 3 publications

References 32 publications

Bed Temperature in Compost Barns Turned With Rotary Hoe and Offset Disc Harrow

Bed Temperature in Compost Barns Turned With Rotary Hoe and Offset Disc Harrow

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

Effect of environmental conditions during transport on chick weight loss and mortality

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