• Abstract

    Enthalpy, physical quantity indicating the amount of thermal energy in the medium, is used by many researchers as an indicator of thermal comfort for humans and production animals. This physical quantity has as input variables the dry bulb temperature, the relative humidity of the air and the local barometric pressure. According to consolidated information of temperature and relative humidity related to the animal homeostasis, it was possible to establish enthalpy ranges for thermal comfort of swine, poultry and cattle, considering the local barometric pressure and its variations, which is not easily accessible in situations of field. Thus, the present study aimed to use multiple linear regression models to estimate enthalpy values by means of easily accessible variables (dry and wet bulb temperatures and relative humidity) which can be obtained by means of psychrometers or even by means of low-cost sensors, currently accessible. Meteorological data from three cities of the Brazilian territory, each representing an animal production system (poultry, swine and cattle) were accessed from the National Institute of Meteorology (INMET) database. According to the analysis of the prediction quality verification indices, the obtained models are efficient in predicting enthalpy values with the use of dry bulb temperature and relative humidity.

  • References

    1. Abreu VMN, Abreu PG (2011) Os desafios da ambiência sobre os sistemas de aves no Brasil. Revista Brasileira de Zootecnia 40: 1-14.
    2. Gomes RCC; Yanagi Júnior T, Lima RR, Yanagi SN, Carvalho VF, Damasceno FA. (2011). Predição do índice de temperatura do globo negro e umidade e do impacto das variações climáticas em galpões avícolas climatizados. Ciência Rural, Santa Maria 41: 1645 – 1651.
    3. Bond TE, Kelly CF (1955) The globe thermometer in agricultural research. St. Joseph: Agricultural Engineering 1: 10.
    4. Buffington DE, Colazzo AA, Canton GH, Pitt D (1981) Black globe-humidity index (BGHI) as comfort equation for dairy cows. Transactions of the ASAE, St. Joseph 24: 711-14.
    5. Conceição MN (2008) Avaliação da influência do sombreamento artificial no desenvolvimento de novilhas leiteiras em pastagens. Thesis - Universidade de São Paulo.
    6. Habeeb AA, Gad AE, Atta MA (2018) Temperature-Humidity Indices as Indicators to Heat Stress of Climatic Conditions with Relation to Production and Reproduction of Farm Animals. International Journal of Biotechnology an Recent Advances 1: 35-50.
    7. Heidari H, Golbabaei F, Shamsipour A, Forushani AR, Gaeni A (2016) Determination of Air Enthalpy Based on Meteorological Data as an Indicator for Heat Stress Assessment in Occupational Outdoor Environments, a Field Study in Iran. Journal of Research in Health Science 16: 133-140.
    8. Lopes AZ (2009) Desenvolvimento de um neuro-controlador para galpões climatizados de frangos de corte. Dissertation - Universidade Federal de Lavras.
    9. Rodrigues VC, Silva IJO, Vieira FMC, Nascimento ST (2011) A correct enthalpy relationship as thermal comfort index for livestock. International Journal of Biometeorology 55:455-459.
    10. Sevegnani KB (1997) Avaliação de tinta cerâmica em telhados de modelos em escala reduzida, simulando galpões para frangos de corte. Dissertation - Universidade Estadual de Campinas.
    11. Slimen IB, Najar T, Ghram A, Abdrrabba M (2015) Heat stress effects on livestock: molecular, cellular and metabolic aspects, a review. Animal Physiology and Animal Nutrition. doi: 10.1111/jpn.12379
    12. Tolon YB, Baracho MS, Naas IA, Rojas M, Moura, DJ (2010) Ambiências térmica, aérea e acústica para reprodutores suínos. Engenharia Agrícola 30: 1-13.
    13. Zeileis A (2016) dynlm: Dynamic Linear Regression. R package version 0.3-5. Available in: http://CRAN.R-project.org/package=dynlm>.

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Copyright (c) 2019 Malque Publishing

How to cite

Sarnighausen, V. C. R. (2019). Estimation of thermal comfort indexes for production animals using multiple linear regression models. Journal of Animal Behaviour and Biometeorology, 7(2), 73–77. https://doi.org/10.31893/2318-1265jabb.v7n2p73-77
  • Article viewed - 48
  • PDF downloaded - 21