Skip to main content
SpringerLink
Log in

Environmental variables responsible for Zebu cattle thermal comfort acquisition

  • Original Paper
  • Published:
International Journal of Biometeorology Aims and scope Submit manuscript

Abstract

The aim of this study was to estimate, using data mining, which microclimate and behavioral variables affect the behavior of animals to seek shaded or sunny areas. The experiment was carried out between January and May 2016 in an integrated crop-livestock-forest system. In this system, we defined two different areas: shaded and sunny. Microclimatic variables (At, BGt, RH, and WS) were measured in each area on 4 consecutive days per month. With these variables, we determined the bioclimatic indicators (THI, BGHI, HLI, MRT, RTL, and ETI). In addition, we calculated the absolute difference (Δ) by subtracting the value recorded in shaded areas from the value recorded in sunny areas for all microclimatic variables and bioclimatic indicators, except for WS. The behaviors (grazing, ruminating, and other activities), posture (standing or lying), and use of areas (shaded or sunny) of 38 Zebu cattle were recorded on 2 consecutive days per month. The data mining technique was applied for analysis in a classification task. The model correctly classified 76% of the instances with a Kappa statistic of 0.51 after features selection from the database. The ΔBGt was the most important feature in the model to classify the decision of Zebu cattle to seek another area or remain in a determined area. The model was built with seven classification rules, being one simple rule, composed of the interaction between ΔBGt and rumination; and other more complex rules, composed of the interactions among the ΔBGt, WS, and rumination. The preference of Zebu cattle to seek or remain in shaded or sunny areas was influenced by eight features: rumination, drinking water, WS, ΔBGt, MRT in shade, BGHI in sun, ΔBGHI, and HLI in sun.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  • Akanmode ER, Oye ND, Celestine HR (2018) Prediction of poultry yield using data mining techniques. Int J Innov Eng Sci Res 2:16–32

    Google Scholar 

  • Allen JD, Hall LW, Collier RJ, Smith JF (2015) Effect of core body temperature, time of day, and climate conditions on behavioral patterns of lactating dairy cows experiencing mild to moderate heat stress. J Dairy Sci 98:118–127. https://doi.org/10.3168/jds.2013-7704

    Article  CAS  Google Scholar 

  • Altmann J (1974) Observation study of behavior: sampling methods. Behavior 49:227–265

    Article  CAS  Google Scholar 

  • Baêta FC, Souza CF (1997) Ambiência em Edificações Rurais: Conforto Animal. UFV, Viçosa, p 246

    Google Scholar 

  • Baeta FC, Meador NF, Shanklin MD, Johnson HD (1987) Equivalent temperature index at temperatures above the thermoneutral for lactating dairy cows. ASAE Pap 87–4015

  • Baggio C, Carvalho PC d F, da Silva JLS et al (2008) Padrões de uso do tempo por novilhos em pastagem consorciada de azevém anual e aveia-preta. Rev Bras Zootec 37:1912–1918. https://doi.org/10.1590/S1516-35982008001100002

    Article  Google Scholar 

  • Baliscei MA, Barbosa OR, Souza W et al (2013) Microclimate without shade and silvopastoral system during summer and winter. Acta Sci Anim Sci 35:37–41

    Article  Google Scholar 

  • Bond TE, Kelly CF, Ittner NR (1976) Radiation studies of painted shade materials. J Agric Eng 35:389–392

    Google Scholar 

  • Buczak AL, Guven E (2016) A survey of data mining and machine learning methods for cyber security intrusion detection. IEEE Commun Surv Tutorials 18:1153–1176. https://doi.org/10.1109/COMST.2015.2494502

    Article  Google Scholar 

  • Buffington DE, Collazo-Arocho A, Canton GH et al (1981) Black globe-humidity index (BGHI) as comfort equation for dairy cows. Trans ASAE 24:711–714

    Article  Google Scholar 

  • Burgos MS, Senn M, Sutter F, Kreuzer M, Langhans W (2001) Effect of water restriction on feeding and metabolism in dairy cows. Am J Physiol Integr Comp Physiol 280:R418–R427. https://doi.org/10.1152/ajpregu.2001.280.2.R418

    Article  CAS  Google Scholar 

  • Cardona CAC, Ramírez JFN, Morales AMT et al (2014) Contribution of intensive silvopastoral systems to animal performance and to adaptation and mitigation of climate change. Rev Colomb Ciencias Pecu 27:76–94

    Google Scholar 

  • Cardoso CC, Peripolli V, Amador SA, Brandão EG, Esteves GIF, Sousa CMZ, França MFMS, Gonçalves FG, Barbosa FA, Montalvão TC, Martins CF, Neto AMF, McManus C (2015) Physiological and thermographic responde to heat stress in zebu cattle. Livest Sci 182:83–92

    Article  Google Scholar 

  • Chapman P, Clinton J, Kerber R et al (2000) CRISP-DM 1.0: step-by-step data mining guide. CRISP, Illinois

  • Coimbra PAD, Machado Filho LCP, Hötzel MJ (2012) Effects of social dominance, water trough location and shade availability on drinking behaviour of cows on pasture. Appl Anim Behav Sci 139:175–182. https://doi.org/10.1016/j.applanim.2012.04.009

    Article  Google Scholar 

  • Collier RJ, Hall LW, Rungruang S, Zimbleman RB (2012) Quantifying heat stress and its impact on metabolism and performance. MidSouth Rumin Nutr Conf 4:74–84. https://doi.org/10.1017/S175173111000090X

    Article  Google Scholar 

  • Costa CCM, Maia ASC, Brown-Brandl TM et al (2018) Thermal equilibrium of Nellore cattle in tropical conditions: an investigation of circadian pattern. J Therm Biol 74:317–324

    Article  Google Scholar 

  • da Silva RG (2000) Um modelo para a determinação do equilíbrio térmico de bovinos em ambientes tropicais. Rev Bras Zootec 29:1244–1252. https://doi.org/10.1590/S1516-35982000000400039

    Article  Google Scholar 

  • de Melo Costa CC, Maia ASC, Nascimento ST, Nascimento CCN, Neto MC, de França Carvalho Fonsêca V (2018) Thermal balance of Nellore cattle. Int J Biometeorol 62:723–731. https://doi.org/10.1007/s00484-017-1349-6

    Article  Google Scholar 

  • Deniz M, Schmitt Filho AL, Farley J, de Quadros SF, Hötzel MJ (2019) High biodiversity silvopastoral system as an alternative to improve the thermal environment in the dairy farms. Int J Biometeorol 63:83–92. https://doi.org/10.1007/s00484-018-1638-8

    Article  Google Scholar 

  • Deniz M, Schmitt Filho AL, Hötzel MJ, de Sousa KT, Pinheiro Machado Filho LC, Sinisgalli PA (2020) Microclimate and pasture area preferences by dairy cows under high biodiversity silvopastoral system in Southern Brazil. Int J Biometeorol 64:1877–1887. https://doi.org/10.1007/s00484-020-01975-0

    Article  Google Scholar 

  • Dominiak KN, Kristensen AR (2017) Prioritizing alarms from sensor-based detection models in livestock production - a review on model performance and alarm reducing methods. Comput Electron Agric 133:46–67. https://doi.org/10.1016/j.compag.2016.12.008

    Article  Google Scholar 

  • Esmay ML (1979) Principles of animal environment. AVI, Westport

    Google Scholar 

  • Fayyad UM, Piatesky-Shapiro G, Smyth P (1996) From data mining to knowledge discovery: an overview. In: Fayyad UM, Piatetsky-Shapiro G, Smyth P, Uthurusamy R (eds) Advances in knowledge discovery and data mining. AAAI Press, Menlo Park, CA, EUA, pp 1–36

    Google Scholar 

  • Fournel S, Rousseau AN, Laberge B (2017) Rethinking environment control strategy of confined animal housing systems through precision livestock farming. Biosyst Eng 155:96–123. https://doi.org/10.1016/j.biosystemseng.2016.12.005

    Article  Google Scholar 

  • Frank E, Hall MA, Witten IH (2016) The WEKA workbench. Online Appendix for “Data Mining: Practical Machine Learning Tools and Techniques”

  • Gaughan JB, Mader TL, Holt SM, Lisle A (2008) A new heat load index for feedlot cattle. J Anim Sci 86:226–234. https://doi.org/10.2527/jas.2007-0305

    Article  CAS  Google Scholar 

  • Gaughan JB, Mader TL, Holt SM, Sullivan ML, Hahn GL (2010) Assessing the heat tolerance of 17 beef cattle genotypes. IntJ Biometeorol 54(6):617–627

    Article  CAS  Google Scholar 

  • Giro A, Pezzopane JRM, Barioni Junior W, Pedroso AF, Lemes AP, Botta D, Romanello N, Barreto AN, Garcia AR (2019) Behavior and body surface temperature of beef cattle in integrated crop-livestock systems with or without tree shading. Sci Total Environ 684:587–596. https://doi.org/10.1016/j.scitotenv.2019.05.377

    Article  CAS  Google Scholar 

  • Hahn GL, Mader TL, Eigenberg RA (2003) Perspectives on development of thermal indices for animal studies and management. In Proc. Symp. Interactions between Climate and Animal Production, pp 31–44. Published as EAAP Technical Series No. 7

  • Hahn GL, Gaughan JB, Mader-Erry L, Eigenberg RA (2013) Thermal indices and their applications for livestock environments. In: ASABE (ed) Livestock energetics and thermal environment management. pp 113–130

  • Hedlund L, Løvlie H (2015) Personality and production: nervous cows produce less milk. J Dairy Sci 98:5819–5828. https://doi.org/10.3168/jds.2014-8667

    Article  CAS  Google Scholar 

  • Herbut P, Angrecka S, Walczak J (2018) Environmental parameters to assessing of heat stress in dairy cattle—a review. Int J Biometeorol 62:2089–2097. https://doi.org/10.1007/s00484-018-1629-9

    Article  Google Scholar 

  • Houari R, Bouncer A, Kechadi M et al (2016) Dimensionality reduction in data mining: a Copula approach. Expert Syst Appl 64:247–260

    Article  Google Scholar 

  • Kadim IT, Mahgoub O, Al-Ajmi DS et al (2004) The influence of season on quality characteristics of hot-boned beef m. longissimus thoracis. Meat Sci 66:831–836

    Article  CAS  Google Scholar 

  • Karvatte N Jr, Klosowski ES, de Almeida RG, Mesquita EE, de Oliveira CC, Alves FV (2016) Shading effect on microclimate and thermal comfort indexes in integrated crop-livestock-forest systems in the Brazilian Midwest. Int J Biometeorol 60:1–9. https://doi.org/10.1007/s00484-016-1180-5

    Article  Google Scholar 

  • Keeling L, Algers B, Blokhuis H, et al (2008) Looking on the bright side of life: reward, positive emotions and animal welfare. Proc. 42nd Congr. Int. Soc. Appl. Ethol., Dublin, Ireland, Wageningen Academic Publishers, Wageningen, the Netherlands. p.3

  • Klein DR, do Vale MM, da Silva MFR et al (2020) Data mining as a hatchery process evaluation tool. Sci Agric 77. https://doi.org/10.1590/1678-992x-2018-0074

  • Kumar A, Hancke GP (2015) A zigbee-based animal health monitoring system. IEEE Sensors J 15:610–617. https://doi.org/10.1109/JSEN.2014.2349073

    Article  Google Scholar 

  • Lehner PN (1996) Handbook of ethological methods, 2nd edn. Cambridge University Press, Cambridge, New York

    Google Scholar 

  • Magalhães CAS, Zolin CA, Lulu J, Lopes LB, Furtini IV, Vendrusculo LG, Zaiatz APSR, Pedreira BC, Pezzopane JRM (2020) Improvement of thermal comfort indices in agroforestry systems in the southern Brazilian Amazon. J Therm Biol 91:102636. https://doi.org/10.1016/j.jtherbio.2020.102636

    Article  Google Scholar 

  • Marino L, Allen K (2017) The psychology of cows. Anim Behav Cogn 4:474–498. https://doi.org/10.26451/abc.04.04.06.2017

    Article  Google Scholar 

  • McHugh ML (2012) Lessons in biostatistics interrater reliability: the kappa statistic. Biochem Med 22:276–282. https://hrcak.srce.hr/89395. Accessed Feb 2021

  • Nordlund KV, Strassburg P, Bennett TB, Oetzel GR, Cook NB (2019) Thermodynamics of standing and lying behavior in lactating dairy cows in freestall and parlor holding pens during conditions of heat stress. J Dairy Sci 102:6495–6507. https://doi.org/10.3168/jds.2018-15891

    Article  CAS  Google Scholar 

  • Oliveira CC (2017) Sistema agrossilvipastoril no Cerrado brasileiro: ambiência e fisiologia de bovinos Nelore. Universidade Federal de Mato Grosso do Sul

  • Oliveira CC, Alves FV, de Almeida RG et al (2018) Thermal comfort indices assessed in integrated production systems in the Brazilian savannah. Agrofor Syst 92:1659–1672. https://doi.org/10.1007/s10457-017-0114-5

    Article  Google Scholar 

  • Pal M, Foody GM (2010) Feature selection for classification of hyperspectral data by SVM. IEEE Trans Geosci Remote Sens 48:2297–2307

    Article  Google Scholar 

  • Pereira DF, Vale MM, Zevolli BR, Salgado DD (2010) Estimating mortality in laying hens as the environmental temperature increases. Braz J Poult Sci 12:265–271

    Article  Google Scholar 

  • Pezzopane JRM, Nicodemo MLF, Bosi C, Garcia AR, Lulu J (2019) Animal thermal comfort indexes in silvopastoral systems with different tree arrangements. J Therm Biol 79:103–111. https://doi.org/10.1016/j.jtherbio.2018.12.015

    Article  Google Scholar 

  • Pezzopane JRM, Bernardi AC d C, Azenha MV et al (2020) Production and nutritive value of pastures in integrated livestock production systems: shading and management effects. Sci Agric 77. https://doi.org/10.1590/1678-992x-2018-0150

  • Phillips CJC, Rind MI (2002) The effects of social dominance on the production and behavior of grazing dairy cows offered forage supplements. J Dairy Sci 85:51–59. https://doi.org/10.3168/jds.S0022-0302(02)74052-6

    Article  CAS  Google Scholar 

  • Rashamol VP, Sejian V, Pragna P, Lees AM, Bagath M, Krishnan G, Gaughan JB (2019) Prediction models, assessment methodologies and biotechnological tools to quantify heat stress response in ruminant livestock. Int J Biometeorol 63:1265–1281. https://doi.org/10.1007/s00484-019-01735-9

    Article  CAS  Google Scholar 

  • Redbo I, Ehrlemark A, Redbo-Torstensson P (2001) Behavioural responses to climatic demands of dairy heifers housed outdoors. Can J Anim Sci 81:9–15. https://doi.org/10.4141/A00-071

    Article  Google Scholar 

  • Riquena R d S, Pereira DF, do Vale MM, Salgado DD (2019) Mortality prediction of laying hens due to heat waves. Rev Ciência Agronômica 50. https://doi.org/10.5935/1806-6690.20190003

  • Sanchez WK, McGuire MA, Beede DK (1994) Macromineral nutrition by heat stress interactions in dairy cattle: review and original research. J Dairy Sci 77:2051–2079. https://doi.org/10.3168/jds.S0022-0302(94)77150-2

    Article  CAS  Google Scholar 

  • SAS Institute Inc (2018) SAS® University Edition Quick Start Guide for Students with Visual Impairments. SAS Institute Inc., Cary, NC

    Google Scholar 

  • Schütz KE, Rogers AR, Cox NR, Tucker CB (2009) Dairy cows prefer shade that offers greater protection against solar radiation in summer: Shade use, behaviour, and body temperature. Appl Anim Behav Sci 116:28–34. https://doi.org/10.1016/j.applanim.2008.07.005

    Article  Google Scholar 

  • Schütz KE, Rogers AR, Poulouin YA, Cox NR, Tucker CB (2010) The amount of shade influences the behavior and physiology of dairy cattle. J Dairy Sci 93:125–133. https://doi.org/10.3168/jds.2009-2416

    Article  CAS  Google Scholar 

  • Sejian V, Bhatta R, Gaughan JB, Dunshea FR, Lacetera N (2018) Review: Adaptation of animals to heat stress. Animal 12:s431–s444. https://doi.org/10.1017/S1751731118001945

    Article  CAS  Google Scholar 

  • Silva RGd, Morais DAEF, Guilhermino MM (2007) Evaluation of thermal stress indexes for dairy cows in tropical regions. Rev Bras de Zootec 36(4 suppl):1192–1198

    Article  Google Scholar 

  • Sim J, Wright CC (2005) The kappa statistic in reliability studies: use, interpretation, and sample size requirements. Phys Ther 85:257–268. https://doi.org/10.1093/ptj/85.3.257

    Article  Google Scholar 

  • Storti AA, De Mattos Nascimento MRB, De Faria CU, Marques da Silva NA (2019) Thermal stress indices in young Nellore bulls raised in tropical environments. Acta Sci Vet 47:10.22456/1679–10.9216.93605

    Google Scholar 

  • Sullivan ML, Cawdell-Smith AJ, Mader TL, Gaughan JB (2011) Effect of shade area on performance and welfare of short fed feedlot cattle. J Anim Sci 89(9):2911–2925

    Article  CAS  Google Scholar 

  • Thom EC (1958) Cooling degress: day air-conditioning, heating and ventilating. Trans Am Soc Heating, Refrig Air Cond Eng 55:65–72

    Google Scholar 

  • Tucker CB, Rogers AR, Schütz KE (2008) Effect of solar radiation on dairy cattle behaviour, use of shade and body temperature in a pasture-based system. Appl Anim Behav Sci 109:141–154. https://doi.org/10.1016/j.applanim.2007.03.015

    Article  Google Scholar 

  • Tullo, Mattachini, Riva et al (2019) Effects of climatic conditions on the lying behavior of a group of primiparous dairy cows. Animals 9:869. https://doi.org/10.3390/ani9110869

    Article  Google Scholar 

  • Vale M, Moura D, Nääs I, Pereira D (2010) Characterization of heat waves affecting mortality rates of broilers between 29 days and market age. Rev Bras Ciência Avícola 12:279–285. https://doi.org/10.1590/S1516-635X2010000400010

    Article  Google Scholar 

  • Veissier I, Van laer E, Palme R et al (2018) Heat stress in cows at pasture and benefit of shade in a temperate climate region. Int J Biometeorol 62:585–595. https://doi.org/10.1007/s00484-017-1468-0

    Article  Google Scholar 

  • Verbaeten S, Assche AV (2003) Ensemble methods for noise elimination in classification problems. In: Windeatt T, Roli F (eds) Multiple classifier systems. Springer-Verlag, Berlin Heidelberg, pp 317–325

    Chapter  Google Scholar 

  • Vieira FMC, Deniz M, Vismara ES, Herbut P, Pilatti JA, Sponchiado MZ, de Oliveira Puretz B (2020) Thermoregulatory and behaviour responses of dairy heifers raised on a silvopastoral system in a subtropical climate. Ann Anim Sci 20:613–627. https://doi.org/10.2478/aoas-2019-0074

    Article  Google Scholar 

  • Vizzotto E, Fischer V, Thaler Neto A et al (2015) Access to shade changes behavioral and physiological attributes of dairy cows during the hot season in the subtropics. Animal 9:1559–1566. https://doi.org/10.1017/S1751731115000877

    Article  CAS  Google Scholar 

  • Wang X, Bjerg BS, Choi CY, Zong C, Zhang G (2018) A review and quantitative assessment of cattle-related thermal indices. J Therm Biol 77:24–37. https://doi.org/10.1016/j.jtherbio.2018.08.005

    Article  Google Scholar 

  • Witten IH, Frank E, Hall MA (2010) Data mining: practical machine learning tools and techniques, 3rd edn. Morgan Kaufmann

  • Witten IH, Frank E, Hall MA, Pal CJ (2017) Data mining: practical machine learning tools and techniques, 4rd edn. Morgan Kaufmann

Download references

Acknowledgements

We would like to thank Embrapa Beef Cattle for the infrastructure and scientific-technical support.

Funding

We would like to thank the State of Mato Grosso do Sul Foundation to Support Education, Science and Technology (FUNDECT) and National Council for Scientific and Technological Development (CNPq) for financial support.

Author information

Authors and Affiliations

  1. Department of Animal Science, Federal University of Parana, 540 Funcionarios St., Curitiba, Parana, 80035050, Brazil

    Denise Volpi, Marcos Martinez do Vale, Matheus Deniz & Maity Zopollatto

  2. Embrapa Beef Cattle, Brazilian Agricultural Research Corporation, Campo Grande, Mato Grosso do Sul, Brazil

    Fabiana Villa Alves

  3. Department of Animal Science, State University of Mato Grosso do Sul, Aquidauana, Mato Grosso do Sul, Brazil

    Alan da Silva Arguelho

Authors
  1. Denise Volpi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Fabiana Villa Alves
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Alan da Silva Arguelho
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Marcos Martinez do Vale
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Matheus Deniz
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Maity Zopollatto
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Denise Volpi.

Ethics declarations

Conflict of interest

The authors declare no competing interests.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Volpi, D., Alves, F.V., da Silva Arguelho, A. et al. Environmental variables responsible for Zebu cattle thermal comfort acquisition. Int J Biometeorol 65, 1695–1705 (2021). https://doi.org/10.1007/s00484-021-02124-x

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00484-021-02124-x

Keywords

Search

Navigation