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Classification of environmental factors potentially motivating for dairy cows to access shade

Published online by Cambridge University Press:  09 July 2021

Matheus Deniz ,
Karolini Tenffen de Sousa ,
Isabelle Cordova Gomes ,
Marcos Martinez do Vale  and
João Ricardo Dittrich
Matheus Deniz*
Affiliation:
Programa de Pós-Graduação em Zootecnia, Departamento de Zootecnia, Universidade Federal do Paraná, Curitiba, Brazil Laboratório de Inovações Tecnológicas em Zootecnia (LITEZ – UFPR), Departamento de Zootecnia, Universidade Federal do Paraná, Curitiba, Brazil
Karolini Tenffen de Sousa
Affiliation:
Programa de Pós-Graduação em Zootecnia, Departamento de Zootecnia, Universidade Federal do Paraná, Curitiba, Brazil Laboratório de Inovações Tecnológicas em Zootecnia (LITEZ – UFPR), Departamento de Zootecnia, Universidade Federal do Paraná, Curitiba, Brazil
Isabelle Cordova Gomes
Affiliation:
Laboratório de Inovações Tecnológicas em Zootecnia (LITEZ – UFPR), Departamento de Zootecnia, Universidade Federal do Paraná, Curitiba, Brazil
Marcos Martinez do Vale
Affiliation:
Laboratório de Inovações Tecnológicas em Zootecnia (LITEZ – UFPR), Departamento de Zootecnia, Universidade Federal do Paraná, Curitiba, Brazil
João Ricardo Dittrich
Affiliation:
Programa de Pós-Graduação em Zootecnia, Departamento de Zootecnia, Universidade Federal do Paraná, Curitiba, Brazil Laboratório de Inovações Tecnológicas em Zootecnia (LITEZ – UFPR), Departamento de Zootecnia, Universidade Federal do Paraná, Curitiba, Brazil
*
Author for correspondence: Matheus Deniz, Email: matheus-utfpr@hotmail.com
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Abstract

The aim of this Research Communication was to apply the data mining technique to classify which environmental factors have the potential to motivate dairy cows to access natural shade. We defined two different areas at the silvopastoral system: shaded and sunny. Environmental factors and the frequency that dairy cows used each area were measured during four days, for 8 h each day. The shaded areas were the most used by dairy cows and presented the lowest mean values of all environmental factors. Solar radiation was the environmental factor with most potential to classify the dairy cow's decision to access shaded areas. Data mining is a machine learning technique with great potential to characterize the influence of the thermal environment in the cows' decision at the pasture.

Keywords

animal distributionbehavioural patterndecision treepastureprecision livestock farming
Type
Research Article
Information
Journal of Dairy Research , Volume 88 , Issue 3 , August 2021 , pp. 274 - 277
Copyright
Copyright © The Author(s), 2021. Published by Cambridge University Press on behalf of Hannah Dairy Research Foundation

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References

Buczak, AL and Guven, E (2016) A survey of data mining and machine learning methods for cybersecurity intrusion detection. IEEE Communications Surveys and Tutorials 18, 1153–1176.10.1109/COMST.2015.2494502CrossRefGoogle Scholar
Cardoso, CS, von Keyserlingk, MAG, Hötzel, MJ, Robbins, J and Weary, DM (2018) Hot and bothered: public attitudes towards heat stress and outdoor access for dairy cows. Plos One 13, 1–14.CrossRefGoogle ScholarPubMed
de Sousa, KT, Deniz, M, Vale, MM, Dittrich, JR and Hötzel, MJ (2021) Influence of microclimate on dairy cows’ behavior in three pasture systems during the winter in south Brazil. Journal of Thermal Biology 97, 1–9.CrossRefGoogle ScholarPubMed
Deniz, M, Schmitt Filho, AL, Hötzel, MJ, de Sousa, KT, Pinheiro Machado Filho, LC and Sinisgalli, PA (2020) Microclimate and pasture area preferences by dairy cows under high biodiversity silvopastoral system in Southern Brazil. International Journal of Biometeorology 64, 1877–1887.CrossRefGoogle ScholarPubMed
Landis, JR and Koch, GG (1977) The measurement of observer agreement for categorical data. Biometrics 33, 159–174.CrossRefGoogle ScholarPubMed
Magalhães, CAS, Zolin, CA, Lulu, J, Lopes, LB, Furtini, IV, Vendrusculo, LG, Zaiatz, APSR, Pedreira, BC and Pezzopane, JRM (2020) Improvement of thermal comfort indices in agroforestry systems in the southern Brazilian Amazon. Journal of Thermal Biology 91, 1–7.CrossRefGoogle ScholarPubMed
McDonald, PV, von Keyserlingk, MAG and Weary, DM (2020) Hot weather increases competition between dairy cows at the drinker. Journal of Dairy Science 103, 3447–3458.CrossRefGoogle Scholar
Neja, W, Piwczyński, D, Krężel-Czopek, S, Sawa, A and Ozkaya, S (2017) The use of data mining techniques for analysing factors affecting cow reactivity during milking. Journal of Central European Agriculture 18, 342–357.CrossRefGoogle Scholar
Schütz, KE, Rogers, AR, Poulouin, YA, Cox, NR and Tucker, CB (2010) The amount of shade influences the behavior and physiology of dairy cattle. Journal of Dairy Science 93, 125–133.CrossRefGoogle ScholarPubMed
Sejian, V, Bhatta, R, Gaughan, JB, Dunshea, FR and Lacetera, N (2018) Review: adaptation of animals to heat stress. Animal: An International Journal of Animal Bioscience 12, S431–S444.CrossRefGoogle ScholarPubMed
Sharifi, S, Pakdel, A, Ebrahimi, M, Reecy, JM, Farsani, SF and Ebrahimie, E (2018) Integration of machine learning and metaanalysis identifies the transcriptomic bio-signature of mastitis disease in cattle. PLoS ONE 13, 1–18.CrossRefGoogle ScholarPubMed
Sharpe, KT, Heins, BJ, Buchanan, ES and Reese, MH (2020) Evaluation of solar photovoltaic systems to shade cows in a pasture-based dairy herd. Journal of Dairy Science 104, 2794–2806.CrossRefGoogle Scholar
Theodoridis, S and Koutroumbas, K (2003) Pattern recognition. In Elsevier (ed.). 2nd Edn., San Diego, CA, USA: Academic Press, pp. 1–881.Google Scholar
Tullo, E, Mattachini, G, Riva, E, Finzi, A, Provolo, G and Guarino, M (2019) Effects of climatic conditions on the lying behavior of a group of primiparous dairy cows. Animals 9, 1–14.CrossRefGoogle ScholarPubMed
Zaborski, D, Proskura, WS and Grzesiak, W (2017) Comparison between data mining methods to assess calving difficulty in cattle. Revista Colombiana de Ciencias Pecuarias 30, 195–208.CrossRefGoogle Scholar
Zaborski, D, Proskura, WS and Grzesiak, W (2018) The use of data mining methods for dystocia detection in Polish Holstein-Friesian Black-and-White cattle. Asian-Australasian Journal of Animal Sciences 31, 1700–1713.CrossRefGoogle ScholarPubMed
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