Euclidean distance: integrated criteria to study sheep behaviour under heat stress

  • Jorge O. SERRANO University of Ciego de Ávila, Faculty of Agriculture, Ciego de Ávila, 69450
  • Asiel VILLARES University of Ciego de Ávila, Faculty of Agriculture, Ciego de Ávila, 69450
  • Francisco D. MANUEL-MALAMBA Universidad de Mandume Ya Ndemufayo, Instituto Superior Politécnico de Huíla (ISPH)
  • Jorge MARTÍNEZ-MELO University of Ciego de Ávila, Faculty of Agriculture, Ciego de Ávila, 69450
  • Carlos MAZORRA University of Ciego de Ávila, Faculty of Agriculture, Ciego de Ávila, 69450
  • Angela BORROTO University of Ciego de Ávila, Faculty of Agriculture, Ciego de Ávila, 69450
  • Elliosha HAJARI Agricultural Research Council-Tropical and Subtropical Crops, Plant Improvement, Private Bag X11208, Nelspruit, 1200
  • Norge FONSECA-FUENTES Universidad de Granma (UDG), Centro de Estudio de Producción Animal (CEPA), Carretera de Manzanillo km 17 ½ CP: 85100, Granma
  • Jose C. LORENZO University of Ciego de Avila, Laboratory for Plant Breeding and Conservation of Genetic Resources, Bioplant Center, Ciego de Ávila, 69450,
Keywords: animal physiological stress; biostatistics; climate change; heat stress; Ovis aries

Abstract

Livestock farming with sheep represents an important income stream. With climate change, domestic sheep are being exposed to heat stress which can have adverse effects on growth. Here, data regarding sheep behaviour in response to high temperature stress was analysed using the Euclidean distance method to integrate all variables into a single representative outcome that could summarize sheep behaviour. We studied the effects of two shepherding conditions either with or without the provision of shade. The number of animals eating grass, ruminating and resting either in the shade or directly in the sun were recorded over one year at two-week intervals. As the ideal behaviour (expert’s criteria), the following conditions were considered: maximum numbers of animals eating grass, ruminating and resting under shaded conditions were desirable; while the numbers of animals ruminating or resting under direct sunlight should be at a minimum. The statistical evaluation undertaken integrated these variables to identify the most significant effects of heat stress. Sheep spent most of the daylight hours engaged in eating and this activity was more intensive where shaded conditions were available. The Euclidean distance calculated for the group of animals maintained under shaded conditions was statistically lower (indicating better behaviour). Based on this, it is possible to accurately rank the treatments in terms of severity. The analysis indicates that the use of the Euclidean distance could be used to summarize a simplified outcome for observational data collected in behavioural studies in response to differing climatic conditions.

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References

Bhatt A, Abbassi B (2021). Review of environmental performance of sheep farming using life cycle assessment. Journal of Cleaner Production:126192.

Blasius J, Eilers P, Gower JC (2009). Better biplots. Computational Statistic Data Analysis 53:3145-3158.

Cerqueira JOL, Araújo JPP, Blanco-Penedo I, Cantalapiedra J, Silvestre AMD, Silva SJCR (2016). Predicción de estrés térmico en vacas lecheras mediante indicadores ambientales y fisiológicos. Archives of Zootechnique 65:357-364.

Czacko J (1980). Metodología sobre diversas conductas en diferentes animales. MTA Biol Oszt Kozl 23:239-253.

Chapman S, Shenk P, Kazan K, Manners J (2001). Using biplots to interpret gene expression pattern in plants. Bioinformatic Application Note 18:202-204. https://doi.org/10.1093/bioinformatics/18.1.202

Duda R, Hart P, Stork D (2001). Pattern classification. John Wiley & Sons Inc, New York.

FAO (2020). Livestock systems. Retrieved 2020 October 08 from http://www.fao.org/livestock-systems/global-distributions/sheep/en/

Faria JC, Demetrio CGB (2008). BPCA: Biplot of multivariate data based on principal components analysis. UESC and ESALQ, Ilheus, Bahia, Brasil and Piracicaba, Sao Paulo, Brasil. R package version 1.02. http://CRAN.R-project.org/package=bpca

Fliege J, Qi H-D, Xiu N (2019). Euclidean distance matrix optimization for sensor network localization. In: Gao C, Zhao G, Fourati H (Eds). Cooperative Localization and Navigation: Theory, Research and Practice. CRC Press, Boca Raton, FL

Gabriel KR (2002). Goodness of fit of biplots and correspondence analysis. Biometrika 89:423-436. https://doi.org/10.1093/biomet/89.2.423

Gomez-Pando L, Jimenez-Davalos J, Eguiluz-de la Barra A, Aguilar-Castellanos E, Falconí-Palomino J, Ibañez-Tremolada M, … Lorenzo JC (2009). Field performance of new in vitro androgenesis-derived double haploids of barley. Euphytica 166:269-276.

Gómez D, Hernández L, Yabor L, Beemster GTS, Tebbe CC, Papenbrock J, Lorenzo JC (2018). Euclidean distance can identify the mannitol level that produces the most remarkable integral effect on sugarcane micropropagation in temporary immersion bioreactors. Journal of Plant Research 131:719-724. https://doi.org/10.1007/s10265-018-1028-7

Granahan J, Sweet J (2001). An evaluation of atmospheric correction techniques using the spectral similarity scale. In: IGARSS 2001. Scanning the Present and Resolving the Future. Proceedings. IEEE 2001 International Geoscience and Remote Sensing Symposium (Cat. No. 01CH37217) 5:2022-2024.

Ichino M (1988). General metrics for mixed features-the cartesian space theory for pattern recognition. In: Proceedings of the 1988 IEEE International Conference on Systems, Man, and Cybernetics 1:494-497.

Ivanov Z (1989). The agricultural experimentation. Pueblo y Educación, Havana, pp 332.

Jafar M, Zilouchian A (2001). Application of soft computing for desalination technology. In: Zilouchian A, Jamshidi M (Eds). Intelligent Control Systems Using Soft Computing Methodologies. CRC Press, Boca Raton, pp 315-353.

Kantardzic M (2003). Data mining: concepts, models, methods and algorithms. Technometrics 45:277. https://doi.org/10.1198/tech.2003.s785

Karthik D, Suresh J, Reddy YR, Sharma G, Ramana J, Gangaraju G, … Reddy PRK (2021). Farming systems in sheep rearing: Impact on growth and reproductive performance, nutrient digestibility, disease incidence and heat stress indices. PloS One 16:e0244922. https://doi.org/10.1371/journal.pone.0244922

Kogan J (2007). Introduction to clustering large and high dimensional data. Cambridge University Press, New York. https://doi.org/10.1111/j.1751-5823.2007.00030_27.x

Krzanowski WJ (2004). Biplots for multifactorial analysis of distance. Biometrics 60:517-524. https://doi.org/10.1111/j.0006-341X.2004.00198.x

López R, Pinto-Santini L, Perozo D, Pineda J, Oliveros I, Chacón T, … Ríos de Álvarez L (2015). Confort térmico y crecimiento de corderas West African pastoreando con y sin acceso a sombra artificial. Archives of Zootechnique 64:139-146.

Lorenzo JC, Varela M, Hernández M, Gutiérrez A, Pérez A, Loyola O (2013). Integrated criteria to identify the best treatment in plant biotechnology experiments. Acta Physiologiae Plantarum 35:3261-3264. https://doi.org/10.1007/s11738-013-1352-4

Lorenzo JC, Yabor L, Medina N, Quintana N, Wells V (2015). Coefficient of variation can identify the most important effects of experimental treatments. Notulae Botanicae Horti Agrobotanici Cluj-Napoca https://doi.org/1015835/NBHA431988143:287-291

Medjekal S, Ghadbane M (2021). Sheep digestive physiology and constituents of feeds. In: Sheep Farming-An Approach to Feed, Growth and Health. IntechOpen. https://doi.org/10.5772/intechopen.92054

Perón N (2010). Manual del ovino. Pelibuey Asociación Cubana de Producción Animal (ACPA), La Habana.

Reyes J, Herrera M, Marquina J, Enjoy D, Pinto-Santini L (2018). Ambiente físico y respuestas fisiológicas de ovinos bajo sombra en horas de máxima radiación. Archives of Zootechnique 67:318-323. https://doi.org/10.21071/az.v67i259.3786

Rojas D, Nejadhashemi A, Harrigan T, Woznicki S (2017). Climate change and livestock: impacts, adaptation, and mitigation. Climatic Risk Management 16:145-163. https://doi.org/10.1016/j.crm.2017.02.001

Sejian V, Bhatta R, Gaughan J, Malik P, Naqvi S, Lal R (2017). Adapting sheep production to climate change. In: Sheep Production Adapting to Climate Change. Springer Singapore, Singapore, pp 1-29.

Silva TPD, Marques CAT, Torreão JNC, Bezerra LR, Araújo MJ, Gottardi FP, … Oliveira RL (2015). Ingestive behaviour of grazing ewes given two levels of concentrate. South African Journal of Animal Science 45:180-187.

Sousa L, Maurício R, Paciullo D, Silveira S, Ribeiro R, Calsavara L, Moreira G (2015). Forage intake, feeding behavior and bio-climatological indices of pasture grass, under the influence of trees, in a silvopastoral system. Tropical Grasslands-Forrajes Tropicales 3:129-141. https://doi.org/10.17138/TGFT(3)129-141

Tavazoie S, Hughes J, Campbell M (1999). Systematic determination of genetic network architecture. Natural Genetics 22:281-285. https://doi.org/10.1038/10343

Villalobos-Olivera A, Hernández L, Martínez J, Quintana N, Zevallos BE, Yabor L, … Sershen JCL (2019). Euclidean distance can recognize the Biojas® concentration that produces the ideal physiological status of pineapple in vitro plantlets. In Vitro Cellular and Developmental Biology-Plant 56(2):259-263. https://doi.org/101007/s11627-019-10023-5

Published
2021-03-05
How to Cite
SERRANO, J. O., VILLARES, A., MANUEL-MALAMBA, F. D., MARTÍNEZ-MELO, J., MAZORRA, C., BORROTO, A., HAJARI, E., FONSECA-FUENTES, N., & LORENZO, J. C. (2021). Euclidean distance: integrated criteria to study sheep behaviour under heat stress. Notulae Scientia Biologicae, 13(1), 10859. https://doi.org/10.15835/nsb13110859
Section
Research articles
CITATION
DOI: 10.15835/nsb13110859