3D numerical modelling of temperature and humidity index distribution in livestock structures: a cattle-barn case study

Published: 12 May 2023
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In dairy cattle farming, heat stress largely impairs production, health, and animal welfare. This study aims to develop a workflow and a numerical analysis procedure to provide a real-time 3D distribution of the temperature and humidity index (THI) in a generic cattle barn based on temperature and humidity monitored in sample points, besides characterising the relationship between indoor THI and outside weather conditions. This research was carried out with reference to the study case of a cattle barn. A model has been developed to define the indoor three-dimensional spatial distribution of the Temperature-Humidity Index of a cattle barn based on environmental measurements at different heights of the building. As a core of the model, the Discrete Sibson Interpolation method was used to render a point cloud representing the THI values in the non-sampled areas. The area between 1-2 meters was emphasised as the region of most significant interest to quantify the heat waves perceived by dairy cows. The model represents an effective tool to distinguish different areas of the animal-occupied zone characterised by different values of THI.

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Barker, Z.E., Vázquez Diosdado, J.A., Codling, E.A., Bell, N.J., Hodges, H.R., Croft, D.P., Amory, J.R., 2018. Use of novel sensors combining local positioning and acceleration to measure feeding behavior differences associated with lameness in dairy cattle. J Dairy Sci 101, 6310–6321. DOI: https://doi.org/10.3168/jds.2016-12172
Becker, C.A., Collier, R.J., Stone, A.E., 2020. Invited review: Physiological and behavioral effects of heat stress in dairy cows. J Dairy Sci 103, 6751–6770. DOI: https://doi.org/10.3168/jds.2019-17929
Benni, S., Pastell, M., Bonora, F., Tassinari, P., Torreggiani, D., 2020. A generalised additive model to characterise dairy cows’ responses to heat stress*. Animal 14, 418–424. DOI: https://doi.org/10.1017/S1751731119001721
Berckmans, D., Guarino, M., 2017. From the Editors: Precision livestock farming for the global livestock sector. Animal Frontiers 7, 4–5. DOI: https://doi.org/10.2527/af.2017.0101
Bernabucci, U., Biffani, S., Buggiotti, L., Vitali, A., Lacetera, N., Nardone, A., 2014. The effects of heat stress in Italian Holstein dairy cattle. J Dairy Sci 97, 471–486. DOI: https://doi.org/10.3168/jds.2013-6611
Bonora, F., Benni, S., Barbaresi, A., Tassinari, P., Torreggiani, D., 2018. A cluster-graph model for herd characterisation in dairy farms equipped with an automatic milking system. Biosyst Eng 167. DOI: https://doi.org/10.1016/j.biosystemseng.2017.12.007
Bonora, Filippo, Pastell, M., Benni, S., Tassinari, P., Torreggiani, D., 2018. ICT monitoring and mathematical modelling of dairy cows performances in hot climate conditions: a study case in Po valley (Italy). Agricultural Engineering International: CIGR Journal 20, 1–12.
Bovo, M., Benni, S., Barbaresi, A., Santolini, E., Agrusti, M., Torreggiani, D., Tassinari, P., 2020. A Smart Monitoring System for a Future Smarter Dairy Farming. In: 2020 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor). IEEE, pp. 165–169. DOI: https://doi.org/10.1109/MetroAgriFor50201.2020.9277547
Carvajal, M.A., Alaniz, A.J., Gutiérrez-Gómez, C., Vergara, P.M., Sejian, V., Bozinovic, F., 2021. Increasing importance of heat stress for cattle farming under future global climate scenarios. Science of The Total Environment 801, 149661. DOI: https://doi.org/10.1016/j.scitotenv.2021.149661
Chung, H., Zhang, X., Jung, S., Zhang, Z., Choi, C.Y., 2022. Application of machine-learned metadata-driven model for dairy barn ventilation simulation. Comput Electron Agric 202. DOI: https://doi.org/10.1016/j.compag.2022.107350
Eigenberg, R.A., Brown-Brandl, T.M., Nienaber, J.A., Hahn, G.L., 2005. Dynamic Response Indicators of Heat Stress in Shaded and Non-shaded Feedlot Cattle, Part 2: Predictive Relationships. Biosyst Eng 91, 111–118. DOI: https://doi.org/10.1016/j.biosystemseng.2005.02.001
Etherington, T.R., Perry, G.L.W., Wilmshurst, J.M., 2021. A History of Open Weather in New Zealand (HOWNZ): an open access 1-km resolution monthly 1910-2019 time-series of interpolated temperature and rainfall grids with associated uncertainty. Earth Syst Sci Data. DOI: https://doi.org/10.5194/essd-2021-303
Frigeri, K.D.M., Kachinski, K.D., Ghisi, N.D.C., Deniz, M., Damasceno, F.A., Barbari, M., Herbut, P., Vieira, F.M.C., 2023. Effects of Heat Stress in Dairy Cows Raised in the Confined System: A Scientometric Review. Animals 13. DOI: https://doi.org/10.3390/ani13030350
Habeeb, A.A., Gad, A.E., Atta, M.A., 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 and Recent Advances 1, 35–50. DOI: https://doi.org/10.18689/ijbr-1000107
Hahn, G.L., 1997. Dynamic responses of cattle to thermal heat loads. J Anim Sci 77, 10. DOI: https://doi.org/10.2527/1997.77suppl_210x
Halachmi, I., Guarino, M., Bewley, J., Pastell, M., 2019. Smart Animal Agriculture: Application of Real-Time Sensors to Improve Animal Well-Being and Production. Annu Rev Anim Biosci 7, 403–425. DOI: https://doi.org/10.1146/annurev-animal-020518-114851
Hofstra, G., Roelofs, J., Rutter, S.M., van Erp-van der Kooij, E., de Vlieg, J., 2022. Mapping Welfare: Location Determining Techniques and Their Potential for Managing Cattle Welfare—A Review. Dairy 3, 776–788. DOI: https://doi.org/10.3390/dairy3040053
James, G., Witten, D., Hastie, T., Tibshirani, R., 2013. An Introduction to Statistical Learning, Springer Texts in Statistics. Springer New York, New York, NY. DOI: https://doi.org/10.1007/978-1-4614-7138-7
Ji, B., Banhazi, T., Perano, K., Ghahramani, A., Bowtell, L., Wang, C., Li, B., 2020. A review of measuring, assessing and mitigating heat stress in dairy cattle. Biosyst Eng 199, 4–26. DOI: https://doi.org/10.1016/j.biosystemseng.2020.07.009
Lees, A.M., Sejian, V., Wallage, A.L., Steel, C.C., Mader, T.L., Lees, J.C., Gaughan, J.B., 2019. The Impact of Heat Load on Cattle. Animals 9, 322. DOI: https://doi.org/10.3390/ani9060322
Leliveld, L.M.C., Provolo, G., 2020. A Review of Welfare Indicators of Indoor-Housed Dairy Cow as a Basis for Integrated Automatic Welfare Assessment Systems. Animals 10, 1430. DOI: https://doi.org/10.3390/ani10081430
Lovarelli, D., Finzi, A., Mattachini, G., Riva, E., 2020. A Survey of Dairy Cattle Behavior in Different Barns in Northern Italy. Animals 10, 713. DOI: https://doi.org/10.3390/ani10040713
McVey, C., Hsieh, F., Manriquez, D., Pinedo, P., Horback, K., 2020. Mind the Queue: A Case Study in Visualizing Heterogeneous Behavioral Patterns in Livestock Sensor Data Using Unsupervised Machine Learning Techniques. Front Vet Sci 7. DOI: https://doi.org/10.3389/fvets.2020.00523
National Research Council (U.S.), 1971. A guide to environmental research on animals. National Academy of Sciences.
Park, S.W., Linsen, L., Kreylos, O., Owens, J.D., Hamann, B., 2006. Discrete Sibson Interpolation. IEEE Trans Vis Comput Graph 12. DOI: https://doi.org/10.1109/TVCG.2006.27
Ravagnolo, O., Misztal, I., 2002. Studies on genetics of heat tolerance in dairy cattle with reduced weather information via cluster analysis. J Dairy Sci 85, 1586–1589. DOI: https://doi.org/10.3168/jds.S0022-0302(02)74228-8
Samal, L., 2005. Heat Stress in Dairy Cows - Reproductive Problems and Control Measures. International Journal of Livestock Research 20, 16–19.
Stewart, M., Wilson, M.T., Schaefer, A.L., Huddart, F., Sutherland, M.A., 2017. The use of infrared thermography and accelerometers for remote monitoring of dairy cow health and welfare. J Dairy Sci 100, 3893–3901. DOI: https://doi.org/10.3168/jds.2016-12055
Thom, E.C., 1959. The Discomfort Index. Weatherwise 12, 57–61. DOI: https://doi.org/10.1080/00431672.1959.9926960
Wang, T., Zhong, R., Zhou, D., 2020. Temporal–Spatial Distribution of Risky Sites for Feeding Cattle in China Based on Temperature/Humidity Index. Agriculture 10, 571. DOI: https://doi.org/10.3390/agriculture10110571
West, J.W., 2003. Effects of Heat-Stress on Production in Dairy Cattle. J Dairy Sci 86, 2131–2144. DOI: https://doi.org/10.3168/jds.S0022-0302(03)73803-X
Yan, G., Shi, Z., Cui, B., Li, H., 2022. Developing a new thermal comfort prediction model and web-based application for heat stress assessment in dairy cows. Biosyst Eng 214, 72–89. DOI: https://doi.org/10.1016/j.biosystemseng.2021.12.006
Yousef, M.K., 1985. Stress physiology in livestock. Volume I. Basic principles. CRC press, Boca Raton, Florida

How to Cite

Perez Garcia, C. A. ., Bovo, M., Torreggiani, D., Tassinari, P. and Benni, S. (2023) “3D numerical modelling of temperature and humidity index distribution in livestock structures: a cattle-barn case study”, Journal of Agricultural Engineering, 54(3). doi: 10.4081/jae.2023.1522.