https://doi.org/10.4081/jae.2021.1229
Assessing the effect of barns structures and environmental conditions in dairy cattle farms monitored in Northern Italy
Published: 23 December 2021
Abstract Views: 1228
PDF: 587
HTML: 14
HTML: 14
Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
Authors
Animal welfare is a fundamental pillar for livestock farming, and it can be endangered by a series of aspects, among which is the presence of undesired microclimates. This condition can be monitored by measuring the temperature-humidity index (THI), an index able to inform about the emergence of heat-stressing conditions in the barns. The THI can be influenced by the external environmental conditions and the barn structure, orientation, thermal buoyancy, and roof insulating materials. In order to evaluate these structural aspects of buildings and the consequent microclimate, in this study, a survey was carried out in 8 dairy cattle barns located in the northern part of Italy that were monitored continuously during thermoneutral, warm, and cold periods. Experts observed the structural aspects ,and the environmental parameters were measured with sensors. From the results emerged that the barns had structural characteristics that considerably affect the internal microclimate, with openings, roof height, forced ventilation, and building orientation playing a significant role in estimating of the THI in the barn. The more critical period was the warm one when the structures could not mitigate the external conditions, and THI exceeded the threshold of 72 for a big share of the period in all monitored farms (range between 50-80% of observations). In the best situation, the cooling systems were able to maintain the external conditions. The results confirm the importance of the barn design and of an appropriate ventilation to improve air exchanges.
Allen J.D., Hall L.W., Collier R.J., Smith J.F. 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. 98:118-27.
DOI: https://doi.org/10.3168/jds.2013-7704
Arcidiacono C., Mancino M., Porto S.M.C. 2020. Moving mean-based algorithm for dairy cow’s oestrus detection from uni-axial-accelerometer data acquired in a free-stall barn. Comp. Electron. Agr. 175:105498.
DOI: https://doi.org/10.1016/j.compag.2020.105498
ASABE. 2006. Design of ventilation systems for poultry and livestock shelters. pp 652-670 in Standards 2006 American Society of Agri-cultural and Biological Engineers (53rd Edition), St. Joseph, MI, USA.
Bar D., Kaim M., Flamenbaum I., Hanochi B., Toaff-Rosenstein R.L. 2019. Technical note: Accelerometer-based recording of heavy breathing in lactating and dry cows as an automated measure of heat load. J. Dairy Sci. 102:3480-86.
DOI: https://doi.org/10.3168/jds.2018-15186
Bellingeri A., Cabrera V., Gallo A., Liang D., Masoero F. 2019. A survey of dairy cattle management, crop planning, and forages cost of production in Northern Italy. Ital. J. Anim. Sci. 18:786-98.
DOI: https://doi.org/10.1080/1828051X.2019.1580153
Berman A. 2019. An overview of heat stress relief with global warming in perspective. Int. J. Biometeorol. 63:493-8.
DOI: https://doi.org/10.1007/s00484-019-01680-7
Bohmanova J., Misztal I., Cole J.B. 2007. Temperature-humidity indices as indicators of milk production losses due to heat stress. J. Dairy Sci. 90:1947-56.
DOI: https://doi.org/10.3168/jds.2006-513
CIGR. 2014. The design of dairy co and replacement heifer housing. Report of the CIGR Section II Working Group n. 14 - cattle housing.
Das R., Sailo L., Verma N., Bharti P., Saikia J. 2016. Impact of heat stress on health and performance of dairy animals: a review. Veterinary World 9:260-8.
DOI: https://doi.org/10.14202/vetworld.2016.260-268
De Paepe M., Pieters J.G., Cornelis W.M., Gabriels D., Merci B., Demeyer P. 2012. Airflow measurements in and around scale model cattle barns in a wind tunnel: effect of ventilation opening height. Biosyst. Eng. 113:22-32.
DOI: https://doi.org/10.1016/j.biosystemseng.2012.06.003
Fernández M.E., Mariño R.A., Carreira X.C. 2008. Relationship between layout and timber structures in freestall dairy cattle barns: influence of internal features. Biosyst. Eng. 100:266-80.
DOI: https://doi.org/10.1016/j.biosystemseng.2008.03.001
Ferraz P.F.P., Ferraz G.A.e.S., Leso L., Klopčič M., Barbari M., Rossi G. 2020. Properties of conventional and alternative bedding materials for dairy cattle. J. Dairy Sci. 103:8661-74.
DOI: https://doi.org/10.3168/jds.2020-18318
Firfiris V.K., Martzopoulou A.G., Kotsopoulos T.A. 2019. Passive cooling systems in livestock buildings towards energy saving: A critical review. Energ. Buildings 202:109368.
DOI: https://doi.org/10.1016/j.enbuild.2019.109368
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. 7:403-25.
DOI: https://doi.org/10.1146/annurev-animal-020518-114851
Hempel S., Menz C., Pinto S., Galán E., Janke D., Estellés F., Müschner-Siemens T., Wang X., Heinicke J., Zhang G., Amon B., del Prado A., Amon T. 2019. Heat stress risk in European dairy cattle husbandry under different climate change scenarios-uncertainties and potential impacts. Earth Syst. Dynam. 10:859-84.
DOI: https://doi.org/10.5194/esd-10-859-2019
Honig H., Miron J., Lehrer H., Jackoby S., Zachut M., Zinou A., Portnick Y., Moallem U. 2012. Performance and welfare of high-yielding dairy cows subjected to 5 or 8 cooling sessions daily under hot and humid climate. J. Dairy Sci. 95:3736-42.
DOI: https://doi.org/10.3168/jds.2011-5054
ISTAT. 2021. Italian National Institute of Statistics. Available from: http://dati.istat.it/?lang=en Accessed: 13 Sept. 2021.
Lovarelli D., Bacenetti J., Guarino M. 2020a. A review on dairy cattle farming: Is precision livestock farming the compromise for an environmental, economic and social sustainable production? J. Clean Prod. 262:121409.
DOI: https://doi.org/10.1016/j.jclepro.2020.121409
Lovarelli D., Finzi A., Mattachini G., Riva E. 2020b. A survey of dairy cattle behavior in different barns in Northern Italy. Animals 10:713.
DOI: https://doi.org/10.3390/ani10040713
Menconi M.E., Grohmann D. 2014. Model integrated of life-cycle costing and dynamic thermal simulation (MILD) to evaluate roof insulation materials for existing livestock buildings. Ener. Buildings 81:48-58.
DOI: https://doi.org/10.1016/j.enbuild.2014.06.005
Pinto S., Hoffmann G., Ammon C., Heuwieser W., Levit H., Halachmi I., Amon T. 2019. Effect of two cooling frequencies on respiration rate in lactating dairy cows under hot and humid climate conditions. Ann. Anim. Sci. 19:821-34.
DOI: https://doi.org/10.2478/aoas-2019-0026
Polsky L., von Keyserlingk M.A. 2017. Invited review: Effects of heat stress on dairy cattle welfare. J. Dairy Sci. 100:8645-57.
DOI: https://doi.org/10.3168/jds.2017-12651
Porto S.M.C., D’Emilio A., Cascone G. 2017. On the influence of the alternation of two different cooling systems on dairy cow daily activities. J. Agr. Eng. 48:21-7.
DOI: https://doi.org/10.4081/jae.2017.577
Provolo G., Riva E. 2008. Influence of temperature and humidity on dairy cow behaviour in freestall barns. Agricultural and Biosystems Engineering for a sustainable world: Conference Proceedings, Hersonissos, Greece.
Tassinari P., Bovo M., Benni S., Franzoni S., Poggi M., Mammi L.M.E., Mattoccia S., Di Stefano L., Bonora F., Barbaresi A., Santolini E., Torreggiani D. 2021. A computer vision approach based on deep learning for the detection of dairy cows in free stall barn. Comp. Electron. Agr. 182:106030.
DOI: https://doi.org/10.1016/j.compag.2021.106030
Van Iaer E., Tuyttens F.A.M., Ampe B., Sonck B., Moons C.P.H., Vandaele L. 2015. Effect of summer conditions and shade on the production. Animal 9:1547-58.
DOI: https://doi.org/10.1017/S1751731115000816
Vox G., Maneta A., Schettini E. 2016. Evaluation of the radiometric properties of roofing materials for livestock buildings and their effect on the surface temperature. Biosyst. Eng. 144:26-37.
DOI: https://doi.org/10.1016/j.biosystemseng.2016.01.016
How to Cite
Lovarelli, D. . (2021) “Assessing the effect of barns structures and environmental conditions in dairy cattle farms monitored in Northern Italy”, Journal of Agricultural Engineering, 52(4). doi: 10.4081/jae.2021.1229.
Copyright (c) 2021 the Author(s)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
PAGEPress has chosen to apply the Creative Commons Attribution NonCommercial 4.0 International License (CC BY-NC 4.0) to all manuscripts to be published.
Similar Articles
- Antonio Comparetti, Pierluigi Febo, Carlo Greco, Santo Orlando, Kestutis Navickas, Arvydas Nekrosius, Kestutis Venslauskas, Biogas yield from Sicilian kitchen waste and cheese whey , Journal of Agricultural Engineering: Vol. 44 No. s2 (2013): Proceedings of the 10th Conference of the Italian Society of Agricultural Engineering
- Alberto Finzi, Elisabetta Riva, Alda Bicoku, Viviana Guido, Seit Shallari, Giorgio Provolo, Comparison of techniques for ammonia emission mitigation during storage of livestock manure and assessment of their effect in the management chain , Journal of Agricultural Engineering: Vol. 50 No. 1 (2019)
- Ildar Badretdinov, Salavat Mudarisov, Eduard Khasanov, Ruslan Nasyrov, Marat Tuktarov, Operation technological process research in the cleaning system of the grain combine , Journal of Agricultural Engineering: Vol. 52 No. 2 (2021)
- Yun Zhu, Shuwen Liu, Xiaojun Wu, Lianfeng Gao, Youyun Xu, Multi-class segmentation of navel orange surface defects based on improved DeepLabv3+ , Journal of Agricultural Engineering: Vol. 55 No. 2 (2024)
- Pietro Picuno, Vernacular farm buildings in landscape planning: a typological analysis in a southern Italian region , Journal of Agricultural Engineering: Vol. 43 No. 3 (2012)
- Rodolfo Piscopia, Andrea Petroselli, Salvatore Grimaldi, A software package for predicting design-flood hydrographs in small and ungauged basins , Journal of Agricultural Engineering: Vol. 46 No. 2 (2015)
- Alessio Cislaghi, Gian Battista Bischetti, Best practices in post-flood surveys: The study case of Pioverna torrent , Journal of Agricultural Engineering: Vol. 53 No. 2 (2022)
- José Luis Morales-Reyes, Héctor-Gabriel Acosta-Mesa, Elia-Nora Aquino-Bolaños, Socorro Herrera Meza, Aldo Márquez Grajales, Anthocyanins estimation in homogeneous bean landrace (Phaseolus vulgaris L.) using probabilistic representation and convolutional neural networks , Journal of Agricultural Engineering: Vol. 54 No. 2 (2023)
- Roberto Romaniello, Alessandro Leone, Giorgio Peri, Measurement of food colour in L*a*b* units from RGB digital image using least squares support vector machine regression , Journal of Agricultural Engineering: Vol. 46 No. 4 (2015)
- Piernicola Masella, Lorenzo Guerrini, Alessandro Parenti, Fabio Baldi, Paolo Spugnoli, Performance of a driven hitch-cart for draft animal power under different power take-off torque and ballast levels condition , Journal of Agricultural Engineering: Vol. 47 No. 4 (2016)
<< < 25 26 27 28 29 30 31 32 33 34 > >>
You may also start an advanced similarity search for this article.
