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Vitali, A.; Lana, E.; Amadori, M.; Bernabucci, U.; Nardone, A.; Lacetera, N. |
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Title  |
Analysis of factors associated with mortality of heavy slaughter pigs during transport and lairage |
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Journal Article |
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Year |
2014 |
Publication |
Journal of Animal Science |
Abbreviated Journal |
J. Anim. Sci. |
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Volume |
92 |
Issue |
11 |
Pages |
5134-5141 |
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Keywords |
Abattoirs/*statistics & numerical data; Animals; *Data Interpretation, Statistical; Humidity/adverse effects; Light/adverse effects; *Mortality; Retrospective Studies; Seasons; Swine/*physiology; Temperature; Time Factors; Transportation/*statistics & numerical data; lairage; mortality; pigs; temperature-humidity index; transport |
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Abstract |
The study was based on data collected during 5 yr (2003-2007) and was aimed at assessing the effects of the month, slaughter house of destination (differing for stocking density, openings, brightness, and cooling device types), length of the journey, and temperature-humidity index (THI) on mortality of heavy slaughter pigs (approximately 160 kg live weight) during transport and lairage. Data were obtained from 24,098 journeys and 3,676,153 pigs transported from 1,618 farms to 3 slaughter houses. Individual shipments were the unit of observation. The terms dead on arrival (DOA) and dead in pen (DIP) refer to pigs that died during transport and in lairage at the abattoir before slaughtering, respectively. These 2 variables were assessed as the dependent counts in separate univariate Poisson regressions. The independent variables assessed univariately in each set of regressions were month of shipment, slaughter house of destination, time traveled, and each combination of the month with the time traveled. Two separate piecewise regressions were done. One used DOA counts within THI levels over pigs transported as a dependent ratio and the second used DIP counts within THI levels over pigs from a transport kept in lairage as a dependent ratio. The THI was the sole independent variable in each case. The month with the greatest frequency of deaths was July with a risk ratio of 1.22 (confidence interval: 1.06-1.36; P < 0.05) and 1.27 (confidence interval: 1.06-1.51; P < 0.05) for DOA and DIP, respectively. The lower mortality risk ratios for DOA and DIP were recorded for January and March (P < 0.05). The aggregated data of the summer (June, July, and August) versus non-summer (January, March, September, and November) months showed a greater risk of pigs dying during the hot season when considering both transport and lairage (P < 0.05). The mortality risk ratio of DIP was lower at the slaughter house with the lowest stocking density (0.64 m(2)/100 kg live weight), large open windows on the roof and sidewalls, low brightness (40 lx) lights, and high-pressure sprinklers as cooling devices. The mortality risk ratio of DOA increased significantly for journeys longer than 2 h, whereas no relationship was found between length of transport and DIP. The piecewise analysis pointed out that 78.5 and 73.6 THI were the thresholds above which the mortality rate increased significantly for DOA and DIP, respectively. These results may help the pig industry to improve the welfare of heavy slaughter pigs during transport and lairage. |
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1525-3163 (Electronic) 0021-8812 (Linking) |
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4641 |
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Özkan, Ş.; Vitali, A.; Lacetera, N.; Amon, B.; Bannink, A.; Bartley, D.J.; Blanco-Penedo, I.; de Haas, Y.; Dufrasne, I.; Elliott, J.; Eory, V.; Fox, N.J.; Garnsworthy, P.C.; Gengler, N.; Hammami, H.; Kyriazakis, I.; Leclère, D.; Lessire, F.; Macleod, M.; Robinson, T.P.; Ruete, A.; Sandars, D.L.; Shrestha, S.; Stott, A.W.; Twardy, S.; Vanrobays, M.L.; Ahmadi, B.V.; Weindl, I.; Wheelhouse, N.; Williams, A.G.; Williams, H.W.; Wilson, A.J.; Østergaard, S.; Kipling, R.P. |
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Title |
Challenges and priorities for modelling livestock health and pathogens in the context of climate change |
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Journal Article |
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Year |
2016 |
Publication |
Environmental Research |
Abbreviated Journal |
Environ. Res. |
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151 |
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130-144 |
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Climate change has the potential to impair livestock health, with consequences for animal welfare, productivity, greenhouse gas emissions, and human livelihoods and health. Modelling has an important role in assessing the impacts of climate change on livestock systems and the efficacy of potential adaptation strategies, to support decision making for more efficient, resilient and sustainable production. However, a coherent set of challenges and research priorities for modelling livestock health and pathogens under climate change has not previously been available. To identify such challenges and priorities, researchers from across Europe were engaged in a horizon-scanning study, involving workshop and questionnaire based exercises and focussed literature reviews. Eighteen key challenges were identified and grouped into six categories based on subject-specific and capacity building requirements. Across a number of challenges, the need for inventories relating model types to different applications (e.g. the pathogen species, region, scale of focus and purpose to which they can be applied) was identified, in order to identify gaps in capability in relation to the impacts of climate change on animal health. The need for collaboration and learning across disciplines was highlighted in several challenges, e.g. to better understand and model complex ecological interactions between pathogens, vectors, wildlife hosts and livestock in the context of climate change. Collaboration between socio-economic and biophysical disciplines was seen as important for better engagement with stakeholders and for improved modelling of the costs and benefits of poor livestock health. The need for more comprehensive validation of empirical relationships, for harmonising terminology and measurements, and for building capacity for under-researched nations, systems and health problems indicated the importance of joined up approaches across nations. The challenges and priorities identified can help focus the development of modelling capacity and future research structures in this vital field. Well-funded networks capable of managing the long-term development of shared resources are required in order to create a cohesive modelling community equipped to tackle the complex challenges of climate change. |
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0013-9351 |
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LiveM |
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MA @ admin @ |
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4766 |
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Vitali, A.; Bernabucci, U.; Nardone, A.; Lacetera, N. |
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Effect of season, month and temperature humidity index on the occurrence of clinical mastitis in dairy heifers |
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2016 |
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Advances in Animal Biosciences |
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Advances in Animal Biosciences |
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7 |
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03 |
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250-252 |
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2040-4700 |
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LiveM, ft_macsur |
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MA @ admin @ |
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4866 |
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Dono, G.; Cortignani, R.; Dell’Unto, D.; Doro, L.; Lacetera, N.; Mula, L.; Pasqui, M.; Quaresima, S.; Vitali, A.; Roggero, P.P. |
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Productive and economic adaptation of Mediterranean agriculture to climate change (Produktive und wirtschaftliche Anpassung der mediterranen Landwirtschaft an den Klimawandel) |
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2014 |
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Jahrbuch der ÖGA |
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24 |
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213-222 |
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24. Jahrestagung der Österreichischen Gesellschaft für Agrarökonomie, 2014-09-25 to 2014-09-26, Vienna |
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MA @ admin @ |
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5027 |
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Lacetera, N.; Vitali, A.; Bernabucci, U.; Nardone, A. |
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Title |
Relationships between temperature humidity index, mortality, milk yield and composition in Italian dairy cows |
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Report |
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2014 |
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FACCE MACSUR Reports |
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3 |
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Sp3-3 |
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The aim of this presentation is to illustrate the activities performed by the LiveM-Task L1.2. group based at the University of Tuscia, Viterbo, Italy. Three different pluriannual databases were built to perform retrospective studies aimed at establishing the relationships between temperature humidity index (THI) and parameters of interest for dairy cow farms. The THI combines temperature and humidity in a single value and has been widely used to quantify heat stress in farm animals. The first database was built to assess the relationships between THI and mortality over a 6 yr period (2002-2007); the second one was a 7 yr database (2001-2007) which was built to establish the relationships between THI and milk yield; the last database included THI, milk somatic cell counts, total bacterial counts, fat and protein percentages data collected over a 7 yr period (2003-2009). The analysis of the three databases provided several equations which demonstrated and quantified an increase of mortality, reduction of milk yield and a worsening of milk quality in hot environment. Results of these analyzes authorized speculations about risks for dairy cows and their productivity in a warming planet. Furthermore, the same results are being utilized by economists also working within MACSUR at the University of Tuscia for an integrated study aimed at establishing the economic impact of climate change in the dairy sector. Combining this information with climate change regional scenarios might permit prediction of the impact of global warming and identification of adaptation measures that are appropriate for specific contexts. No Label |
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