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Lacetera, N.; Vitali, A.; Bernabucci, U.; Nardone, A. |
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Title |
Report on the analysis of interannual and seasonal variations in productive, reproductive and health data |
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2015 |
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FACCE MACSUR Reports |
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4 |
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D-L1.2.2 |
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The work carried out under LiveM, L1.2 and described herein was based on construction and query of large databases which included multiannual productive and health field data. Productive data referred to dairy cows, whereas health data were relative both to dairy cows and pigs. The analysis pointed out significant seasonal variations of parameters under study. In synthesis, summer/hot season was associated with significant worsening of dairy cows milk composition and with significant higher risk of death in pigs. These results may help to predict consequences of climate change in economically important sectors of the livestock industry and also to identify and target adaptation options that are appropriate for specific contexts, and that can contribute to environmental sustainability as well as to economic development. No Label |
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MA @ admin @ |
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2216 |
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Author |
Lacetera, N. |
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Title |
Season and temperature humidity index related changes of productive and health parameters in dairy cows and pigs |
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2015 |
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FACCE MACSUR Reports |
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5 |
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Sp5-33 |
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The work described herein was based on construction and query of four different large databases which included multiannual (5-7 years) meteorological, productive and health data from the field. Productive data were referred to dairy cows and included milk yield and composition (total bacterial count, fat and protein percentages) whereas health data were relative both to dairy cows (milk somatic cell counts and mortality data) and pigs (mortality data during transport and at lairage). The analysis pointed out significant seasonal variations of parameters under study. In synthesis, summer/hot season was associated with significant worsening of cows’ milk composition and with significant higher risk of death in pigs. The analysis also permitted to establish the themperature humidity index values above which a significant decline of performance and health of dairy cows or pigs has to expected. These results may help to predict the consequences of climate change in economically important sectors of the livestock industry, to identify and target adaptation options that are appropriate for specific contexts and that can contribute to environmental sustainability as well as to economic development. No Label |
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MACSUR Science Conference 2015 »Integrated Climate Risk Assessment in Agriculture & Food«, 8–9+10 April 2015, Reading, UK |
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MA @ admin @ |
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2148 |
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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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Bertocchi, L.; Vitali, A.; Lacetera, N.; Nardone, A.; Varisco, G.; Bernabucci, U. |
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Seasonal variations in the composition of Holstein cow’s milk and temperature-humidity index relationship |
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2014 |
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Animal |
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Animal |
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8 |
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4 |
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667-674 |
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Animal Husbandry/*methods; Animals; Cattle/*physiology; Cell Count/veterinary; Dairying; Female; Hot Temperature; Humidity; Italy; Lactation/*physiology; Milk/cytology/*physiology; Retrospective Studies; Seasons |
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A retrospective study on seasonal variations in the characteristics of cow’s milk and temperature-humidity index (THI) relationship was conducted on bulk milk data collected from 2003 to 2009. The THI relationship study was carried out on 508 613 bulk milk data items recorded in 3328 dairy farms form the Lombardy region, Italy. Temperature and relative humidity data from 40 weather stations were used to calculate THI. Milk characteristics data referred to somatic cell count (SCC), total bacterial count (TBC), fat percentage (FA%) and protein percentage (PR%). Annual, seasonal and monthly variations in milk composition were evaluated on 656 064 data items recorded in 3727 dairy farms. The model highlighted a significant association between the year, season and month, and the parameters analysed (SCC, TBC, FA%, PR%). The summer season emerged as the most critical season. Of the summer months, July presented the most critical conditions for TBC, FA% and PR%, (52 054 ± 183 655, 3.73% ± 0.35% and 3.30% ± 0.15%, respectively), and August presented higher values of SCC (369 503 ± 228 377). Each milk record was linked to THI data calculated at the nearest weather station. The analysis demonstrated a positive correlation between THI and SCC and TBC, and indicated a significant change in the slope at 57.3 and 72.8 maximum THI, respectively. The model demonstrated a negative correlation between THI and FA% and PR% and provided breakpoints in the pattern at 50.2 and 65.2 maximum THI, respectively. The results of this study indicate the presence of critical climatic thresholds for bulk tank milk composition in dairy cows. Such indications could facilitate the adoption of heat management strategies, which may ensure the health and production of dairy cows and limit related economic losses. |
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1751-7311 |
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LiveM, ft_macsur |
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MA @ admin @ |
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4618 |
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Kipling, R.P.; Bannink, A.; Bellocchi, G.; Dalgaard, T.; Fox, N.J.; Hutchings, N.J.; Kjeldsen, C.; Lacetera, N.; Sinabell, F.; Topp, C.F.E.; van Oijen, M.; Virkajärvi, P.; Scollan, N.D. |
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Title |
Modelling European ruminant production systems: Facing the challenges of climate change |
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Report |
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2017 |
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FACCE MACSUR Reports |
Abbreviated Journal |
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10 |
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Pages |
L1.1-D1 |
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Ruminant production systems are important producers of food, support rural communities and culture, and help to maintain a range of ecosystem services including the sequestering of carbon in grassland soils. However, these systems also contribute significantly to climate change through greenhouse gas (GHG) emissions, while intensi- fication of production has driven biodiversity and nutrient loss, and soil degradation. Modeling can offer insights into the complexity underlying the relationships between climate change, management and policy choices, food production, and the maintenance of ecosystem services. This paper 1) provides an overview of how ruminant systems modeling supports the efforts of stakeholders and policymakers to predict, mitigate and adapt to climate change and 2) provides ideas for enhancing modeling to fulfil this role. Many grassland models can predict plant growth, yield and GHG emissions from mono-specific swards, but modeling multi-species swards, grassland quality and the impact of management changes requires further development. Current livestock models provide a good basis for predicting animal production; linking these with models of animal health and disease is a prior- ity. Farm-scale modeling provides tools for policymakers to predict the emissions of GHG and other pollutants from livestock farms, and to support the management decisions of farmers from environmental and economic standpoints. Other models focus on how policy and associated management changes affect a range of economic and environmental variables at regional, national and European scales. Models at larger scales generally utilise more empirical approaches than those applied at animal, field and farm-scales and include assumptions which may not be valid under climate change conditions. It is therefore important to continue to develop more realistic representations of processes in regional and global models, using the understanding gained from finer-scale modeling. An iterative process of model development, in which lessons learnt from mechanistic models are ap- plied to develop ‘smart’ empirical modeling, may overcome the trade-off between complexity and usability. De- veloping the modeling capacity to tackle the complex challenges related to climate change, is reliant on closer links between modelers and experimental researchers, and also requires knowledge-sharing and increasing technical compatibility across modeling disciplines. Stakeholder engagement throughout the process of model development and application is vital for the creation of relevant models, and important in reducing problems re- lated to the interpretation of modeling outcomes. Enabling modeling to meet the demands of policymakers and other stakeholders under climate change will require collaboration within adequately-resourced, long-term inter-disciplinary research networks |
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LiveM |
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MA @ admin @ |
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4947 |
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