Rethinking Heat Index Tools for Livestock

Gaughan, J. B.; Mader, T. L.; Gebremedhin, K. G.

doi:10.1002/9781119949091.ch14

Cited by 28 publications

(25 citation statements)

References 43 publications

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“…Climatic data from 1999 through 2011, including hourly temperature, humidity, and dew-point observations, were retrieved from the National Climatic Data Center ( http://www.ncdc.noaa.gov/ ). Temperature-humidity index values were calculated using an equation derived for use with dairy cattle [ 10 ].…”

Section: Methodsmentioning

confidence: 99%

Periconceptional Heat Stress of Holstein Dams Is Associated with Differences in Daughter Milk Production and Composition during Multiple Lactations

et al. 2015

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Heat stress at the time of conception affects the subsequent milk production of primiparous Holstein cows; however, it is unknown whether these effects are maintained across multiple lactations. Therefore, the objective of the current study was to examine the relationship between periconceptional heat stress and measurements of milk production and composition in cows retained within a herd for multiple lactations. National Dairy Herd Improvement Association data was obtained from Dairy Records Management Systems. Records included milk production data and milk composition data from over 75,000 and 44,000 Holstein cows, respectively, born between 2000 and 2010 in Florida, Georgia, and Texas. Conception dates were calculated by subtracting 276 d from the recorded birth date. Records for cows conceived within the months of June, July, and August were retained as heat stress conceived (HSC) cows; cows conceived within the months of December, January, and February were retained as thermoneutral conceived (TNC) contemporaries. Adjusted 305-d mature equivalent milk, protein percent and fat percent were evaluated with a mixed model ANOVA using SAS. Milk production was significantly affected by periconceptional heat stress. When a significant difference or tendency for a difference was detected between the HSC and TNC cows, the TNC produced more milk in all but one comparison. The advantage in milk production for the TNC cows over the HSC cows ranged from 82 ± 42 to 399 ± 61 kg per lactation. Alterations in fat and protein percentage were variable and most often detected in first lactations (first > second or third). Overall, the most striking result of this study is the consistency of the relationship between HSC and milk production. The nature of this relationship suggests that heat stress at or around the time of conception impairs cow milk yield throughout her lifetime.

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Section: Methodsmentioning

confidence: 99%

Periconceptional Heat Stress of Holstein Dams Is Associated with Differences in Daughter Milk Production and Composition during Multiple Lactations

et al. 2015

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“…age, level of production). Gaughan et al [20] concluded that the THI may not adequately describe the effect of hot climatic conditions on livestock (and much less on the effects of cold conditions). Furthermore, with the exception of the case of cattle, most THIs have not been specifically designed for their own species (and much less for a specific breed exploited in certain geographic and climatic conditions).…”

Section: Climatic Indexes and Characterisation Of The Effect Of Heat mentioning

confidence: 99%

Characterisation of Goats’ Response to Heat Stress: Tools to Improve Heat Tolerance

Serradilla¹,

Carabaño²,

Ramón³

et al. 2018

Goat Science

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This chapter aims to review present knowledge about the effects of heat stress on goats, summarising what is known about its measurement, its impact on the performances of the animals, mainly milk traits, the physiological and genetic bases of the animals' response and the improvement of resilience through selection. A short historic review of the climatic indexes used to measure heat stress, with special emphasis on the temperature humidity indexes, and the main consequences on milk yield and composition are followed by a description of the results of experiments carried out to study the physiological and metabolic consequences of heat stress. The results of the quantitative analyses of the genetic bases of heat stress using norm of reaction models and of the application of omic techniques, particularly transcriptomic and genomic, to understand the complexity of the genetic background of animal's reaction to thermal stress, constitute the next points. The chapter ends treating the possible ways and difficulties of applying selection to increase resilience to heat stress.

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“…The Temperature Humidity Index (THI) has been widely used to explore these relationships in livestock, and to model expected responses to climatic change (Gaughan and Hahn, 2010). THI has some recognised limitations, including the assumption that all animals respond to thermal stressors in the same way, and a lack of consideration of other important variables (including solar radiation, wind speed, duration of exposure) (Gaughan et al, 2012). Improved indices have been proposed, including THI adjusted for wind speed and solar radiation, a number of respiration rate indices and the heat load index (Gaughan et al, 2012).…”

Section: Modeling Heat Stress In Cattlementioning

confidence: 99%

“…THI has some recognised limitations, including the assumption that all animals respond to thermal stressors in the same way, and a lack of consideration of other important variables (including solar radiation, wind speed, duration of exposure) (Gaughan et al, 2012). Improved indices have been proposed, including THI adjusted for wind speed and solar radiation, a number of respiration rate indices and the heat load index (Gaughan et al, 2012). Whatever the index used, climate change is expected to raise average temperatures and increase the frequency of temperature extremes.…”

Section: Modeling Heat Stress In Cattlementioning

confidence: 99%

Modeling European ruminant production systems: Facing the challenges of climate change

Kipling

Bannink

Bellocchi³

et al. 2016

Agricultural Systems

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This is the author accepted manuscript. The final version is available from Elsevier via http://dx.doi.org/10.1016/j.agsy.2016.05.007Ruminant 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 intensification 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 priority. 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 applied to develop ?smart? empirical modeling, may overcome the trade-off between complexity and usability. Developing 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 related 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.authorsversionPeer reviewe

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Rethinking Heat Index Tools for Livestock

Cited by 28 publications

References 43 publications

Periconceptional Heat Stress of Holstein Dams Is Associated with Differences in Daughter Milk Production and Composition during Multiple Lactations

Periconceptional Heat Stress of Holstein Dams Is Associated with Differences in Daughter Milk Production and Composition during Multiple Lactations

Characterisation of Goats’ Response to Heat Stress: Tools to Improve Heat Tolerance

Modeling European ruminant production systems: Facing the challenges of climate change

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