1,082 research outputs found

    Coherent phonon transport in short-period two-dimensional superlattices of graphene and boron nitride

    Get PDF
    Promoting coherent transport of phonons at material interfaces is a promising strategy for controlling thermal transport in nanostructures and an alternative to traditional methods based on structural defects. Coherent transport is particularly relevant in short-period heterostructures with smooth interfaces and long-wavelength heat-carrying phonons, such as two-dimensional superlattices of graphene and boron nitride. In this work, we predict phonon properties and thermal conductivities in these superlattices using a normal mode decomposition approach. We study the variation of the frequency dependence of these properties with the periodicity and interface configuration (zigzag and armchair) for superlattices with period lengths within the coherent regime. Our results showed that the thermal conductivity decreases significantly from the first period length (0.44 nm) to the second period length (0.87 nm), 13% across the interfaces and 16% along the interfaces. For greater periods, the conductivity across the interfaces continues decreasing at a smaller rate of 11 W/mK per period length increase (0.43 nm), driven by changes in the phonon group velocities (coherent effects). In contrast, the conductivity along the interfaces slightly recovers at a rate of 2 W/mK per period, driven by changes in the phonon relaxation times (diffusive effects). By changing the interface configuration from armchair to zigzag, the conductivities for all period lengths increase by approximately 7% across the interfaces and 19% along the interfaces

    Emergent Collectivity in Nuclei and Enhanced Proton-Neutron Interactions

    Full text link
    Enhanced proton-neutron interactions occur in heavy nuclei along a trajectory of approximately equal numbers of valence protons and neutrons. This is also closely aligned with the trajectory of the saturation of quadrupole deformation. The origin of these enhanced p-n interactions is discussed in terms of spatial overlaps of proton and neutron wave functions that are orbit-dependent. It is suggested for the first time that nuclear collectivity is driven by synchronized filling of protons and neutrons with orbitals having parallel spins, identical orbital and total angular momenta projections, belonging to adjacent major shells and differing by one quantum of excitation along the z-axis. These results may lead to a new approach to symmetry-based theoretical calculations for heavy nuclei.Comment: 6 pages, 4 figure

    Uncertainty in the measurement of indoor temperature and humidity in naturally ventilated dairy buildings as influenced by measurement technique and data variability

    Get PDF
    [EN] The microclimatic conditions in dairy buildings affect animal welfare and gaseous emissions. Measurements are highly variable due to the inhomogeneous distribution of heat and humidity sources (related to farm management) and the turbulent inflow (associated with meteorologic boundary conditions). The selection of the measurement strategy (number and position of the sensors) and the analysis methodology adds to the uncertainty of the applied measurement technique. To assess the suitability of different sensor positions, in situations where monitoring in the direct vicinity of the animals is not possible, we collected long-term data in two naturally ventilated dairy barns in Germany between March 2015 and April 2016 (horizontal and vertical profiles with 10 to 5 min temporal resolution). Uncertainties related to the measurement setup were assessed by comparing the device outputs under lab conditions after the on-farm experiments. We found out that the uncertainty in measurements of relative humidity is of particular importance when assessing heat stress risk and resulting economic losses in terms of temperature-humidity index. Measurements at a height of approximately 3 m-3.5 m turned out to be a good approximation for the microclimatic conditions in the animal occupied zone (including the air volume close to the emission active zone). However, further investigation along this cross-section is required to reduce uncertainties related to the inhomogeneous distribution of humidity. In addition, a regular sound cleaning (and if possible recalibration after few months) of the measurement devices is crucial to reduce the instrumentation uncertainty in long-term monitoring of relative humidity in dairy barns (C) 2017 The Authors. Published by Elsevier Ltd on behalf of IAgrE.The work was financially supported by the German Federal Ministry of Food and Agriculture (BMEL) through the Federal Office for Agriculture and Food (BLE), grant number 2814ERA02C.Hempel, S.; König, M.; Menz, C.; Janke, D.; Amon, B.; Banhazi, T.; Estellés, F.... (2018). Uncertainty in the measurement of indoor temperature and humidity in naturally ventilated dairy buildings as influenced by measurement technique and data variability. Biosystems Engineering. 166:58-75. https://doi.org/10.1016/j.biosystemseng.2017.11.004S587516

    Evaluating three-pillar sustainability modelling approaches for dairy cattle production systems

    Get PDF
    Milk production in Europe is facing major challenges to ensure its economic, environmental, and social sustainability. It is essential that holistic concepts are developed to ensure the future sustainability of the sector and to assist farmers and stakeholders in making knowledge-based decisions. In this study, integrated sustainability assessment by means of whole-farm modelling is presented as a valuable approach for identifying factors and mechanisms that could be used to improve the three pillars (3Ps) of sustainability in the context of an increasing awareness of economic profitability, social well-being, and environmental impacts of dairy production systems (DPS). This work aims (i) to create an evaluation framework that enables quantitative analysis of the level of integration of 3P sustainability indicators in whole-farm models and (ii) to test this method. Therefore, an evaluation framework consisting of 35 indicators distributed across the 3Ps of sustainability was used to evaluate three whole-farm models. Overall, the models integrated at least 40% of the proposed indicators. Different results were obtained for each sustainability pillar by each evaluated model. Higher scores were obtained for the environmental pillar, followed by the economic and the social pillars. In conclusion, this evaluation framework was found to be an effective tool that allows potential users to choose among whole-farm models depending on their needs. Pathways for further model development that may be used to integrate the 3P sustainability assessment of DPS in a more complete and detailed way were identified. © 2021 by the authors.This study was financially supported by the German Federal Ministry of Food and Agriculture (BMEL) through the Federal Office for Agriculture and Food (BLE) under grant number 2819ERA08A (MilKey project, funded under the Joint Call 2018 ERA-GAS, SusAn and ICT-AGRI 2 on ?Novel technologies, solutions and systems to reduce the greenhouse gas emissions in animal production systems?). BC3-Research is supported by the Spanish Government through Mar?a de Maeztu excellence accreditation 2018-2022 (Ref. MDM-2017-0714) and by the Basque Government through the BERC 2018-2021 program. Agustin del Prado is financed through the Ramon y Cajal program by the Spanish Ministry of Economy, Industry, and Competitiveness (RYC-2017-22143)

    Determinants of Early Initiation of Breastfeeding in Rural Tanzania.

    Get PDF
    Breastfeeding is widely known for its imperative contribution in improving maternal and newborn health outcomes. However, evidence regarding timing of initiation of breastfeeding is limited in Tanzania. This study examines the extent of and factors associated with early initiation of breastfeeding in three rural districts of Tanzania. Data were collected in 2011 in a cross-sectional survey of random households in Rufiji, Kilombero and Ulanga districts of Tanzania. From the survey, 889 women who had given birth within 2 years preceding the survey were analyzed. Both descriptive and inferential statistical analyses were conducted. Associations between the outcome variable and each of the independent variables were tested using chi-square. Logistic regression was used for multivariate analysis. Early initiation of breastfeeding (i.e. breastfeeding initiation within 1 h of birth) stood at 51 %. The odds of early initiation of breastfeeding was significantly 78 % lower following childbirth by caesarean section than vaginal birth (adjusted odds ratio (OR) = 0.22; 95 % confidence interval (CI) 0.14, 0.36). However, this was almost twice as high for women who gave birth in health facilities as for those who gave birth at home (OR = 1.75; 95 % CI 1.25, 2.45). Furthermore, maternal knowledge of newborn danger signs was negatively associated with early initiation of breastfeeding (moderate vs. high: OR = 1.73; 95 % CI 1.23, 2.42; low vs. high: OR = 2.06; 95 % CI 1.43, 2.96). The study found also that early initiation of breastfeeding was less likely in Rufiji compared to Kilombero (OR = 0.52; 95 % CI 0.31, 0.89), as well as among ever married than currently married women (OR = 0.46; 95 % CI 0.25, 0.87). To enhance early initiation of breastfeeding, using health facilities for childbirth must be emphasized and facilitated among women in rural Tanzania. Further, interventions to promote and enforce early initiation of breastfeeding should be devised especially for caesarean births. Women residing in rural locations and women who are not currently married should be specifically targeted with interventions aimed at enhancing early initiation of breastfeeding to ensure healthy outcomes for newborns

    Sociodemographic Drivers of Multiple Sexual Partnerships among Women in three Rural Districts of Tanzania

    Get PDF
    This study examines prevalence and correlates of multiple sexual partnerships (MSP) among women aged 15+ years in Rufiji, Kilombero, and Ulanga districts of Tanzania. Data were collected in a cross-sectional household survey in Rufiji, Kilombero, and Ulanga districts in Tanzania in 2011. From the survey, a total of 2,643 sexually active women ages 15+ years were selected for this analysis. While the chi-square test was used for testing association between MSP and each of the independent variables, logistic regression was used for multivariate analysis. Number of sexual partners reported ranged from 1 to 7, with 7.8% of the women reporting multiple sexual partners (2+) in the past year. MSP was more likely among both ever married women (adjusted odds ratio [AOR] =3.83, 95% confidence interval [CI] 1.40–10.49) and single women (AOR =6.13, 95% CI 2.45–15.34) than currently married women. There was an interaction between marital status and education, whereby MSP was 85% less likely among single women with secondary or higher education compared to married women with no education (AOR =0.15, 95% CI 0.03–0.61). Furthermore, women aged 40+ years were 56% less likely compared to the youngest women (,20 years) to report MSP (AOR =0.44, 95% CI 0.24–0.80). The odds of MSP among Muslim women was 1.56 times as high as that for Christians women (AOR =1.56, 95% CI 1.11–2.21). Ndengereko women were 67% less likely to report MSP compared to Pogoro women (AOR =0.33, 95% CI 0.18–0.59). Eight percent of the women aged 15+ in Rufiji, Kilombero, and Ulanga districts of Tanzania are engaged in MSP. Encouraging achievement of formal education, especially at secondary level or beyond, may be a viable strategy toward partner reduction among unmarried women. Age, religion, and ethnicity are also important dimensions for partner reduction efforts

    Neutrino Oscillations in a Supersymmetric SO(10) Model with Type-III See-Saw Mechanism

    Full text link
    The neutrino oscillations are studied in the framework of the minimal supersymmetric SO(10) model with Type-III see-saw mechanism by additionally introducing a number of SO(10) singlet neutrinos. The light Majorana neutrino mass matrix is given by a combination of those of the singlet neutrinos and the SU(2)LSU(2)_L active neutrinos. The minimal SO(10) model gives an unambiguous Dirac neutrino mass matrix, which enables us to predict the masses and the other parameters for the singlet neutrinos. These predicted masses take the values accessible and testable by near future collider experiments under the reasonable assumptions. More comprehensive calculations on these parameters are also given.Comment: 14 pages, 5 figures; the version to appear in JHE

    Statistiques de pêche en lagune Ebrié (Côte d'Ivoire): 1976 et 1977

    Get PDF
    In the Ebrié lagoon fishes are mostly caught by means of 6 kinds of fishing gear: small and large mesh gillnets (respectively 1.5-2 and 6.5-8 inches stretched mesh), cast-nets and multi-hooked lines for individual fishermen, and beach seines and ring-nets for collective fishing. Statistical data gathered during 1977 allowed an estimation of total catches: about 6700 tons. The bulk of the catch, 4800 tons, came from beach nets and ring nets, the contribution of which is nearly the same. Individual fishing gear, small mesh gillnets repesenting the main part, account for 25 to 30% of total catch; about 1900 tons for year 1977. Six species, or species groups, comprise more than 85% of the catch. In the Abidjan area, where marine influence is the more noticeable, ring nets are more numerous and their catches increased since 1975. On the other hand, fish captures in unsalted and brackish waters seem to show a stagnancy and a decrease in fish lengths; this phenomenon is probably in connection with beach-seine excessive fishing effort and/or their small meshes (one inch stretched)

    Heat stress risk in European dairy cattle husbandry under different climate change scenarios - uncertainties and potential impacts

    Get PDF
    [EN] In the last decades, a global warming trend was observed. Along with the temperature increase, modifications in the humidity and wind regime amplify the regional and local impacts on livestock husbandry. Direct impacts include the occurrence of climatic stress conditions. In Europe, cows are economically highly relevant and are mainly kept in naturally ventilated buildings that are most susceptible to climate change. The high-yielding cows are particularly vulnerable to heat stress. Modifications in housing management are the main measures taken to improve the ability of livestock to cope with these conditions. Measures are typically taken in direct reaction to uncomfortable conditions instead of in anticipation of a long-term risk for climatic stress. Measures that balance welfare, environmental and economic issues are barely investigated in the context of climate change and are thus almost not available for commercial farms. Quantitative analysis of the climate change impacts on animal welfare and linked economic and environmental factors is rare. Therefore, we used a numerical modeling approach to estimate the future heat stress risk in such dairy cattle husbandry systems. The indoor climate was monitored inside three reference barns in central Europe and the Mediterranean regions. An artificial neuronal network (ANN) was trained to relate the outdoor weather conditions provided by official meteorological weather stations to the measured indoor microclimate. Subsequently, this ANN model was driven by an ensemble of regional climate model projections with three different greenhouse gas concentration scenarios. For the evaluation of the heat stress risk, we considered the number and duration of heat stress events. Based on the changes in the heat stress events, various economic and environmental impacts were estimated. The impacts of the projected increase in heat stress risk varied among the barns due to different locations and designs as well as the anticipated climate change (considering different climate models and future greenhouse gas concentrations). There was an overall increasing trend in number and duration of heat stress events. At the end of the century, the number of annual stress events can be expected to increase by up to 2000, while the average duration of the events increases by up to 22 h compared to the end of the last century. This implies strong impacts on economics, environment and animal welfare and an urgent need for mid-term adaptation strategies. We anticipated that up to one-tenth of all hours of a year, correspondingly one-third of all days, will be classified as critical heat stress conditions. Due to heat stress, milk yield may decrease by about 2.8 % relative to the present European milk yield, and farmers may expect financial losses in the summer season of about 5.4 % of their monthly income. In addition, an increasing demand for emission reduction measures must be expected, as an emission increase of about 16 Gg of ammonia and 0.1 Gg of methane per year can be expected under the anticipated heat stress conditions. The cattle respiration rate increases by up to 60 %, and the standing time may be prolonged by 1 h. This causes health issues and increases the probability of medical treatments. The various impacts imply feedback loops in the climate system which are presently underexplored. Hence, future in-depth studies on the different impacts and adaptation options at different stress levels are highly recommended.This research has been supported by the German Federal Ministry of Food and Agriculture (BMEL) through the Federal Office for Agriculture and Food (BLE) (grant nos. 2814ERA02C and 2814ERA03C), the Instituto Nacional de Investigacion Tecnologia Agraria y Alimentaria (INIA) (grant no. 618105), the Basque Government (grant no. BERC 2018-2021), the Spanish Ministry of Economy, Industry and Competitiveness MINECO (grant nos. MDM-2017-0714, FJCI-2016-30263, and RYC-2017-22143), and the Innovation Foundation Denmark (grant no. 4215-00004B).Hempel, S.; Menz, C.; Pinto, S.; Galán, E.; Janke, D.; Estellés, F.; Müschner-Siemens, T.... (2019). Heat stress risk in European dairy cattle husbandry under different climate change scenarios - uncertainties and potential impacts. Earth System Dynamics. 10(4):859-884. https://doi.org/10.5194/esd-10-859-2019S859884104Acatincăi, S., Gavojdian, D., Stanciu, G., Cziszter, L. T., Tripon, I., and Baul, S.: Study Regarding Rumination Behavior in Cattle–Position Adopted by Cows During Rumination Process, Scientific Papers Animal Science and Biotechnologies, 43, 199–202, 2010. aAllen, J., Anderson, S., Collier, R., and Smith, J.: Managing heat stress and its impact on cow behavior, in: 28th Annual Southwest Nutrition and Management Conference, 6–8 March 2013, Reno, Nevada, USA, 2013. aAllen, J., Hall, L., Collier, R., and Smith, J.: 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, 118–127, 2015. a, bAmon, B., Kryvoruchko, V., Fröhlich, M., Amon, T., Pöllinger, A., Mösenbacher, I., and Hausleitner, A.: Ammonia and greenhouse gas emissions from a straw flow system for fattening pigs: Housing and manure storage, Livest. Sci., 112, 199–207, 2007. aAnderson, S., Bradford, B., Harner, J., Tucker, C., Choi, C., Allen, J., Hall, L., Rungruang, S., Collier, R., and Smith, J.: Effects of adjustable and stationary fans with misters on core body temperature and lying behavior of lactating dairy cows in a semiarid climate, J. Dairy Sci., 96, 4738–4750, 2013. a, bAngrecka, S. and Herbut, P.: Conditions for cold stress development in dairy cattle kept in free stall barn during severe frosts, Czech J. Anim. Sci., 60, 81–87, https://doi.org/10.17221/7978-CJAS, 2015. aBailey, K., Jones, C., and Heinrichs, A.: Economic returns to Holstein and Jersey herds under multiple component pricing, J. Dairy Sci., 88, 2269–2280, 2005. aBerman, A.: Estimates of heat stress relief needs for Holstein dairy cows 1, J. Anim. Sci., 83, 1377–1384, 2005. aBerman, A., Folman, Y., Kaim, M., Mamen, M., Herz, Z., Wolfenson, D., Arieli, A., and Graber, Y.: Upper critical temperatures and forced ventilation effects for high-yielding dairy cows in a subtropical climate, J. Dairy Sci., 68, 1488–1495, 1985. aBernabucci, U., Biffani, S., Buggiotti, L., Vitali, A., Lacetera, N., and Nardone, A.: The effects of heat stress in Italian Holstein dairy cattle, J. Dairy Sci., 97, 471–486, 2014. a, bBianca, W.: Relative importance of dry- and wet-bulb temperatures in causing heat stress in cattle, Nature, 195, 251–252, 1962. aBohmanova, J., Misztal, I., and Cole, J.: Temperature-humidity indices as indicators of milk production losses due to heat stress, J. Dairy Sci., 90, 1947–1956, 2007. a, b, c, dBouraoui, R., Lahmar, M., Majdoub, A., Djemali, M., and Belyea, R.: The relationship of temperature-humidity index with milk production of dairy cows in a Mediterranean climate, Anim. Res., 51, 479–491, 2002. a, bBroucek, J.: Production of methane emissions from ruminant husbandry: a review, J. Environ. Prot., 5, 1482–1493, https://doi.org/10.4236/jep.2014.515141, 2014. aBrouček, J., Letkovičová, M., and Kovalčuj, K.: Estimation of cold stress effect on dairy cows, Int. J. Biometeorol., 35, 29–32, 1991. aBroucek, J., Ryba, S., Mihina, S., Uhrincat, M., and Kisac, P.: Impact of thermal-humidity index on milk yield under conditions of different dairy management, J. Anim. Feed Sci., 16, 329–344, https://doi.org/10.22358/jafs/66755/2007, 2007. a, bBrown-Brandl, T., Eigenberg, R., Nienaber, J., and Hahn, G. L.: Dynamic response indicators of heat stress in shaded and non-shaded feedlot cattle, Part 1: Analyses of indicators, Biosyst. Eng., 90, 451–462, 2005. aBrügemann, K., Gernand, E., König von Borstel, U., and König, S.: Defining and evaluating heat stress thresholds in different dairy cow production systems, Arch. Anim. Breed., 55, 13–24, 2012. aCannon, A. J.: Multivariate quantile mapping bias correction: an N-dimensional probability density function transform for climate model simulations of multiple variables, Clim. Dynam., 50, 31–49, https://doi.org/10.1007/s00382-017-3580-6, 2018. aCarabano, M.-J., Logar, B., Bormann, J., Minet, J., Vanrobays, M.-L., Diaz, C., Tychon, B., Gengler, N., and Hammami, H.: Modeling heat stress under different environmental conditions, J. Dairy Sci., 99, 3798–3814, 2016. a, b, cChristensen, J., Hewitson, B., Busuioc, A., Chen, A., Gao, X., Held, I., Jones, R., Kolli, R., Kwon, W.-T., Laprise, R., Magaña Rueda, V., Mearns, L., Menéndez, C., Räisänen, J., Rinke, A., Sarr, A., and Whetton, P.: Regional Climate Projections, in: IPCC Climate Change 2007: The Physical Science Basis, edited by: Solomon, S., Qin, D., Manning, M., Hen, Z., Marquis, M., Averyt, K., Tignor, M., and Miller, H., Cambridge University Press, Cambridge, UK and New York, NY, USA, 2007. aCollier, R. J., Hall, L. W., Rungruang, S., and Zimbleman, R. B.: Quantifying heat stress and its impact on metabolism and performance, Proc. Florida Ruminant Nutrition Symp, Department of Animal Sciences, University of Arizona, Gainesville, USA, p. 68, 2012. a, b, cCook, N., Mentink, R., Bennett, T., and Burgi, K.: The effect of heat stress and lameness on time budgets of lactating dairy cows, J. Dairy Sci., 90, 1674–1682, 2007. a, bCurtis, A., Scharf, B., Eichen, P., and Spiers, D.: Relationships between ambient conditions, thermal status, and feed intake of cattle during summer heat stress with access to shade, J. Therm. Biol., 63, 104–111, 2017. ada Costa, A. N. L., Feitosa, J. V., Montezuma, P. A., de Souza, P. T., and de Araújo, A. A.: Rectal temperatures, respiratory rates, production, and reproduction performances of crossbred Girolando cows under heat stress in northeastern Brazil, Int. J. Biometeorol., 59, 1647–1653, 2015. a, bDa Silva, R. G., Maia, A. S. C., and de Macedo Costa, L. L.: Index of thermal stress for cows (ITSC) under high solar radiation in tropical environments, Int. J. Biometeorol., 59, 551–559, 2015. aDavison, T., Jonsson, N., Mayer, D., Gaughan, J., Ehrlich, W., and McGowan, M.: Comparison of the impact of six heat-load management strategies on thermal responses and milk production of feed-pad and pasture fed dairy cows in a subtropical environment, Int. J. Biometeorol., 60, 1961–1968, 2016. aDel Prado A., Scholefield D., Chadwick D., Misselbrook T., Haygarth P., Hopkins A., Dewhurst R., Jones R., Moorby J., Davison P., Lord E., Turner M., Aikman P., and Schröder J.: A modelling framework to identify new integrated dairy production systems, in: 21st General Meeting of the European Grassland Federation (EGF), 3–6 April 2006, Badajoz, Spain, 766–768, 2006. aDe Rensis, F. and Scaramuzzi, R. J.: Heat stress and seasonal effects on reproduction in the dairy cow – a review, Theriogenology, 60, 1139–1151, 2003. aDe Rensis, F., Garcia-Ispierto, I., and López-Gatius, F.: Seasonal heat stress: Clinical implications and hormone treatments for the fertility of dairy cows, Theriogenology, 84, 659–666, 2015. aDiepen, C. v., Wolf, J., Keulen, H. V., and Rappoldt, C.: WOFOST: a simulation model of crop production, Soil Use Manage., 5, 16–24, 1989. aDikmen, S. and Hansen, P.: Is the temperature-humidity index the best indicator of heat stress in lactating dairy cows in a subtropical environment?, J. Dairy Sci., 92, 109–116, 2009. a, bDirksen, G., Gründer, H., Grunert, E., Krause, D., and Stöber, M.: Clinical examination of cattle, 3rd edn., Verlag Paul Parey, Berlin, Germany, 1990. aDosio, A.: Projections of climate change indices of temperature and precipitation from an ensemble of bias-adjusted high-resolution EURO-CORDEX regional climate models, J. Geophys. Res.-Atmos., 121, 5488–5511, 2016. aEfron, B.: Bootstrap Methods: Another Look at the Jackknife, Ann. Stat., 7, 1–26, https://doi.org/10.1214/aos/1176344552, 1979. aEfron, B. and Tibshirani, R.: Bootstrap Methods for Standard Errors, Confidence Intervals, and Other Measures of Statistical Accuracy, Stat. Sci., 1, 54–75, https://doi.org/10.1214/ss/1177013815, 1986. aEuropean Commission – EU FADN: EU Dairy Farms Report Based on 2016 FADN Data, avaialble at: https://ec.europa.eu/agriculture/fadn_en (last access: 11 April 2019), 2018. aFiedler, A., Fischer, J., Hempel, S., Saha, C., Loebsin, C., Berg, W., Amon, B., Brunsch, R., and Amon, T.: Flow fields within a dairy barn – Measurements, physical modelling and numerical simulation, in: Proceedings of the International Conference of Agricultural Engineering AgEng, 6–10 July 2014, Zürich, Switzerland, 1–5, 2014. a, bFood and Agriculture Organization of the United Nations (FAO): The Impact of Disasters on Agriculture – Assessing the information gap, available at: http://www.fao.org/3/a-i7279e.pdf (last access: 10 September 2018), 2017. aFord, B.: An Overview of Hot-Deck Procedures, in: Incomplete Data in Sample Surveys: Theory and Bibliographies, edited by: Madow, W., Olkin, I., and Rubin, D., Academic Press, New York, USA, 1983. aFournel, S., Ouellet, V., and Charbonneau, É.: Practices for alleviating heat stress of dairy cows in humid continental climates: a literature review, Animals, 7, 37, https://doi.org/10.3390/ani7050037, 2017. aGalán, E., Llonch, P., Villagrá, A., Levit, H., Pinto, S., and del Prado, A.: A systematic review of non-productivity-related animal-based indicators of heat stress resilience in dairy cattle, PloS one, 13, e0206520, https://doi.org/10.1371/journal.pone.0206520, 2018. a, b, c, dGaughan, J., Mader, T. L., Holt, S., and Lisle, A.: A new heat load index for feedlot cattle, J. Anim. Sci., 86, 226–234, 2008. aGebremedhin, K. and Wu, B.: Simulation of flow field of a ventilated and occupied animal space with different inlet and outlet conditions, J. Therm. Biol., 30, 343–353, 2005. aGiorgi, F. and Gutowski Jr., W. J.: Regional dynamical downscaling and the CORDEX initiative, Annu. Rev. Env. Resour., 40, 467–490, 2015. aGroenestein, C., Hutchings, N., Haenel, H., Amon, B., Menzi, H., Mikkelsen, M., Misselbrook, T., van Bruggen, C., Kupper, T., and Webb, J.: Comparison of ammonia emissions related to nitrogen use efficiency of livestock production in Europe, J. Clean. Prod., 211, 1162–1170, 2019. aGurney, K.: An Introduction to Neural Networks, UCL Press Limited an imprint of Taylor & Francis group, London, UK, 1997. aHahn, G.: Dynamic responses of cattle to thermal heat loads, J. Anim. Sci., 77, 10–20, 1999. aHammami, H., Carabaño, M.-J., Logar, B., Vanrobays, M.-L., and Gengler, N.: Genotype x Climate interactions for protein yield using four European Holstein Populations, in: Proceedings of 10th World Congress of Genetics Applied to Livestock Production, 17–22 August 2014, Vancouver, Canada, 2014. aHeaton, J.: Artificial Intelligence for Humans, Volume 3: Deep Learning and Neural Networks, Artificial Intelligence for Humans Series, CreateSpace Independent Publishing Platform, Heaton Research, Inc., Chesterfield, USA, 2015. aHeinicke, J., Hoffmann, G., Ammon, C., Amon, B., and Amon, T.: Effects of the daily heat load duration exceeding determined heat load thresholds on activity traits of lactating dairy cows, J. Therm. Biol., 77, 67–74, 2018. a, b, c, dHeinicke, J., Ibscher, S., Belik, V., and Amon, T.: Cow individual activity response to the accumulation of heat load duration, J. Therm. Biol., 82, 23–32, https://doi.org/10.1016/j.jtherbio.2019.03.011, 2019. aHempel, S. and Menz, C.: Indoor climate projections for European cattle barns, Mendeley Data, v1, https://doi.org/10.17632/tjp8h523p7.1, 2019. aHempel, S., Frieler, K., Warszawski, L., Schewe, J., and Piontek, F.: A trend-preserving bias correction – the ISI-MIP approach, Earth Syst. Dynam., 4, 219–236, https://doi.org/10.5194/esd-4-219-2013, 2013. aHempel, S., Wiedemann, L amd Ammon, C., Fiedler, A., Saha, C.and Janke, D. L. C., Fischer, J., Amon, B., Hoffmann, G., Menz, C., Zhang, G., Halachmi, I., Del Prado, A., Estelles, F., Berg, W., Brunsch, R., and Amon, T.: Determine the flow characteristics of naturally ventilated dairy barns to optimize barn climate, in: 12. Tagung: Bau, Technik und Umwelt 2015 in der landwirtschaftlichen Nutztierhaltung, 8–10 September, 2015, KTBL, Darmstadt, Germany, 346–351, 2015a. aHempel, S., Wiedemann, L., Ammon, C., Fiedler, M., Saha, C., Loebsin, C., Fischer, J., Berg, W., Brunsch, R., and Amon, T.: Assessment of the through-flow patterns in naturally ventilated dairy barns – Three methods, one complex approach, in: RAMIRAN 2015 – Rural-Urban Symbiosis, edited by: Körner, I., TC-O_16, TUTech Verlag, Hamburg, Germany, Hamburg University of Technology, Germany, 356–359, e-book, 2015b. aHempel, S., Janke, D., König, M., Menz, C., Englisch, A., Pinto, S., Sibony, V., Halachmi, I., Rong, L., Zong, C., Zhang, G., Sanchis, E., Estelle, F., Calvet, S., Galan, E., del Prado, A., Ammon, C., Amon, B., and Amon, T.: Integrated modelling to assess optimisation potentials for cattle housing climate, Advances in Animal Biosciences, 7, 261–262, https://doi.org/10.1017/S2040470016000352, 2016a. a, bHempel, S., Saha, C. K., Fiedler, M., Berg, W., Hansen, C., Amon, B., and Amon, T.: Non-linear temperature dependency of ammonia and methane emissions from a naturally ventilated dairy barn, Biosyst. Eng., 145, 10–21, 2016b. a, b, c, dHempel, S., Menz, C., Halachmi, I., Zhang, G., del Prado, A., Estelles, F., Amon, B., and Amon, T.: Report on FACCE-JPI valorisation meeting, available at: https://www.faccejpi.com/content/download/5161/48933/version/1/file/FACCE-JPI_Synthesis-valorisation-survey-results-FINAL.pdf (last access: 11 April 2019), 2017a. aHempel, S., Menz, C., Halachmi, I., Zhang, G., del Prado, A., Estelles, F., Amon, B., and Amon, T.: Report on ERANET+ mid-term meeting, available at: https://www.faccejpi.com/content/download/5163/48955/version/2/file/Projects+booklet_updated+08+May+2017.pdf (last access: 11 April 2019), 2017b. aHempel, S., Menz, C., Halachmi, I., Zhang, G., del Prado, A., Estelles, F., Amon, B., and Amon, T.: Report on ERANET+ mid-term meeting, available at: https://www.faccejpi.com/content/download/5295/50720/version/1/file/OptiBarn_presentation_ERA_NET+final+meeting+March18[1].pdf (last access: 11 April 2019), 2017c. aHempel, S., König, M., Menz, C., Janke, D., Amon, B., Banhazi, T. M., Estellés, F., and Amon, T.: Uncertainty in the measurement of indoor temperature and humidity in naturally ventilated dairy buildings as influenced by measurement technique and data variability, Biosyst. Eng., 166, 58–75, 2018. a, b, c, dHerbut, P. and Angrecka, S.: Relationship between THI level and dairy cows’ behaviour during summer period, Ital. J. Anim. Sci., 17, 226–233, 2018. aHerbut, P., Angrecka, S., Nawalany, G., and Adamczyk, K.: Spatial and temporal distribution of temperature, relative humidity and air velocity in a parallel milking parlour during summer period, Ann. Anim. Sci., 15, 517–526, 2015. aHoffmann, I.: Climate change and the characterization, breeding and conservation of animal genetic resources, Anim. Genet., 41, 32–46, 2010. aHonig, H., Miron, J., Lehrer, H., Jackoby, S., Zachut, M., Zinou, A., Portnick, Y., and Moallem, U.: 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, 3736–3742, 2012. a, b, c, dHübener, H., Bülow, K., Fooken, C., Früh, B., Hoffmann, P., Höpp, S., Keuler, K., Menz, C., Mohr, V., Radtke, K., Ramthun, H., Spekat, A., Steger, C., Toussaint, F., Warrach-Sagi, K., and Woldt, M.: ReKliEs-De Ergebnisbericht, Tech. rep., World Data Center for Climate (WDCC) at DKRZ, Hamburg, Germany, https://doi.org/10.2312/WDCC/ReKliEsDe_Ergebnisbericht, 2017. aHutchings, N., Sommer, S. G., and Jarvis, S.: A model of ammonia volatilization from a grazing livestock farm, Atmos. Environ., 30, 589–599, 1996. aJackson, P. and Cockcroft, P.: Clinical examination of farm animals, Wiley-Backwell, Hoboken, USA, 2008. aKadzere, C., Murphy, M., Silanikove, N., and Maltz, E.: Heat stress in lactating dairy cows: a review, Livest. Sci., 77, 59–91, 2002. a, b, c, d, eKafle, G. K., Joo, H., and Ndegwa, P. M.: Sampling Duration and Frequency for Determining Emission Rates from Naturally Ventilated Dairy Barns, T. ASABE, 61, 681–691, https://doi.org/10.13031/trans.12543, 2018. aKendall, P., Nielsen, P., Webster, J., Verkerk, G., Littlejohn, R., and Matthews, L.: The effects of providing shade to lactating dairy cows in a temperate climate, Livest. Sci., 103, 148–157, 2006. aKjellström, E., Nikulin, G., Strandberg, G., Christensen, O. B., Jacob, D., Keuler, K., Lenderink, G., van Meijgaard, E., Schär, C., Somot, S., Sørland, S. L., Teichmann, C., and Vautard, R.: European climate change at global mean temperature increases of 1.5 and 2 °C above pre-industrial conditions as simulated by the EURO-CORDEX regional climate models, Earth Syst. Dynam., 9, 459–478, https://doi.org/10.5194/esd-9-459-2018, 2018. a, bKurukulasuriya, P. and Rosenthal, S.: Climate change and agriculture: A review of impacts and adaptations, Environment department papers, no. 91, Climate change series, World Bank, Washington, D.C., USA, 2013. aLees, J., Lees, A., and Gaughan, J.: Developing a heat load index for lactating dairy cows, Anim. Prod. Sci., 58, 1387–1391, https://doi.org/10.1071/AN17776, 2018. a, bLelieveld, J., Evans, J. S., Fnais, M., Giannadaki, D., and Pozzer, A.: The contribution of outdoor air pollution sources to premature mortality on a global scale, Nature, 525, 367–371, 2015. aMader, T. L., Davis, M., and Brown-Brandl, T.: Environmental factors influencing heat stress in feedlot cattle, J. Anim. Sci., 84, 712–719, 2006. a, b, cMader, T. L., Johnson, L., and Gaughan, J.: A
    corecore