Relevant bibliographies by topics / Dairy

Academic literature on the topic 'Dairy'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 June 2024

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Journal articles on the topic "Dairy"

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Cheng, Lijun, C. Jing, T. H. Duan, and F. Z. Li. "ResNet-based dairy daily behavior recognition." EAI Endorsed Transactions on Internet of Things 9, no. 2 (July 31, 2023): e5. http://dx.doi.org/10.4108/eetiot.v9i2.2901.

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With the rapid development of China ’s dairy farming industry, it becomes difficult to breed and manage the increasing number of dairy cows. The smart agricultural enabled by edging techniques such as smart sensor, IoT, machine learning, etc. shows great potential to improve the scientific breeding and management of dairy cows. Using machine learning assisted computer vision to identify and classify the behavior of dairy cows can quickly determine the health status of dairy cows and improve management efficiency. However, there are still some challenges need to be addressed in the current behavior recognition of dairy cows. Due to the more complex background of dairy farms, the increase in the number of cows makes the mutual shading problem of dairy cows serious, which leads to the low efficiency of dairy cow behavior recognition. To address this challenge, this paper collected and labeled four types of 1,660 dairy daily behavior datasets and proposed a residual neural network (ResNet)-based dairy daily behavior recognition model. Experiments show that the proposed method is far superior to the baseline method in accuracy performance, and it provides inspiration for the behavior recognition of cows.
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Shivley, C. B., N. J. Urie, and J. E. Lombard. "1227 Factors associated with average daily gain in dairy heifer calves on U.S. dairy operations." Journal of Animal Science 94, suppl_5 (October 1, 2016): 590. http://dx.doi.org/10.2527/jam2016-1227.

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Schönleben, Manfred, Joachim Mentschel, and Luboš Střelec. "Towards smart dairy nutrition: Improving sustainability and economics of dairy production." Czech Journal of Animal Science 65, No. 5 (May 31, 2020): 153–61. http://dx.doi.org/10.17221/16/2020-cjas.

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To satisfy increasing food and nutrient supply requirements for our growing future human population, farmers and staple food producers carry vital responsibilities. Especially farmers with ruminant livestock like dairy cows transform otherwise for human consumption unsuitable fibre into highly nutritious milk and meat. Nevertheless, dairy farmers are challenged increasingly by the consequences of global warming. Economic risks like feed supply and volatile commodity prices need to be balanced, also taking into account the increasing environmental awareness of end-customers. Focusing just on emissions, dairy production is contributing an essential part of the total carbon footprint emitted by the agricultural sector. Since rumen degradability of feed was identified by the Food and Agriculture Organization of the United Nations as one of the most influential parameters in reducing the carbon footprint of dairy farming, the desire to exploit leverage potential for efficiency increases can be considered exceptionally high. Although the positive effects of improved feed, in other words, neutral detergent fibre rumen degradability for dairy farming are well understood, detailed information on the correct management to obtain well digestible feed sources is still missing. Using the smart dairy nutrition ration formulation concept, applying readily on-farm available digitized data and management information the objectives of this study were: 1) to assess the influential parameters which govern neutral detergent fibre rumen degradability of corn silage, using a set of 584 corn silages from multiple years, and 2) to evaluate within an integrated dairy production set up the economic and ecological improvement potential by feeding a subset of 28 different corn silages, including detailed variety information. Results show that the neutral detergent fibre rumen degradability is primarily governed by variety choice and can be four times more important than the correct harvest stage decision. By feeding corn silage varieties with high neutral detergent fibre rumen degradability, monetary income could be increased by ~10% while simultaneously reducing manure accumulation.
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Horáčková, Šárka, Blanka Vrchotová, Daniel Koval, Akkenzhe Omarova, Marcela Sluková, and Jiří Štětina. "Use of Lactiplantibacillus plantarum for dairy and non-dairy fermented products." Czech Journal of Food Sciences 40, No. 5 (October 26, 2022): 392–99. http://dx.doi.org/10.17221/132/2022-cjfs.

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In this study, two strains of Lactiplantibacillus plantarum 299v and CCDM 181 were tested for their ability to grow in milk and soy beverage, for stability during cold storage of fermented beverages, compatibility with yoghurt culture and activity against yeasts. Both strains grew better in soy drink compared to milk. During co-culturing with the yoghurt culture, sufficient acidification of milk and soy beverage necessary for the production of fermented products was achieved. The stability of tested strains in media at pH 4.5 for 28 days at 5 °C was good. L. plantarum was effective in the inhibition of undesirable yeast growth, but the ability was strain-specific. Tested strains demonstrated also a strain-specific ability to suppress the growth of yoghurt culture bacteria. For a possible application of co-culturing L. plantarum with the yoghurt culture, verification of the mutual compatibility of specific strains is necessary.
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Thomas, C. V., M. A. DeLorenzo, D. K. Beede, and T. H. Spreen. "Predicting Daily Feed Costs for Dairy Management Models." Journal of Dairy Science 75, no. 11 (November 1992): 3109–21. http://dx.doi.org/10.3168/jds.s0022-0302(92)78074-6.

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Bellingtier, J. A., and S. D. Neupert. "COMPARING DAILY DAIRY METHODOLOGIES: MTURK VS. TRADITIONAL APPROACHES." Innovation in Aging 1, suppl_1 (June 30, 2017): 234. http://dx.doi.org/10.1093/geroni/igx004.870.

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Dittrich, I., M. Gertz, and J. Krieter. "Alterations in sick dairy cows’ daily behavioural patterns." Heliyon 5, no. 11 (November 2019): e02902. http://dx.doi.org/10.1016/j.heliyon.2019.e02902.

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Pimentel, Luana S., Jeremias Moraes, Aderval S. Luna, Diego B. Barros, Tatiana C. Pimentel, Jonas T. Guimarães, Hugo L. A. Silva, et al. "Brazilian infant dairy foods: mineral content and daily intake contribution." British Food Journal 120, no. 10 (October 1, 2018): 2454–65. http://dx.doi.org/10.1108/bfj-11-2017-0649.

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Purpose The purpose of this paper is to investigate the mineral content of seven Brazilian infant dairy product categories (petit Suisse cheeses, fermented milks, yogurts, fermented dairy beverage, dairy dessert, Requeijão cremoso spreadable cheese and UHT dairy beverages) and estimate their contribution to daily intake. Design/methodology/approach The composition of major (Ca, K, Mg, and Na) and trace (Pb, Cd, Cu and Mn) minerals was determined using Inductively Coupled Plasma Optical Emission Spectrometry. Furthermore, a comparative analysis of the mineral levels with the Reference Daily Intake (RDI) at different child development stages was carried out. Findings High Ca levels were observed in petit Suisse (3.44±1.66 mg g−1), dairy dessert (3.88±0.02 mg g−1) and Requeijão cremoso (4.14±0.07 mg g−1). Dairy dessert presented the highest K level (2.57±0.07 mg g−1), while the Requeijão cremoso presented the highest Na content (4.78±0.10 mg g−1), and both products had the highest Mg contents (238.55±16.27 and 197.39±5.18 µg g−1, respectively). Trace elements (Cd, Cu, Mn and Pb) were below the limit of detection for all commercial dairy foods. Among food products analyzed, petit Suisse cheese and dairy dessert can be considered good sources of calcium, while Requeijão cremoso is high in both calcium and sodium. Originality/value This study is the first to analyze the mineral levels of several Brazilian infant dairy foods and the daily intake contribution during important child development stages. These findings provide valuable guidance for researchers and practitioners trying to develop healthy and nutritious dairy products for infants and children.
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Kandylis, Panagiotis, Katerina Pissaridi, Argyro Bekatorou, Maria Kanellaki, and Athanasios A. Koutinas. "Dairy and non-dairy probiotic beverages." Current Opinion in Food Science 7 (February 2016): 58–63. http://dx.doi.org/10.1016/j.cofs.2015.11.012.

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Misra, Sukant K., Dale H. Carley, and Stanley M. Fletcher. "Dairy farmers' evaluation of dairy cooperatives." Agribusiness 9, no. 4 (July 1993): 351–61. http://dx.doi.org/10.1002/1520-6297(199307)9:4<351::aid-agr2720090406>3.0.co;2-4.

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Dissertations / Theses on the topic "Dairy"

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Rodrigues, Luiz Henrique Antunes. "CAP-DAIRY : computer aided planning of dairy farms." Thesis, Cranfield University, 1995. http://dspace.lib.cranfield.ac.uk/handle/1826/7708.

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A linear programming (LP) model has been developed (CAP-DAIRY) to describe the grass utilisation and feeding system on a dairy farm. It links several components of the system and optimizes the system as a whole. The model links a grass utilisation model, a feed ration model and a novel model which relates on-going milk yield to on-going feeding level. The main feature of the model is the approach adopted to relate feeding levels, milk yield and weight changes. When cows are fed more than they require for maintenance and the current level of milk yield, the excess energy becomes increased bodyweight and cause an increase in milk yield. When fed less than they require, they mobilize reserves into energy for milk production and lose weight and tend to reduce milk yield. At the start of the lactation some weight loss is tolerated. This is treated in the model as a requirements for up to 0.5 kg/day weight loss in addition to maintenance so that a lower weight loss is the increase case. A linear mathematical model that represents this mechanism was developed and incorporated into the LP and fitted to data which changed the level of feeding of dairy cows during the lactation. This gives a greater flexibility to the LP and allows the model to determine optimal feeding levels at all stages of the lactation and as a consequence optimal milk yields and optimal stages for weight changes, which vary depending on calving date and feed availability The grass utilisation model permits the successive utilisation of grass for grazing or silage making. Grazing can take place two, three or four weeks after the previous use and silage making five, six or seven weeks after the previous use. To allow for the effect of silage making on regrowth, use after this is delayed by one week. Data on energy and dry matter yields at any time is required and the model determines the optimum schedule of use and frequency. Silage is made in a number of separate silos but the feeding-out energy value makes the model non-linear. This is solved by using a recursive approach in which the initially unknown feeding value is calculated from successive solutions and the model reoptimized to convergence. The feed ration model determines the amount of grass, silage and concentrates required based on the maximum dry matter intake, which is a function of yield and the energy required for maintenance, milk yield and any weight change. The model could be easily extended to also use protein given suitable data. The LP determines the optimal land use for forage and cash crops, calving pattern and feeding strategy according to specific farm conditions such as farm area, milk quota and availability of forage maize. Several scenarios were studied and the effects of changes of different parameters analysed. Results indicated that net margins increased with maize crop areas and gave higher optimum milk yields replacing concentrates up to an optimum area of maize. The seasonality of milk prices affected particularly calving pattern and milk yield and the results suggested they led to more even milk production due to encouraging Autumn calving. Results also showed that the optimal feeding levels is different for cows calving in different periods of the year resulting in different weight change pattern and milk yields. Spring calving cows lost more weight than cows calving in any other period, but regained the weight lost quickly. They also produced the lowest level of milk. Autumns calving cows had the highest milk yield and the lowest weight losses, although a longer period to regain that weight was optimal. Summer calving cows produced slightly less milk and lost slightly more weight than Autumn cows. Another important aspect that results showed was the influence that maize silage has on farm decisions. The larger the maize crop area the higher the marginal price of milk quota, showing that milk quota constraint was more severe for those cases and consequently higher prices for extra milk quota could be paid. CAP-DAIRY is suitable for analysing the impact that changes such as milk prices, fertilizer prices or concentrates prices would cause on the optimal plans. The model is also helpful to evaluate research priorities by analysing the effects caused by biological and technical changes such as grass varieties and machinery.
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Gresser, Melissa R. "The Consumption of Dairy and Dairy Alternatives and the Perception of Dairy in College Students." Kent State University / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=kent1428574455.

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Olori, Victor Enishede. "Utilisation of daily milk records in genetic evaluation of dairy cattle." Thesis, University of Edinburgh, 1997. http://hdl.handle.net/1842/12729.

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The aim of this study was to determine how daily yield records might be used to improve the genetic evaluation of dairy cattle, which is currently based on 305-day yield predicted from monthly test day records. Daily milk yield records of 488 first lactation Holstein Friesian cows were obtained from one UK herd and summarised into weekly averages. Weekly fat, protein and lactose content records, from the same herd, were also obtained and each multiplied by test day yield to estimate fat, protein and lactose yields. Analysis of variance indicated that residual standard deviation (RSD) for each trait was lower when season of production was included in the model instead of the season of calving. The difference in RSD was more for fat and protein yields than the other traits. Lactation stage and season were the most important environmental factors affecting daily milk yield and composition while calving age had a small but significant (P<0.05) effect. Pregnancy accounted for 1.4 to 1.7% of the variation in yield traits but less than 0.4% of the variation in content traits. Its effects varied with gestation stage causing daily milk yield to decline by 3kg in the 8th month of gestation. A significant interaction between lactation and gestation stage was observed which suggested that the negative effect of pregnancy was higher in mid than late stages of lactation. Standard models of the lactation curve studied accounted for a substantial proportion of the variation in daily milk yields of typical lactations, which made up about half of the lactations studied. The other half was made up of atypical lactations such as highly persistent animals with almost flat curves whose lactation could not be adequately modelled with the standard curve functions. A regression spline model was derived which was as good as the best 3 parameter model and more flexible. These results indicate that models which make rigid assumptions about the shape of the lactation curve may not be very effective in accounting for the effect of lactation stage on daily yields. Yields of the same trait at different stages of lactation were positively correlated throughout but the correlation between yield and content traits was negative. Average correlation between milk yield and adjacent weeks was 0.93 declining to 0.61 between yields 41 weeks apart. Daily milk yields in mid and late stages of lactation were more highly correlated with 305-day yield than yields in early lactation. Covariance functions, using orthogonal polynomials up to the order of 4, were used to model genetic and permanent environmental covariances in a restricted maximum likelihood (REML) random regression (RR) model. Genetic parameters and breeding values were estimated for yield in every week of lactation. There were compared with estimates from a multivariate model, which considered yield at different stages as different traits, and a repeatability model without random regressions.
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Kronberg, Kari. "Dairy Princess handbook /." Click here to view, 2009. http://digitalcommons.calpoly.edu/dscisp/1/.

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McMillan, Allison. "Dairy cattle grouping /." Click here to view, 2009. http://digitalcommons.calpoly.edu/dscisp/7.

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Thesis (B.S.)--California Polytechnic State University, 2009.
Project advisor: Edwin Jaster. Title from PDF title page; viewed on Jan. 21, 2010. Includes bibliographical references. Also available on microfiche.
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Boda, Gayatri. "Benchmarking dairy information using interactive visualization for dairy farm decision making." Thesis, McGill University, 2005. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=97910.

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The main goal of this research was to explore the use of benchmarking in the dairy industry. This includes descriptions of the various sectors in North America where benchmarking has been used successfully on a continuous basis or in research. Benchmarking methods that are currently used in the Quebec dairy industry are examined. An improvement to such methods is proposed through the use of visualization, coupled with interactivity, and with a focus on adaptability and usage. The advantages of such an interactive tool are discussed in light of on-farm decision-making, and a further use of visual slider applications is described to help with parameters of known economic importance.
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Casebow, Andrew. "The Guernsey Dairy Industry." Thesis, University of Reading, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.502585.

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Renfrow, Crystal. "Keeping Dairy Cows Cool." College of Agriculture and Life Sciences, University of Arizona (Tucson, AZ), 1995. http://hdl.handle.net/10150/622379.

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Ravaglia, Marco, Justin Dinh, Michelle Frandsen, and Alyssa Garcia. "Dairy Processing Plant Production." Thesis, The University of Arizona, 2011. http://hdl.handle.net/10150/144925.

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Roxström, Anki. "Genetic aspects of fertility and longevity in dairy cattle /." Uppsala : Swedish Univ. of Agricultural Sciences (Sveriges lantbruksuniv.), 2001. http://epsilon.slu.se/avh/2001/91-576-5812-9.pdf.

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Books on the topic "Dairy"

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Dairy Queen: Dairy Queen #1. Boston: Houghton Mifflin, 2006.

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Dairy. Mankato, MN: Amicus High Interest, 2015.

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Dickmann, Nancy. Dairy. Chicago, Ill: Heinemann Library, 2012.

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The dairy. London: Murdoch, 2008.

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American Dairy Association of Illinois., ed. Our dairy specialties: A collection of dairy recipes from Illinois dairy farmers and Illinois dairy farm wives. El Paso, IL: American Dairy Association of Illinois, 1988.

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Canada, Industry Science and Technology Canada. Dairy products. Ottawa, Ont: Industry, Science and Technology Canada, 1992.

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The dairy. Leicester: Thorpe, 2015.

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Warren, J. Fred. Dairy produce. Washington, DC: Office of Industries, U.S. International Trade Commission, 1992.

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Henderson, Kathy. Dairy cows. Chicago: Childrens Press, 1988.

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Flynn, Padraig. Dairy husbandry. (Eire): ACOT, 1985.

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Book chapters on the topic "Dairy"

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Schoenfuss, Tonya C., and Ramesh C. Chandan. "Dairy Ingredients in Dairy Food Processing." In Dairy Ingredients for Food Processing, 421–72. Oxford, UK: Wiley-Blackwell, 2011. http://dx.doi.org/10.1002/9780470959169.ch17.

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Otero Rodríguez, Telmo, and Daniel Lanero Táboas. "From dairy innovation to dairy specialization." In Rural History in Europe, 107–34. Turnhout, Belgium: Brepols Publishers, 2023. http://dx.doi.org/10.1484/m.rurhe-eb.5.135023.

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White, S., and G. White. "Dairy flavourings." In Food Flavourings, 222–56. Boston, MA: Springer US, 1991. http://dx.doi.org/10.1007/978-1-4613-0499-9_9.

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Boylston, Terri D. "Dairy Products." In Food Biochemistry and Food Processing, 425–41. Oxford, UK: Wiley-Blackwell, 2012. http://dx.doi.org/10.1002/9781118308035.ch23.

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Lapp, Julia. "Dairy Ethics." In Encyclopedia of Food and Agricultural Ethics, 1–6. Dordrecht: Springer Netherlands, 2016. http://dx.doi.org/10.1007/978-94-007-6167-4_537-1.

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Barre, H. J., L. L. Sammet, and G. L. Nelson. "Dairy Housing." In Environmental and Functional Engineering of Agricultural Buildings, 220–48. Boston, MA: Springer US, 1988. http://dx.doi.org/10.1007/978-1-4684-1443-1_11.

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Xiong, Youling L. "Dairy Proteins." In Ingredients in Meat Products, 131–44. New York, NY: Springer New York, 2009. http://dx.doi.org/10.1007/978-0-387-71327-4_6.

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Mayorga, Bonastre Oliete, and Manuel Gómez. "Dairy Ingredients." In Bakery Products Science and Technology, 259–74. Chichester, UK: John Wiley & Sons, Ltd, 2014. http://dx.doi.org/10.1002/9781118792001.ch14.

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Gordon, J. "Dairy Products." In Food Industries Manual, 74–131. Boston, MA: Springer US, 1993. http://dx.doi.org/10.1007/978-1-4615-2099-3_3.

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Duveen, Michael. "Dairy products." In Handbook of Organic Food Processing and Production, 111–25. Boston, MA: Springer US, 1994. http://dx.doi.org/10.1007/978-1-4615-2107-5_7.

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Conference papers on the topic "Dairy"

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Bruning, Stephanie. "Dairy crest "cityside"." In ACM SIGGRAPH 2006 Computer animation festival. New York, New York, USA: ACM Press, 2006. http://dx.doi.org/10.1145/1179196.1179213.

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Yalçın, Zülkif, and Bahattin Taş. "Use and Accounting of Activity-Based Costing in Dairy and Dairy Production Enterprises." In 4th International Symposium on Innovative Approaches in Social, Human and Administrative Sciences. SETSCI, 2019. http://dx.doi.org/10.36287/setsci.4.8.008.

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"Assessment of Dairy Waste as Co-substrate for Anaerobic Digestion with Dairy Manure." In 2014 ASABE International Meeting. American Society of Agricultural and Biological Engineers, 2014. http://dx.doi.org/10.13031/aim.20141888006.

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Nusai, C., W. Chankeaw, and B. Sangkaew. "Dairy cow-vet: A mobile expert system for disease diagnosis of dairy cow." In 2015 IEEE/SICE International Symposium on System Integration (SII). IEEE, 2015. http://dx.doi.org/10.1109/sii.2015.7405063.

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Ray A. Bucklin and David R. Bray. "Dairy Cooling Systems in Florida." In 2005 Tampa, FL July 17-20, 2005. St. Joseph, MI: American Society of Agricultural and Biological Engineers, 2005. http://dx.doi.org/10.13031/2013.19906.

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Lin, Bin. "SCM in China dairy industry." In the 7th international conference. New York, New York, USA: ACM Press, 2005. http://dx.doi.org/10.1145/1089551.1089709.

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Anayo Thomas Ukwuani, Wendong Tao, and Jianqiu Han. "Ammonia Recovery from Dairy Manure." In 2013 Kansas City, Missouri, July 21 - July 24, 2013. St. Joseph, MI: American Society of Agricultural and Biological Engineers, 2013. http://dx.doi.org/10.13031/aim.20131590424.

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Peter Wright, Curt Gooch, and Scott Inglis. "Biodrying Dairy Manure: Initial Experiences." In 2001 Sacramento, CA July 29-August 1,2001. St. Joseph, MI: American Society of Agricultural and Biological Engineers, 2001. http://dx.doi.org/10.13031/2013.4207.

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Rozhkova, Svetlana Mikhailovna, and Alexandra Viktorovna Shchutskaya. "INVESTMENT CLIMATE IN DAIRY FARMING." In Трансформация экономической и правовой системы России: проблемы и перспективы. Самара: Самарский государственный экономический университет, 2021. http://dx.doi.org/10.46554/sno-teslsr:p-p-23/25.11.2020.pp.103.

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Danchuk, V. V., I. I. Antonik, and V. O. Danchuk. "HEAT STRESS IN DAIRY CATTLE." In CLIMATE-SMART AGRICULTURE: SCIENCE AND PRACTICE. Baltija Publishing, 2023. http://dx.doi.org/10.30525/978-9934-26-389-7-5.

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Reports on the topic "Dairy"

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Beldman, A., S. van Berkum, H. Kortstee, and J. Zijlstra. Dairy farming and dairy industry in Iran. Wageningen: Wageningen Economic Research, 2017. http://dx.doi.org/10.18174/417175.

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Schulte, Kristen, Jennifer A. Bentley, and Laffy F. Tranel. Women Managing Dairy Cattle: Educating Dairy Women. Ames (Iowa): Iowa State University, January 2015. http://dx.doi.org/10.31274/ans_air-180814-1301.

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Mondak, Chris. Collegiate Dairy Career Opportunity Day and Dairy Tours Connects Students to Dairy Industry. Ames (Iowa): Iowa State University, January 2008. http://dx.doi.org/10.31274/ans_air-180814-82.

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Bentley, Jennifer, and Larry Tranel. ISU Extension Dairy Team Newsletters Inform Dairy Industry. Ames (Iowa): Iowa State University, January 2012. http://dx.doi.org/10.31274/ans_air-180814-199.

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Tranel, Larry. Increasing Dairy Base with Millionaire Model Dairy Farms. Ames (Iowa): Iowa State University, January 2006. http://dx.doi.org/10.31274/ans_air-180814-672.

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Hall, Fred M., and Larry F. Tranel. Siouxland Dairy Lenders Seminar Focus On Dairy Enterprise Issues. Ames (Iowa): Iowa State University, January 2018. http://dx.doi.org/10.31274/ans_air-180814-387.

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Bentley, Jennifer A., and Ronald A. Lenth. Tri-State Dairy Youth Expo Hones in on Dairy Judging Skills. Ames (Iowa): Iowa State University, January 2014. http://dx.doi.org/10.31274/ans_air-180814-1161.

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Bentley, Jennifer A., and Ronald A. Lenth. Tri-State Dairy Youth Expo Hones in on Dairy Judging Skills. Ames (Iowa): Iowa State University, January 2015. http://dx.doi.org/10.31274/ans_air-180814-1304.

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Gorden, Patrick J., Cassandra Rice, and Adlai Schuler. A Survey of Dairy Farm Treatment Practices on Midwest Dairy Farms. Ames (Iowa): Iowa State University, January 2016. http://dx.doi.org/10.31274/ans_air-180814-215.

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10

Bentley, Jennifer A., and Ronald A. Lenth. Tri-State Dairy Youth Expo Hones in on Dairy Judging Skills. Ames (Iowa): Iowa State University, January 2016. http://dx.doi.org/10.31274/ans_air-180814-219.

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