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Journal articles on the topic 'Dairy'

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Author: Grafiati

Published: 4 June 2021

Last updated: 1 June 2024

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1

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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2

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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3

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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4

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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5

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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6

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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7

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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8

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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9

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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10

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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11

KATSURA, Yuki. "Dairy, Fun Dairy, and Love Dairy in Nakashibetsu Town, Hokkaido Prefecture." JOURNAL OF RURAL PLANNING ASSOCIATION 38, no. 2 (September 30, 2019): 114–15. http://dx.doi.org/10.2750/arp.38.114.

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12

Deshmukh, Aditya, Aditya Purohit, Aman Pratap Singh, and Anirudha Singh. "Implementation of Android Application In React-Native: Daily Dairy." International Journal for Research in Applied Science and Engineering Technology 10, no. 4 (April 30, 2022): 1846–52. http://dx.doi.org/10.22214/ijraset.2022.41655.

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Abstract: This paper describes the whole process of android application development using the react-native framework and firebase of google and developed the application “DailyDairy''. The Application is developed for medium-sized and digitally illiterate dairy farmers. The software is divided into milk distribution management and payment transactions, statistics and analysis can be a variety of technical data to analyze, synthesize, organize, and the User Interface is practical, economic, and user-friendly. This application also solves the differences between Vendor and his Customers. This paper also describes the integration of firebase firestore (database provided by google) with android application development with javascript framework react-native and code editor used in is visual studio code by Microsoft. Keywords: Dairy Management, Android application, Vendor, Customer, Milk, Dairy Farmer, Digital Literacy

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13

Z Saricicek, B. "Possibilities of using dairy compounds exposed to different treatments in dairy cow feeding." Czech Journal of Animal Science 49, No. 5 (December 12, 2011): 205–10. http://dx.doi.org/10.17221/4301-cjas.

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This study was conducted to investigate the effects of untreated dairy compound feeds; DCF (control) and DCFs subjected to 2% fat (F); 2.5% tannic acid (TA); 2% fat + 2.5% tannic acid (F + TA) on the milk yield and composition. In the study, 4 cows of Jersey breed were used and 4 × 4 Latin square experimental design was applied. Normal milk yield, 4% fat corrected milk yield (FCM), dry matter content of milk, non-fat solids content of milk, CP content of milk, fat content of milk, lactose content of milk, crude ash content of milk, daily DM consumption of cows, feed efficiency according to normal milk yield of cows, feed efficiency according to the FCM of cows were 11.96, 12.14, 10.89 and 11.94 kg/day (P < 0.05); 14.16, 14.50, 13.06 and 13.70 kg/day (P < 0.05); 13.37, 13.34, 13.54 and 13.41% (P > 0.05); 8.12, 8.00, 8.35 and 8.19% (P > 0.05); 3.67, 3.70, 3.87 and 3.63% (P < 0.05); 5.25, 5.34, 5.19 and 5.22% (P > 0.05); 4.97, 4.812, 5.01 and 5.042% (P > 0.05); 0.75, 0.73, 0.74 and 0.73% (P > 0.05); 15.97, 15.84, 15.94 and 15.59 kg/day (P > 0.05); 1.34, 1.31, 1.46 and 1.31 kg feed DM/kg milk (P > 0.05); 1.13, 1.09, 1.22 and 1.14 kg feed DM/kg milk (P > 0.05), respectively. According to the results it can be stated that 2.5TA and 2F treatments had positive effects on FCM milk yield and milk protein yield.  

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14

Corredig, Milena. "Advanced Dairy Chemistry. Volume 1. Dairy Proteins." Trends in Food Science & Technology 15, no. 5 (May 2004): 282. http://dx.doi.org/10.1016/j.tifs.2003.10.003.

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15

Vedamuthu, E. R. "The Dairy Leuconostoc: Use in Dairy Products." Journal of Dairy Science 77, no. 9 (September 1994): 2725–37. http://dx.doi.org/10.3168/jds.s0022-0302(94)77215-5.

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16

Hamdilah, Sri Ratu Nurul, Maulidian, and Rukavina Baskh. "PENGEMBANGAN MODEL BISNIS PETERNAKAN SUSU SAPI PERAH MELALUI PERSPEKTIF BLUE OCEAN (STUDI KASUS: PETERNAKAN SAPI PERAH CIBUGARY DI PONDOK RANGGON CIPAYUNG JAKARTA TIMUR)." Jurnal Bioindustri 4, no. 1 (November 30, 2021): 25–40. http://dx.doi.org/10.31326/jbio.v4i1.706.

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Cibugary dairy farm is one of the dairy farms that actively produces milk and has an agritourism concept. Cibugary Farm as an old player in the midst of the many players in the dairy industry and is currently trapped in fierce competition (red ocean). The purpose of this study was to analyze the condition of business model, analyze the internal and external environment on Cibugary dairy farm and analyze the right business model for Cibugary dairy farm The analytical method used is the business model canvas, IFAS and EFAs analysis, SWOT and Blue Ocean Strategy. The results of this study are to find several alternative business strategies through a SWOT matrix combined with a blue ocean perspective that is eliminate-reduce-increase-create that will be used by companies to encounter the future. To avoid fierce competition and create blue oceans, Cibugary dairy farms must try to differentiate from existing competitors. In Cibugary product elements is diversifying date palm milk products and memberships cards. While the Cibugary agro-tourism element is creating adventure and Cibugary creation shelter.

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17

Synytska, A. A. "THE SELECTION INDEX OF DAILY PROFIT FOR THE EVALUATION OF BREEDING BULLS OF DAIRY AND DAIRY-MEAT BREEDS." Scientific and Technical Bulletin of the Institute of Animal Science NAAS of Ukraine, no. 120 (2018): 135–42. http://dx.doi.org/10.32900/2312-8402-2018-120-135-142.

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18

Žgajnar, J., L. Juvančič, and S. Kavčič. "Combination of linear and weighted goal programming with penalty function in optimisation of a daily dairy cow ration." Agricultural Economics (Zemědělská ekonomika) 55, No. 10 (November 9, 2009): 492–500. http://dx.doi.org/10.17221/2601-agricecon.

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The aim of the paper is to present a developed spreadsheet tool for the formulation of a daily dairy cow ration. It is constructed on the basis of two linked sub-models developed on the MS Excel platform. It merges the common linear programming model and the weighted-goal programming model with a penalty function. The first sub-model is included in the tool to make an estimate of the least-cost magnitude that might be expected. The obtained result is entered into the second sub-model as the goal that should be met as closely as possible. The tool was tested at two different values of preferential weights for dairy cows with a 25 kg daily milk yield. The results obtained confirm the benefits of the applied approach. In contrast to the common linear program tools, which terminate at formulation of the least-cost ration, our tool provides more efficient rations (in both economic and nutritive terms) by fine-tuning the nutritive goals and by allowing for harmless deviations from these goals by application of penalty functions.

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19

Breen, J. E. "Dairy youngstock." Veterinary Record 167, no. 22 (November 26, 2010): 871. http://dx.doi.org/10.1136/vr.c6697.

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20

PHELAN, J. A. "Dairy spreads." International Journal of Dairy Technology 39, no. 4 (October 1986): 110–15. http://dx.doi.org/10.1111/j.1471-0307.1986.tb02393.x.

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21

Stepaniak, Leszek. "Dairy enzymology." International Journal of Dairy Technology 57, no. 2-3 (May 2004): 153–71. http://dx.doi.org/10.1111/j.1471-0307.2004.00144.x.

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22

Hammond, Paula. "Delightful dairy!" 5 to 7 Educator 2008, no. 37 (January 2008): viii—xi. http://dx.doi.org/10.12968/ftse.2008.7.1.28425.

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23

Jenner, Brian. "Dairy Daze." British Journalism Review 14, no. 4 (December 2003): 33–37. http://dx.doi.org/10.1177/0956474803144006.

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24

Bradley, Robert L. "Dairy Chemistry." Journal of AOAC INTERNATIONAL 82, no. 2 (March 1, 1999): 429–30. http://dx.doi.org/10.1093/jaoac/82.2.429.

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25

Jelen, P. "Dairy Technology." International Dairy Journal 10, no. 8 (January 2000): 585–86. http://dx.doi.org/10.1016/s0958-6946(00)00077-7.

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26

Bailey, Kenneth W. "Dairy processing." Veterinary Clinics of North America: Food Animal Practice 19, no. 2 (July 2003): 295–317. http://dx.doi.org/10.1016/s0749-0720(03)00026-4.

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27

Van Saun, Robert J. "Dairy Nutrition." Veterinary Clinics of North America: Food Animal Practice 30, no. 3 (November 2014): xi—xii. http://dx.doi.org/10.1016/j.cvfa.2014.08.004.

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28

Richardson, Gary H. "Dairy Chemistry." Journal of AOAC INTERNATIONAL 72, no. 1 (January 1, 1989): 70–71. http://dx.doi.org/10.1093/jaoac/72.1.70a.

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29

Bradley, Robert L. "Dairy Chemistry." Journal of AOAC INTERNATIONAL 74, no. 1 (January 1, 1991): 115. http://dx.doi.org/10.1093/jaoac/74.1.115.

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30

Bradley, Robert L. "Dairy Chemistry." Journal of AOAC INTERNATIONAL 75, no. 1 (January 1, 1992): 90. http://dx.doi.org/10.1093/jaoac/75.1.90.

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31

Bradley, Robert L. "Dairy Chemistry." Journal of AOAC INTERNATIONAL 76, no. 1 (January 1, 1993): 106–7. http://dx.doi.org/10.1093/jaoac/76.1.106.

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32

Bradley, Robert L. "Dairy Chemistry." Journal of AOAC INTERNATIONAL 77, no. 1 (January 1, 1994): 160. http://dx.doi.org/10.1093/jaoac/77.1.160.

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33

Bradley, Robert L. "Dairy Chemistry." Journal of AOAC INTERNATIONAL 78, no. 1 (January 1, 1995): 160–61. http://dx.doi.org/10.1093/jaoac/78.1.160.

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34

Bekele, Wondewsen, Gebeyehu Goshu, Berhan Tamir, Tilaye Demissie, and Zemelak Sahle. "Characterization of Dairy Production Constraints, Existing Feeding Practices and Mineral Supplementation in Dairy Feeds in Two Districts of East Shoa Zone, Ethiopia." Advances in dairy Research 07, no. 01 (2019). http://dx.doi.org/10.35248/advances-dairy.1000219.

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35

"Dairy." British Dental Journal 200, no. 1 (January 2006): 9. http://dx.doi.org/10.1038/sj.bdj.4813161.

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36

"Dairy." British Dental Journal 196, no. 3 (February 2004): 130. http://dx.doi.org/10.1038/sj.bdj.4811016.

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37

"Dairy." British Dental Journal 197, no. 5 (September 2004): 228. http://dx.doi.org/10.1038/sj.bdj.4811661.

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38

"Dairy." Pigment & Resin Technology 36, no. 4 (July 10, 2007). http://dx.doi.org/10.1108/prt.2007.12936dac.003.

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"Dairy." Pigment & Resin Technology 36, no. 5 (September 18, 2007). http://dx.doi.org/10.1108/prt.2007.12936eac.007.

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40

"Dairy." Anti-Corrosion Methods and Materials 54, no. 5 (September 18, 2007). http://dx.doi.org/10.1108/acmm.2007.12854eac.001.

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41

"Dairy." Journal of Manufacturing Technology Management 19, no. 1 (December 24, 2007). http://dx.doi.org/10.1108/jmtm.2008.06819aac.002.

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42

"Dairy." Literary and Linguistic Computing 3, no. 4 (1988): 269–70. http://dx.doi.org/10.1093/llc/3.4.269.

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43

"Dairy." Literary and Linguistic Computing 7, no. 2 (1992): 152–53. http://dx.doi.org/10.1093/llc/7.2.152.

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44

"DAIRY." Reviews in Religion & Theology 2, no. 3 (August 1995): 28–29. http://dx.doi.org/10.1111/j.1467-9418.1995.tb00151.x.

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45

"Dairy." Pigment & Resin Technology 42, no. 3 (May 17, 2013). http://dx.doi.org/10.1108/prt.2013.12942caa.010.

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46

"Dairy." British Journal of Urology 67, no. 1 (January 1991): 111–12. http://dx.doi.org/10.1111/j.1464-410x.1991.tb15090.x.

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47

"Dairy." British Journal of Urology 70, no. 6 (December 1992): 701–2. http://dx.doi.org/10.1111/j.1464-410x.1992.tb15855.x.

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48

"Dairy." Medicine, Science and the Law 36, no. 3 (July 1996): 275–76. http://dx.doi.org/10.1177/002580249603600320.

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"Dairy." Medicine, Science and the Law 36, no. 4 (October 1996): 368. http://dx.doi.org/10.1177/002580249603600420.

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"Dairy." Medicine, Science and the Law 37, no. 1 (January 1997): 92. http://dx.doi.org/10.1177/002580249703700125.

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