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AIMS Agriculture and Food

2020, Volume 5, Issue 1: 20-29. doi: 10.3934/agrfood.2020.1.20
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Research article

Beef quality indicators and their dependence on keeping technology of bull calves of different genotypes

  • 1.
    Department of Technology of Meat, Milk Products and Chemistry, Federal State Budgetary Educational Institution of Higher Education «Bashkir State Agrarian University», Ufa, 50-letia Octiabria str, 34, 450001, Russian Federation
  • 2.
    Federal State Budgetary Scientific Institution All-Russian Research Institute of Animal Husbandry Mechanization, Moscow, Riazanovskoe settl, Znamia Oktiabria, 31, 108823, Russian Federation
  • The paper presents the comparative analysis of meat productivity of Black-and-white, Bestuzhev and Simmental bull calves. Morphological and chemical composition of meat, its variety assortment, anatomical body parts correlation during carcass dressing have been studied depending on the keeping technology. The results of these studies prove that young stock grown and fed indoors are superior to young stock of the same age grown out-door in main production indicators to be estimated. The calves of Bestuzhev and Simmental breeds grown indoors had the highest values of fleshing index which was 4.6–4.8. The mass of meat content of calves grown indoors was 6.3–16.1 kg (P < 0.05–P < 0.01) more than the mass of meat content of calves grown outdoors. The bull calves of Simmental breed were characterized by the largest weight of muscle tissue. For this indicator the superiority over the herd-mates of other evaluated breeds amounted to 12.0 kg (P < 0.01) and 6.6 kg (P < 0.05) when keeping animals on outdoor feedlot and 21.8 (P < 0.01) and 14.3 kg (P < 0.01) when keeping animals indoors. The high-quality beef with a favorable ratio of protein and fat meeting the current consumer requirements was received from young stock of all groups.

    Citation: Artyom Lamanov, Yurij Ivanov, Rishat Iskhakov, Liliya Zubairova, Khamit Tagirov, Azat Salikhov. Beef quality indicators and their dependence on keeping technology of bull calves of different genotypes[J]. AIMS Agriculture and Food, 2020, 5(1): 20-29. doi: 10.3934/agrfood.2020.1.20

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  • The paper presents the comparative analysis of meat productivity of Black-and-white, Bestuzhev and Simmental bull calves. Morphological and chemical composition of meat, its variety assortment, anatomical body parts correlation during carcass dressing have been studied depending on the keeping technology. The results of these studies prove that young stock grown and fed indoors are superior to young stock of the same age grown out-door in main production indicators to be estimated. The calves of Bestuzhev and Simmental breeds grown indoors had the highest values of fleshing index which was 4.6–4.8. The mass of meat content of calves grown indoors was 6.3–16.1 kg (P < 0.05–P < 0.01) more than the mass of meat content of calves grown outdoors. The bull calves of Simmental breed were characterized by the largest weight of muscle tissue. For this indicator the superiority over the herd-mates of other evaluated breeds amounted to 12.0 kg (P < 0.01) and 6.6 kg (P < 0.05) when keeping animals on outdoor feedlot and 21.8 (P < 0.01) and 14.3 kg (P < 0.01) when keeping animals indoors. The high-quality beef with a favorable ratio of protein and fat meeting the current consumer requirements was received from young stock of all groups.




    [1] Kick EL, Zering K, Classen J (2017) Approaches to agricultural innovation and their effectiveness. AIMS Agric Food 2: 370-373. doi: 10.3934/agrfood.2017.4.370
    [2] González-Sánchez E, Kassam A, Basch G, et al. (2016) Conservation agriculture and its contribution to the achievement of agri-environmental and economic challenges in Europe. AIMS Agric Food 1:387-408. doi: 10.3934/agrfood.2016.4.387
    [3] Gurina R, Nikitchenko V, Nikitchenko D, et al. (2019) Evaluation of economic efficiency of keeping and raising young cattle in Russia. Eng Rur Develop 18: 343-348.
    [4] Hocquette JF, Ellies-Oury MP, Lherm M, et al. (2018) Current situation and future prospects for beef production in Europe-A review. Asian Austral J Anim Sci 31: 1017. doi: 10.5713/ajas.18.0196
    [5] Wang G, Hua L, Squires VR, et al. (2017) What road should the grazing industry take on pastoral land in China? AIMS Agric Food 2: 354-369. doi: 10.3934/agrfood.2017.4.354
    [6] Dementyev EP, Bazekin GV, Tokarev IN, et al. (2018) The application of physical and biological stimulants in livestock breeding. J Eng Appl Sci 13: 8325-8330.
    [7] Khaziakhmetov F, Khabirov A, Avzalov R, et al. (2018) Effect of probiotics on calves, weaned pigs and lamb growth. Res J Pharm Bio Chem Sci 9: 866-870.
    [8] Tagirov HH, Gubaidullin NM, Fakhretdinov IR, et al. (2018) Carcass quality and yield attributesvof bull calves fed on fodder concentrate «Zolotoi Felutsen». J Eng Appl Sci 13: 6597.
    [9] Tan JBL, Lim YY (2015) Critical analysis of current methods for assessing the in vitro antioxidant and antibacterial activity of plant extracts. Food Chem 172: 814-822. doi: 10.1016/j.foodchem.2014.09.141
    [10] Khaziakhmetov F, Khabirov A, Avzalov R, et al. (2018) Valuable effect of using probiotics in poultry farming. Ann Res Rev Bio 25: 1-7.
    [11] Babii AV, Arkhipova AL, Andreichenko IN, et al. (2018) A TaqMan PCR assay for detection of DGAT1 K232A polymorphism in cattle. AIMS Agric Food 3: 306-312. doi: 10.3934/agrfood.2018.3.306
    [12] Ellies-Oury MP, Lee A, Jacob H, et al. (2019) Meat consumption-what French consumers feel about the quality of beef? Ital J Anim Sci 18: 646-656. doi: 10.1080/1828051X.2018.1551072
    [13] Gadiev RR, Khaziev DD, Galina CR, et al. (2019) The use of chlorella in goose breeding. AIMS Agric Food 4: 349-361. doi: 10.3934/agrfood.2019.2.349
    [14] Creemers S, Van Passel S, Vigani M, et al. (2019) Relationship between farmers' perception of sustainability and future farming strategies: A commodity-level comparison. AIMS Agric Food 4: 613-642. doi: 10.3934/agrfood.2019.3.613
    [15] Trofimtsev YI, Pomortsev OA, Popov VF, et al. (2017) Numerical modeling of harmonics in meteorological time series. Proceedings of the 3rd international conference on construction and building engineering (Iconbuild) 2017: Smart construction towards global challenges, AIP Publishing.
    [16] Vestergaard M, Jørgensen KF, Çakmakçı C, et al. (2019) Performance and carcass quality of crossbred beef x Holstein bull and heifer calves in comparison with purebred Holstein bull calves slaughtered at 17 months of age in an organic production system. Livest Sci 223: 184-192. doi: 10.1016/j.livsci.2019.03.018
    [17] Sedykh TA, Gizatullin R, Kosilov V, et al. (2018) Adapting australian Hereford cattle to the conditions of the Southern Urals. Res J Phar Biol Chem Sci 9: 885-898.
    [18] McDonnell RP, Edmunds BE, Morris R (2017) Feeding management, production and performance of 13 pasture-based dairy farms in a Mediterranean environment. Anim Prod Sci 57: 1940-1951. doi: 10.1071/AN15768
    [19] Guidelines for the evaluation of meat productivity and meat quality (1990) Moscow: VASKHNIL, All-Russian Academy of Agricultural Sciences, 86.
    [20] Kurchaeva E, Ovsynnikova G, Vostroilov A, et al. (2018) Influence of probiotic preparations on meat quality of farm animals. International scientific and practical conference "Agro-SMART-Smart solutions for agriculture" (Agro-SMART 2018), Atlantis Press.
    [21] Sedykh TA, Kalashnikova LA, Gusev IV, et al. (2016) Influence of TG5 and LEP gene polymorphism on quantitative and qualitative meat composition in beef calves. Iraqi J Vet Sci 30: 41-48. doi: 10.33899/ijvs.2016.121382
    [22] Phillips CJ (2018) Principles of cattle production. CABI.
    [23] Losada-Espinosa N, Villarroel M, María GA, et al. (2018) Pre-slaughter cattle welfare indicators for use in commercial abattoirs with voluntary monitoring systems: A systematic review Meat Sci 138: 34-48.
    [24] Jaeger M, Brügemann K, Brandt H, et al. (2019) Associations between precision sensor data with productivity, health and welfare indicator traits in native black and white dual-purpose cattle under grazing conditions. Appl Anim Behav Sci 212: 9-18. doi: 10.1016/j.applanim.2019.01.008
    [25] Gorlov I, Azhmuldinov E, Karpenko E, et al. (2017) Comparative assessment of nutritional and biological value of beef from calves of various breeds. Proceedings of the international scientific conference, Latvia University of Agriculture.

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