Systems and methods for assessing the homogeneity of feed mixtures for farm animals (review)

Relevance.  When  forming  a  diet  for  feeding  animals  that  are  kept  in  modern  livestock  complexes,  the farmer  pursues  an  exceptionally  rational  approach  in  terms  of  livestock  productivity  and  the  period  of productive  existence  of  animals.  First  of  all,  the  provision  of  these  indicators  forms  the  quality  of  animal feeding, expressed in the total nutritional value of the diet and the quality of mixing the components that make up its composition.

Methods. The updating of the direction of scientific research was carried out on the basis of a preliminary literary  review  of  modern  publications  in  the  international  journals  Journal  of  dairy  science,  Robotics and  autonomous  systems  and  Agriculture.  In  addition,  the  functionality  of  modern  analytical  equipment from  world  manufacturers  used  in  agriculture  was  analyzed.  The  technical  solutions  of  the  exhibits  of industry  exhibitions,  as  well  as  the  accompanying  documentation,  are  considered.  The  existing  methods for  determining  the  homogeneity  of  feed  mixtures  for  farm  animals  have  been  studied,  advantages  and disadvantages have been identified.

Results .  The  existing  methods  for  estimating  the  homogeneity  of  feed  mixtures  are  considered.  A  new concept has been proposed for constructing a system for determining the homogeneity of feed mixtures by  optical  methods,  which  is  based  on  express  measurement  of  the  optical  properties  of  feed  mixture components on the surface of a conveyor belt or mixing unit, the use of which will allow you to manage the temporary modes of mixing feeds and assess the serviceability of the machines and units used.

1. Kosilov V.I., Irgashev T.A., Rebezov M.B., Klochkova M.A. Cost of feed and age-related dynamics of a live weight of young sheep Tsigay breed and its insalivate with Edilbaev breed. Kishovarz. 2020; (4): 56–60 (in Russian). https://elibrary.ru/jfxgpc

2. Irgashev T.A., Baigenov F.N., Karimova M.O., Olimov S.H., Rebezov M.B., Bykova O.A. Influence of bentonite and bentonite-containing premix on feed consumption, growth and development of calves. Michurinsk agronomy bulletin. 2022; (1): 43–48 (in Russian). https://elibrary.ru/okaczb

3. Kosilov V.I., Rakhimzhanova I.A., Salihov A.A., Rebezov M.B., Mironova I.V., Perevoiko Zh.A. Influence of feeding level and genotype on age dynamics of live weight of purebred and crossbred heifers. Izvestia Orenburg State Agrarian University. 2022; (1): 208–212 (in Russian). https://doi.org/10.37670/2073-0853-2022-93-1-208-212

4. Konganbaev E.K., Asenova B.K., Smolnikova F.Kh., Rebezov M.B. Study on grain monocomponent feed used in feeding broiler chickens. International scientific and practical conference dedicated to the memory of Vasily Matveevich Gorbatov. Moscow: Gorbatov›s All-Russian Meat Research Institute. 2015; 235–237 (in Russian). https://elibrary.ru/vbdkrh

5. Trepalina E., Galkin A. Test systems for the control of ionophoric coccidiostatics in feed. Compound feeds. 2015; (5): 83–85 (in Russian). https://www.elibrary.ru/trlhkf

6. Djuragic O., Levic J., Sredanovic S., Lević L. Evaluation of homogeneity in feed by method of microtracers®. Archiva Zootechnica. 2009; 12(4): 85–91.

7. Rocha A.G., Montanhini R.N., Dilkin P., Tamiosso C.D., Mallmann C.A. Comparison of different indicators for the evaluation of feed mixing efficiency. Animal Feed Science and Technology. 2015; 209: 249–256. https://doi.org/10.1016/j.anifeedsci.2015.09.005

8. Latvietis J., Priekulis J., Eihvalde I. Problems of cow feeding in robotic milking and loose handling conditions. Engineering for Rural Development. Proceedings of the 7th International Scientific Conference. Jelgava. 2008; 270–274.

9. Gayathri S.L., Panda N. Chelated minerals and its effect on animal production: A review. Agricultural Reviews. 2018; 39(4): 314–320. https://doi.org/10.18805/ag.R-1823

10. Yegorov B.V., Маkаrinskaya A.V., Gоntsa N.V. Features of production technology of highly homogenous feed additives. Zernovì produkti ì kombìkorma. 2014; (2): 37–40 (in Ukrainian). https://www.elibrary.ru/wjxqft

11. Herrman T., Behnke K. Testing Mixer Performance. MF-1172. Kansas State University Agricultural Experiment Station and Cooperative Extension Service. 1994; 4.

12. Robles V., González L.A., Ferret A., Manteca X., Calsamiglia S. Effects of feeding frequency on intake, ruminal fermentation, and feeding behavior in heifers fed high-concentrate diets. Journal of Animal Science. 2007; 85(10): 2538–2547. https://doi.org/10.2527/jas.2006-739

13. Rego G. et al. A portable IoT NIR spectroscopic system to analyze the quality of dairy farm forage. Computers and Electronics in Agriculture. 2020; 175: 105578. https://doi.org/10.1016/j.compag.2020.105578

14. Samadi, Wajizah S., Munawar A.A. Rapid and Simultaneous Determination of Feed Nutritive Values by Means of Near Infrared Spectroscopy. Tropical Animal Science Journal. 2018; 41(2): 121–127. https://doi.org/10.5398/tasj.2018.41.2.121

15. Yang Z. et al. Development and validation of near-infrared spectroscopy for the prediction of forage quality parameters in Lolium multiflorum. PeerJ. 2017; 5: e3867. https://doi.org/10.7717/peerj.3867

16. Marchesini G., Serva L., Garbin E., Mirisola M., Andrighetto I. Near-infrared calibration transfer for undried whole maize plant between laboratory and on-site spectrometers. Italian Journal of Animal Science. 2018; 17(1): 66–72. https://doi.org/10.1080/1828051x.2017.1345660

17. Ren G., Sun Y., Li M., Ning J., Zhang Z. Cognitive spectroscopy for evaluating Chinese black tea grades (Camellia sinensis): near‐infrared spectroscopy and evolutionary algorithms. Journal of the Science of Food and Agriculture. 2020; 100(10): 3950–3959. https://doi.org/10.1002/jsfa.10439

18. Sánchez M.-T., Torres I., de la Haba M.-J., Chamorro A., Garrido-Varo A., Pérez-Marín D. Rapid, simultaneous, and in situ authentication and quality assessment of intact bell peppers using near-infrared spectroscopy technology. Journal of the Science of Food and Agriculture. 2019; 99(4): 1613–1622. https://doi.org/10.1002/jsfa.9342

19. Andueza D., Picard F., Martin-Rosset W., Aufrère J. Near-infrared spectroscopy calibrations performed on oven-dried green forages for the prediction of chemical composition and nutritive value of preserved forage for ruminants. Applied Spectroscopy. 2016; 70(8): 1321–1327. https://doi.org/10.1177/0003702816654056

20. Harper M.T. et al. Short communication: Preference for flavored concentrate premixes by dairy cows. Journal of Dairy Science. 2016; 99(8): 6585–6589. https://doi.org/10.3168/jds.2016-11001

21. Królczyk J., Tukiendorf M. Using the methods of geostatic function and Monte Carlo in estimating the randomness of distribution of a two-component granular mixture during the flow mixing. Electronic Journal of Polish Agricultural Universities. 2005; 8(4): 78.

22. Eisenberg D. Mix with Confidence. International Milling Flour & Feed. 1994; 31–33.

23. Kumar S., Lahlali R., Liu X., Karunakaran C. Infrared spectroscopy combined with imaging: A new developing analytical tool in health and plant science. Applied Spectroscopy Reviews. 2016; 51(6): 466–483. https://doi.org/10.1080/05704928.2016.1157808

24. Berzaghi P. Cherney J.H., Casler M.D. Prediction performance of portable near infrared reflectance instruments using preprocessed dried, ground forage samples. Computers and Electronics in Agriculture. 2021; 182: 106013. https://doi.org/10.1016/j.compag.2021.106013

25. Barashkov N.N., Pysarenko P.V., Krikunova V.Yu., Sakhno T.V., Krikunov О.A. Ferromagnetic microtracers and their use for evaluation of the homogeneity of feed for agricultural animals and poultry. Zernovì produkti ì kombìkorma. 2016; (3): 34–40 (in Russian). https://www.elibrary.ru/xchsvx

26. Sakhno T., Krykunova V., Sakhno Y., Barashkov N., Eisenberg D. Preparation of ferromagnetic liquid containing mixed iron oxide/manganese oxide nanoparticles and its use for mixer studies in liquids feeds. Physics of Liquid Matter: Modern Problems. 7th International Conference. Abstracts. Kyiv. 2016; 147.

27. Barashkov N., Eisenberg D., Eisenberg S., Mohnke J. Ferromagnetic microtracers and their use in feed applications. Feed Technology. XII International Symposium. Novi Sad. 2008.

28. Krolczyk J. The effect of mixing time on the homogeneity of multi-component granular systems. Transactions of FAMENA. 2016; 40(1): 45–56.

29. Corrigan O.I., Wilkinson M.L., Ryan J., Harte K., Corrigan O.F. The Use of Microtracers® in a Medicated Premix to Determine the Presence of Tiamulin in Final Feed. Drug Development and Industrial Pharmacy. 1994; 20(8): 1503–1509. https://doi.org/10.3109/03639049409038386

30. Bagliacca M., Paci G., Marzoni M., Lisi E. Impiego di particelle di ferro colorate (Microtracers©) come traccianti dei mangimi e per il controllo della miscelazione. Large Animals Review. 2002; 8(2): 9–12.

31. Nikkhah A. Barley grain for ruminants: A global treasure or tragedy. Journal of Animal Science and Biotechnology. 2012; 3: 22. https://doi.org/10.1186/2049-1891-3-22

32. Cherkasov R.I., Adigamov K.A., Voronin V.V., Gapon N.V., Sizyakin R.A. Quality assessment of loose mix materials with different grain size. Modern problems of science and education. 2015; (2–2): 169 (in Russian). https://www.elibrary.ru/uzjahn

33. Demin O.V., Smolin D.O., Pershin V.F. Qualification mixes bulk materials based on their digital images. Modern problems of science and education. 2013; (2): 157 (in Russian). https://www.elibrary.ru/rxuokp

34. Nikkhah A. Optimizing Barley Grain Use by Dairy Cows: A Betterment of Current Perceptions. Progress in Food Science and Technology. New York: Nova Science Publishers Inc. 2011; 1: 165–178.

35. Restle J., Faturi C., Pascoal L.L., Rosa J.R.P., Brondani I.L., Filho D.C.A. Processing oats grain for cull cows finished in feedlot. Ciência Animal Brasileira. 2009; 10(2): 497–503 (in Portuguese).

36. Nikitin E.A., Pavkin D.Yu., Izmailov A.Yu., Aksenov A.G. Assessing the Homogeneity of Forage Mixtures Using an RGB Camera as Exemplified by Cattle Rations. Applied Sciences. 2022; 12(7): 3230. https://doi.org/10.3390/app12073230

37. Pavkin D.Yu., Belyakov M.V., Nikitin E.A., Efremenkov I.Yu., Golyshkov I.A. Determination of the Dependences of the Nutritional Value of Corn Silage and Photoluminescent Properties. Applied Sciences. 2023; 13(18): 10444. https://doi.org/10.3390/app131810444

38. Nikitin E.A., Semenyuk V.S. Analysis of feed mixture effective preparation’s problems in the modern farming. Journal of VNIIMZH. 2019; (2): 158–163 (in Russian). https://www.elibrary.ru/kjpyil

39. Belyakov M.V., Nikitin E.A., Efremenkov I.Yu. Efficiency of the Photoluminescent Method for Monitoring the Homogeneity of Feed Mixtures in Animal Husbandry. Agricultural Machinery and Technologies. 2022; 16(3): 55–61 (in Russian). https://doi.org/10.22314/2073-7599-2022-16-3-55-61

40. Kirsanov V.V., Belyakov M.V., Nikitin E.A., Blagov D.A., Mikhailichenko S.M. Spectral analysis as a tool for determining the mixing quality of a multicomponent feed mixture. Vestnik Bashkir State Agrarian University. 2023; (3): 41–46 (in Russian). https://www.elibrary.ru/rcobbx

41. Kirsanov V.V., Pavkin D.Yu., Nikitin E.A., Kiryushin I.A. Application of technical vision systems for diagnosing the quality of cattle feed. Agricultural Science Euro-North-East. 2021; 22(5): 770–776 (in Russian). https://doi.org/10.30766/2072-9081.2021.22.5.770-776

42. Lednev V.N., Sdvizhensky P.A., Grishin M.Yu., Nikitin E.A., Gudkov S.V., Pershin S.M. Improving Calibration Strategy for LIBS Heavy Metals Analysis in Agriculture Applications. Photonics. 2021; 8(12): 563. https://doi.org/10.3390/photonics8120563

43. Gorlov I.F. et al. The effect of new prebiotic feed additive on large white breed pigs natural resistance and productivity. Vestnik Bashkir State Agrarian University. 2023; (3): 36–41 (in Russian). https://www.elibrary.ru/cbmmhi