Математическое моделирование влияния показателей микроклимата на интенсификацию агропромышленного производства на примере выращивания бройлеров
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Научный журнал Моделирование, оптимизация и информационные технологииThe scientific journal Modeling, Optimization and Information Technology
Online media
issn 2310-6018

Mathematical modeling of microclimate indicator influence on the intensification of agricultural production on the example of broiler farming

Belykh T.I.   Burdukovskaya A.V.   Ivonina O.Y.   Rodionov A.V.  

UDC 519.8:51-76
DOI: 10.26102/2310-6018/2023.40.1.018

  • Abstract
  • List of references
  • About authors

The article deals with the regression mathematical models that describe the influence of mechanical and automatic microclimate control systems on the growth and development of Arbor Acres cross broiler chickens in Sayansky Broiler agro-industrial complex with outdoor maintenance. The paper regards the influence of such parameters as microclimate, temperature, humidity and illumination. To test the statistical hypothesis of homogeneity of the two considered samples, the Cramer-Welch and Wilcoxon tests are employed. Chou's test is presented concerning the possibility of constructing two different mathematical models of the same type that illustrate the patterns of the modeled indicators development. Statistical estimates of the significance of the constructed models and the factors included in the models are calculated. An interpretation of the results of regression analysis in relation to the subject area under study is given. In addition, a graphical visualization of the analysis of the initial and output data of the constructed models was performed. The ranking of factors is carried out according to the degree of their impact on the resulting indicator using elasticity coefficients and the shares of their influence. The main production indicators are calculated based on the results of livestock rearing: average daily gain, absolute gain, relative growth rate, safety. The article calculates the economic effect for one full cycle of farming broiler chickens.

1. Egorova T.A. The development of Russian poultry farming in the global trend. Ptitsevodstvo. 2019;2:4–9. DOI: 10.33845/0033-3239-2019-68-2-4-9 (In Russ.).

2. Fisinin V.I., Buyarov V.S., Buyarov A.V., Shumetov V.G. Meat poultry farming in the regions of Russia: current state and prospects for innovative development. Agrarnaya nauka = Agrarian science. 2018;2:30–38. (In Russ.).

3. Akbaev M., Malofeev N., Tsyplyaev A. etc. Reserves for increasing the productivity of broilers. Ptitsevodstvo. 2003;7:5–7. (In Russ.).

4. Astrakhantsev A.A. The productivity of broiler chickens with different technological options for growing. Ptitsevodstvo. 2019;1:26–30. DOI: 10.33845/0033-3239-2019-68-1-26-30 (In Russ.).

5. Zykov S.A. Modern trends in the development of poultry farming. Effektivnoe zhivotnovodstvo. 2019;4:51–54. (In Russ.).

6. Buyarov B.C., Godymenko V.I., Buyarov A.V., Nozdrin A.E. The effectiveness of innovative technologies for the industrial production of broiler meat. Vestnik agrarnoi nauki. 2017;2(65):36–47. (In Russ.).

7. Pisarev Yu., Batov V. Fattening of poultry in outdoor conditions. Ptitsevodstvo. 2003;5:13–14. (In Russ.).

8. Mikhalev P.V., Vasilevsky N.M. The effectiveness of the application of new methods of microclimate control in the cultivation of broiler chickens. Uchenye zapiski Kazanskoi Gosudarstvennoi Akademii veterinarnoi meditsiny im. N.E. Baumana. 2012;212:319–323. (In Russ.).

9. Karelina L.N., Vlasov B.Ya., Susloparova N.V. Light regime as an ecological and stress factor in the development of farm birds. Ob"edinennyi nauchnyi zhurnal. 2011;11(12):111–113. (In Russ.).

10. Draper N., Smith G. Applied regression analysis. M.: Dialectics; 2017. 912 p. (In Russ.).

11. Merkuryeva E.K. Fundamentals of biometrics. M.: Publishing house of Moscow State University; 1963. 242 p. (In Russ.).

12. Plokhinsky N.A. Biometrics. Novosibirsk: Publishing House of SO AN SSR; 1961. 364 p. (In Russ.).

13. Aivazyan S.A. Applied statistics. Fundamentals of econometrics: Proc. In 2 vols. 2nd ed., corr. T. 2. M : UNITI-DANA; 2001. 432 p. (In Russ.).

14. Brandt Z. Data analysis. Statistical and computational methods for scientists. M.: Mir; 2003. 686 p. (In Russ.).

15. Strizhov V.V., Krymova E.A. Methods for choosing regression models. M.: VTS RAN; 2010. 60 p. (In Russ.).

Belykh Tatyana Ivanovna
Candidate of Physical and Mathematical Sciences Associate Professor

Baikal State University

Irkutsk, Russian Federation

Burdukovskaya Anna Valerievna
Candidate of Physical and Mathematical Sciences Associate Professor

Irkutsk State University

Irkutsk, Russian Federation

Ivonina Olga Yurievna
Candidate of Agricultural Sciences Associate Professor

Irkutsk State Agrarian University named after A.A. Yezhevsky

Irkutsk, Russian Federation

Rodionov Aleksei Vladimirovich

Baikal State University

Irkutsk, Russian Federation

Keywords: mathematical modeling, regression model, determination coefficient, statistical significance of the model, arbor Acres cross broilers, microclimate

For citation: Belykh T.I. Burdukovskaya A.V. Ivonina O.Y. Rodionov A.V. Mathematical modeling of microclimate indicator influence on the intensification of agricultural production on the example of broiler farming. Modeling, Optimization and Information Technology. 2023;11(1). Available from: https://moitvivt.ru/ru/journal/pdf?id=1305 DOI: 10.26102/2310-6018/2023.40.1.018 (In Russ).

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Full text in PDF

Received 11.01.2023

Revised 09.02.2023

Accepted 02.03.2023

Published 03.03.2023