Agriculture (Sep 2022)

Technology of Microclimate Regulation in Organic and Energy-Sustainable Livestock Production

  • Zbynek Havelka,
  • Radim Kunes,
  • Yevhen Kononets,
  • Jessica Elizabeth Stokes,
  • Lubos Smutny,
  • Pavel Olsan,
  • Jan Kresan,
  • Radim Stehlik,
  • Petr Bartos,
  • Maohua Xiao,
  • Pavel Kriz,
  • Pavol Findura,
  • David Roztocil

DOI
https://doi.org/10.3390/agriculture12101563
Journal volume & issue
Vol. 12, no. 10
p. 1563

Abstract

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The control of climatic conditions where cattle are kept is one of the challenges in the livestock sector regarding the digital automation of the process. (1) Background: The main purpose of this study is to define the optimal foundations for automatic climatic systems in organic and energy-sustainable livestock production. In particular, the following components are suggested: (a) the determination of current deviations and interdependency between factors; (b) an algorithm for defining the possible sources of regulation; (c) the ranking approach of the optimal sequence of possible sources; and (d) ensuring transparency and coordination of the model with organic and energy certificates. (2) Methods: This investigation accumulates information on the characteristics of the main microclimatic parameters and simulates their possible combinations in a livestock building in Poland within 24 h of a spring day. A few indices are considered that signal the impact on the thermal comfort of cattle based on the example of recommended measures for the Angus steer genotype. (3) Results: The proposed transparent algorithm is designed for selecting and ranking potential sources of microclimate control according to three criteria. (4) Conclusions: This paper potentially contributes to determining the most optimal digital algorithm for managing microclimate conditions to ensure acceptable comfort for animals, meeting the requirements of organic certification with minimum costs of production, and switching to sustainable types of energy with consideration of technologies’ efficiency. The algorithm is scalable and adjustable to the individual conditions of any livestock premise with a digitally controlled environment.

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