Journal of Robotics Spectrum


An Evaluation of Smart Livestock Feeding Strategies



Journal of Robotics Spectrum

Received On : 25 January 2023

Revised On : 28 February 2023

Accepted On : 05 March 2023

Published On : 16 March 2023

Volume 01, 2023

Pages : 066-077


Abstract


The wasteful utilization of feeds is associated with a decrease in profitability. As the demand for feed increases in the future and the competition between food, feed, and fuel intensifies, it is anticipated that there will be significant environmental and social ramifications. The increasing demand for cattle products has given rise to various social, economic, and ecological concerns. This article examines various feeding techniques, encompassing the utilization of smart technology. The implementation of digital technology has facilitated the adoption of a farming technique known as "smart livestock feeding," which ensures the provision of nutritionally balanced food to animals. The result is the production of animals that exhibit improved health conditions and require reduced amounts of both sustenance and medical attention. Farmers can enhance their profits from the trade of leaner and more efficient cattle through the reduction of costs. The significance of this issue arises from the challenges faced by numerous farms worldwide, including factors such as disease outbreaks and insufficient availability of animal feed. The practice of intelligently feeding cattle incorporates advanced technologies such as predictive analytics, big data, and Internet of Things (IoT), information and communication technology (ICT), artificial intelligence, and genomics.


Keywords


Smart Livestock Feeding, Scheduled Feeding, Limit Feeding, Full Feeding, Free Access Feeding, Supplemental Feeding.


  1. A. Wilfart, S. Espagnol, S. Dauguet, A. Tailleur, A. Gac, and F. Garcia-Launay, “ECOALIM: A dataset of environmental impacts of feed ingredients used in french animal production,” PLoS One, vol. 11, no. 12, p. e0167343, 2016.
  2. M. Trotter and Central Queensland University Institute for Future Farming Systems, Australia, “Precision livestock farming and pasture management systems,” in Precision agriculture for sustainability, Burleigh Dodds Science Publishing, 2018, pp. 421–459.
  3. K. Satyanarayan, V. Jagadeeswary, P. Belakeri, A. Babu, and Y. Srinivas, “Develop and evaluate use of mobile application for cattle farming: A new generation technology transfer for sustainable dairy production,” Int. J. Livest. Res., no. 0, p. 1, 2018.
  4. G. H. Hopson, “The importance of cooperation between the milking machine industry and the milk sanitarian in milking machine sanitation,” J. Milk Food Technol., vol. 6, no. 1, pp. 39–44, 1943.
  5. D. M. Karcher and J. A. Mench, “Overview of commercial poultry production systems and their main welfare challenges,” in Advances in Poultry Welfare, Elsevier, 2018, pp. 3–25.
  6. V. F. Fedorenko et al., “Analysis of different options of use of milking robots in dairy livestock,” Mach. Equip. Rural Area, no. 7, pp. 33–37, 2021.
  7. E. Yurtman et al., “Archaeogenetic analysis of Neolithic sheep from Anatolia suggests a complex demographic history since domestication,” Commun. Biol., vol. 4, no. 1, p. 1279, 2021.
  8. M. T. Konrad, H. Ø. Nielsen, A. B. Pedersen, and K. Elofsson, “Drivers of farmers’ investments in nutrient abatement technologies in five Baltic sea countries,” Ecol. Econ., vol. 159, pp. 91–100, 2019.
  9. “Research of qualitative indicators of use of milking robots,” SCIENCE IN THE CENTRAL RUSSIA, no. 6, pp. 35–42, 2019.
  10. C. B. Nam, “Report on statistical uses of administrative records (statistical policy working paper 6 of the office of federal statistical policy and standards),” J. Am. Stat. Assoc., vol. 78, no. 382, p. 496, 1983.
  11. B. F. Stanton, E. O. Heady, G. L. Johnson, and L. S. Hardin, “Resource productivity, returns to scale, and farm size,” J. Farm Econ., vol. 38, no. 4, p. 1068, 1956.
  12. E. Pellegrino, S. Bedini, M. Nuti, and L. Ercoli, “Author Correction: Impact of genetically engineered maize on agronomic, environmental and toxicological traits: a meta-analysis of 21 years of field data,” Sci. Rep., vol. 8, no. 1, 2018.
  13. C. J. Byrd, J. S. Johnson, J. S. Radcliffe, B. A. Craig, S. D. Eicher, and D. C. Lay Jr, “Nonlinear analysis of heart rate variability for evaluating the growing pig stress response to an acute heat episode,” Animal, vol. 14, no. 2, pp. 379–387, 2020.
  14. M. D. Salman, M. E. King, K. G. Odde, and R. G. Mortimer, “Annual costs associated with disease incidence and prevention in Colorado cow-calf herds participating in rounds 2 and 3 of the National Animal Health Monitoring System from 1986 to 1988,” J. Am. Vet. Med. Assoc., vol. 198, no. 6, pp. 968–973, 1991.
  15. J. S. Mogil, D. S. J. Pang, G. G. Silva Dutra, and C. T. Chambers, “The development and use of facial grimace scales for pain measurement in animals,” Neurosci. Biobehav. Rev., vol. 116, pp. 480–493, 2020.
  16. C. Guo, S. Su, K.-K. R. Choo, P. Tian, and X. Tang, “A provably secure and efficient range query scheme for outsourced encrypted uncertain data from cloud-based internet of things systems,” IEEE Internet Things J., vol. 9, no. 3, pp. 1848–1860, 2022.
  17. X. Ning and J. Jiang, “Defense-in-depth against insider attacks in cyber-physical systems,” Internet of Things and Cyber-Physical Systems, vol. 2, pp. 203–211, 2022.
  18. E. Nalon and P. Stevenson, “Addressing lameness in farmed animals: An urgent need to achieve compliance with EU animal welfare law,” Animals (Basel), vol. 9, no. 8, p. 576, 2019.
  19. L. V. Alarcón, A. Allepuz, and E. Mateu, “Correction to: Biosecurity in pig farms: a review,” Porcine Health Manag., vol. 7, no. 1, p. 24, 2021.
  20. E. S. Klappe, R. Cornet, D. A. Dongelmans, and N. F. de Keizer, “Inaccurate recording of routinely collected data items influences identification of COVID-19 patients,” Int. J. Med. Inform., vol. 165, no. 104808, p. 104808, 2022.
  21. D. Lefebvre, D. Lips, and J. M. Giffroy, “The European Convention for the Protection of Pet Animals and tail docking in dogs,” Rev. Sci. Tech., vol. 26, no. 3, pp. 619–628, 2007.
  22. “Advances in farm animal genomic resources (GENOMIC-RESOURCES),” Esf.org. [Online]. Available: http://archives.esf.org/coordinating-research/research-networking-programmes/life-earth-and-environmental-sciences-lee/current-esf-research-networking-programmes-in-life-earth-and-environmental-sciences/advances-in-farm-animal-genomic-resources-genomic-resources.html. [Accessed: 22-Jul-2023].
  23. J. Amin, M. Sharif, A. Haldorai, M. Yasmin, and R. S. Nayak, “Brain tumor detection and classification using machine learning: a comprehensive survey,” Complex & Intelligent Systems, vol. 8, no. 4, pp. 3161–3183, Nov. 2021, doi: 10.1007/s40747-021-00563-y.
  24. A. Haldorai and A. Ramu, “Canonical Correlation Analysis Based Hyper Basis Feedforward Neural Network Classification for Urban Sustainability,” Neural Processing Letters, vol. 53, no. 4, pp. 2385–2401, Aug. 2020, doi: 10.1007/s11063-020-10327-3.
  25. R. Subha, A. Haldorai, and A. Ramu, “An Optimal Approach to Enhance Context Aware Description Administration Service for Cloud Robots in a Deep Learning Environment,” Wireless Personal Communications, vol. 117, no. 4, pp. 3343–3358, Feb. 2021, doi: 10.1007/s11277-021-08073-3.
  26. A. Kumar, K. Abhishek, X. Liu, and A. Haldorai, “An Efficient Privacy-Preserving ID Centric Authentication in IoT Based Cloud Servers for Sustainable Smart Cities,” Wireless Personal Communications, vol. 117, no. 4, pp. 3229–3253, Nov. 2020, doi: 10.1007/s11277-020-07979-8.
  27. K. N. Durai, R. Subha, and A. Haldorai, “A Novel Method to Detect and Prevent SQLIA Using Ontology to Cloud Web Security,” Wireless Personal Communications, vol. 117, no. 4, pp. 2995–3014, Mar. 2020, doi: 10.1007/s11277-020-07243-z.
  28. A. K. Gupta, T. Maity, A. H, and Y. K. Chauhan, “An electromagnetic strategy to improve the performance of PV panel under partial shading,” Computers & Electrical Engineering, vol. 90, p. 106896, Mar. 2021, doi: 10.1016/j.compeleceng.2020.106896.
  29. S. Pal, D. K. Mishra, A. Haldorai, L. R. Parvathy, S. Janupriya, and D. V. Babu, “Machine Learning Based Real Time-Heuristic Sensor Data Analytics For Early Warning Prediction,” Oct. 2021, doi: 10.21203/rs.3.rs-1012679/v1.

Acknowledgements


Author(s) thanks to Dr.Yoni Danieli for this research completion and support.


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Cite this article


Sim Sze Yin and Yoni Danieli, “An Evaluation of Smart Livestock Feeding Strategies”, Journal of Robotics Spectrum, vol.1, pp. 066-077, 2023. doi: 10.53759/9852/JRS202301007.


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© 2023 Sim Sze Yin and Yoni Danieli. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.