Research Article
BibTex RIS Cite

Ration Preparation of Dairy Cows with an Innovative Method: A Multi-Objective Optimization Approach

Year 2023, Volume: 19 Issue: 2, 90 - 108, 30.12.2023

Abstract

The livestock sector, particularly in the realm of dairy farming, is actively immersed in diverse research initiatives aimed at grappling with economic challenges and refining feeding strategies. The process of ration preparation within this sector involves meticulous planning, intending to fulfill the daily nutritional needs of animals and optimize their performance. This comprehensive planning takes into account various factors, including the animals' characteristics, physiological conditions, productivity levels, and environmental influences. The critical aspect of selecting and determining the quantity of feeds in the ration preparation process significantly influences animal health, efficiency, and economic viability. Employing artificial intelligence, specifically the multi-objective optimization method, proves highly effective in tackling these intricate challenges. This method strives to formulate optimal rations by concurrently considering diverse objectives, such as different nutrients and cost factors. In this article, a feeding strategy within the livestock sector is crafted using Multi-Objective PSO (MOPSO), a notable multi-objective optimization method, to generate cost-effective rations. In comparison with several existing systems, the proposed method consistently demonstrates noteworthy effectiveness.

Project Number

121E098

Thanks

The experiment was supported by TÜBİTAK-ARDEB under project number of 121E098.

References

  • [1] Buryakov, N. P., Aleshin, D. E., Buryakova, M. A., Zaikina, A. S., Laptev, G. Y., Ilina, L. A., ... & Fathala, M. M. (2022). Influence of using various levels of protein concentrate in rations of Ayrshire dairy cows on rumen microbiome, reproductive traits and economic efficiency. Veterinary Sciences, 9(10), 534.
  • [2] Lee, J. H., Kim, T. K., Cha, J. Y., Jang, H. W., Yong, H. I., & Choi, Y. S. (2022). How to develop strategies to use insects as animal feed: digestibility, functionality, safety, and regulation. Journal of Animal Science and Technology, 64(3), 409.
  • [3] Gasco, L., Acuti, G., Bani, P., Dalle Zotte, A., Danieli, P. P., De Angelis, A., ... & Roncarati, A. (2020). Insect and fish by-products as sustainable alternatives to conventional animal proteins in animal nutrition. Italian Journal of Animal Science, 19(1), 360-372.
  • [4] Hristov, A. N., Melgar, A., Wasson, D., & Arndt, C. (2022). Symposium review: Effective nutritional strategies to mitigate enteric methane in dairy cattle. Journal of dairy science.
  • [5] Dumas, A., Dijkstra, J., & France, J. (2008). Mathematical modelling in animal nutrition: a centenary review. The Journal of Agricultural Science, 146(2), 123-142.
  • [6] Brodie, G., Bootes, N., Dunshea, F., & Leury, B. (2019). Microwave processing of animal feed: a brief review. Transactions of the ASABE, 62(3), 705-717.
  • [7] Monteiro, A., Santos, S., & Gonçalves, P. (2021). Precision Agriculture for Crop and Livestock Farming—Brief Review. Animals 2021, 11, 2345.
  • [8] Menendez III, H. M., Brennan, J. R., Gaillard, C., Ehlert, K., Quintana, J., Neethirajan, S., ... & Tedeschi, L. O. (2022). ASAS–NANP Symposium: Mathematical Modeling in Animal Nutrition: Opportunities and challenges of confined and extensive precision livestock production. Journal of Animal Science, 100(6), skac160.
  • [9] Poppi, D. P., and McLennan, S. R. (2010). Nutritional research to meet future challenges. Anim. Prod. Sci. 50, 329–338. doi: 10.1071/AN09230
  • [10] [NRC (2006). Nutrient Requirements of Dogs and Cats. Washington, DC: The National Academies Press.
  • [11] NRC (2016). Nutrient Requirements of Beef Cattle. Washington, DC: National Academy Press.
  • [12] INRA Noziere, P., Sauvant, D., and Delaby, L. (2018). INRA Feeding System for Ruminants. Wageningen: Wageningen Academic Publishers.
  • [13] Daniel, J. B., Van Laar, H., Dijkstra, J., and Sauvant, D. (2020). Evaluation of predicted ration nutritional values by NRC (2001) and INRA (2018) feed evaluation systems, and implications for the prediction of milk response. J. Dairy Sci. 103,1–17. doi: 10.3168/jds.2020-18286
  • [14] Harmon, D. L. (2020). Grand challenge in animal nutrition. Frontiers in Animal Science, 1, 621638.
  • [15] Fister Jr, I., Yang, X. S., Fister, I., Brest, J., & Fister, D. (2013). A brief review of nature-inspired algorithms for optimization. arXiv preprint arXiv:1307.4186.
  • [16] Uyeh, D. D., Pamulapati, T., Mallipeddi, R., Park, T., Asem-Hiablie, S., Woo, S., ... & Ha, Y. (2019). Precision animal feed formulation: An evolutionary multi-objective approach. Animal Feed Science and Technology, 256, 114211.
  • [17] Rahman, R. A., Ramli, R., Jamari, Z., & Ku-Mahamud, K. R. (2015, August). Evolutionary algorithm approach for solving animal diet formulation. In Proc. 5th Int. Conf. Comput. Informatics, ICOCI (No. 32, pp. 274-279).
  • [18] Coello, C. A. C., Pulido, G. T., & Lechuga, M. S. (2004). Handling multiple objectives with particle swarm optimization. IEEE Transactions on evolutionary computation, 8(3), 256-279.
  • [19] Serkan, K. A. Y. A., & FIĞLALI, N. (2016). Çok Amaçlı Optimizasyon Problemlerinde Pareto Optimal Kullanımı. Sosyal Bilimler Araştırma Dergisi, 5(2), 9-18.
  • [20] Kutlu, H. R., Görgülü, M., & Çelik, L. B. (2005). Genel hayvan besleme ders notu. Çukurova Üniversitesi Ziraat

Süt İneklerinin Yenilikçi Bir Yöntemle Rasyon Hazırlanması: Çok Amaçlı Bir Optimizasyon Yaklaşımı

Year 2023, Volume: 19 Issue: 2, 90 - 108, 30.12.2023

Abstract

Hayvancılık sektörü, özellikle süt sığırcılığında karşılaşılan ekonomik zorluklarla baş etmek ve besleme stratejilerini geliştirmek amacıyla çeşitli araştırmalara odaklanmaktadır. Bu sektörde rasyon hazırlama süreci, hayvanların günlük besin ihtiyaçlarını karşılamayı ve optimal verim elde etmeyi amaçlayan kapsamlı bir planlamayı içermektedir. Bu süreç, hayvanların özellikleri, fizyolojik durumları, verim düzeyleri ve çevresel faktörler dikkate alınarak gerçekleştirilir. Rasyon hazırlama sürecinde kullanılacak yemlerin seçimi ve miktarı, hayvan sağlığı, verimliliği ve ekonomik etkinlik açısından kritik öneme sahiptir. Yapay zekanin çok amaçlı optimizasyon yöntemi, bu tur problemlerini çözmek için özellikle etkilidir. Bu yöntem, farklı besin maddeleri ve maliyet faktörleri gibi çeşitli amaçları gözeterek, optimal rasyonları oluşturmayı amaçlar. Bu makalede çok amaçlı optimizasyon yöntemlerinde biri olan MOPSO ile hayvancılık sektöründe bir besleme stratejisini geliştirerek, maliyeti etkin rasyonlar oluşturmaktadır. Geliştirilen yöntem birkaç mevcut sistem ile karşilaştirildiğinda önerilen yöntemin etkili olduğu gözukmektedir.

Project Number

121E098

References

  • [1] Buryakov, N. P., Aleshin, D. E., Buryakova, M. A., Zaikina, A. S., Laptev, G. Y., Ilina, L. A., ... & Fathala, M. M. (2022). Influence of using various levels of protein concentrate in rations of Ayrshire dairy cows on rumen microbiome, reproductive traits and economic efficiency. Veterinary Sciences, 9(10), 534.
  • [2] Lee, J. H., Kim, T. K., Cha, J. Y., Jang, H. W., Yong, H. I., & Choi, Y. S. (2022). How to develop strategies to use insects as animal feed: digestibility, functionality, safety, and regulation. Journal of Animal Science and Technology, 64(3), 409.
  • [3] Gasco, L., Acuti, G., Bani, P., Dalle Zotte, A., Danieli, P. P., De Angelis, A., ... & Roncarati, A. (2020). Insect and fish by-products as sustainable alternatives to conventional animal proteins in animal nutrition. Italian Journal of Animal Science, 19(1), 360-372.
  • [4] Hristov, A. N., Melgar, A., Wasson, D., & Arndt, C. (2022). Symposium review: Effective nutritional strategies to mitigate enteric methane in dairy cattle. Journal of dairy science.
  • [5] Dumas, A., Dijkstra, J., & France, J. (2008). Mathematical modelling in animal nutrition: a centenary review. The Journal of Agricultural Science, 146(2), 123-142.
  • [6] Brodie, G., Bootes, N., Dunshea, F., & Leury, B. (2019). Microwave processing of animal feed: a brief review. Transactions of the ASABE, 62(3), 705-717.
  • [7] Monteiro, A., Santos, S., & Gonçalves, P. (2021). Precision Agriculture for Crop and Livestock Farming—Brief Review. Animals 2021, 11, 2345.
  • [8] Menendez III, H. M., Brennan, J. R., Gaillard, C., Ehlert, K., Quintana, J., Neethirajan, S., ... & Tedeschi, L. O. (2022). ASAS–NANP Symposium: Mathematical Modeling in Animal Nutrition: Opportunities and challenges of confined and extensive precision livestock production. Journal of Animal Science, 100(6), skac160.
  • [9] Poppi, D. P., and McLennan, S. R. (2010). Nutritional research to meet future challenges. Anim. Prod. Sci. 50, 329–338. doi: 10.1071/AN09230
  • [10] [NRC (2006). Nutrient Requirements of Dogs and Cats. Washington, DC: The National Academies Press.
  • [11] NRC (2016). Nutrient Requirements of Beef Cattle. Washington, DC: National Academy Press.
  • [12] INRA Noziere, P., Sauvant, D., and Delaby, L. (2018). INRA Feeding System for Ruminants. Wageningen: Wageningen Academic Publishers.
  • [13] Daniel, J. B., Van Laar, H., Dijkstra, J., and Sauvant, D. (2020). Evaluation of predicted ration nutritional values by NRC (2001) and INRA (2018) feed evaluation systems, and implications for the prediction of milk response. J. Dairy Sci. 103,1–17. doi: 10.3168/jds.2020-18286
  • [14] Harmon, D. L. (2020). Grand challenge in animal nutrition. Frontiers in Animal Science, 1, 621638.
  • [15] Fister Jr, I., Yang, X. S., Fister, I., Brest, J., & Fister, D. (2013). A brief review of nature-inspired algorithms for optimization. arXiv preprint arXiv:1307.4186.
  • [16] Uyeh, D. D., Pamulapati, T., Mallipeddi, R., Park, T., Asem-Hiablie, S., Woo, S., ... & Ha, Y. (2019). Precision animal feed formulation: An evolutionary multi-objective approach. Animal Feed Science and Technology, 256, 114211.
  • [17] Rahman, R. A., Ramli, R., Jamari, Z., & Ku-Mahamud, K. R. (2015, August). Evolutionary algorithm approach for solving animal diet formulation. In Proc. 5th Int. Conf. Comput. Informatics, ICOCI (No. 32, pp. 274-279).
  • [18] Coello, C. A. C., Pulido, G. T., & Lechuga, M. S. (2004). Handling multiple objectives with particle swarm optimization. IEEE Transactions on evolutionary computation, 8(3), 256-279.
  • [19] Serkan, K. A. Y. A., & FIĞLALI, N. (2016). Çok Amaçlı Optimizasyon Problemlerinde Pareto Optimal Kullanımı. Sosyal Bilimler Araştırma Dergisi, 5(2), 9-18.
  • [20] Kutlu, H. R., Görgülü, M., & Çelik, L. B. (2005). Genel hayvan besleme ders notu. Çukurova Üniversitesi Ziraat
There are 20 citations in total.

Details

Primary Language English
Subjects Algorithms and Calculation Theory
Journal Section Articles
Authors

Muhammed Milani 0000-0003-2450-0280

Muhlis Macit 0000-0002-5055-1156

Feyza Hepkarşı 0000-0001-6891-8502

Project Number 121E098
Publication Date December 30, 2023
Submission Date December 20, 2023
Acceptance Date December 30, 2023
Published in Issue Year 2023 Volume: 19 Issue: 2

Cite

APA Milani, M., Macit, M., & Hepkarşı, F. (2023). Ration Preparation of Dairy Cows with an Innovative Method: A Multi-Objective Optimization Approach. Electronic Letters on Science and Engineering, 19(2), 90-108.