https://doi.org/10.4081/jae.2025.1718
A study comparing energy consumption and environmental emissions in ostrich meat and egg production
Published: 11 February 2025
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All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
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The assessment of energy usage in the production of ostrich meat and eggs provides a comprehensive analysis of energy consumption and production efficiency. The energy consumption per 1000 units is 1,086,825.54 MJ for meat and 1,197,794.25 MJ for egg. When considering protein supply, egg production seems to be more justifiable in terms of energy efficiency compared to meat production. This study delves into the impact of egg and meat production on human health, revealing a slight difference of 0.23 disability adjusted life years (DALY), hinting that egg production could potentially have marginally more negative health effects than meat production. Artificial neural network (ANN) analysis indicates that optimizing machinery, diesel fuel, and energy usage can enhance the productivity of meat production. It also suggests that there is a possibility for greater resource efficiency in egg production as opposed to meat production, highlighting that a focus on resource efficiency within egg production could yield positive environmental and energy benefits. Additionally, the coefficient of determination in the adaptive neuro-fuzzy inference system (ANFI) 4 model indicates a more favorable outcome for factors related to meat production compared to those for eggs production. Moreover, the low mean squared error value reflects the high accuracy of the results obtained in the analysis.
Abdollahizad, S., Balafar, M.A., Feizizadeh, B., et al. 2021. Using hybrid artificial intelligence approach based on a neuro-fuzzy system and evolutionary algorithms for modeling landslide susceptibility in East Azerbaijan Province, Iran. Earth Sci. Inform. 14:1861–82.
DOI: https://doi.org/10.1007/s12145-021-00644-z
Adams, J., Revell, B.J. 1998. Ostrich farming:-a review and feasibility study of opportunities in the EU. Newport, School of Management Harper Adams University College. Available from: https://macaulay.webarchive.hutton.ac.uk/livestocksystems/feasibility/osr.pdf
Alluvione F, Moretti B, Sacco D, Grignani C. 2011. EUE (energy use efficiency) of cropping systems for a sustainable agriculture. Energy 36:4468–81.
DOI: https://doi.org/10.1016/j.energy.2011.03.075
Alves, E.C., dos Santos Alves, I.H., Soares, B.B., et al. 2023. Resource recovery of biological residues from the Brazilian poultry industry in mitigating environmental impacts: A life cycle assessment (LCA) approach. J. Clean Prod. 416:137895.
DOI: https://doi.org/10.1016/j.jclepro.2023.137895
Asem-Hiablie, S., Battagliese, T., Stackhouse-Lawson, K.R., Alan Rotz, C. 2019. A life cycle assessment of the environmental impacts of a beef system in the USA. Int J Life Cycle Assess 24:441–55.
DOI: https://doi.org/10.1007/s11367-018-1464-6
Barends-Jones, V., Pienaar, L. 2020. The South African Ostrich Industry Footprint. Available from https://www.elsenburg.com/wp-content/uploads/2022/03/2020-Ostrich-Industry-Footprint.pdf
Bhavani, Y.V.K., Hatture, D.S.M., Pagi, D.V.B., Saboji, D.S.V. 2023. An analytical review on traditional farming and smart farming: various technologies around smart farming. Int. J. Innov. Eng. Manag. 12:66-85.
DOI: https://doi.org/10.2139/ssrn.4381020
Brand, Z., Brand, T.S., Brown, C.R. 2003. The effect of dietary energy and protein levels on production in breeding female ostriches. Br Poult Sci 44:598–606.
DOI: https://doi.org/10.1080/00071660310001618343
Chau, C.K., Leung, T.M., Ng, W.Y. 2015. A review on life cycle assessment, life cycle energy assessment and life cycle carbon emissions assessment on buildings. Appl. Ener. 143:395–413.
DOI: https://doi.org/10.1016/j.apenergy.2015.01.023
Cochran, W.G. 1977. The estimation of sample size. Sampl.Tech. 3:72-90.
de Vries, M., de Boer, I.J.M. 2010. Comparing environmental impacts for livestock products: A review of life cycle assessments. Livest. Sci. 128:1–11.
DOI: https://doi.org/10.1016/j.livsci.2009.11.007
Dongare, A.D., Kharde, R.R., Kachare, A.D. 2008. Introduction to artificial neural network. Certif. Int. J. Eng. Innov. Technol. 9001:2277–3754.
Estrada-González, I.E., Taboada-González, P.A., Guerrero-García-Rojas, H., Márquez-Benavides, L. 2020. Decreasing the environmental impact in an egg-producing farm through the application of LCA and lean tools. Appl Sci 10:1352.
DOI: https://doi.org/10.3390/app10041352
Fnais, A., Rezgui, Y., Petri, I., et al. 2022. The application of life cycle assessment in buildings: challenges, and directions for future research. Int. J. Life Cycle Assess. 27:627–54.
DOI: https://doi.org/10.1007/s11367-022-02058-5
Ghasemi-Mobtaker, H., Kaab, A., Rafiee, S., Nabavi-Pelesaraei, A. 2022. A comparative of modeling techniques and life cycle assessment for prediction of output energy, economic profit, and global warming potential for wheat farms. Energy Rep. 8:4922–34.
DOI: https://doi.org/10.1016/j.egyr.2022.03.184
Guinée, J.B., Heijungs, R., Huppes, G., et al. 2010. Life cycle assessment: past, present, and future. Environ. Sci. Technol. 45:90–6.
DOI: https://doi.org/10.1021/es101316v
Hakim, S.J.S., Razak, H.A. 2013. Adaptive neuro fuzzy inference system (ANFIS) and artificial neural networks (ANNs) for structural damage identification. Struct. Eng. Mech. 45:779–802.
DOI: https://doi.org/10.12989/sem.2013.45.6.779
Kaab, A., Sharifi, M., Mobli, H., et al. 2019. Combined life cycle assessment and artificial intelligence for prediction of output energy and environmental impacts of sugarcane production. Sci. Total Environ. 664:1005-1019.
DOI: https://doi.org/10.1016/j.scitotenv.2019.02.004
Kaab, A., Sharifi, M., Moradi, H. 2021. Analysis of energy indicators and environmental impacts of dryland cantaloupe production with life cycle assessment approach (case study: Ilam). J. Agric. Mach. 11:491–504.
Kanwisher, N., Khosla, M., Dobs, K. 2023. Using artificial neural networks to ask ‘why’ questions of minds and brains. Trends Neurosci. 46:240–54.
DOI: https://doi.org/10.1016/j.tins.2022.12.008
Khalid, M., ur Rehman, S., Muhammad Azam, S., et al. 2022. Effect of commercial & natural feeds on the growth performance of ostrich (Struthio camelus) in captivity. Front Chem. Sci. 3:25–30.
DOI: https://doi.org/10.52700/fcs.v3i1.37
Kitani, O. 1999. CIGR handbook of agricultural engineering, Volume V Energy and Biomass Engineering, Chapter 1 Natural Energy and Biomass, Part 1.3 Biomass Resources. St. Joseph, American Society of Agricultural and Biological Engineers. pp. 6-11.
Kolawole, A.S., Iyiola, A.O. 2023. Environmental pollution: threats, impact on biodiversity, and protection strategies. In: Izah S.C., Ogwu M.C. (eds.), Sustainable utilization and conservation of Africa’s biological resources and environment. Sustainable development and biodiversity. Singapore, Springer. pp. 377–409.
DOI: https://doi.org/10.1007/978-981-19-6974-4_14
Kumar, S., Priyadarshini, M., Ahmad, A., Ghangrekar, M.M. 2023. Advanced biological and non-biological technologies for carbon sequestration, wastewater treatment, and concurrent valuable recovery: A review. J. CO2 Util. 68:102372.
DOI: https://doi.org/10.1016/j.jcou.2022.102372
Lai, X., Chen, Q., Tang, X., et al. 2022. Critical review of life cycle assessment of lithium-ion batteries for electric vehicles: A lifespan perspective. ETransportation 12:100169.
DOI: https://doi.org/10.1016/j.etran.2022.100169
Llonch, P., Haskell, M.J., Dewhurst, R.J., Turner, S.P. 2017. Current available strategies to mitigate greenhouse gas emissions in livestock systems: an animal welfare perspective. Animal 11:274–84.
DOI: https://doi.org/10.1017/S1751731116001440
Liu, X., Elgowainy, A., Wang, M. 2020. Life cycle energy use and greenhouse gas emissions of ammonia production from renewable resources and industrial by-products. Green Chem. 22:5751–61.
DOI: https://doi.org/10.1039/D0GC02301A
López-Andrés, J.J., Aguilar-Lasserre, A.A., Morales-Mendoza, L.F., et al. 2018. Environmental impact assessment of chicken meat production via an integrated methodology based on LCA, simulation and genetic algorithms. J. Clean Prod. 174:477–91.
DOI: https://doi.org/10.1016/j.jclepro.2017.10.307
Maind, S.B., Wankar, P. 2014. Research paper on basic of artificial neural network. Int. J. Recent Innov. Trends Comput. Commun. 2:96-100.
Manap, K., Serikkyzy, M. 2023. Production of ostrich meat pâtés: Design of a food safety management system. Food Sci Technol Int. 29:847-856.
DOI: https://doi.org/10.1177/10820132221124195
May, G., Barletta, I., Stahl, B., Taisch, M.2015. Energy management in production: A novel method to develop key performance indicators for improving energy efficiency. Appl Energy 149:46–61.
DOI: https://doi.org/10.1016/j.apenergy.2015.03.065
Menghi, R., Papetti, A., Germani, M., Marconi, M. 2019. Energy efficiency of manufacturing systems: A review of energy assessment methods and tools. J. Clean Prod. 240:118276.
DOI: https://doi.org/10.1016/j.jclepro.2019.118276
Ministry of Agriculture Jihad, 2001. [Annual Agricultural Statistics].[in Persian]. Available from: www.maj.ir
Mohammadi Kashka, F., Tahmasebi Sarvestani, Z., Pirdashti, H., et al. 2023. Sustainable systems engineering using life cycle assessment: application of artificial intelligence for predicting agro-environmental footprint. Sustainability (Basel) 15:6326.
DOI: https://doi.org/10.3390/su15076326
Nabavi-Pelesaraei, A., Amid, S., Shoja, H. 2014. Energy use and greenhouse gas emission analysis for sugar beet production under three cultivated area levels. Trends Life Sci. 3:10–22.
Niaze, A.A., Sahu, R., Sunkara, M.K., Upadhyayula, S. 2023. Model construction and optimization for raising the concentration of industrial bioethanol production by using a data-driven ANN model. Renew. Energy 216:119031.
DOI: https://doi.org/10.1016/j.renene.2023.119031
Pahlavani hamid, Dehghani, A.A., Bahremand, A.R., Shojaei, S. 2017. Intelligent estimation of flood hydrographs using an adaptive neuro–fuzzy inference system (ANFIS). Model. Earth Syst. Environ. 3:35.
DOI: https://doi.org/10.1007/s40808-017-0305-0
Ponnampalam, E.N., Holman, B.W.B. 2023. Sustainability II: Sustainable animal production and meat processing. Lawrie’s Meat Sci. 2023:727–98.
DOI: https://doi.org/10.1016/B978-0-323-85408-5.00001-7
Ramedani, Z., Alimohammadian, L., Kheialipour, K., et al. 2019. Comparing energy state and environmental impacts in ostrich and chicken production systems. Environ. Sci. Pollut. Res. 26:28284–93.
DOI: https://doi.org/10.1007/s11356-019-05972-8
Shibak, A., Maghsoudi, A., Rokouei, M., et al. 2023. Investigation of egg production curve in ostrich using nonlinear functions. Poult. Sci.102:102333.
DOI: https://doi.org/10.1016/j.psj.2022.102333
Subha, S., Nagalakshmi, S. 2021. Design of ANFIS controller for intelligent energy management in smart grid applications. J. Ambient. Intell. Humaniz. Comput. 12:6117–27.
DOI: https://doi.org/10.1007/s12652-020-02180-y
van der Werf, H.M.G., Knudsen, M.T., Cederberg, C. 2020. Towards better representation of organic agriculture in life cycle assessment. Nat. Sustain. 3:419–425.
DOI: https://doi.org/10.1038/s41893-020-0489-6
Zhang, Z., Jin, Y., Chen, B., Brown, P. 2019. California almond yield prediction at the orchard level with a machine learning approach. Front. Plant Sci. 10:444608.
DOI: https://doi.org/10.3389/fpls.2019.00809
Zhu, X., Labianca, C., He, M., et al. 2022. Life-cycle assessment of pyrolysis processes for sustainable production of biochar from agro-residues. Bioresour. Technol. 360:127601.
DOI: https://doi.org/10.1016/j.biortech.2022.127601
How to Cite
Behboodi, B. (2025) “A study comparing energy consumption and environmental emissions in ostrich meat and egg production”, Journal of Agricultural Engineering, 56(1). doi: 10.4081/jae.2025.1718.
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