Global trends and research frontiers on machine learning in sustainable animal production in times of climate change: Bibliometric analysis aimed at insights and orientations for the coming decades
According to topics, such as climate change, global population, animal production and food security, it is important improving food production systems' sustainability and getting to know that using machine learning in sustainable animal production in times of climate change will be a useful too...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-06-01
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| Series: | Environmental and Sustainability Indicators |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2665972724002319 |
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| author | Robson Mateus Freitas Silveira Concepta Mcmanus Iran José Oliveira da Siva |
| author_facet | Robson Mateus Freitas Silveira Concepta Mcmanus Iran José Oliveira da Siva |
| author_sort | Robson Mateus Freitas Silveira |
| collection | DOAJ |
| description | According to topics, such as climate change, global population, animal production and food security, it is important improving food production systems' sustainability and getting to know that using machine learning in sustainable animal production in times of climate change will be a useful tool to increase food production with guaranteed animal welfare by reducing carbon and water footprints. The present pioneering review provides a longitudinal perspective on the current state of academic research in the emerging machine learning field linked to sustainable animal production in times of climate change. The study will provide scholars and professionals with a holistic view of the current state of studies, opportunities and associated risks on this topic, and pathways for future research in this emerging and promising field. In total, 1082 documents published in the last 70 years, in Scopus Database, were selected for the study. The annual growth rate recorded for publications in this field reached 3.78% per year, with 31.52% international contribution and 22.2 citations per document. The main insights generated in the bibliometric analysis were (i) sustainable animal production changed from unidisciplinary science to multidisciplinary science linked to agricultural, environmental and engineering sciences, mainly to genetics and computing; (ii) the concept of sustainable animal production emerged from animal welfare and climate change concepts found in UN's 2030 Agenda; (iii) omics sciences, greenhouse gases, energy efficiency and animal welfare are the main keywords for bibliometric analyses in future studies related to sustainable animal production in the coming centuries; (iii) prediction and classification analyses, i.e., supervised machine learning models used as main tools in animal production; (iv) residual feed intake applied to measure sustainable feed efficiency in animal farming in the past and nowadays; and (v) The United States, China, Brazil and Australia are the main countries publishing studies on sustainability in animal production, but only China has been gaining prominence in publications in this field, in recent years, and it will turn this country into an emerging leader in future publications on this topic. The present study provides new insights that were not previously fully captured or assessed in other reviews. Finally, improving livestock production sustainability is particularly important, because a significant part of the projected increases in the global food demand is expected to come from livestock, and artificial intelligence will certainly help producers in decision-making processes, mainly in times of climate change. |
| format | Article |
| id | doaj-art-3f717cc6f2564191907bc1bd6027863b |
| institution | Kabale University |
| issn | 2665-9727 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Environmental and Sustainability Indicators |
| spelling | doaj-art-3f717cc6f2564191907bc1bd6027863b2025-02-12T05:32:46ZengElsevierEnvironmental and Sustainability Indicators2665-97272025-06-0126100563Global trends and research frontiers on machine learning in sustainable animal production in times of climate change: Bibliometric analysis aimed at insights and orientations for the coming decadesRobson Mateus Freitas Silveira0Concepta Mcmanus1Iran José Oliveira da Siva2University of São Paulo (USP), “Luiz de Queiroz” Agriculture College (ESALQ), Department of Animal Science, 13418-900, Piracicaba, São Paulo State, Brazil; University of São Paulo (USP), “Luiz de Queiroz” Agriculture College (ESALQ), Department of Biosystems Engineering, Environment Livestock Research Group (NUPEA), 13418-900, Piracicaba, São Paulo State, Brazil; Corresponding author. Department of Animal Science, “Luiz de Queiroz” Agriculture College (ESALQ), University of São Paulo (USP), 13418900, Piracicaba, São Paulo State, Brazil.Center for Nuclear Energy in Agriculture, University of São Paulo (CENA), 13418-900, Piracicaba, São Paulo State, BrazilUniversity of São Paulo (USP), “Luiz de Queiroz” Agriculture College (ESALQ), Department of Biosystems Engineering, Environment Livestock Research Group (NUPEA), 13418-900, Piracicaba, São Paulo State, BrazilAccording to topics, such as climate change, global population, animal production and food security, it is important improving food production systems' sustainability and getting to know that using machine learning in sustainable animal production in times of climate change will be a useful tool to increase food production with guaranteed animal welfare by reducing carbon and water footprints. The present pioneering review provides a longitudinal perspective on the current state of academic research in the emerging machine learning field linked to sustainable animal production in times of climate change. The study will provide scholars and professionals with a holistic view of the current state of studies, opportunities and associated risks on this topic, and pathways for future research in this emerging and promising field. In total, 1082 documents published in the last 70 years, in Scopus Database, were selected for the study. The annual growth rate recorded for publications in this field reached 3.78% per year, with 31.52% international contribution and 22.2 citations per document. The main insights generated in the bibliometric analysis were (i) sustainable animal production changed from unidisciplinary science to multidisciplinary science linked to agricultural, environmental and engineering sciences, mainly to genetics and computing; (ii) the concept of sustainable animal production emerged from animal welfare and climate change concepts found in UN's 2030 Agenda; (iii) omics sciences, greenhouse gases, energy efficiency and animal welfare are the main keywords for bibliometric analyses in future studies related to sustainable animal production in the coming centuries; (iii) prediction and classification analyses, i.e., supervised machine learning models used as main tools in animal production; (iv) residual feed intake applied to measure sustainable feed efficiency in animal farming in the past and nowadays; and (v) The United States, China, Brazil and Australia are the main countries publishing studies on sustainability in animal production, but only China has been gaining prominence in publications in this field, in recent years, and it will turn this country into an emerging leader in future publications on this topic. The present study provides new insights that were not previously fully captured or assessed in other reviews. Finally, improving livestock production sustainability is particularly important, because a significant part of the projected increases in the global food demand is expected to come from livestock, and artificial intelligence will certainly help producers in decision-making processes, mainly in times of climate change.http://www.sciencedirect.com/science/article/pii/S2665972724002319Animal welfareCleaner productionFood productionTrend researchSustainability |
| spellingShingle | Robson Mateus Freitas Silveira Concepta Mcmanus Iran José Oliveira da Siva Global trends and research frontiers on machine learning in sustainable animal production in times of climate change: Bibliometric analysis aimed at insights and orientations for the coming decades Environmental and Sustainability Indicators Animal welfare Cleaner production Food production Trend research Sustainability |
| title | Global trends and research frontiers on machine learning in sustainable animal production in times of climate change: Bibliometric analysis aimed at insights and orientations for the coming decades |
| title_full | Global trends and research frontiers on machine learning in sustainable animal production in times of climate change: Bibliometric analysis aimed at insights and orientations for the coming decades |
| title_fullStr | Global trends and research frontiers on machine learning in sustainable animal production in times of climate change: Bibliometric analysis aimed at insights and orientations for the coming decades |
| title_full_unstemmed | Global trends and research frontiers on machine learning in sustainable animal production in times of climate change: Bibliometric analysis aimed at insights and orientations for the coming decades |
| title_short | Global trends and research frontiers on machine learning in sustainable animal production in times of climate change: Bibliometric analysis aimed at insights and orientations for the coming decades |
| title_sort | global trends and research frontiers on machine learning in sustainable animal production in times of climate change bibliometric analysis aimed at insights and orientations for the coming decades |
| topic | Animal welfare Cleaner production Food production Trend research Sustainability |
| url | http://www.sciencedirect.com/science/article/pii/S2665972724002319 |
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