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 - Robson Mateus Freitas Silveira; Concepta Mcmanus; Iran José Oliveira da Siva (Environmental and Sustainability Indicators)
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.