sustAInable: AI-Driven Research for Sustainable Agri-Food Futures

This proposal investigates how artificial intelligence (AI) is reshaping scientific research within sustainable agri-food systems, focusing on workforce transformation and AI adoption across sectors. AI holds transformative potential for science—enhancing prediction, discovery, and optimisation—but the agri-food sector remains behind more digitally mature fields (e.g., finance) in integrating these capabilities into research and innovation ecosystems. Despite growing interest in AI across research, extension, and education, global and UK-specific evidence shows the agri-food sector lags significantly behind in adoption. In the UK, agri-food accounts for only 1.2% of AI development and implementation, compared to 17% in finance and 31% in IT[1]. Barriers such as limited rural connectivity, institutional fragmentation, and suboptimal research infrastructures constrain the development and application of AI-based research[2]. Moreover, a concern in the UK is the lack of translation of AI advancements into successful real-world applications[3]. These gaps limit the real-world impact of agri-food innovation and the ability to respond to urgent global challenges, including extreme temperatures, food insecurity, and shifting global diets. At the same time, the potential of AI to accelerate agri-food innovation is increasingly evident. AI tools are used to speed up crop breeding, identify bioactive molecules, detect methane-reducing compounds in livestock diets, and support early-stage discovery of functional ingredients[4]. In research, AI enables more efficient synthesis of metadata, detection of research gaps, and wiser allocation of funding[5]. These emerging tools could transform how knowledge is produced, validated, and applied across agri-food systems—from soil management to supply chains. This project responds to these trends with two core objectives: To analyse research-based employment dynamics and the demand for AI researchers within agri-food compared to other sectors globally (including the UK). To explore AI-driven science adoption through comparative case studies in mature sectors (e.g., finance and healthcare), identifying the barriers and enablers that influence AI integration into scientific research. The research will address the following questions: What are the sector-specific demands for AI researchers in agri-food systems compared to other industries, and how do skill requirements differ? What factors influence AI-driven research in agri-food compared to other sectors, and how can these be addressed to enhance research capacity and adoption? Combining job posting analytics, researcher profile analysis, case studies, interviews, and bibliometric mapping, the project offers a novel, empirically grounded understanding of how AI reshapes the agri-food research landscape.? Moreover, the fellowship is supported by robust institutional backing, interdisciplinary mentorship, and the applicant's proficiency with extensive dataset analytics. Interdisciplinary collaboration further ensures effective project delivery.? The outcomes will inform educational, workforce, and funding policies, guide research—based organisations and governments, and provide a roadmap for aligning AI capabilities with the specific needs of agri-food science. If acted upon, these insights will accelerate sustainable innovation, enhance food systems' resilience, reduce environmental externalities' impact, and build a research landscape that addresses some of society's most pressing challenges. References [1] Department for Science, Innovation & Technology, UK. (2024). Artificial Intelligence sector study 2023. [Link] [2] DEFRA (2022). Automation in horticulture review [Link] [3] Cambridge Industrial Innovation Policy. (2024). UK Innovation Report 2024. [Link] [4] Chowdhury et al.(2024). Animal Frontiers, 14(6), 33–41. https://doi.org/10.1093/af/vfae025; Doherty, A., et al. (2021). Frontiers in Genetics, 12. https://doi.org/10.3389/fgene.2021.768979 Kong, Y., et al (2024). Journal of Agricultural and Food Chemistry, 72(10), 5145–5152. https://doi.org/10.1021/acs.jafc.3c06895 [5] Raliya, R. (2024). ACS Agricultural Science & Technology, 4(6), 628–630. https://doi.org/10.1021/acsagscitech.4c00290