With the improvement of residents’ income and the upgrading of consumption structure, the demand for livestock and poultry products in China has grown rapidly, but the environmental pressure brought by animal husbandry has become increasingly prominent.

Water scarcity has become a critical constraint on global agricultural production. Particularly in tropical and subtropical regions, major crops such as maize frequently face the dual threats of drought and high temperatures.

In agricultural production, microplastics have become environmental pollutants that cannot be ignored. They may not only enter the food chain through the soil-plant system, threatening food safety, but also alter the physical and chemical properties of soil, interfere with microbial functions, and thereby affect soil health and crop growth.

Chili is a vital cash crop in many countries, and Sri Lanka is no exception. As one of the pillar industries of the country’s agriculture, chili not only meets domestic consumption needs but also is exported to markets such as India, the Middle East, and Europe in the form of processed products like dried chili and chili powder, supporting the livelihoods of numerous smallholder farmers.

As a crucial agricultural yield-increasing technique in arid and semi-arid regions, plastic film mulching (PFM) has significantly enhanced crop yield and quality by increasing soil temperature, reducing water evaporation, and optimizing nutrient cycling. However, with the increasingly prominent issue of farmland microplastic pollution caused by residual plastic films, this "white revolution" is now facing severe challenges to sustainable development.

Using Artificial Intelligence (AI) to Advance Translational Research (HEPI Policy Note 67), authored by Rose Stephenson, Director of Policy and Strategy at HEPI, and Lan Murdock, Senior Corporate Communications Manager at Taylor & Francis, draws on discussions at a roundtable of higher education leaders, researchers, AI innovators and funders, as well as a range of research case studies, to evaluate the future role of AI in translational research.