Completed Food & Agriculture Engineering

Achieving Sustainable Agricultural Systems (ASSIST)

In plain English

AI plain-English summary

Farmers and policymakers are testing new ways to grow more food while shrinking agriculture’s environmental footprint, but they lack long-term, large-scale evidence to know which methods actually work. This project builds a ten-year national research capability—linking crop scientists, ecologists, and geologists—to fill that gap. Current studies tend to be short-term or focused on single factors like soil chemistry, leaving unanswered questions about how novel farming systems perform across entire landscapes and over decades. The programme will develop validated computer models, field-scale data, and a UK-wide network of commercial farms as living laboratories. If successful, it will give the agricultural industry tools to design systems that maintain or increase crop yields, reduce pollution and water use, and build resilience to climate shocks—all while keeping farms economically competitive. The work also aims to resolve trade-offs between food production and other benefits from the same land, such as carbon storage, flood control, and wildlife habitat. For the public, the payoff is a more secure food supply without sacrificing the natural systems that clean water and air.

View original technical description
Feeding the rapidly growing global population in a sustainable way is one of the greatest challenges facing humanity. Addressing this will require a flexible mix of strategies, including minimising wasteful losses of food, reducing the amount of animal products in diets, together with increasing the productivity and efficiency of the existing farmed area whilst maintaining natural capital on which it and other ecosystem services depend. The latter concept of 'sustainable intensification' (SI) of agriculture is considered central to ensuring food security and a healthy environment for future generations. However, how best to achieve this is the subject of vigorous debate. Testing fully the concept of SI will require world-leading research into agro-ecosystems, which integrates environmental, agricultural and social sciences, and is applied across a range of scales. To support the research community in achieving this goal we will build a long-term (10 year+) national capability between BBSRC and NERC institutes, Rothamsted Research (RRes), Centre for Ecology and Hydrology (CEH) and British Geological Survey (BGS). This will require a critical and stable mass of expertise from a number of disciplines, together with capacity building by training and developing early career researchers. Only by bringing together expertise in management of natural resources (CEH, BGS) with that of crop production (RRes) will we be able to examine fully the impacts of intensification on the wider environment, and develop synergistic farming systems that contribute towards environmental sustainability. The ASSIST programme - Achieving Sustainable Agricultural Systems - will develop innovative farming systems that maintain or increase productivity and resilience to future perturbations, while reducing the environmental and ecological footprint of agriculture. It will advance knowledge in five core areas: 1) identifying current and future biophysical limitations on crop productivity; 2) predicting the impacts of changes in agricultural management the wider environment, 3) understanding and enhancing the ecological processes underpinning food production; 4) developing innovative farming systems which minimise inputs while maximising yield, and build resilience to future environmental change; and 5) providing tools and data for planning future multi-functional land use that optimises benefits to food production whilst minimising trade-offs and conflicts with other ecosystem services. SI is currently an area of considerable research investment. However, current initiatives are justifiably short-term and have a narrow focus on existing policies and practice (Defra), or on single components of the agro-ecosystem, such as soil processes (RCUK). While they form an invaluable starting point and provide detailed understanding of critical processes, we will complement and go beyond these initiatives through consideration of the longer-term (10 year+), large-scale effectiveness, impacts and robustness of novel mid- and far-horizon agricultural systems and technologies. This national capability will provide the community with validated biophysical models and data at the field and national scale, a UK-wide research infrastructure using commercial farms, and tools to explore and synthesise the data generated. It will provide opportunities for new partnerships between the wider academic community and industry to address critical knowledge gaps, including the socio-economic barriers to uptake and implementation of these new farming systems, and the integration of advances in crop breeding. Together these activities will support the agricultural industry in assessing the potential to meet ambitious goals for environmental sustainability whilst remaining competitive in the global market.

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Researchers

Adrian Collins (Co-Investigator)Alice Milne (Co-Investigator)Andrew Neal (Co-Investigator)Andrew Whitmore (Co-Investigator)Angela Karp (Co-Investigator)Chris Watts (Co-Investigator)Goetz Richter (Co-Investigator)Ian Clark (Co-Investigator)Ian Shield (Co-Investigator)Jennifer Dungait (Co-Investigator)John Crawford (Co-Investigator)Jonathan Storkey (Principal Investigator)Kim Hammond-Kosack (Co-Investigator)Laura Cardenas (Co-Investigator)Lianhai Wu (Co-Investigator)Malcolm Hawkesford (Co-Investigator)Mikhail Semenov (Co-Investigator)Paul Neve (Co-Investigator)Philip Murray (Co-Investigator)Samantha Cook (Co-Investigator)Stephen Paul McGrath (Co-Investigator)Tim Mauchline (Co-Investigator)Tom Misselbrook (Co-Investigator)W Whalley (Co-Investigator)

Related Research

Grants with similar aims, by meaning.

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Optimising UK Landscapes for Agroecosystem Resilience

Original classification

Research Grant

Plain English summaries and category classifications on this site are generated by AI and may not perfectly reflect the original research.