Given the commitment by Government to Sustainable Development, the performance of UK agriculture is increasingly judged against a broad mix of social, economic and environmental criteria. These include contributions to rural livelihoods and communities, supply of healthy foods, welfare of farm workers and animals, wise use of natural resources and protection of the natural environment. Information, knowledge and analytical methods are required to guide decision makers, whether at the farm or policy making level, in order to help formulate and implement strategies in pursuit of sustainability. Future agricultural systems that are inherently more environmentally and economically sustainable will depend on rapid access to knowledge and support for critical farming decisions. The availability of improved communication channels, via the Internet and including the Defra portal, and greater computing power in the hands of the industry provide the context in which scientific solutions can be made available and intelligible to the industry, delivering real benefit from research outputs. This project involves the maintenance, further development and use of mathematical and operational research methods (and related data management systems) with the broad purpose of helping to improve the quality of decision making in the agricultural, and particularly farming, sector. More specifically its objectives are: • To sustain and develop the scientific and technical basis for the delivery of knowledge and guidance to decision makers in the agricultural sector through decision support systems. • To develop operational research models of agricultural and related environmental systems which accurately describe the systems’ behaviour in diverse, dynamic and often uncertain circumstances in order to improve management and thereby enhance the delivery of location-specific scientific knowledge to individual decision makers in the agricultural sector. In order to achieve these objectives, the project will: • develop new techniques to underpin decision support systems, including those which address the management of risk and uncertainty as well as the integration of different objectives and decision criteria, such as decision maker preferences • develop quantitative models to be included in decision support systems • develop and extend systems for whole-farm environmental assessment and optimisation • support the integration of other modules which simulate decision problems • investigate alternative approaches to the delivery of decision support systems, such as choice of dissemination methods and guidance for users • incorporate new knowledge into existing models • advance methods for keeping models in decision support systems up-to-date Most of the techniques being developed are applicable to a wide range of systems beyond the examples being used in the project. For example, team members have worked on LINK projects on the tactical management of feeding to pigs and poultry, which can help to reduce ammonia and other gaseous nitrogen emissions by reducing overfeeding of protein. Similar methods could be applied to other types of diffuse pollution and also to the management of limited resources, for example decision support systems based on Cranfield’s research on irrigation planning at farm level to optimise resource use in water-scarce environments. The general philosophy is to model decision making at the level of the farm manager using parsimonious science-based models. Working at this level allows the development of decision support tools for farm use, and also application within larger-scale systems that look at the likely behaviour of farmers in response to external forces, such as price changes, regulation and climate change. The use of science-based models helps to ensure that decision support systems are easily updated and can be applied to new questions and novel and farm-specific situations. The project will ensure that there is a continuing stream of tools and integrated approaches to delivery of decision support for farmers and relevant advisory, research and policy making communities. The work will integrate with and also catalyse innovation in decision support across the UK community and beyond. It will provide a central resource to advise Defra and other research teams on agricultural modelling, operational research and decision support. Objective O1. Develop new techniques to underpin decision support systems O1.1 Methods to reduce the complexity of dynamic models O1.2 Methods for keeping models in decision support systems up-to-date O1.3 Methods of updating in response to user observations O1.4 Methods for optimising decisions incorporating variability O2. Develop new models for use in decision support systems O2.1 Incorporate new knowledge into existing models O2.2 Common growth stage model for cereals O2.3 Barley development models to underpin a disease models O2.4 Other models of processes to underpin new systems O3. Develop and extend decision support systems for whole-farm environmental assessment and optimisation O3.1 Decision support systems that enable the optimization of environmental performance across whole farm systems, rotations etc. O4. Investigate and test alternative approaches to delivery and integration of decision support systems O4.1 Assess delivery mechanisms O5. Provide a central resource to advise Defra on agricultural modelling, operational research and decision support O5.1 Provide a hub for a network of DSS activities – through the coordination of Defra funded work with other R&D providers, particularly the Agricultural Decision Support delivery team based at ADAS O5.2 Provide advice to Defra on developments in this area more widely and on the management of relevant research and its integration with that of other funders O5.3 Appropriate biomathematics research to be negotiated with Defra to support Defra’s response to the Research Priorities Group