JDec: Joint decision models for citizens, crops, and environment

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This project will adapt decision-theoretic tools to agri-environmental management, a domain that has been underserved by mathematical methodology. The process of decision-making within an agricultural context is complex, because it spans multiple interdependent stages, and involves many risks along the way. Decisions - when to apply pesticides, how much to apply, when to prune, when to water, even when to harvest - can affect crucially the produce quantity and quality, and hence the short-term success of the enterprise. The decisions will also determine the extent of environmental harm, which have been challenging to define, as is the value of "services" provided by the ecosystem. To facilitate their inclusion in decision-making we develop models that are more flexible and more holistic than common frameworks in operational research. First, outcomes need to be valued by utility functions that reflect costs and benefits comprehensively. Among other things, they need to be evaluated along decision trajectories, including appropriate levels of memory and foresight, and interdependencies along the way. For example, a herbicide treatment may look effective only as long as its indirect effect is ignored on the wild pollinators that had visited the weeds, and whose loss will need to be compensated with new costs.Second, in an agricultural-environmental context, decisions are not taken by humans alone. A modelling approach looking at decisions being taken jointly by all three --- the farmer, the crop and the environment --- opens the flexibility needed to deal with interactions. We further allow for a higher level of uncertainty, in that the influence each of these agents has may itself depend on random events.Third, our models acknowledge the temporal dimension and potential resource allocation constraints. In a large, interconnected, multi-stage system of land and resource management, past actions influence future decisions. Adding rapidly changing environment, with extreme weather events increasing in frequency, shifting pest and pollinator ranges, and resource depletion, we need to take account of the need for robust approximate solutions in model development. In other words, the challenges of having to make decisions in the "real world in real time" requires a paradigm for "good enough" decision-making, and a conceptualisation of the gap it has to optimal solutions. Our major objective is to build the mathematical and statistical framework for decision modelling that covers these three aspects. Our work extends existing approaches by building in more flexible mechanisms for uncertainty and interdependencies. Key ideas from behavioural sciences will move us beyond a narrow rationality framework. Subject to data availability, our resulting theory will be applicable to both small and large landscape scales.We will explore these ideas in two case studies. The first is a system of wild pollinators in apple orchards, a particularly suitable testing ground for understanding indirect effects at the frontier between managed land and its surrounding landscape. The second case study explores the use of decision modelling in a large farm scale experiment with 4 crops and multiple intervention methods. It provides a rich data set for comparing decision strategies. Our work can directly benefit many citizens: not only crop scientists and land managers, but also ecologists, conservationists, local authorities, charities and policy-makers. The tools we are designing will open up to the field sciences an approach that has been used with great success in a variety of other disciplines. With better tools, such as the ones we are proposing, environmentally-conscious actions taken to feed a growing population in a changing climate can be dynamic, adaptable, and sustainable.