This project will adapt decision-theoretic tools to agri-environmental management. 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.
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 holistic than common frameworks in operational research and emphasises the inclusion of uncertainties and inter-dependencies.
• Suitable utility functions for outcomes reflect costs and benefits comprehensively through evaluated along decision trajectories, including appropriate levels of memory and foresight, and interdependencies. 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.
• Suitable utility functions for outcomes reflect costs and benefits comprehensively through evaluated along decision trajectories, including appropriate levels of memory and foresight, and inter-dependencies. 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.
• 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.
• Acknowledgment of a temporal dimension and potential resource allocation constraints. In a large, interconnected, multi-stage system of land and resource management, past actions influence future decisions. Addition of rapidly changing environment, with extreme weather events increasing in frequency, shifting pest and pollinator ranges, and resource depletion.
• Concepts for robust approximate solutions. 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 tools aim to inform environmentally-conscious actions taken to feed a growing population in a changing climate can be dynamic, adaptable and sustainable.
We are illustrating our decision methods in the context of agri-environmental situations on two case studies:
• Wild pollinators in apple orchards, a particularly suitable testing ground for understanding indirect effects at the frontier between managed land and its surrounding landscape.
• 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.
PI Julia Brettschneider https://warwick.ac.uk/fac/sci/statistics/staff/academic-research/brettschneider/
|Link to the project page on the UKRI Gateway to Research https://gtr.ukri.org/projects?ref=NE%2FT004134%2F1|