Characterising the agricultural mosaic for effective disease control: landscape structure and crop disease
Lead Research Organisation:
University of Cambridge
Department Name: Plant Sciences
Abstract
Control of crop disease, be it by spraying chemicals, by planting resistant varieties, or by any other method, is deployed at the scale of the individual field. However, successful control depends on the management of disease at regional and national scales. How far and how fast a pathogen spreads through a landscape and, indeed, whether or not it persists and for how long, depends upon the topology of susceptible host and how this compares to the typical dispersal scale of pathogens. This details of the interaction changes in both time and space as the susceptible crops are moved around the landscape. Attempting to understand the effect of spatial patterning of host on pathogen dynamics has a long history (van der Plank, 1948), and is still an active area of reseach (Cunniffe et al., 2015). Exploratory work has shown how to adapt the work of DeWoody et al. (2005) to include crop rotation and overwintering in a spatially-structured metapopulation-type model of the agricultural mosaic. A starting point for this project would involve understanding how this can be used to inform strategies for deployment of control. The framework could also be extended, most notably to understand the effects of within-field dynamics, synchronicity in planting and harvesting, and stochasticity in disease spread. Understanding how the landscape structure affects the spread of disease would have a number of applications, feeding into an improved understanding not only of how crop diseases can be controlled effectively, but also of pathogen evolutionary change (e.g. fungicide resistance).
Organisations
Studentship Projects
| Project Reference | Relationship | Related To | Start | End | Student Name |
|---|---|---|---|---|---|
| BB/M011194/1 | 01/10/2015 | 31/03/2024 | |||
| 1643594 | Studentship | BB/M011194/1 | 01/10/2015 | 30/09/2019 | Elliott Bussell |
| Description | Mathematical models of tree diseases often have little to say about how to manage established epidemics. Models often show that it is too late for successful disease eradication, but few study what management could still be beneficial. This study focusses on finding effective control strategies for managing sudden oak death, a tree disease caused Phytophthora ramorum. Sudden oak death is a devastating disease spreading through forests in California and southwestern Oregon. The disease is well established and eradication is no longer possible. The ongoing spread of sudden oak death is threatening high value tree resources, including national parks, and culturally and ecologically important species like tanoak. In this thesis we show how the allocation of limited resources for controlling sudden oak death can be optimised to protect these valuable trees. We use simple, approximate models of sudden oak death dynamics, to which we apply the mathematical framework of optimal control theory. Applying the optimised controls from the approximate model to a complex, spatial simulation model, we demonstrate that the framework finds effective strategies for protecting tanoak, whilst also conserving biodiversity. When applied to the problem of protecting Redwood National Park, which is under threat from a nearby outbreak of sudden oak death, the framework finds spatial strategies that balance protective barriers with control at the epidemic wavefront. Because of the number of variables in the system, computational and numerical limitations restrict the control optimisation to relatively simple approximate models. We show how a lack of accuracy in the approximate model can be accounted for by using model predictive control, from control systems engineering: an approach coupling feedback with optimal control theory. Continued surveillance of the complex system, and re-optimisation of the control strategy, ensures that the result remains close to optimal, and leads to highly effective disease management. In this thesis we show how the machinery of optimal control theory can inform plant disease management, protecting valuable resources from sudden oak death. Incorporating feedback into the application of the resulting strategies ensures control remains effective over long timescales, and is robust to uncertainties and stochasticity in the system. Local management of sudden oak death is still possible, and our results show how this can be achieved. |
| Exploitation Route | Improve optimisation of disease management across animal, plant and human health. Methods to couple optimisations with complex models to provide useful, actionable advice for decision makers. |
| Sectors | Agriculture, Food and Drink,Digital/Communication/Information Technologies (including Software),Healthcare |
| Title | Landscape Optimal Control |
| Description | Computational algorithm and associated models for optimising control of plant disease at the landscape scale, using optimal control theory. Using numerical methods we have extended the range over which optimisation is possible and created infrastructure for combining models of different scales. |
| Type Of Material | Computer model/algorithm |
| Year Produced | 2018 |
| Provided To Others? | No |
| Impact | Allows the possibility of applying optimal control theory to much larger problems than has been done previously. In particular previous models have been based on 2-3 'patches' whereas this new method can optimise over 50+ regions. |
| Title | Optimal & Model Predictive Control |
| Description | Code implementing models and optimisations described in 2018 Biorxiv preprint. Allows reproduction of figures from paper as well as further exploration of techniques and results. |
| Type Of Technology | Software |
| Year Produced | 2018 |
| Open Source License? | Yes |
| Impact | Improved reproducibility of published work. |
| Description | Science Festival - tree trail |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | Local |
| Primary Audience | Public/other audiences |
| Results and Impact | Tree trail held at Cambridge Botanic Garden, engaging members of the public to take an interest in tree identification. Used as a platform to introduce epidemiological concepts |
| Year(s) Of Engagement Activity | 2019 |
| Description | Science Festival Cambridge |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | Local |
| Primary Audience | Public/other audiences |
| Results and Impact | Stall about mathematical modelling of plant diseases at Cambridge Science Festival, Life Sciences marquee. Intended purpose to engage public, getting children interested in computational models and maths, whilst informing parents and other adults about plant disease impacts and methods for control. |
| Year(s) Of Engagement Activity | 2016,2017 |
| Description | Science Museum Lates (London) |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | Regional |
| Primary Audience | Public/other audiences |
| Results and Impact | Attendance with Epidemiology and Modelling group at Science Museum Lates event in London. Engaging with public about mathematical models of plant disease spread through a tablet game. Participants aimed to minimise effects of disease whilst learning about disease impacts across the world and methods of control. Intended purpose to inform the public and increase awareness. Much positive feedback about the stall. |
| Year(s) Of Engagement Activity | 2016 |
| URL | https://www.sciencemuseum.org.uk/see-and-do/lates |