Overview

4C is a modelling framework used to make quantitative climate change risk assessments for crop pests and pathogens. It acts as a core modelling activity for plant pathologists who generate data on the complex interaction of climatic conditions, cultivation systems, host variety, and strain-specific aptitudes in causing disease. This knowledge is synthesised in an overarching, modular risk assessment framework comprised of (sub-)models for crop growth, pest or pathogen risk, and dispersal. These can be added, removed, or adapted depending on your pathosystem of interest and your research questions. 4C can easily incorporate risk models developed using your own experimental data, and link them with existing and accessible future climate scenario and crop distribution datasets. If you are interested in developing a risk model for 4C or in running a particular set of climate change / adaptation scenarios, then please contact us.

Crop distribution data

4C uses data on the spatial coverage (location and area) of all the major crop species in Scotland to provide a proxy indicator for future crop distributions. This ensures that projections are made for the appropriate geographic areas and allows us to test the robustness of current crop production systems to future disease threats. We also use these data to account for realistic distributions of pest and pathogen populations, and where relevant, the potential for spread of disease among production areas. We obtain these data from various sources, such as the Scottish Integrated Administration and Control System (IACS), and the June Agricultural Census (JAC). They are not available to the public. 4C can also accommodate any crop distribution dataset, or simply make projections for specific coordinates or regions.

Climate change data

4C uses 25 km gridded monthly climate projections from the UK Met. Office (UKCP09). These are based on sophisticated scientific methodologies and are produced using the best available climate models. A large set of plausible CO2 emissions scenarios and time-periods are available. These data are probabilistic, allowing the likelihood of climate impacts to be evaluated. Spatially coherent climate projections are also available, and these are important in 4C as they allow the user to capture relationships between different locations, such as epidemic spread. 4C also utilises a weather generator should any risk assessment require climate data at a finer temporal or spatial resolution. We also use extensive soil temperature records to convert future air temperatures to future soil temperatures for soil pathosystems.

Risk models

The pest or pathogen risk models we develop take many forms; from simple look-up tables, to empirically derived curves from controlled environment experiments, right up to full life-cycle models. Unlike other risk assessment frameworks, we do not assume that pests or inoculum are abundant and ubiquitous. 4C utilises a range of dispersal models to simulate all the relevant processes for pest  / pathogen transportation among crop locations. We also use crop growth models, as canopy coverage affects micro-climate, which in turn affects pest or pathogen risk. Crop height and leaf density also affect wind and turbulence characteristics both within and above the canopy, which  have an impact on dispersal risk. 4C is therefore unique in providing an integrated view on the risk of epidemic occurrence and spread under climate change.