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Crop Connectivity under Climate Change

Basic overview

A free desktop app for performing climate change risk assessments in real crop locations in Great Britain.

  • Build your own risk model in a few easy clicks.
  • Choose a raster distribution of crop species / land use locations to apply your model in, or generate your own landscape.
  • Select a climate variable, a future time-period and month, and pick your greenhouse gas emissions scenario.
  • Define the level of connectivity among grid cell locations, if desired.
  • Hit the run button, visualise your results, and save them to create your own graphics.


A risk function is selected from the drop-down list and fit to user-supplied data for the proportion of plants infected as a function of temperature. If you don't have any experimental data you can still select a model from the drop-down list and manually adjust the parameter values till you get the weather-dependent response you desire, or you can simply type in your own function in the box provided.

Once your model is defined in the Model Tab, you can move on to the Projections Tab. Temperature is selected as the variable of interest, for a low emissions scenario in the 2040s, and potato crops are selected from the list of available crop species / land-use types. That selection is narrowed further to locations in the English Midlands (map shown). In order to adjust projected values from the model according to the connectivity of the crops (e.g. for pest dispersal), dispersal is switched on and set to a 2D Gaussian dispersal function with an average dispersal distance of 10km. The five boxplots show the distribution of projected model values in the selected potato crops for May through to September. In this example the results are presented as the percentage increase in risk compared to the current (baseline) climate, whereas you can opt to display the absolute values from the model.


This app was developed as a tool to help non-modellers perform state-of-the-art climate change risk assessments. At your fingertips are real crop / land-use data from the Integrated Administration and Control System (IACS), the June Scottish Agricultural Census (JACS), the Crop Map of England (CROME), and UK Climate Projections (UKCP09) spatially coherent probabilistic climate change data. Building a model and defining your future scenarios is just a matter of hitting a few switches. I've used this framework to produce peer-reviewed journal articles, and you can too: 

Installation and documentation

You can download the desktop app and get full documentation from GitHub using the following link:

Project Information
Project Type: 
Active Project


Areas of Interest

Printed from /research/projects/crop-connectivity-under-climate-change on 01/12/23 11:58:54 PM

The James Hutton Research Institute is the result of the merger in April 2011 of MLURI and SCRI. This merger formed a new powerhouse for research into food, land use, and climate change.