Abstract
Combining climate change, crop growth and crop disease models to predict impacts of climate change on crop diseases can guide planning of climate change adaptation strategies to ensure future food security. This review summarises recent developments in modelling climate change impacts on crop diseases, emphasises some major challenges and highlights recent trends. The use of multi-model ensembles in climate change modelling and crop modelling is contributing towards measures of uncertainty in climate change impact projections but other aspects of uncertainty remain largely unexplored. Impact assessments are still concentrated on few crops and few diseases but are beginning to investigate arable crop disease dynamics at the landscape level.
Highlights
Modelling impacts of climate change on arable crop diseases: progress, challenges and applications§ Fay Newbery1, Aiming Qi2,3 and Bruce DL Fitt3
This review summarises recent developments in modelling climate change impacts on crop diseases, emphasises some major challenges and highlights recent trends
The use of multi-model ensembles in climate change modelling and crop modelling is contributing towards measures of uncertainty in climate change impact projections but other aspects of uncertainty remain largely unexplored
Summary
An illustration of how climate, crop growth and disease models can be combined to produce projections of crop growth stages and disease incidence/severity for different climate change scenarios. (1) Observed data for weather (e.g. daily minimum and maximum temperature (8C), total rainfall (mm) and solar radiation (MJ dayÀ1)), crop growth stage and disease incidence can be collated from a number of sources for different regions for a period of years. (2) The crop growth stages predicted using the crop growth model can be validated by comparing predicted crop growth stages, generated by the model using observed weather data, with observed crop growth stages for the same sites for a given period. (3) A disease model can be developed from data for disease incidence from sites within a certain distance of the site for which there is observed weather for a given period. (4) Predictions of disease incidence can be validated by comparing predictions made using observed weather to observed disease incidence data for a given period for different regions. (5) Weather data can be generated for each of the sites for each climate scenario. (6) The crop growth stages can be projected for each site for each climate scenario using the crop growth model, allowing maps to be generated to show the effect of climate change on crop growth. (7) Using the weather generated and crop growth stage projected using the crop growth model for each of the sites for each of the climate scenarios, the disease model can be used to project disease incidence for each site for each of the climate scenarios. A recent Climate Change, Agriculture and Food Security (CCAFS) working paper [36] highlights the need for trained plant pathologists, data gathering, modelling of crop diseases and pests, and pre-emptive crop resistance against serious new disease and pest threats to give Africa the best support to maintain its food security. Modelling impacts of climate change on arable crop diseases at the landscape scale Modelling is producing tools for policy planners that will facilitate investigation of possible consequences of human adaptation to the threats to food security of climate warming and diseases This investigation needs to be done at the landscape level since disease inoculum is often widely dispersed. It is important that these traits can be accurately represented in climate change impact assessments
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