Abstract
The impact of climate change on dispersal processes is largely ignored in risk assessments for crop diseases, as inoculum is generally assumed to be ubiquitous and non-limiting. We suggest that consideration of the impact of climate change on the connectivity of crops for inoculum transmission may provide additional explanatory and predictive power in disease risk assessments, leading to improved recommendations for agricultural adaptation to climate change. In this study a crop growth model was combined with aerobiological models and a newly developed infection risk model to provide a framework for quantifying the impact of future climates on the risk of disease occurrence and spread. The integrated model uses standard meteorological variables and can be easily adapted to various crop pathosystems characterized by airborne inoculum. In a case study, the framework was used with data defining the spatial distribution of potato crops in Scotland and spatially coherent, probabilistic climate change data to project the future connectivity of crop distributions for Phytophthora infestans (causal agent of potato late blight) inoculum, and the subsequent risk of infection. Projections and control recommendations are provided for multiple combinations of potato cultivar and CO2 emissions scenario, and temporal and spatial averaging schemes. Overall, we found that relative to current climatic conditions, the risk of late blight will increase in Scotland during the first half of the potato growing season, and decrease during the second half. To guide adaptation strategies we also investigated the potential impact of climate-change driven shifts in the cropping season. Advancing the start of the potato growing season by one month proved to be an effective strategy from both an agronomic and late blight management perspective. This article is protected by copyright. All rights reserved.
Original language | English |
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Pages (from-to) | 3724-3738 |
Number of pages | 15 |
Journal | Global Change Biology |
Volume | 22 |
Issue number | 11 |
Early online date | 23 May 2016 |
DOIs | |
Publication status | Published - Nov 2016 |