Fungal infections of rice, wheat and grape in Europe in 2030-2050.

2013 - Agronomy for Sustainable Development, 33, 767-776
Bregaglio, S., Donatelli, M., Confalonieri, R.


Although models to predict climate impact on crop production have been used since the 1980s, spatial and temporal diffusion of plant diseases are poorly known. This lack of knowledge is due to few models of plant epidemics, high biophysical complexity, and difficulty to couple disease
models to crop simulators. The first step is the evaluation of disease potential growth in response to climate drivers only. Here, we estimated the evolution of potential infection events of fungal pathogens of wheat, rice, and grape in Europe. A generic process-based infection model driven by air temperature and leaf wetness data was parameterized with the thermal and moisture requirements of the pathogens. The model was
run on current climate as baseline, and on two time frames centered on 2030 and 2050. Our results show an overall increase in the number of infection events, with differences among the pathogens, and showing complex geographical patterns. For wheat, Puccinia recondita, or brown rust, is forecasted to increase +20–100 % its pressure on the crop. Puccinia striiformis, or yellow rust, will increase 5–20 % in the cold areas. Rice pathogens Pyricularia oryzae, or blast disease, and Bipolaris oryzae, or brown spot, will be favored in all European rice districts, with the most critical situation in Northern Italy (+100 %). For grape, Plasmopara viticola, or downy mildew, will increase +5–20 % throughout Europe. Whereas Botrytis cinerea, or bunch rot, will have heterogeneous impacts ranging from −20 to +100 % infection events. Our findings represents the first attempt to provide extensive estimates on disease pressure on crops under climate change, providing information on possible future challenges European farmers will face with in the coming years.

Keywords: Infection process, plant fungal diseases, potential infection, process-based models, spatialized simulation, SRES scenarios
DOI: 10.1007/s13593-013-0149-6