Future development of apricot blossom blight under climate change in Southern France

Summary in the original language of the document

Climate change will have several consequences for agro-systems, one of which will concern changes to the development of pathogens. Because of the losses it causes, particularly in organic farming, Monilinia laxa is an important pathogen affecting apricot crops. This study focuses on the consequences of climate change regarding blossom and twig blight (Monilinia laxa) of apricot. To achieve this, a Climatic Index of cumulated Blight risk (CIB) was built, to obtain the weighted sum of blossom blight incidence throughout the blooming period. An epidemiological model to calculate the incidence of blossom blight during every potentially infectious episode and based on biological parameters, was calibrated using a trap pot experiment where trees were placed in orchards and subject to various meteorological conditions. The CIB derived from this model was evaluated on field data, and was shown to be a robust and useful tool to predict the effects of climate change on the development of apricot blight. Then, using the CIB with a phenological model to predict blooming periods in the future, we estimated the risks of apricot blight until 2100 on four contrasted apricot cultivars and in three geographical zones under climate change scenarios RCP 4.5 and 8.5. This study revealed different effects of climate change depending on the cultivar and altitude. Apricot trees would bloom earlier (up to a difference of 50 days between 1950 and 2100) under climate change. Under the combined effects of these shifts of blooming period and changing climatic conditions, late cultivars such as Bergarouge might see a reduction in the risk of blossom blight (down to 31%) because of warmer but dryer blooming periods. Other varieties (e.g.: Bergeron) could experience an increase in this risk by up to 27% with a shift of the blooming period towards rainier conditions at the highest altitudes. The results of this study could be used to anticipate future changes as well as be used at present as a decision-support tool for farmers.