DOI: 10.1016/j.agsy.2019.102733
论文题名: Robustness of crop disease response to climate change signal under modeling uncertainties
作者: Launay M. ; Zurfluh O. ; Huard F. ; Buis S. ; Bourgeois G. ; Caubel J. ; Huber L. ; Bancal M.-O.
刊名: Agricultural Systems
ISSN: 0308521X
出版年: 2020
卷: 178 语种: 英语
英文关键词: Global warming
; Infection
; Models
; Multimodel
; Puccinia triticina
Scopus关键词: climate change
; disease incidence
; epidemic
; food security
; fungal disease
; global warming
; infectivity
; prediction
; signaling
; uncertainty analysis
; yield response
; Puccinia triticina
; Triticum aestivum
英文摘要: Crop fungal diseases threaten food security in the dual context of a growing global population and a warming climate. Leaf rust is one of the most important wheat diseases which can result in yield losses of more than 40 %. When considering these crucial questions, innovative approaches to crop cultivation are clearly required. One essential prerequisite before the development of adaptive strategies to climate change, is to understand and forecast the potential impact of this change on fungal diseases, based on the use of modelling approaches. However, numerous epidemiological models are available; they vary considerably in terms of their complexity, and are based on hypotheses that oversimplify factors that influence the prediction of epidemics. During this study, we implemented six combinations of leaf wetness duration and infection efficiency models to simulate the future evolution of leaf rust of wheat, and compared the resulting trends. Daily and seasonal climatic indicators were inferred from the simulated infection efficiencies, from 1950 to 2100, with two contrasted Representative Concentration Pathways, RCP 4.5 and RCP 8.5, at three sites representative of traditional French wheat production areas. The inferred indicators characterize the intensity and frequency of leaf rust infection, the length and calendar positioning of the longest sequences without infection, and the relevant microclimate. Their absolute values varied considerably depending on the model combinations used, even more than between the present and future climatic periods or RCP scenarios. However, the same trends were observed in the future, with climate change being a significant explanatory variable of the evolution of the six climatic indicators simulated. The results of combining these models showed that the climatic risk of both the frequency and intensity of leaf rust infection would increase during the autumn and winter seasons, and a distinct drop should be expected during the summer, enabling a longer risk-free period. Some important common trends were thus highlighted, reinforcing confidence in the robustness of the results. These findings should be taken into account when designing adaptive strategies that will sustain production under future abiotic stresses while minimizing sanitary risks. © 2019 Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/159479
Appears in Collections: 气候变化与战略
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作者单位: US AgroClim, INRA, Domaine St. Paul, Site Agroparc, Avignon Cedex 9, 84914, France; UMR ECOSYS, INRA, AgroParisTech, Université Paris-Saclay, Thiverval-Grignon, 78850, France; UMR EMMAH, INRA, Domaine St. Paul, Site Agroparc, Avignon Cedex 9, 84914, France; Agriculture and Agri-Food Canada, Horticulture Research and Development Centre, Saint-Jean-sur-Richelieu, Quebec, Canada; EcoClimaSol, Immeuble MIBI, 672 Rue du Mas Verchant, Montpellier Cedex 02, 34967, France
Recommended Citation:
Launay M.,Zurfluh O.,Huard F.,et al. Robustness of crop disease response to climate change signal under modeling uncertainties[J]. Agricultural Systems,2020-01-01,178