Crop yield as a bioclimatic index of El Nino impact in Europe: Crop forecast implications

被引:13
|
作者
Capa-Morocho, Mirian [1 ,2 ]
Rodriguez-Fonseca, Belen [1 ,3 ,4 ]
Ruiz-Ramos, Margarita [1 ,2 ]
机构
[1] Univ Politecn Madrid, UCM, Campus Int Excellence Moncloa, E-28040 Madrid, Spain
[2] Univ Politecn Madrid, CEIGRAM Agsyst Prod Vegetal Fitotecnia, ETSI Agron, E-28040 Madrid, Spain
[3] Univ Complutense Madrid, Fac Ciencias Fis, Dept Geofis & Meteorol, E-28040 Madrid, Spain
[4] UCM, CSIC, Inst Geociencias, E-28040 Madrid, Spain
关键词
El Nino teleconnections in Europe; Seasonal crop yield prediction; Bioclimatic index; Crop forecasting; Potential yield; Crop model; NORTH-ATLANTIC OSCILLATION; MAIZE YIELD; SEASONAL RAINFALL; CLIMATE; ENSO; TEMPERATURE; VARIABILITY; NAO;
D O I
10.1016/j.agrformet.2014.07.012
中图分类号
S3 [农学(农艺学)];
学科分类号
0901 ;
摘要
Crop yield is partially determined by atmospheric climate variability, which in turn depends on changes in the sea surface temperature (SST). El Nino is the leading mode of SST interannual variability, and its impacts extend worldwide. Nevertheless, the predictability of these impacts is controversial, especially those associated with European climate variability, which have been found to be non-stationary and non-linear. The present study shows how potential crop yield obtained from reanalysis data and crop models serves as an alternative and more effective index of El Nino teleconnections because it integrates the nonlinearities between the climate variables in a unique time series. The relationships between El Nino and crop yield anomalies are more significant than the individual contributions of each of the atmospheric variables used as input in the crop model. Additionally, the non-stationarities between El Nino and European climate variability are more clearly detected when analyzing crop-yield variability. The understanding of this relationship allows for some predictability up to one year before the crop is harvested. This predictability is not constant, but depends on both high and low frequency modulation. These findings enhance the importance of crop models for impact studies, for the improvement of crop forecasting, and as generators of a climate variability index (the potential yield) for analyzing climate variability and change. (C) 2014 Elsevier B.V. All rights reserved.
引用
收藏
页码:42 / 52
页数:11
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