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欧盟小麦预测在极端影响下的季节性表现。

In-season performance of European Union wheat forecasts during extreme impacts.

机构信息

European Commission, Joint Research Centre, Via E. Fermi 2749, 21027, Ispra, Italy.

出版信息

Sci Rep. 2018 Oct 18;8(1):15420. doi: 10.1038/s41598-018-33688-1.

DOI:10.1038/s41598-018-33688-1
PMID:30337571
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6194012/
Abstract

Here we assess the quality and in-season development of European wheat (Triticum spp.) yield forecasts during low, medium, and high-yielding years. 440 forecasts were evaluated for 75 wheat forecast years from 1993-2013 for 25 European Union (EU) Member States. By July, years with median yields were accurately forecast with errors below 2%. Yield forecasts in years with low yields were overestimated by ~10%, while yield forecasts in high-yielding years were underestimated by ~8%. Four-fifths of the lowest yields had a drought or hot driver, a third a wet driver, while a quarter had both. Forecast accuracy of high-yielding years improved gradually during the season, and drought-driven yield reductions were anticipated with lead times of ~2 months. Single, contrasting successive in-season, as well as spatially distant dry and wet extreme synoptic weather systems affected multiple-countries in 2003, '06, '07, '11 and 12', leading to wheat losses up to 8.1 Mt (>40% of total EU loss). In these years, June forecasts ( 1-month lead-time) underestimated these impacts by 10.4 to 78.4%. To cope with increasingly unprecedented impacts, near-real-time information fusion needs to underpin operational crop yield forecasting to benefit from improved crop modelling, more detailed and frequent earth observations, and faster computation.

摘要

在这里,我们评估了低、中、高产年份欧洲小麦(Triticum spp.)产量预测的质量和季节性发展。我们评估了 1993 年至 2013 年期间 25 个欧盟成员国 75 个小麦预测年份的 440 个预测。到 7 月,中等产量年份的预测误差低于约 2%,预测结果较为准确。低产年份的预测结果高估了约 10%,而高产年份的预测结果低估了约 8%。五分之四的最低产量与干旱或高温驱动因素有关,三分之一与潮湿驱动因素有关,四分之一与两者都有关。高产年份的预测精度在整个季节逐渐提高,干旱驱动的产量减少可提前约 2 个月预测。2003 年、2006 年、2007 年、2011 年和 2012 年,单一的、连续的、季节性的、空间上相隔的干旱和潮湿极端天气系统影响了多个国家,导致小麦损失高达 810 万吨(占欧盟总损失的 40%以上)。在这些年份,6 月的预测(约 1 个月的提前期)低估了这些影响 10.4%至 78.4%。为了应对越来越多的前所未有的影响,近实时信息融合需要为作物产量预测提供支持,以受益于改进的作物模型、更详细和更频繁的地球观测以及更快的计算。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3c4a/6194012/c736a447f5ec/41598_2018_33688_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3c4a/6194012/dc6f1ddc5dab/41598_2018_33688_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3c4a/6194012/7a1de6f929fd/41598_2018_33688_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3c4a/6194012/588881b17abd/41598_2018_33688_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3c4a/6194012/ad827b16ea27/41598_2018_33688_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3c4a/6194012/c736a447f5ec/41598_2018_33688_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3c4a/6194012/dc6f1ddc5dab/41598_2018_33688_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3c4a/6194012/7a1de6f929fd/41598_2018_33688_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3c4a/6194012/588881b17abd/41598_2018_33688_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3c4a/6194012/ad827b16ea27/41598_2018_33688_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3c4a/6194012/c736a447f5ec/41598_2018_33688_Fig5_HTML.jpg

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本文引用的文献

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2
Causes and implications of the unforeseen 2016 extreme yield loss in the breadbasket of France.法国粮食主产区 2016 年意外大幅减产的原因及其影响。
Nat Commun. 2018 Apr 24;9(1):1627. doi: 10.1038/s41467-018-04087-x.
3
Linking crop yield anomalies to large-scale atmospheric circulation in Europe.
利用哨兵-1和哨兵-2时间序列检测油菜地块的开花物候。
Remote Sens Environ. 2020 Mar 15;239:111660. doi: 10.1016/j.rse.2020.111660.
将欧洲的作物产量异常与大规模大气环流联系起来。
Agric For Meteorol. 2017 Jun 15;240-241:35-45. doi: 10.1016/j.agrformet.2017.03.019.
4
Technology: The Future of Agriculture.技术:农业的未来。
Nature. 2017 Apr 26;544(7651):S21-S23. doi: 10.1038/544S21a.
5
Influence of extreme weather disasters on global crop production.极端天气灾害对全球作物生产的影响。
Nature. 2016 Jan 7;529(7584):84-7. doi: 10.1038/nature16467.
6
The fingerprint of climate trends on European crop yields.欧洲作物产量上气候趋势的特征
Proc Natl Acad Sci U S A. 2015 Mar 3;112(9):2670-5. doi: 10.1073/pnas.1409606112. Epub 2015 Feb 17.
7
Climate variation explains a third of global crop yield variability.气候变化解释了全球作物产量变异性的三分之一。
Nat Commun. 2015 Jan 22;6:5989. doi: 10.1038/ncomms6989.
8
Global crop forecasting.全球作物预测。
Science. 1980 May 16;208(4445):670-9. doi: 10.1126/science.208.4445.670.
9
Operational seasonal forecasting of crop performance.作物生长表现的业务性季节预测。
Philos Trans R Soc Lond B Biol Sci. 2005 Nov 29;360(1463):2109-24. doi: 10.1098/rstb.2005.1753.