Civil Engineering Department, The City College of New York, The City University of New York, 10031 New York City, USA; NOAA Center for Earth System Sciences and Remote Sensing Technologies (NOAA-CREST), The City College of New York, The City University of New York, 10031 New York City, USA.
NOAA Center for Earth System Sciences and Remote Sensing Technologies (NOAA-CREST), The City College of New York, The City University of New York, 10031 New York City, USA; Columbia Water Center, Columbia University, 10025 New York City, USA.
Sci Total Environ. 2019 Apr 20;662:361-372. doi: 10.1016/j.scitotenv.2019.01.172. Epub 2019 Jan 17.
In this study, long-term national-based yields of maize, rice, sorghum and soybean (MRSS) from 1961 to 2013 are decomposed using Robust Principal Component Analysis (RPCA). After removing outliers, the first three principal components (PC) of the persistent yield anomalies are scrutinized to assess their association with climate and to identify co-varying countries and crops. Sea surface temperature anomalies (SSTa), atmospheric and oceanic indices, air temperature anomalies (ATa) and Palmer Drought Severity Index (PDSI) are used to study the association between the PCs and climate. Results show that large-scale climate, especially El Niño-Southern Oscillation (ENSO) and North Atlantic Oscillation (NAO) are strongly correlated with crop yield variability. Extensive maize harvesting regions in Europe and North America, rice in South America, Oceania and east of Asia, sorghum in west and southeast of Asia, North America and Caribbean and soybean in North and South America, Oceania and south of Asia experienced the influence of local climate variability in this period. Sorghum yield variability across the globe exhibits significant correlations with many atmospheric and oceanic indices. Results indicate that not only do the same crops in many countries co-vary significantly, but different crops, in particular maize, in different PCs also co-vary with other crops. Identifying the association between climate and crop yield variability and recognizing similar and dissimilar countries in terms of yield fluctuations can be informative for the identified nations with regard to the periodic and predictable nature of many large-scale climatic patterns.
本研究采用稳健主成分分析（RPCA）方法，对 1961 年至 2013 年玉米、水稻、高粱和大豆的长期全国产量进行了分解。在去除异常值后，研究了持久产量异常的前三个主成分（PC），以评估它们与气候的关系，并确定协同变化的国家和作物。本研究使用海表温度异常（SSTa）、大气和海洋指数、气温异常（ATa）和帕尔默干旱严重指数（PDSI）来研究 PC 与气候之间的关系。结果表明，大规模气候，特别是厄尔尼诺-南方涛动（ENSO）和北大西洋涛动（NAO）与作物产量变化密切相关。在这一时期，欧洲和北美的大片玉米收获区、南美的水稻、大洋洲和东亚的水稻、亚洲西部和东南部、北美和加勒比地区的高粱以及南北美洲、大洋洲和南亚的大豆都受到了当地气候变化的影响。全球高粱产量变化与许多大气和海洋指数显著相关。结果表明，不仅许多国家的同种作物变化显著，而且不同的作物，特别是不同 PC 中的玉米，也与其他作物协同变化。识别气候与作物产量变化之间的关系，以及在产量波动方面识别相似和不同的国家，可以为这些国家提供有关许多大规模气候模式周期性和可预测性的信息。
