Abstract
We present the output data of Robust Principal Component Analysis (RPCA) applied to global crop yield variability of maize, rice, sorghum and soybean (MRSS) as presented in the publication “Climate drives variability and joint variability of global crop yields” (Najafi et al., 2019). Global maps of the correlation between all the principal components (PCs) acquired from the low rank matrix (L) of MRSS and Palmer Drought Severity Index (PDSI), air temperature anomalies (ATa) and sea surface temperature anomalies (SSTa) are provided in this article. We present co-varying countries, impacted cropland areas across global countries, and 10 global regions by climate and the association between PCs and multiple atmospheric and oceanic indices. Moreover, the joint dependency between PCs of MRSS yields are presented using two different approaches.
Highlights
We present the output data of Robust Principal Component Analysis (RPCA) applied to global crop yield variability of maize, rice, sorghum and soybean (MRSS) as presented in the publication “Climate drives variability and joint variability of global crop yields” (Najafi et al, 2019)
Analyzed, processed Annual country-based crops yields, export, import and production are collected from the Food and Agriculture Organization of the United Nations statistical databases
Value of the Data Global maps of the association between crop yield variability and Palmer Drought Severity Index, air temperature anomalies and sea surface temperature anomalies are valuable since they improve our understanding about the impact of large-scale and local-scale climate on crops productivity
Summary
We present the output data of Robust Principal Component Analysis (RPCA) applied to global crop yield variability of maize, rice, sorghum and soybean (MRSS) as presented in the publication “Climate drives variability and joint variability of global crop yields” (Najafi et al, 2019). Earth and Planetary Sciences, Environmental Science Atmospheric Science, Global and Planetary Change Table, figure This data is acquired applying Robust Principal Component Analysis (RPCA) on detrended crop yields of maize, rice, sorghum and soybean of global countries with a complete dataset from 1961 to 2013.
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