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Data of variability and joint variability of global crop yields and their association with climate

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...

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Detalles Bibliográficos
Autores principales: Najafi, Ehsan, Pal, Indrani, Khanbilvardi, Reza
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6660635/
https://www.ncbi.nlm.nih.gov/pubmed/31372410
http://dx.doi.org/10.1016/j.dib.2019.103745
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author Najafi, Ehsan
Pal, Indrani
Khanbilvardi, Reza
author_facet Najafi, Ehsan
Pal, Indrani
Khanbilvardi, Reza
author_sort Najafi, Ehsan
collection PubMed
description 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.
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spelling pubmed-66606352019-08-01 Data of variability and joint variability of global crop yields and their association with climate Najafi, Ehsan Pal, Indrani Khanbilvardi, Reza Data Brief Earth and Planetary Science 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. Elsevier 2019-03-08 /pmc/articles/PMC6660635/ /pubmed/31372410 http://dx.doi.org/10.1016/j.dib.2019.103745 Text en © 2019 The Author(s) http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Earth and Planetary Science
Najafi, Ehsan
Pal, Indrani
Khanbilvardi, Reza
Data of variability and joint variability of global crop yields and their association with climate
title Data of variability and joint variability of global crop yields and their association with climate
title_full Data of variability and joint variability of global crop yields and their association with climate
title_fullStr Data of variability and joint variability of global crop yields and their association with climate
title_full_unstemmed Data of variability and joint variability of global crop yields and their association with climate
title_short Data of variability and joint variability of global crop yields and their association with climate
title_sort data of variability and joint variability of global crop yields and their association with climate
topic Earth and Planetary Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6660635/
https://www.ncbi.nlm.nih.gov/pubmed/31372410
http://dx.doi.org/10.1016/j.dib.2019.103745
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