Climatic indicators and soybean yield in Rio Grande do Sul

Abstract The main of this work was to identify teleconnection patterns that influence soybean yield variability in Rio Grande do Sul in order to find potential predictors of agricultural yield in the State. Soybean yield data from 87 municipalities provided by the Brazilian Institute of Geography an...

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Main Authors: Diogo, Alessandro Arsego, Ferraz, Simone Erotildes Teleginski, Nereu Augusto Streck, Cardoso, Andréa De Oliveira, Junior Zanon Alencar
Format: Dataset
Language:unknown
Published: SciELO journals 2019
Subjects:
Online Access:https://dx.doi.org/10.6084/m9.figshare.9276119.v1
https://scielo.figshare.com/articles/Climatic_indicators_and_soybean_yield_in_Rio_Grande_do_Sul/9276119/1
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spelling ftdatacite:10.6084/m9.figshare.9276119.v1 2023-05-15T15:05:58+02:00 Climatic indicators and soybean yield in Rio Grande do Sul Diogo, Alessandro Arsego Ferraz, Simone Erotildes Teleginski Nereu Augusto Streck Cardoso, Andréa De Oliveira Junior Zanon Alencar 2019 https://dx.doi.org/10.6084/m9.figshare.9276119.v1 https://scielo.figshare.com/articles/Climatic_indicators_and_soybean_yield_in_Rio_Grande_do_Sul/9276119/1 unknown SciELO journals https://dx.doi.org/10.1590/0102-77863340024 https://dx.doi.org/10.6084/m9.figshare.9276119 Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 CC-BY 40107 Meteorology FOS Earth and related environmental sciences dataset Dataset 2019 ftdatacite https://doi.org/10.6084/m9.figshare.9276119.v1 https://doi.org/10.1590/0102-77863340024 https://doi.org/10.6084/m9.figshare.9276119 2021-11-05T12:55:41Z Abstract The main of this work was to identify teleconnection patterns that influence soybean yield variability in Rio Grande do Sul in order to find potential predictors of agricultural yield in the State. Soybean yield data from 87 municipalities provided by the Brazilian Institute of Geography and Statistics were used. These series were grouped into three homogeneous groups of yield by cluster analysis. Lag correlations between climatic indexes associated with teleconnection patterns and mean soybean yield of each of the three groups evidenced the importance of each index for the crop in Rio Grande do Sul. Among the teleconnection patterns, the ones that presented most significant correlations, confidence higher than 90%, and persistent with soybean yield were the Arctic Oscillation (negative between October and December), North Atlantic Oscillation (negative between October and January) and Sea Surface Temperature anomalies in the Atlantic Ocean, between 20°S-30°S and 20°O-40°W, (positive between December and March). These indices also showed a high correlation with the precipitation between December and March for each homogeneous group evidencing the importance of their monitoring for the planning of the soybean farming in Rio Grande do Sul. Dataset Arctic North Atlantic North Atlantic oscillation DataCite Metadata Store (German National Library of Science and Technology) Arctic
institution Open Polar
collection DataCite Metadata Store (German National Library of Science and Technology)
op_collection_id ftdatacite
language unknown
topic 40107 Meteorology
FOS Earth and related environmental sciences
spellingShingle 40107 Meteorology
FOS Earth and related environmental sciences
Diogo, Alessandro Arsego
Ferraz, Simone Erotildes Teleginski
Nereu Augusto Streck
Cardoso, Andréa De Oliveira
Junior Zanon Alencar
Climatic indicators and soybean yield in Rio Grande do Sul
topic_facet 40107 Meteorology
FOS Earth and related environmental sciences
description Abstract The main of this work was to identify teleconnection patterns that influence soybean yield variability in Rio Grande do Sul in order to find potential predictors of agricultural yield in the State. Soybean yield data from 87 municipalities provided by the Brazilian Institute of Geography and Statistics were used. These series were grouped into three homogeneous groups of yield by cluster analysis. Lag correlations between climatic indexes associated with teleconnection patterns and mean soybean yield of each of the three groups evidenced the importance of each index for the crop in Rio Grande do Sul. Among the teleconnection patterns, the ones that presented most significant correlations, confidence higher than 90%, and persistent with soybean yield were the Arctic Oscillation (negative between October and December), North Atlantic Oscillation (negative between October and January) and Sea Surface Temperature anomalies in the Atlantic Ocean, between 20°S-30°S and 20°O-40°W, (positive between December and March). These indices also showed a high correlation with the precipitation between December and March for each homogeneous group evidencing the importance of their monitoring for the planning of the soybean farming in Rio Grande do Sul.
format Dataset
author Diogo, Alessandro Arsego
Ferraz, Simone Erotildes Teleginski
Nereu Augusto Streck
Cardoso, Andréa De Oliveira
Junior Zanon Alencar
author_facet Diogo, Alessandro Arsego
Ferraz, Simone Erotildes Teleginski
Nereu Augusto Streck
Cardoso, Andréa De Oliveira
Junior Zanon Alencar
author_sort Diogo, Alessandro Arsego
title Climatic indicators and soybean yield in Rio Grande do Sul
title_short Climatic indicators and soybean yield in Rio Grande do Sul
title_full Climatic indicators and soybean yield in Rio Grande do Sul
title_fullStr Climatic indicators and soybean yield in Rio Grande do Sul
title_full_unstemmed Climatic indicators and soybean yield in Rio Grande do Sul
title_sort climatic indicators and soybean yield in rio grande do sul
publisher SciELO journals
publishDate 2019
url https://dx.doi.org/10.6084/m9.figshare.9276119.v1
https://scielo.figshare.com/articles/Climatic_indicators_and_soybean_yield_in_Rio_Grande_do_Sul/9276119/1
geographic Arctic
geographic_facet Arctic
genre Arctic
North Atlantic
North Atlantic oscillation
genre_facet Arctic
North Atlantic
North Atlantic oscillation
op_relation https://dx.doi.org/10.1590/0102-77863340024
https://dx.doi.org/10.6084/m9.figshare.9276119
op_rights Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
cc-by-4.0
op_rightsnorm CC-BY
op_doi https://doi.org/10.6084/m9.figshare.9276119.v1
https://doi.org/10.1590/0102-77863340024
https://doi.org/10.6084/m9.figshare.9276119
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