Altitude and geographic coordinates to estimate monthly rainfall in the state of Mato Grosso do Sul
Autor(a) principal: | |
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Data de Publicação: | 2016 |
Outros Autores: | , , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Bioscience journal (Online) |
Texto Completo: | https://seer.ufu.br/index.php/biosciencejournal/article/view/29387 |
Resumo: | Adjustment of multiple linear regression equations has allowed estimating the value of a certain climatological variable according to geographical coordinates with acceptable degree of accuracy. The aim of this study was to verify if the average monthly rainfall could be estimated according to the altitude, latitude and longitude in Mato Grosso do Sul State (MS). Rainfall data of 32 stations of MS were collected from 1954 to 2013. It were formed 384 time series (12 months × 32 sites), with different numbers of years of observations in each series. On each of the 384 monthly rainfall time series it was calculated the average (a), at least 30 years of observation, forming 12 matrices 32 x 4 (32 sites x 4 variables: altitude, latitude, longitude and monthly rainfall). It was estimated for each matrix the Pearson's linear correlation coefficient among the variables, performing the multicollinearity diagnosis for each matrix. Correlations were unfolded by path analysis in direct and indirect effects and in each month it was used the multiple linear regression model. The altitude and latitude have greater effect on the spatial distribution of rainfall in MS. The multiple linear regression equations generated in this study will subsidize researches of crop zoning, indication for sowing times, irrigation, determination of yield potential, climate risks zoning and credit and agricultural insurance. |
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Altitude and geographic coordinates to estimate monthly rainfall in the state of Mato Grosso do Sul correlationmultiple linear regressionpath analysis.Adjustment of multiple linear regression equations has allowed estimating the value of a certain climatological variable according to geographical coordinates with acceptable degree of accuracy. The aim of this study was to verify if the average monthly rainfall could be estimated according to the altitude, latitude and longitude in Mato Grosso do Sul State (MS). Rainfall data of 32 stations of MS were collected from 1954 to 2013. It were formed 384 time series (12 months × 32 sites), with different numbers of years of observations in each series. On each of the 384 monthly rainfall time series it was calculated the average (a), at least 30 years of observation, forming 12 matrices 32 x 4 (32 sites x 4 variables: altitude, latitude, longitude and monthly rainfall). It was estimated for each matrix the Pearson's linear correlation coefficient among the variables, performing the multicollinearity diagnosis for each matrix. Correlations were unfolded by path analysis in direct and indirect effects and in each month it was used the multiple linear regression model. The altitude and latitude have greater effect on the spatial distribution of rainfall in MS. The multiple linear regression equations generated in this study will subsidize researches of crop zoning, indication for sowing times, irrigation, determination of yield potential, climate risks zoning and credit and agricultural insurance.EDUFU2016-01-20info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://seer.ufu.br/index.php/biosciencejournal/article/view/2938710.14393/BJ-v32n1a2016-29387Bioscience Journal ; Vol. 32 No. 1 (2016): Jan./Feb.; 41-47Bioscience Journal ; v. 32 n. 1 (2016): Jan./Feb.; 41-471981-3163reponame:Bioscience journal (Online)instname:Universidade Federal de Uberlândia (UFU)instacron:UFUenghttps://seer.ufu.br/index.php/biosciencejournal/article/view/29387/17749Copyright (c) 2016 Paulo Eduardo Teodoro, Elias Rodrigues da Cunha, Caio Cézar Guedes Corrêa, Larissa Pereira Ribeiro, Francisco Eduardo Torres, José Francisco de Oliveira-Junior, Givanildo Gois, Victor Matheus Bacanihttps://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccessTeodoro, Paulo Eduardoda Cunha, Elias RodriguesCorrêa, Caio Cézar GuedesRibeiro, Larissa PereiraTorres, Francisco EduardoOliveira-Junior, José Francisco deGois, GivanildoBacani, Victor Matheus2022-02-23T11:16:57Zoai:ojs.www.seer.ufu.br:article/29387Revistahttps://seer.ufu.br/index.php/biosciencejournalPUBhttps://seer.ufu.br/index.php/biosciencejournal/oaibiosciencej@ufu.br||1981-31631516-3725opendoar:2022-02-23T11:16:57Bioscience journal (Online) - Universidade Federal de Uberlândia (UFU)false |
dc.title.none.fl_str_mv |
Altitude and geographic coordinates to estimate monthly rainfall in the state of Mato Grosso do Sul |
title |
Altitude and geographic coordinates to estimate monthly rainfall in the state of Mato Grosso do Sul |
spellingShingle |
Altitude and geographic coordinates to estimate monthly rainfall in the state of Mato Grosso do Sul Teodoro, Paulo Eduardo correlation multiple linear regression path analysis. |
title_short |
Altitude and geographic coordinates to estimate monthly rainfall in the state of Mato Grosso do Sul |
title_full |
Altitude and geographic coordinates to estimate monthly rainfall in the state of Mato Grosso do Sul |
title_fullStr |
Altitude and geographic coordinates to estimate monthly rainfall in the state of Mato Grosso do Sul |
title_full_unstemmed |
Altitude and geographic coordinates to estimate monthly rainfall in the state of Mato Grosso do Sul |
title_sort |
Altitude and geographic coordinates to estimate monthly rainfall in the state of Mato Grosso do Sul |
author |
Teodoro, Paulo Eduardo |
author_facet |
Teodoro, Paulo Eduardo da Cunha, Elias Rodrigues Corrêa, Caio Cézar Guedes Ribeiro, Larissa Pereira Torres, Francisco Eduardo Oliveira-Junior, José Francisco de Gois, Givanildo Bacani, Victor Matheus |
author_role |
author |
author2 |
da Cunha, Elias Rodrigues Corrêa, Caio Cézar Guedes Ribeiro, Larissa Pereira Torres, Francisco Eduardo Oliveira-Junior, José Francisco de Gois, Givanildo Bacani, Victor Matheus |
author2_role |
author author author author author author author |
dc.contributor.author.fl_str_mv |
Teodoro, Paulo Eduardo da Cunha, Elias Rodrigues Corrêa, Caio Cézar Guedes Ribeiro, Larissa Pereira Torres, Francisco Eduardo Oliveira-Junior, José Francisco de Gois, Givanildo Bacani, Victor Matheus |
dc.subject.por.fl_str_mv |
correlation multiple linear regression path analysis. |
topic |
correlation multiple linear regression path analysis. |
description |
Adjustment of multiple linear regression equations has allowed estimating the value of a certain climatological variable according to geographical coordinates with acceptable degree of accuracy. The aim of this study was to verify if the average monthly rainfall could be estimated according to the altitude, latitude and longitude in Mato Grosso do Sul State (MS). Rainfall data of 32 stations of MS were collected from 1954 to 2013. It were formed 384 time series (12 months × 32 sites), with different numbers of years of observations in each series. On each of the 384 monthly rainfall time series it was calculated the average (a), at least 30 years of observation, forming 12 matrices 32 x 4 (32 sites x 4 variables: altitude, latitude, longitude and monthly rainfall). It was estimated for each matrix the Pearson's linear correlation coefficient among the variables, performing the multicollinearity diagnosis for each matrix. Correlations were unfolded by path analysis in direct and indirect effects and in each month it was used the multiple linear regression model. The altitude and latitude have greater effect on the spatial distribution of rainfall in MS. The multiple linear regression equations generated in this study will subsidize researches of crop zoning, indication for sowing times, irrigation, determination of yield potential, climate risks zoning and credit and agricultural insurance. |
publishDate |
2016 |
dc.date.none.fl_str_mv |
2016-01-20 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://seer.ufu.br/index.php/biosciencejournal/article/view/29387 10.14393/BJ-v32n1a2016-29387 |
url |
https://seer.ufu.br/index.php/biosciencejournal/article/view/29387 |
identifier_str_mv |
10.14393/BJ-v32n1a2016-29387 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
https://seer.ufu.br/index.php/biosciencejournal/article/view/29387/17749 |
dc.rights.driver.fl_str_mv |
https://creativecommons.org/licenses/by/4.0 info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
https://creativecommons.org/licenses/by/4.0 |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
EDUFU |
publisher.none.fl_str_mv |
EDUFU |
dc.source.none.fl_str_mv |
Bioscience Journal ; Vol. 32 No. 1 (2016): Jan./Feb.; 41-47 Bioscience Journal ; v. 32 n. 1 (2016): Jan./Feb.; 41-47 1981-3163 reponame:Bioscience journal (Online) instname:Universidade Federal de Uberlândia (UFU) instacron:UFU |
instname_str |
Universidade Federal de Uberlândia (UFU) |
instacron_str |
UFU |
institution |
UFU |
reponame_str |
Bioscience journal (Online) |
collection |
Bioscience journal (Online) |
repository.name.fl_str_mv |
Bioscience journal (Online) - Universidade Federal de Uberlândia (UFU) |
repository.mail.fl_str_mv |
biosciencej@ufu.br|| |
_version_ |
1797069075440992256 |