Modeling the impact of agrometeorological variables on soybean yield in the Mato Grosso Do Sul: 2000–2019
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Outros Autores: | , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1007/s10668-020-00807-w http://hdl.handle.net/11449/198977 |
Resumo: | The study of the soybean yield variability influenced by the climate contributes to the planning of strategies to mitigate its negative effects. Thus, our aim was to calibrate agrometeorological models for soybean yield forecast and identify the weather variables that most influence soybean yield. This study used historical series of climate and soybean yield data from soybean-producing locations in the Mato Grosso do Sul state, Brazil. The historical climate series was 20 years (2000–2019). The soybean production, yield, and planted area data of the localities were in the period from 2009–2018. Multiple linear regression analysis was the statistical tool used for data modeling. The models from the north and central regions forecast of anticipation of 2 months since the final data necessary to apply the model were EXCJANc and PJANc, respectively. The models calibrated for the southern region reported anticipation of one month since the final data necessary to apply the model was EXCFEVc. The calibrated models used to forecast soybean yield as a function of climatic conditions have a high degree of significance (p < 0.05), high accuracy and errors lower. The models for the northern and central regions show a prevision of anticipation of 2 months before soybean harvest, a period that is essential for producers to be able to conduct pre- and post-harvest planning. The climate variable with the greatest negative influence (r = − 0.54) on soybean yield in Mato Grosso do Sul state was water stress in December. |
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Modeling the impact of agrometeorological variables on soybean yield in the Mato Grosso Do Sul: 2000–2019ClimateCrop modelingGlycine max LSpatial error modelYield zoningThe study of the soybean yield variability influenced by the climate contributes to the planning of strategies to mitigate its negative effects. Thus, our aim was to calibrate agrometeorological models for soybean yield forecast and identify the weather variables that most influence soybean yield. This study used historical series of climate and soybean yield data from soybean-producing locations in the Mato Grosso do Sul state, Brazil. The historical climate series was 20 years (2000–2019). The soybean production, yield, and planted area data of the localities were in the period from 2009–2018. Multiple linear regression analysis was the statistical tool used for data modeling. The models from the north and central regions forecast of anticipation of 2 months since the final data necessary to apply the model were EXCJANc and PJANc, respectively. The models calibrated for the southern region reported anticipation of one month since the final data necessary to apply the model was EXCFEVc. The calibrated models used to forecast soybean yield as a function of climatic conditions have a high degree of significance (p < 0.05), high accuracy and errors lower. The models for the northern and central regions show a prevision of anticipation of 2 months before soybean harvest, a period that is essential for producers to be able to conduct pre- and post-harvest planning. The climate variable with the greatest negative influence (r = − 0.54) on soybean yield in Mato Grosso do Sul state was water stress in December.Federal Institute of Mato Grosso do Sul (IFMS) - NaviraiState University of Sao Paulo (FCAV/UNESP) - JaboticabalState University of Sao Paulo (FCAV/UNESP) - JaboticabalFederal Institute of Mato Grosso do Sul (IFMS) - NaviraiUniversidade Estadual Paulista (Unesp)de Oliveira Aparecido, Lucas EduardoTorsoni, Guilherme Botegada Silva Cabral de Moraes, José Reinaldode Meneses, Kamila Cunha [UNESP]Lorençone, João AntonioLorençone, Pedro Antonio2020-12-12T01:27:15Z2020-12-12T01:27:15Z2020-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://dx.doi.org/10.1007/s10668-020-00807-wEnvironment, Development and Sustainability.1573-29751387-585Xhttp://hdl.handle.net/11449/19897710.1007/s10668-020-00807-w2-s2.0-85086386676Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengEnvironment, Development and Sustainabilityinfo:eu-repo/semantics/openAccess2021-10-22T21:54:14Zoai:repositorio.unesp.br:11449/198977Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T15:40:01.530115Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Modeling the impact of agrometeorological variables on soybean yield in the Mato Grosso Do Sul: 2000–2019 |
title |
Modeling the impact of agrometeorological variables on soybean yield in the Mato Grosso Do Sul: 2000–2019 |
spellingShingle |
Modeling the impact of agrometeorological variables on soybean yield in the Mato Grosso Do Sul: 2000–2019 de Oliveira Aparecido, Lucas Eduardo Climate Crop modeling Glycine max L Spatial error model Yield zoning |
title_short |
Modeling the impact of agrometeorological variables on soybean yield in the Mato Grosso Do Sul: 2000–2019 |
title_full |
Modeling the impact of agrometeorological variables on soybean yield in the Mato Grosso Do Sul: 2000–2019 |
title_fullStr |
Modeling the impact of agrometeorological variables on soybean yield in the Mato Grosso Do Sul: 2000–2019 |
title_full_unstemmed |
Modeling the impact of agrometeorological variables on soybean yield in the Mato Grosso Do Sul: 2000–2019 |
title_sort |
Modeling the impact of agrometeorological variables on soybean yield in the Mato Grosso Do Sul: 2000–2019 |
author |
de Oliveira Aparecido, Lucas Eduardo |
author_facet |
de Oliveira Aparecido, Lucas Eduardo Torsoni, Guilherme Botega da Silva Cabral de Moraes, José Reinaldo de Meneses, Kamila Cunha [UNESP] Lorençone, João Antonio Lorençone, Pedro Antonio |
author_role |
author |
author2 |
Torsoni, Guilherme Botega da Silva Cabral de Moraes, José Reinaldo de Meneses, Kamila Cunha [UNESP] Lorençone, João Antonio Lorençone, Pedro Antonio |
author2_role |
author author author author author |
dc.contributor.none.fl_str_mv |
Federal Institute of Mato Grosso do Sul (IFMS) - Navirai Universidade Estadual Paulista (Unesp) |
dc.contributor.author.fl_str_mv |
de Oliveira Aparecido, Lucas Eduardo Torsoni, Guilherme Botega da Silva Cabral de Moraes, José Reinaldo de Meneses, Kamila Cunha [UNESP] Lorençone, João Antonio Lorençone, Pedro Antonio |
dc.subject.por.fl_str_mv |
Climate Crop modeling Glycine max L Spatial error model Yield zoning |
topic |
Climate Crop modeling Glycine max L Spatial error model Yield zoning |
description |
The study of the soybean yield variability influenced by the climate contributes to the planning of strategies to mitigate its negative effects. Thus, our aim was to calibrate agrometeorological models for soybean yield forecast and identify the weather variables that most influence soybean yield. This study used historical series of climate and soybean yield data from soybean-producing locations in the Mato Grosso do Sul state, Brazil. The historical climate series was 20 years (2000–2019). The soybean production, yield, and planted area data of the localities were in the period from 2009–2018. Multiple linear regression analysis was the statistical tool used for data modeling. The models from the north and central regions forecast of anticipation of 2 months since the final data necessary to apply the model were EXCJANc and PJANc, respectively. The models calibrated for the southern region reported anticipation of one month since the final data necessary to apply the model was EXCFEVc. The calibrated models used to forecast soybean yield as a function of climatic conditions have a high degree of significance (p < 0.05), high accuracy and errors lower. The models for the northern and central regions show a prevision of anticipation of 2 months before soybean harvest, a period that is essential for producers to be able to conduct pre- and post-harvest planning. The climate variable with the greatest negative influence (r = − 0.54) on soybean yield in Mato Grosso do Sul state was water stress in December. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020-12-12T01:27:15Z 2020-12-12T01:27:15Z 2020-01-01 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1007/s10668-020-00807-w Environment, Development and Sustainability. 1573-2975 1387-585X http://hdl.handle.net/11449/198977 10.1007/s10668-020-00807-w 2-s2.0-85086386676 |
url |
http://dx.doi.org/10.1007/s10668-020-00807-w http://hdl.handle.net/11449/198977 |
identifier_str_mv |
Environment, Development and Sustainability. 1573-2975 1387-585X 10.1007/s10668-020-00807-w 2-s2.0-85086386676 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Environment, Development and Sustainability |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.source.none.fl_str_mv |
Scopus reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1808128547796025344 |