Soybean yield in the Matopiba region under climate changes

Detalhes bibliográficos
Autor(a) principal: Silva,Vicente de P. R. da
Data de Publicação: 2020
Outros Autores: Silva,Roberta A. e, Maciel,Girlene F., Souza,Enio P. de, Braga,Célia C., Holanda,Romildo M. de
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Revista Brasileira de Engenharia Agrícola e Ambiental (Online)
Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1415-43662020000100008
Resumo: ABSTRACT The climatic conditions along the cycle are the main factors responsible for the final production of any crop. This study aimed to evaluate the current conditions and the effects of climate change scenarios on the yield of soybean grown in the Matopiba region, located between the states of Tocantins, south and northeast of Maranhão, south of Piauí and west of Bahia, Brazil. The AquaCrop model of FAO, version 5.0, was calibrated with data of 2014 and validated with those of 2016, using climate, soil and crop management parameters collected in two experimental campaigns conducted between June and October in 2014 and 2016 in Palmas, TO, Brazil. The performance of the model was evaluated using the following statistical indicators: prediction error (PE), coefficient of determination (R2), normalized root mean square error (NRMSE), Nash-Sutcliffe model efficiency coefficient (EF) and Willmott’s index of agreement (d). It was verified that the AquaCrop model underestimates soybean grain yield under severe water stress conditions throughout the growing cycle. The increase in CO2 concentration and in the air temperature, projected by the climate models HadGEM2-ES and MIROC5 under the scenario of stabilization (RCP 4.5) and the scenario of progression (RCP 8.5), have contributed to the increase in soybean yield by the end of this century.
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spelling Soybean yield in the Matopiba region under climate changesGlycine maxyieldAquaCrop modelclimate variabilityABSTRACT The climatic conditions along the cycle are the main factors responsible for the final production of any crop. This study aimed to evaluate the current conditions and the effects of climate change scenarios on the yield of soybean grown in the Matopiba region, located between the states of Tocantins, south and northeast of Maranhão, south of Piauí and west of Bahia, Brazil. The AquaCrop model of FAO, version 5.0, was calibrated with data of 2014 and validated with those of 2016, using climate, soil and crop management parameters collected in two experimental campaigns conducted between June and October in 2014 and 2016 in Palmas, TO, Brazil. The performance of the model was evaluated using the following statistical indicators: prediction error (PE), coefficient of determination (R2), normalized root mean square error (NRMSE), Nash-Sutcliffe model efficiency coefficient (EF) and Willmott’s index of agreement (d). It was verified that the AquaCrop model underestimates soybean grain yield under severe water stress conditions throughout the growing cycle. The increase in CO2 concentration and in the air temperature, projected by the climate models HadGEM2-ES and MIROC5 under the scenario of stabilization (RCP 4.5) and the scenario of progression (RCP 8.5), have contributed to the increase in soybean yield by the end of this century.Departamento de Engenharia Agrícola - UFCG2020-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S1415-43662020000100008Revista Brasileira de Engenharia Agrícola e Ambiental v.24 n.1 2020reponame:Revista Brasileira de Engenharia Agrícola e Ambiental (Online)instname:Universidade Federal de Campina Grande (UFCG)instacron:UFCG10.1590/1807-1929/agriambi.v24n1p8-14info:eu-repo/semantics/openAccessSilva,Vicente de P. R. daSilva,Roberta A. eMaciel,Girlene F.Souza,Enio P. deBraga,Célia C.Holanda,Romildo M. deeng2019-12-04T00:00:00Zoai:scielo:S1415-43662020000100008Revistahttp://www.scielo.br/rbeaaPUBhttps://old.scielo.br/oai/scielo-oai.php||agriambi@agriambi.com.br1807-19291415-4366opendoar:2019-12-04T00:00Revista Brasileira de Engenharia Agrícola e Ambiental (Online) - Universidade Federal de Campina Grande (UFCG)false
dc.title.none.fl_str_mv Soybean yield in the Matopiba region under climate changes
title Soybean yield in the Matopiba region under climate changes
spellingShingle Soybean yield in the Matopiba region under climate changes
Silva,Vicente de P. R. da
Glycine max
yield
AquaCrop model
climate variability
title_short Soybean yield in the Matopiba region under climate changes
title_full Soybean yield in the Matopiba region under climate changes
title_fullStr Soybean yield in the Matopiba region under climate changes
title_full_unstemmed Soybean yield in the Matopiba region under climate changes
title_sort Soybean yield in the Matopiba region under climate changes
author Silva,Vicente de P. R. da
author_facet Silva,Vicente de P. R. da
Silva,Roberta A. e
Maciel,Girlene F.
Souza,Enio P. de
Braga,Célia C.
Holanda,Romildo M. de
author_role author
author2 Silva,Roberta A. e
Maciel,Girlene F.
Souza,Enio P. de
Braga,Célia C.
Holanda,Romildo M. de
author2_role author
author
author
author
author
dc.contributor.author.fl_str_mv Silva,Vicente de P. R. da
Silva,Roberta A. e
Maciel,Girlene F.
Souza,Enio P. de
Braga,Célia C.
Holanda,Romildo M. de
dc.subject.por.fl_str_mv Glycine max
yield
AquaCrop model
climate variability
topic Glycine max
yield
AquaCrop model
climate variability
description ABSTRACT The climatic conditions along the cycle are the main factors responsible for the final production of any crop. This study aimed to evaluate the current conditions and the effects of climate change scenarios on the yield of soybean grown in the Matopiba region, located between the states of Tocantins, south and northeast of Maranhão, south of Piauí and west of Bahia, Brazil. The AquaCrop model of FAO, version 5.0, was calibrated with data of 2014 and validated with those of 2016, using climate, soil and crop management parameters collected in two experimental campaigns conducted between June and October in 2014 and 2016 in Palmas, TO, Brazil. The performance of the model was evaluated using the following statistical indicators: prediction error (PE), coefficient of determination (R2), normalized root mean square error (NRMSE), Nash-Sutcliffe model efficiency coefficient (EF) and Willmott’s index of agreement (d). It was verified that the AquaCrop model underestimates soybean grain yield under severe water stress conditions throughout the growing cycle. The increase in CO2 concentration and in the air temperature, projected by the climate models HadGEM2-ES and MIROC5 under the scenario of stabilization (RCP 4.5) and the scenario of progression (RCP 8.5), have contributed to the increase in soybean yield by the end of this century.
publishDate 2020
dc.date.none.fl_str_mv 2020-01-01
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
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dc.language.iso.fl_str_mv eng
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dc.publisher.none.fl_str_mv Departamento de Engenharia Agrícola - UFCG
publisher.none.fl_str_mv Departamento de Engenharia Agrícola - UFCG
dc.source.none.fl_str_mv Revista Brasileira de Engenharia Agrícola e Ambiental v.24 n.1 2020
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