Agrometeorological models for groundnut crop yield forecasting in the Jaboticabal, São Paulo State region, Brazil
Autor(a) principal: | |
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Data de Publicação: | 2015 |
Outros Autores: | |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Acta Scientiarum. Agronomy (Online) |
Texto Completo: | http://www.periodicos.uem.br/ojs/index.php/ActaSciAgron/article/view/19766 |
Resumo: | Forecast is the act of estimating a future event based on current data. Ten-day period (TDP) meteorological data were used for modeling: mean air temperature, precipitation and water balance components (water deficit (DEF) and surplus (EXC) and soil water storage (SWS)). Meteorological and yield data from 1990-2004 were used for calibration, and 2005-2010 were used for testing. First step was the selection of variables via correlation analysis to determine which TDP and climatic variables have more influence on the crop yield. The selected variables were used to construct models by multiple linear regression, using a stepwise backwards process. Among all analyzed models, the following was notable: Yield = - 4.964 x [SWS of 2° TDP of December of the previous year (OPY)] – 1.123 x [SWS of 2° TDP of November OPY] + 0.949 x [EXC of 1° TDP of February of the productive year (PY)] + 2.5 x [SWS of 2° TDP of February OPY] + 19.125 x [EXC of 1° TDP of May OPY] – 3.113 x [EXC of 3° TDP of January OPY] + 1.469 x [EXC of 3 TDP of January of PY] + 3920.526, with MAPE = 5.22%, R2 = 0.58 and RMSEs = 111.03 kg ha-1. |
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Acta Scientiarum. Agronomy (Online) |
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Agrometeorological models for groundnut crop yield forecasting in the Jaboticabal, São Paulo State region, BrazilAgrometeorological models for groundnut crop yield forecasting in the Jaboticabal, São Paulo State region, Brazilcrop modelwater balancepredictionproduction.agrometeorologiaAgrometeorologiaForecast is the act of estimating a future event based on current data. Ten-day period (TDP) meteorological data were used for modeling: mean air temperature, precipitation and water balance components (water deficit (DEF) and surplus (EXC) and soil water storage (SWS)). Meteorological and yield data from 1990-2004 were used for calibration, and 2005-2010 were used for testing. First step was the selection of variables via correlation analysis to determine which TDP and climatic variables have more influence on the crop yield. The selected variables were used to construct models by multiple linear regression, using a stepwise backwards process. Among all analyzed models, the following was notable: Yield = - 4.964 x [SWS of 2° TDP of December of the previous year (OPY)] – 1.123 x [SWS of 2° TDP of November OPY] + 0.949 x [EXC of 1° TDP of February of the productive year (PY)] + 2.5 x [SWS of 2° TDP of February OPY] + 19.125 x [EXC of 1° TDP of May OPY] – 3.113 x [EXC of 3° TDP of January OPY] + 1.469 x [EXC of 3 TDP of January of PY] + 3920.526, with MAPE = 5.22%, R2 = 0.58 and RMSEs = 111.03 kg ha-1. Forecast is the act of estimating a future event based on current data. Ten-day period (TDP) meteorological data were used for modeling: mean air temperature, precipitation and water balance components (water deficit (DEF) and surplus (EXC) and soil water storage (SWS)). Meteorological and yield data from 1990-2004 were used for calibration, and 2005-2010 were used for testing. First step was the selection of variables via correlation analysis to determine which TDP and climatic variables have more influence on the crop yield. The selected variables were used to construct models by multiple linear regression, using a stepwise backwards process. Among all analyzed models, the following was notable: Yield = - 4.964 x [SWS of 2° TDP of December of the previous year (OPY)] – 1.123 x [SWS of 2° TDP of November OPY] + 0.949 x [EXC of 1° TDP of February of the productive year (PY)] + 2.5 x [SWS of 2° TDP of February OPY] + 19.125 x [EXC of 1° TDP of May OPY] – 3.113 x [EXC of 3° TDP of January OPY] + 1.469 x [EXC of 3 TDP of January of PY] + 3920.526, with MAPE = 5.22%, R2 = 0.58 and RMSEs = 111.03 kg ha-1.Universidade Estadual de Maringá2015-10-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionmodelos agrometeorologicosmodelagem agrometeorologicaapplication/pdfhttp://www.periodicos.uem.br/ojs/index.php/ActaSciAgron/article/view/1976610.4025/actasciagron.v37i4.19766Acta Scientiarum. Agronomy; Vol 37 No 4 (2015); 403-410Acta Scientiarum. Agronomy; v. 37 n. 4 (2015); 403-4101807-86211679-9275reponame:Acta Scientiarum. Agronomy (Online)instname:Universidade Estadual de Maringá (UEM)instacron:UEMenghttp://www.periodicos.uem.br/ojs/index.php/ActaSciAgron/article/view/19766/pdf_91Moreto, Victor BruniniRolim, Glauco de Souzainfo:eu-repo/semantics/openAccess2015-10-29T10:21:25Zoai:periodicos.uem.br/ojs:article/19766Revistahttp://www.periodicos.uem.br/ojs/index.php/ActaSciAgronPUBhttp://www.periodicos.uem.br/ojs/index.php/ActaSciAgron/oaiactaagron@uem.br||actaagron@uem.br|| edamasio@uem.br1807-86211679-9275opendoar:2015-10-29T10:21:25Acta Scientiarum. Agronomy (Online) - Universidade Estadual de Maringá (UEM)false |
dc.title.none.fl_str_mv |
Agrometeorological models for groundnut crop yield forecasting in the Jaboticabal, São Paulo State region, Brazil Agrometeorological models for groundnut crop yield forecasting in the Jaboticabal, São Paulo State region, Brazil |
title |
Agrometeorological models for groundnut crop yield forecasting in the Jaboticabal, São Paulo State region, Brazil |
spellingShingle |
Agrometeorological models for groundnut crop yield forecasting in the Jaboticabal, São Paulo State region, Brazil Moreto, Victor Brunini crop model water balance prediction production. agrometeorologia Agrometeorologia |
title_short |
Agrometeorological models for groundnut crop yield forecasting in the Jaboticabal, São Paulo State region, Brazil |
title_full |
Agrometeorological models for groundnut crop yield forecasting in the Jaboticabal, São Paulo State region, Brazil |
title_fullStr |
Agrometeorological models for groundnut crop yield forecasting in the Jaboticabal, São Paulo State region, Brazil |
title_full_unstemmed |
Agrometeorological models for groundnut crop yield forecasting in the Jaboticabal, São Paulo State region, Brazil |
title_sort |
Agrometeorological models for groundnut crop yield forecasting in the Jaboticabal, São Paulo State region, Brazil |
author |
Moreto, Victor Brunini |
author_facet |
Moreto, Victor Brunini Rolim, Glauco de Souza |
author_role |
author |
author2 |
Rolim, Glauco de Souza |
author2_role |
author |
dc.contributor.author.fl_str_mv |
Moreto, Victor Brunini Rolim, Glauco de Souza |
dc.subject.por.fl_str_mv |
crop model water balance prediction production. agrometeorologia Agrometeorologia |
topic |
crop model water balance prediction production. agrometeorologia Agrometeorologia |
description |
Forecast is the act of estimating a future event based on current data. Ten-day period (TDP) meteorological data were used for modeling: mean air temperature, precipitation and water balance components (water deficit (DEF) and surplus (EXC) and soil water storage (SWS)). Meteorological and yield data from 1990-2004 were used for calibration, and 2005-2010 were used for testing. First step was the selection of variables via correlation analysis to determine which TDP and climatic variables have more influence on the crop yield. The selected variables were used to construct models by multiple linear regression, using a stepwise backwards process. Among all analyzed models, the following was notable: Yield = - 4.964 x [SWS of 2° TDP of December of the previous year (OPY)] – 1.123 x [SWS of 2° TDP of November OPY] + 0.949 x [EXC of 1° TDP of February of the productive year (PY)] + 2.5 x [SWS of 2° TDP of February OPY] + 19.125 x [EXC of 1° TDP of May OPY] – 3.113 x [EXC of 3° TDP of January OPY] + 1.469 x [EXC of 3 TDP of January of PY] + 3920.526, with MAPE = 5.22%, R2 = 0.58 and RMSEs = 111.03 kg ha-1. |
publishDate |
2015 |
dc.date.none.fl_str_mv |
2015-10-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion modelos agrometeorologicos modelagem agrometeorologica |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://www.periodicos.uem.br/ojs/index.php/ActaSciAgron/article/view/19766 10.4025/actasciagron.v37i4.19766 |
url |
http://www.periodicos.uem.br/ojs/index.php/ActaSciAgron/article/view/19766 |
identifier_str_mv |
10.4025/actasciagron.v37i4.19766 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
http://www.periodicos.uem.br/ojs/index.php/ActaSciAgron/article/view/19766/pdf_91 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Universidade Estadual de Maringá |
publisher.none.fl_str_mv |
Universidade Estadual de Maringá |
dc.source.none.fl_str_mv |
Acta Scientiarum. Agronomy; Vol 37 No 4 (2015); 403-410 Acta Scientiarum. Agronomy; v. 37 n. 4 (2015); 403-410 1807-8621 1679-9275 reponame:Acta Scientiarum. Agronomy (Online) instname:Universidade Estadual de Maringá (UEM) instacron:UEM |
instname_str |
Universidade Estadual de Maringá (UEM) |
instacron_str |
UEM |
institution |
UEM |
reponame_str |
Acta Scientiarum. Agronomy (Online) |
collection |
Acta Scientiarum. Agronomy (Online) |
repository.name.fl_str_mv |
Acta Scientiarum. Agronomy (Online) - Universidade Estadual de Maringá (UEM) |
repository.mail.fl_str_mv |
actaagron@uem.br||actaagron@uem.br|| edamasio@uem.br |
_version_ |
1799305909350957056 |