Spatial variability of meteorological observations and impacts on regional estimates of soybean grain productivity

Detalhes bibliográficos
Autor(a) principal: Ferreira, Rodrigo Cornacini
Data de Publicação: 2017
Outros Autores: Sibaldelli, Rubson Natal Ribeiro, Morais, Heverly, Abi Saab, Otávio Jorge Grigoli, Farias, José Renato Bouças
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Semina. Ciências Agrárias (Online)
Texto Completo: https://ojs.uel.br/revistas/uel/index.php/semagrarias/article/view/26165
Resumo: Brazil requires a fully representative weather network station; it is common to use data observed in locations distant from the region of interest. However, few studies have evaluated the efficiency and precision associated with the use of climate data, either estimated or interpolated, from stations far from the agricultural area of interest. Hence, this study aimed to demonstrate the impacts of spatial variability of the main meteorological elements on the regional estimate of soybean productivity. Regression analysis was used to compare data recorded at three weather stations located throughout Londrina, PR, Brazil. The water balance of the soybean crop was calculated at 10-day periods and grain productivity losses estimated using the Agro-Ecological Zones (AEZ) methodology. Temperatures at the three locations were similar, while the relative air humidity, and particularly, the rainfall data, were less correlated. A high degree of caution is recommended in the use and choice of a single weather station to represent a municipality or region, particularly in countries, such as Brazil, with multiple regions of agricultural and environmental importance. Models and crop season estimates that do not consider such a recommendation are vulnerable to errors in their forecasts. The volumetric and temporal variability in the spatial rainfall distribution resulted in soybean yield discrepancies, estimated at the municipal level. The consistency of the data series, the location of weather stations and their distance to the location of interest determine the ability of crop models to accurately estimate soybean production based on meteorological data, particularly the rainfall data. This study contributes to future regional research using climate data, and highlights the importance of a weather station network throughout Brazil, demonstrating the urgent need to increase the number of weather stations, particularly for recording rainfall data.
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spelling Spatial variability of meteorological observations and impacts on regional estimates of soybean grain productivityVariabilidade espacial das observações meteorológicas e impactos na estimativa regional da produtividade de sojaGlycine max LMerrillWeather stationRainfallClimate variabilityAgro-ecological zone.Glycine max LMerrillEstação meteorológicaPrecipitação pluvialVariabilidade climáticaZona agroecológica.Brazil requires a fully representative weather network station; it is common to use data observed in locations distant from the region of interest. However, few studies have evaluated the efficiency and precision associated with the use of climate data, either estimated or interpolated, from stations far from the agricultural area of interest. Hence, this study aimed to demonstrate the impacts of spatial variability of the main meteorological elements on the regional estimate of soybean productivity. Regression analysis was used to compare data recorded at three weather stations located throughout Londrina, PR, Brazil. The water balance of the soybean crop was calculated at 10-day periods and grain productivity losses estimated using the Agro-Ecological Zones (AEZ) methodology. Temperatures at the three locations were similar, while the relative air humidity, and particularly, the rainfall data, were less correlated. A high degree of caution is recommended in the use and choice of a single weather station to represent a municipality or region, particularly in countries, such as Brazil, with multiple regions of agricultural and environmental importance. Models and crop season estimates that do not consider such a recommendation are vulnerable to errors in their forecasts. The volumetric and temporal variability in the spatial rainfall distribution resulted in soybean yield discrepancies, estimated at the municipal level. The consistency of the data series, the location of weather stations and their distance to the location of interest determine the ability of crop models to accurately estimate soybean production based on meteorological data, particularly the rainfall data. This study contributes to future regional research using climate data, and highlights the importance of a weather station network throughout Brazil, demonstrating the urgent need to increase the number of weather stations, particularly for recording rainfall data.O Brasil ainda não possui uma rede de estações meteorológicas suficientemente representativas, sendo comum a utilização de dados observados em locais distantes da região de interesse. Contudo, são escassos estudos que avaliem a eficácia e precisão da utilização de dados climáticos estimados ou interpolados a partir de estações distantes da área agrícola de interesse. Assim, este estudo teve como objetivo demonstrar os impactos da variabilidade espacial dos principais elementos meteorológicos sobre a estimativa regional da produtividade de grãos de soja. Utilizaram-se dados observados em três estações meteorológicas em diferentes locais de Londrina, comparados por meio de análise de regressão. Calculou-se o balanço hídrico decendial para soja e estimaram-se as perdas de produtividade de grãos pelo método Zona Agroecológica. As temperaturas nos diferentes locais apresentaram semelhanças, enquanto a umidade relativa do ar e, principalmente, precipitação pluvial foram mais discrepantes. Recomenda-se muita cautela no uso e na escolha de uma única estação meteorológica para representar um município ou região, como acontece em várias regiões de importância agrícola e ambiental no Brasil. Modelos e resultados de estimativas de safras que não consideram tal recomendação estão vulneráveis a erros em suas previsões. A variabilidade volumétrica e temporal na distribuição espacial das precipitações pluviais provocaram diferentes estimativas de produtividade de soja em escala municipal. A consistência da série de dados, a localização das estações meteorológicas e a distância destas ao ponto de interesse são fatores determinantes da precisão em modelos para estimativas da produtividade de grãos de soja com base em dados meteorológicos, com destaque para a precipitação pluvial. Este estudo contribui para futuras pesquisas regionais que utilizem dados climáticos, além de evidenciar a importância da rede de estações meteorológicas em todo Brasil, demonstrando a necessidade urgente do incremento no número dessas estações, em especial para o registro de dados das precipitações pluviais.UEL2017-08-25info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionPesquisa Empírica de Campoapplication/pdfhttps://ojs.uel.br/revistas/uel/index.php/semagrarias/article/view/2616510.5433/1679-0359.2017v38n4Supl1p2265Semina: Ciências Agrárias; Vol. 38 No. 4Supl1 (2017); 2265-2278Semina: Ciências Agrárias; v. 38 n. 4Supl1 (2017); 2265-22781679-03591676-546Xreponame:Semina. Ciências Agrárias (Online)instname:Universidade Estadual de Londrina (UEL)instacron:UELenghttps://ojs.uel.br/revistas/uel/index.php/semagrarias/article/view/26165/21481Copyright (c) 2017 Semina: Ciências Agráriashttp://creativecommons.org/licenses/by-nc/4.0info:eu-repo/semantics/openAccessFerreira, Rodrigo CornaciniSibaldelli, Rubson Natal RibeiroMorais, HeverlyAbi Saab, Otávio Jorge GrigoliFarias, José Renato Bouças2022-10-21T14:56:14Zoai:ojs.pkp.sfu.ca:article/26165Revistahttp://www.uel.br/revistas/uel/index.php/semagrariasPUBhttps://ojs.uel.br/revistas/uel/index.php/semagrarias/oaisemina.agrarias@uel.br1679-03591676-546Xopendoar:2022-10-21T14:56:14Semina. Ciências Agrárias (Online) - Universidade Estadual de Londrina (UEL)false
dc.title.none.fl_str_mv Spatial variability of meteorological observations and impacts on regional estimates of soybean grain productivity
Variabilidade espacial das observações meteorológicas e impactos na estimativa regional da produtividade de soja
title Spatial variability of meteorological observations and impacts on regional estimates of soybean grain productivity
spellingShingle Spatial variability of meteorological observations and impacts on regional estimates of soybean grain productivity
Ferreira, Rodrigo Cornacini
Glycine max L
Merrill
Weather station
Rainfall
Climate variability
Agro-ecological zone.
Glycine max L
Merrill
Estação meteorológica
Precipitação pluvial
Variabilidade climática
Zona agroecológica.
title_short Spatial variability of meteorological observations and impacts on regional estimates of soybean grain productivity
title_full Spatial variability of meteorological observations and impacts on regional estimates of soybean grain productivity
title_fullStr Spatial variability of meteorological observations and impacts on regional estimates of soybean grain productivity
title_full_unstemmed Spatial variability of meteorological observations and impacts on regional estimates of soybean grain productivity
title_sort Spatial variability of meteorological observations and impacts on regional estimates of soybean grain productivity
author Ferreira, Rodrigo Cornacini
author_facet Ferreira, Rodrigo Cornacini
Sibaldelli, Rubson Natal Ribeiro
Morais, Heverly
Abi Saab, Otávio Jorge Grigoli
Farias, José Renato Bouças
author_role author
author2 Sibaldelli, Rubson Natal Ribeiro
Morais, Heverly
Abi Saab, Otávio Jorge Grigoli
Farias, José Renato Bouças
author2_role author
author
author
author
dc.contributor.author.fl_str_mv Ferreira, Rodrigo Cornacini
Sibaldelli, Rubson Natal Ribeiro
Morais, Heverly
Abi Saab, Otávio Jorge Grigoli
Farias, José Renato Bouças
dc.subject.por.fl_str_mv Glycine max L
Merrill
Weather station
Rainfall
Climate variability
Agro-ecological zone.
Glycine max L
Merrill
Estação meteorológica
Precipitação pluvial
Variabilidade climática
Zona agroecológica.
topic Glycine max L
Merrill
Weather station
Rainfall
Climate variability
Agro-ecological zone.
Glycine max L
Merrill
Estação meteorológica
Precipitação pluvial
Variabilidade climática
Zona agroecológica.
description Brazil requires a fully representative weather network station; it is common to use data observed in locations distant from the region of interest. However, few studies have evaluated the efficiency and precision associated with the use of climate data, either estimated or interpolated, from stations far from the agricultural area of interest. Hence, this study aimed to demonstrate the impacts of spatial variability of the main meteorological elements on the regional estimate of soybean productivity. Regression analysis was used to compare data recorded at three weather stations located throughout Londrina, PR, Brazil. The water balance of the soybean crop was calculated at 10-day periods and grain productivity losses estimated using the Agro-Ecological Zones (AEZ) methodology. Temperatures at the three locations were similar, while the relative air humidity, and particularly, the rainfall data, were less correlated. A high degree of caution is recommended in the use and choice of a single weather station to represent a municipality or region, particularly in countries, such as Brazil, with multiple regions of agricultural and environmental importance. Models and crop season estimates that do not consider such a recommendation are vulnerable to errors in their forecasts. The volumetric and temporal variability in the spatial rainfall distribution resulted in soybean yield discrepancies, estimated at the municipal level. The consistency of the data series, the location of weather stations and their distance to the location of interest determine the ability of crop models to accurately estimate soybean production based on meteorological data, particularly the rainfall data. This study contributes to future regional research using climate data, and highlights the importance of a weather station network throughout Brazil, demonstrating the urgent need to increase the number of weather stations, particularly for recording rainfall data.
publishDate 2017
dc.date.none.fl_str_mv 2017-08-25
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
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format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv https://ojs.uel.br/revistas/uel/index.php/semagrarias/article/view/26165
10.5433/1679-0359.2017v38n4Supl1p2265
url https://ojs.uel.br/revistas/uel/index.php/semagrarias/article/view/26165
identifier_str_mv 10.5433/1679-0359.2017v38n4Supl1p2265
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv https://ojs.uel.br/revistas/uel/index.php/semagrarias/article/view/26165/21481
dc.rights.driver.fl_str_mv Copyright (c) 2017 Semina: Ciências Agrárias
http://creativecommons.org/licenses/by-nc/4.0
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Copyright (c) 2017 Semina: Ciências Agrárias
http://creativecommons.org/licenses/by-nc/4.0
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv UEL
publisher.none.fl_str_mv UEL
dc.source.none.fl_str_mv Semina: Ciências Agrárias; Vol. 38 No. 4Supl1 (2017); 2265-2278
Semina: Ciências Agrárias; v. 38 n. 4Supl1 (2017); 2265-2278
1679-0359
1676-546X
reponame:Semina. Ciências Agrárias (Online)
instname:Universidade Estadual de Londrina (UEL)
instacron:UEL
instname_str Universidade Estadual de Londrina (UEL)
instacron_str UEL
institution UEL
reponame_str Semina. Ciências Agrárias (Online)
collection Semina. Ciências Agrárias (Online)
repository.name.fl_str_mv Semina. Ciências Agrárias (Online) - Universidade Estadual de Londrina (UEL)
repository.mail.fl_str_mv semina.agrarias@uel.br
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