Model for soybean production forecast based on prevailing physical conditions

Detalhes bibliográficos
Autor(a) principal: Gusso, Anibal
Data de Publicação: 2017
Outros Autores: Arvor, Damien, Ducati, Jorge Ricardo
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Pesquisa Agropecuária Brasileira (Online)
Texto Completo: https://seer.sct.embrapa.br/index.php/pab/article/view/22912
Resumo: The objective of this work was to evaluate the reliability of the physiological meaning of the enhanced vegetation index (EVI) data for the development of a remote sensing-based procedure to estimate soybean production prior to crop harvest. Time-series data from the moderate resolution imaging spectroradiometer (Modis) were applied to investigate the relationship between local yield fluctuations of soybean and the prevailing physically-driven conditions in the state of Mato Grosso, located in the south of the Brazilian Amazon. The developed methodology was based on the coupled model (CM). The CM provides production estimates for early January, using images from the maximum crop development period. Production estimates were validated at three different spatial scales: state, municipality, and local. At the state and municipality levels, the results obtained from the CM were compared with official agricultural statistics from Instituto Brasileiro de Geografia e Estatística and Companhia Nacional de Abastecimento, from 2001 to 2011. The coefficients of determination ranged from 0.91 to 0.98, with overall result of R2=0.96 (p≤0.01), indicating that the model adheres to official statistics. At the local level, spatially distributed data were compared with production data from 422 crop fields. The coefficient of determination (R2=0.87) confirmed the reliability of the EVI for its applicability on remote sensing-based models for soybean production forecast.
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spelling Model for soybean production forecast based on prevailing physical conditionsModelo para previsão da produção de soja baseado em condições físicas predominantesagriculture; EVI; Modis; remote sensing; satelliteagricultura; EVI; Modis; sensoriamento remoto; satéliteThe objective of this work was to evaluate the reliability of the physiological meaning of the enhanced vegetation index (EVI) data for the development of a remote sensing-based procedure to estimate soybean production prior to crop harvest. Time-series data from the moderate resolution imaging spectroradiometer (Modis) were applied to investigate the relationship between local yield fluctuations of soybean and the prevailing physically-driven conditions in the state of Mato Grosso, located in the south of the Brazilian Amazon. The developed methodology was based on the coupled model (CM). The CM provides production estimates for early January, using images from the maximum crop development period. Production estimates were validated at three different spatial scales: state, municipality, and local. At the state and municipality levels, the results obtained from the CM were compared with official agricultural statistics from Instituto Brasileiro de Geografia e Estatística and Companhia Nacional de Abastecimento, from 2001 to 2011. The coefficients of determination ranged from 0.91 to 0.98, with overall result of R2=0.96 (p≤0.01), indicating that the model adheres to official statistics. At the local level, spatially distributed data were compared with production data from 422 crop fields. The coefficient of determination (R2=0.87) confirmed the reliability of the EVI for its applicability on remote sensing-based models for soybean production forecast.O objetivo deste trabalho foi avaliar a confiabilidade do significado fisiológico de dados do índice de vegetação “enhanced vegetation index” (EVI) no desenvolvimento de um procedimento baseado em sensoriamento remoto para estimar a produção de soja antes da colheita. Foram aplicados dados de séries temporais do “moderate resolution imaging spectroradiometer” (Modis) para investigar a relação entre as flutuações locais na produtividade da soja e as condições físicas predominantes no Estado de Mato Grosso, localizado no sul da Amazônia brasileira. A metodologia desenvolvida foi baseada no modelo acoplado (CM). O CM fornece estimativas de produção para o início de janeiro, ao utilizar imagens do período de máximo desenvolvimento da cultura. As estimativas de produção foram validadas em três escalas espaciais diferentes: estadual, municipal e local. Nos níveis estadual e municipal, os resultados obtidos a partir do CM foram comparados às estatísticas agrícolas oficiais do Instituto Brasileiro de Geografia e Estatística e da Companhia Nacional de Abastecimento, de 2001 a 2011. Os coeficientes de determinação variaram entre 0,91 e 0,98, com resultado global de R2=0,96 (p≤0,01), o que indica que o modelo se ajusta às estatísticas oficiais. No nível local, os dados espacialmente distribuídos foram comparados a dados de produção de 422 lavouras. O coeficiente de determinação (R2=0,87) confirmou a confiabilidade do EVI para ser aplicado em modelos baseados em sensoriamento remoto, para previsão da produção de soja.Pesquisa Agropecuaria BrasileiraPesquisa Agropecuária BrasileiraLand Processes Distributed Active Archive Center (LP DAAC) of National Aeronautical and Space Administration (Nasa)Earth Resources Observation and Science (EROS) Center from U.S. Geological Survey (USGS)Gusso, AnibalArvor, DamienDucati, Jorge Ricardo2017-03-24info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://seer.sct.embrapa.br/index.php/pab/article/view/22912Pesquisa Agropecuaria Brasileira; v.52, n.2, fev. 2017; 95-103Pesquisa Agropecuária Brasileira; v.52, n.2, fev. 2017; 95-1031678-39210100-104xreponame:Pesquisa Agropecuária Brasileira (Online)instname:Empresa Brasileira de Pesquisa Agropecuária (Embrapa)instacron:EMBRAPAenghttps://seer.sct.embrapa.br/index.php/pab/article/view/22912/13707Direitos autorais 2017 Pesquisa Agropecuária Brasileirainfo:eu-repo/semantics/openAccess2017-03-24T19:50:52Zoai:ojs.seer.sct.embrapa.br:article/22912Revistahttp://seer.sct.embrapa.br/index.php/pabPRIhttps://old.scielo.br/oai/scielo-oai.phppab@sct.embrapa.br || sct.pab@embrapa.br1678-39210100-204Xopendoar:2017-03-24T19:50:52Pesquisa Agropecuária Brasileira (Online) - Empresa Brasileira de Pesquisa Agropecuária (Embrapa)false
dc.title.none.fl_str_mv Model for soybean production forecast based on prevailing physical conditions
Modelo para previsão da produção de soja baseado em condições físicas predominantes
title Model for soybean production forecast based on prevailing physical conditions
spellingShingle Model for soybean production forecast based on prevailing physical conditions
Gusso, Anibal
agriculture; EVI; Modis; remote sensing; satellite
agricultura; EVI; Modis; sensoriamento remoto; satélite
title_short Model for soybean production forecast based on prevailing physical conditions
title_full Model for soybean production forecast based on prevailing physical conditions
title_fullStr Model for soybean production forecast based on prevailing physical conditions
title_full_unstemmed Model for soybean production forecast based on prevailing physical conditions
title_sort Model for soybean production forecast based on prevailing physical conditions
author Gusso, Anibal
author_facet Gusso, Anibal
Arvor, Damien
Ducati, Jorge Ricardo
author_role author
author2 Arvor, Damien
Ducati, Jorge Ricardo
author2_role author
author
dc.contributor.none.fl_str_mv Land Processes Distributed Active Archive Center (LP DAAC) of National Aeronautical and Space Administration (Nasa)
Earth Resources Observation and Science (EROS) Center from U.S. Geological Survey (USGS)
dc.contributor.author.fl_str_mv Gusso, Anibal
Arvor, Damien
Ducati, Jorge Ricardo
dc.subject.por.fl_str_mv agriculture; EVI; Modis; remote sensing; satellite
agricultura; EVI; Modis; sensoriamento remoto; satélite
topic agriculture; EVI; Modis; remote sensing; satellite
agricultura; EVI; Modis; sensoriamento remoto; satélite
description The objective of this work was to evaluate the reliability of the physiological meaning of the enhanced vegetation index (EVI) data for the development of a remote sensing-based procedure to estimate soybean production prior to crop harvest. Time-series data from the moderate resolution imaging spectroradiometer (Modis) were applied to investigate the relationship between local yield fluctuations of soybean and the prevailing physically-driven conditions in the state of Mato Grosso, located in the south of the Brazilian Amazon. The developed methodology was based on the coupled model (CM). The CM provides production estimates for early January, using images from the maximum crop development period. Production estimates were validated at three different spatial scales: state, municipality, and local. At the state and municipality levels, the results obtained from the CM were compared with official agricultural statistics from Instituto Brasileiro de Geografia e Estatística and Companhia Nacional de Abastecimento, from 2001 to 2011. The coefficients of determination ranged from 0.91 to 0.98, with overall result of R2=0.96 (p≤0.01), indicating that the model adheres to official statistics. At the local level, spatially distributed data were compared with production data from 422 crop fields. The coefficient of determination (R2=0.87) confirmed the reliability of the EVI for its applicability on remote sensing-based models for soybean production forecast.
publishDate 2017
dc.date.none.fl_str_mv 2017-03-24
dc.type.none.fl_str_mv
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.sct.embrapa.br/index.php/pab/article/view/22912
url https://seer.sct.embrapa.br/index.php/pab/article/view/22912
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv https://seer.sct.embrapa.br/index.php/pab/article/view/22912/13707
dc.rights.driver.fl_str_mv Direitos autorais 2017 Pesquisa Agropecuária Brasileira
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Direitos autorais 2017 Pesquisa Agropecuária Brasileira
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Pesquisa Agropecuaria Brasileira
Pesquisa Agropecuária Brasileira
publisher.none.fl_str_mv Pesquisa Agropecuaria Brasileira
Pesquisa Agropecuária Brasileira
dc.source.none.fl_str_mv Pesquisa Agropecuaria Brasileira; v.52, n.2, fev. 2017; 95-103
Pesquisa Agropecuária Brasileira; v.52, n.2, fev. 2017; 95-103
1678-3921
0100-104x
reponame:Pesquisa Agropecuária Brasileira (Online)
instname:Empresa Brasileira de Pesquisa Agropecuária (Embrapa)
instacron:EMBRAPA
instname_str Empresa Brasileira de Pesquisa Agropecuária (Embrapa)
instacron_str EMBRAPA
institution EMBRAPA
reponame_str Pesquisa Agropecuária Brasileira (Online)
collection Pesquisa Agropecuária Brasileira (Online)
repository.name.fl_str_mv Pesquisa Agropecuária Brasileira (Online) - Empresa Brasileira de Pesquisa Agropecuária (Embrapa)
repository.mail.fl_str_mv pab@sct.embrapa.br || sct.pab@embrapa.br
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