Using Geospatial Information to Map Yield Gain from the Use of Azospirillum brasilense in Furrow
Autor(a) principal: | |
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Data de Publicação: | 2023 |
Outros Autores: | , , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.3390/agronomy13030808 http://hdl.handle.net/11449/248678 |
Resumo: | The application of biological products in agricultural crops has become increasingly prominent. The growth-promoting bacterium Azospirillum brasilense has been used as an alternative to promote greater yield in maize crops. In the context of precision agriculture, interpreting geospatial data has allowed for monitoring the effect of the application of products that increase the yield of corn crops. The objective of this work was to evaluate the potential of Kriging techniques and spectral models through images in estimating the gain in yield of maize crop after applying A. brasilense. Analyses were carried out in two commercial areas treated with A. brasilense. The results revealed that models of yield prediction by Kriging with a high volume of training data estimated the yield gain with a root-mean-square error deviation (RMSE%), mean absolute percentage error (MAPE%), and R2 to be 6.67, 5.42, and 0.88, respectively. For spectral models with a low volume of training data, yield gain was estimated with RMSE%, MAPE%, and R2 to be 9.3, 7.71, and 0.80, respectively. The results demonstrate the potential to map the spatial distribution of productivity gains in corn crops following the application of A. brasilense. |
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Repositório Institucional da UNESP |
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spelling |
Using Geospatial Information to Map Yield Gain from the Use of Azospirillum brasilense in Furrowbiological productgeospatial data analysisyield gain distribution mapThe application of biological products in agricultural crops has become increasingly prominent. The growth-promoting bacterium Azospirillum brasilense has been used as an alternative to promote greater yield in maize crops. In the context of precision agriculture, interpreting geospatial data has allowed for monitoring the effect of the application of products that increase the yield of corn crops. The objective of this work was to evaluate the potential of Kriging techniques and spectral models through images in estimating the gain in yield of maize crop after applying A. brasilense. Analyses were carried out in two commercial areas treated with A. brasilense. The results revealed that models of yield prediction by Kriging with a high volume of training data estimated the yield gain with a root-mean-square error deviation (RMSE%), mean absolute percentage error (MAPE%), and R2 to be 6.67, 5.42, and 0.88, respectively. For spectral models with a low volume of training data, yield gain was estimated with RMSE%, MAPE%, and R2 to be 9.3, 7.71, and 0.80, respectively. The results demonstrate the potential to map the spatial distribution of productivity gains in corn crops following the application of A. brasilense.Instutute of Geography Unversidade Federal de Uberlândia, BR-MGPost Graduate Program in Agriculture and Geospatial Information Institute of Agrarian Sciences Unversidade Federal de Uberlândia, BR-MGLallemand Soluções Biológicas LTDA, BR-MGCartography Departament Faculdade de Ciências e Tecnologia Universidade Estadual PaulistaInstitute of Agrarian Sciences Unversidade Federal de Uberlândia, MGCartography Departament Faculdade de Ciências e Tecnologia Universidade Estadual PaulistaUnversidade Federal de UberlândiaLallemand Soluções Biológicas LTDAUniversidade Estadual Paulista (UNESP)Martins, George DerocoXavier, Laura Cristina Mourade Oliveira, Guilherme Pereirade Lourdes Bueno Trindade Gallo, Maria [UNESP]de Abreu Júnior, Carlos Alberto MatiasVieira, Bruno SérgioMarques, Douglas Joséda Silva, Filipe Vieira2023-07-29T13:50:33Z2023-07-29T13:50:33Z2023-03-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://dx.doi.org/10.3390/agronomy13030808Agronomy, v. 13, n. 3, 2023.2073-4395http://hdl.handle.net/11449/24867810.3390/agronomy130308082-s2.0-85152358383Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengAgronomyinfo:eu-repo/semantics/openAccess2024-06-18T15:02:07Zoai:repositorio.unesp.br:11449/248678Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T23:57:51.152912Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Using Geospatial Information to Map Yield Gain from the Use of Azospirillum brasilense in Furrow |
title |
Using Geospatial Information to Map Yield Gain from the Use of Azospirillum brasilense in Furrow |
spellingShingle |
Using Geospatial Information to Map Yield Gain from the Use of Azospirillum brasilense in Furrow Martins, George Deroco biological product geospatial data analysis yield gain distribution map |
title_short |
Using Geospatial Information to Map Yield Gain from the Use of Azospirillum brasilense in Furrow |
title_full |
Using Geospatial Information to Map Yield Gain from the Use of Azospirillum brasilense in Furrow |
title_fullStr |
Using Geospatial Information to Map Yield Gain from the Use of Azospirillum brasilense in Furrow |
title_full_unstemmed |
Using Geospatial Information to Map Yield Gain from the Use of Azospirillum brasilense in Furrow |
title_sort |
Using Geospatial Information to Map Yield Gain from the Use of Azospirillum brasilense in Furrow |
author |
Martins, George Deroco |
author_facet |
Martins, George Deroco Xavier, Laura Cristina Moura de Oliveira, Guilherme Pereira de Lourdes Bueno Trindade Gallo, Maria [UNESP] de Abreu Júnior, Carlos Alberto Matias Vieira, Bruno Sérgio Marques, Douglas José da Silva, Filipe Vieira |
author_role |
author |
author2 |
Xavier, Laura Cristina Moura de Oliveira, Guilherme Pereira de Lourdes Bueno Trindade Gallo, Maria [UNESP] de Abreu Júnior, Carlos Alberto Matias Vieira, Bruno Sérgio Marques, Douglas José da Silva, Filipe Vieira |
author2_role |
author author author author author author author |
dc.contributor.none.fl_str_mv |
Unversidade Federal de Uberlândia Lallemand Soluções Biológicas LTDA Universidade Estadual Paulista (UNESP) |
dc.contributor.author.fl_str_mv |
Martins, George Deroco Xavier, Laura Cristina Moura de Oliveira, Guilherme Pereira de Lourdes Bueno Trindade Gallo, Maria [UNESP] de Abreu Júnior, Carlos Alberto Matias Vieira, Bruno Sérgio Marques, Douglas José da Silva, Filipe Vieira |
dc.subject.por.fl_str_mv |
biological product geospatial data analysis yield gain distribution map |
topic |
biological product geospatial data analysis yield gain distribution map |
description |
The application of biological products in agricultural crops has become increasingly prominent. The growth-promoting bacterium Azospirillum brasilense has been used as an alternative to promote greater yield in maize crops. In the context of precision agriculture, interpreting geospatial data has allowed for monitoring the effect of the application of products that increase the yield of corn crops. The objective of this work was to evaluate the potential of Kriging techniques and spectral models through images in estimating the gain in yield of maize crop after applying A. brasilense. Analyses were carried out in two commercial areas treated with A. brasilense. The results revealed that models of yield prediction by Kriging with a high volume of training data estimated the yield gain with a root-mean-square error deviation (RMSE%), mean absolute percentage error (MAPE%), and R2 to be 6.67, 5.42, and 0.88, respectively. For spectral models with a low volume of training data, yield gain was estimated with RMSE%, MAPE%, and R2 to be 9.3, 7.71, and 0.80, respectively. The results demonstrate the potential to map the spatial distribution of productivity gains in corn crops following the application of A. brasilense. |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023-07-29T13:50:33Z 2023-07-29T13:50:33Z 2023-03-01 |
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/10.3390/agronomy13030808 Agronomy, v. 13, n. 3, 2023. 2073-4395 http://hdl.handle.net/11449/248678 10.3390/agronomy13030808 2-s2.0-85152358383 |
url |
http://dx.doi.org/10.3390/agronomy13030808 http://hdl.handle.net/11449/248678 |
identifier_str_mv |
Agronomy, v. 13, n. 3, 2023. 2073-4395 10.3390/agronomy13030808 2-s2.0-85152358383 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Agronomy |
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_ |
1808129566801133568 |