Índices de vegetação, rendimento de grãos e seus componentes em soja, em área com avaliação de agentes de controle biológico
Ano de defesa: | 2020 |
---|---|
Autor(a) principal: | |
Orientador(a): | |
Banca de defesa: | , |
Tipo de documento: | Dissertação |
Tipo de acesso: | Acesso aberto |
Idioma: | por |
Instituição de defesa: |
Universidade Federal de Santa Maria
Colégio Politécnico da UFSM |
Programa de Pós-Graduação: |
Programa de Pós-Graduação em Agricultura de Precisão
|
Departamento: |
Agronomia
|
País: |
Brasil
|
Palavras-chave em Português: | |
Palavras-chave em Inglês: | |
Área do conhecimento CNPq: | |
Link de acesso: | http://repositorio.ufsm.br/handle/1/23672 |
Resumo: | The use of Remote Sensing (RS), especially in Precision Agriculture (PA) is in full expansion. Sensors embedded in the remotely piloted aircraft (RPA), complement the orbital and proximal RS, combined with the techniques of spatial statistical analysis and data modeling generate numerous information, which can assist in the evolution of agriculture. In a similar way, but with research carried out for a longer time, but not jointly, the use of biological control agents can fill the need to reduce the use of chemical inputs in crops, so that the activity approaches more a balance between production and sustainability, mainly in soybean (Glicine max (L.)), currently the main commodities produced in Brazil and in the world. This research presents a study on the effects of biological control on the vegetation indexes (VI) and on the of soybean yield components (YC). The research starts from the reality of conventional agriculture and seeks, through the use of biotechnology and disruptive technologies of Precision Agriculture, as is the case of RS through multispectral sensors embedded in RPA, as a way of non-destructive monitoring, to assist in making decision-making. The general objective of this research was to test biological control agents in the soybean culture, and to use vegetation indexes obtained with a multispectral sensor embedded in a RPA to identify the spatial variability produced by the use of these products and to estimate the yield of grains and their components. An experiment was implemented in the municipality of Tupanciretã, in the central region of RS, with treatments carried out during the sowing of a soybean crop through the application of biological control agents in the sowing furrow. Three treatments and a control were used (without application of the products). The first, using the fungus Trihcoderma harzianun, the second, using the same fungus mixed with the bacterium Bacillus amyloliquefaciens, and the third, with only that bacterium. The crop was imaged using RPA, of the fixed wing type, and an embedded multispectral sensor. Six images were taken, three in the vegetative stages of the crop (V4, V6 and V9) and three in the reproductive stages (R1, R2 and R6). From the images, five VI were generated, NDVI, NDRE, MPRI and SAVI, the latter with two soil adjustment constants (0.25 and 0.5). The data of the YC were obtained, with the measurement of crop characteristics, at the time of harvest, in which the vegetable samples were collected, in two planting lines x 0.80 m, making 120 sample units of 0.720 m2, 30 for each treatment and witness. From each sample, the number of plants, viable and non-viable pods (manually), the number of grains (electronic grain counter) were counted and, after determining the humidity, the dry matter masses of a thousand grains and total were measured, and their results were converted into m2. For statistical analysis of the data, descriptive statistics were used, which gave a general idea of the data, Pearson's Correlation analysis between the generated IV and the YC, analysis of variance or average rank, to compare the effect of treatments on the IV and YC, and regression analysis, to estimate productivity from the VI (with zonal statistic data). The results showed that the biological control agents applied in the experiment provided the treatments with a larger population of plants, when compared to the control, with possible vigor and health superior to the same, and statistically different values were detected in the IV, YC and productivity. The IV that best estimated productivity was NDRE, at the R1 stage, with a correlation of 0.718 and a coefficient of determination with productivity of 0.804. It is concluded that the best treatment was obtained with the mixture of T. harzianun and B. amyloliquefaciens (greater number of plants, viable pods, grains and greater productivity), than the multispectral sensor embedded in RPA, proved to be useful for monitoring the culture development in various phenological stages and that the best IV were NDRE, NDVI and SAVI, in the phenological stages R1 and R2. |
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2022-02-16T18:54:06Z2022-02-16T18:54:06Z2020-06-01http://repositorio.ufsm.br/handle/1/23672The use of Remote Sensing (RS), especially in Precision Agriculture (PA) is in full expansion. Sensors embedded in the remotely piloted aircraft (RPA), complement the orbital and proximal RS, combined with the techniques of spatial statistical analysis and data modeling generate numerous information, which can assist in the evolution of agriculture. In a similar way, but with research carried out for a longer time, but not jointly, the use of biological control agents can fill the need to reduce the use of chemical inputs in crops, so that the activity approaches more a balance between production and sustainability, mainly in soybean (Glicine max (L.)), currently the main commodities produced in Brazil and in the world. This research presents a study on the effects of biological control on the vegetation indexes (VI) and on the of soybean yield components (YC). The research starts from the reality of conventional agriculture and seeks, through the use of biotechnology and disruptive technologies of Precision Agriculture, as is the case of RS through multispectral sensors embedded in RPA, as a way of non-destructive monitoring, to assist in making decision-making. The general objective of this research was to test biological control agents in the soybean culture, and to use vegetation indexes obtained with a multispectral sensor embedded in a RPA to identify the spatial variability produced by the use of these products and to estimate the yield of grains and their components. An experiment was implemented in the municipality of Tupanciretã, in the central region of RS, with treatments carried out during the sowing of a soybean crop through the application of biological control agents in the sowing furrow. Three treatments and a control were used (without application of the products). The first, using the fungus Trihcoderma harzianun, the second, using the same fungus mixed with the bacterium Bacillus amyloliquefaciens, and the third, with only that bacterium. The crop was imaged using RPA, of the fixed wing type, and an embedded multispectral sensor. Six images were taken, three in the vegetative stages of the crop (V4, V6 and V9) and three in the reproductive stages (R1, R2 and R6). From the images, five VI were generated, NDVI, NDRE, MPRI and SAVI, the latter with two soil adjustment constants (0.25 and 0.5). The data of the YC were obtained, with the measurement of crop characteristics, at the time of harvest, in which the vegetable samples were collected, in two planting lines x 0.80 m, making 120 sample units of 0.720 m2, 30 for each treatment and witness. From each sample, the number of plants, viable and non-viable pods (manually), the number of grains (electronic grain counter) were counted and, after determining the humidity, the dry matter masses of a thousand grains and total were measured, and their results were converted into m2. For statistical analysis of the data, descriptive statistics were used, which gave a general idea of the data, Pearson's Correlation analysis between the generated IV and the YC, analysis of variance or average rank, to compare the effect of treatments on the IV and YC, and regression analysis, to estimate productivity from the VI (with zonal statistic data). The results showed that the biological control agents applied in the experiment provided the treatments with a larger population of plants, when compared to the control, with possible vigor and health superior to the same, and statistically different values were detected in the IV, YC and productivity. The IV that best estimated productivity was NDRE, at the R1 stage, with a correlation of 0.718 and a coefficient of determination with productivity of 0.804. It is concluded that the best treatment was obtained with the mixture of T. harzianun and B. amyloliquefaciens (greater number of plants, viable pods, grains and greater productivity), than the multispectral sensor embedded in RPA, proved to be useful for monitoring the culture development in various phenological stages and that the best IV were NDRE, NDVI and SAVI, in the phenological stages R1 and R2.O uso de Sensoriamento Remoto (SR), em especial na Agricultura de Precisão (AP) está em plena expansão. Sensores embarcados nas aeronaves remotamente pilotadas (RPA), complementam o SR orbital e proximal, aliados as técnicas de análise estatística espacial e modelagem de dados geram inúmeras informações, que podem auxiliar na evolução da agricultura. De maneira análoga, mas com pesquisas realizadas há mais tempo, porém não conjuntas, o uso de agentes de controle biológico pode preencher a necessidade de redução do uso de insumos químicos nas lavouras, para que atividade se aproxime mais de um equilíbrio entre a produção e a sustentabilidade, principalmente na cultura de soja (Glicine max (L.)), atualmente principal comodities produzida no Brasil e no mundo. Esta pesquisa apresenta um estudo sobre os efeitos do controle biológico nos índices de vegetação (IV) e nos componentes de rendimento (CR) da soja. A pesquisa parte da realidade da agricultura convencional e busca, através do uso da biotecnologia e das tecnologias disruptivas da Agricultura de Precisão, como é o caso do SR através de sensores multiespectrais embarcados em RPA, como forma de monitoramento não destrutivo, para auxiliar na tomada de decisão. O objetivo geral desta pesquisa foi testar agentes de controle biológicos na cultura da soja, e utilizar índices de vegetação obtidos com sensor multiespectral embarcado em aeronave remotamente pilotada para identificar a variabilidade espacial produzida pelo uso desses produtos e estimar o rendimento de grãos e de seus componentes. Foi implantado um experimento, no município de Tupanciretã, região central do RS, com tratamentos realizados durante a semeadura de uma lavoura de soja através da aplicação de agentes de controle biológico no sulco da semeadura. Foram utilizados três tratamentos e uma testemunha (sem aplicação destes produtos). O primeiro, com o uso do fungo Trihcoderma harzianun, o segundo, com uso do mesmo fungo misturado com a bactéria Bacillus amyloliquefaciens e o terceiro, com somente a referida bactéria. O imageamento da lavoura foi realizado com RPA, do tipo asa fixa, e sensor multiespectral embarcado. Foram realizados seis imageamentos, três nos estágios vegetativos da cultura (V4, V6 e V9) e três nos estágios reprodutivos (R1, R2 e R6). Das imagens, foram gerados cinco IV, NDVI, NDRE, MPRI e SAVI, este último com duas constantes de ajuste ao solo (0,25 e 0,5). Os dados dos CR foram obtidos, com a mensuração de características da cultura, no momento da colheita, em que foram coletadas as amostras vegetais, em duas linhas de plantio x 0,80 m, perfazendo 120 unidades amostrais de 0,720 m2, 30 para cada tratamento e testemunha. De cada amostra foram contadas o número de plantas, de vagens viáveis e não viáveis (de forma manual), o número de grãos (contador eletrônico de grãos) e, após a determinação da umidade, foram medidas as massas da matéria seca de mil grãos e total, e seus resultados convertidos em m2. Para análise estatística dos dados, foi utilizada a estatística descritiva, que deu uma ideia geral dos dados, análise de Correlação de Pearson entre os IV gerados e os CR, análise de variância ou de postos médios, para comparação do efeito dos tratamentos nos IV e CR, e análise de regressão, para estimar a produtividade a partir dos IV (com dados de estatística zonal). Os resultados demonstraram que os agentes de controle biológico aplicados no experimento proporcionaram aos tratamentos uma maior população de plantas, quando comparados com a testemunha, com possível vigor e sanidade superiores à mesma, e estatisticamente foram detectados diferentes valores nos IV, nos CR e na produtividade. O IV que melhor estimou a produtividade foi o NDRE, no estádio R1, apresentou correlação de 0,718 e coeficiente de determinação com a produtividade de 0,804. Conclui-se que o melhor tratamento foi obtido com a mistura de T. harzianun e B. amyloliquefaciens (maior número de plantas, vagens viáveis, grãos e maior produtividade), que o sensor multiespectral embarcado em RPA, mostrou-se útil para monitorar o desenvolvimento da cultura em vários estágios fenológicos e, que os melhores IV foram NDRE, NDVI e SAVI, nos estágios fenológicos R1 e R2.porUniversidade Federal de Santa MariaColégio Politécnico da UFSMPrograma de Pós-Graduação em Agricultura de PrecisãoUFSMBrasilAgronomiaAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessAgricultura de precisãoInsumos biológicosAeronave remotamente pilotadaSensor multiespectralPrecision agricultureBiological suppliesRemotely piloted aircraftMultispectral sensor in ARPCNPQ::CIENCIAS AGRARIAS::AGRONOMIAÍndices de vegetação, rendimento de grãos e seus componentes em soja, em área com avaliação de agentes de controle biológicoVegetation indices, grain yield and their components in soybean, in an area evaluation of biological control agentsinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisAmaral, Lúcio de Paulahttp://lattes.cnpq.br/6612592358172016Bredemeier, ChristianZamberlan, João Fernandohttp://lattes.cnpq.br/6884507489333712Oliveira Neto, Deoclides de500100000009600600600abc8d702-44d0-410b-907e-d93c28b7fb00b83c4c79-c823-446e-82a2-40daaca088d6e52de4a9-eedb-4207-9793-d43dab5b6cbc8d262e83-4e24-44c1-8fb9-17fdb4754e94reponame:Biblioteca Digital de Teses e Dissertações do UFSMinstname:Universidade Federal de Santa Maria (UFSM)instacron:UFSMORIGINALDIS_PPGAP_2021_OLIVEIRA NETO_DEOCLIDES.pdfDIS_PPGAP_2021_OLIVEIRA NETO_DEOCLIDES.pdfDissertação de Mestradoapplication/pdf8125418http://repositorio.ufsm.br/bitstream/1/23672/1/DIS_PPGAP_2021_OLIVEIRA%20NETO_DEOCLIDES.pdf8eaf20babfb91bb5c3a31b80e3368b99MD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; 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dc.title.por.fl_str_mv |
Índices de vegetação, rendimento de grãos e seus componentes em soja, em área com avaliação de agentes de controle biológico |
dc.title.alternative.eng.fl_str_mv |
Vegetation indices, grain yield and their components in soybean, in an area evaluation of biological control agents |
title |
Índices de vegetação, rendimento de grãos e seus componentes em soja, em área com avaliação de agentes de controle biológico |
spellingShingle |
Índices de vegetação, rendimento de grãos e seus componentes em soja, em área com avaliação de agentes de controle biológico Oliveira Neto, Deoclides de Agricultura de precisão Insumos biológicos Aeronave remotamente pilotada Sensor multiespectral Precision agriculture Biological supplies Remotely piloted aircraft Multispectral sensor in ARP CNPQ::CIENCIAS AGRARIAS::AGRONOMIA |
title_short |
Índices de vegetação, rendimento de grãos e seus componentes em soja, em área com avaliação de agentes de controle biológico |
title_full |
Índices de vegetação, rendimento de grãos e seus componentes em soja, em área com avaliação de agentes de controle biológico |
title_fullStr |
Índices de vegetação, rendimento de grãos e seus componentes em soja, em área com avaliação de agentes de controle biológico |
title_full_unstemmed |
Índices de vegetação, rendimento de grãos e seus componentes em soja, em área com avaliação de agentes de controle biológico |
title_sort |
Índices de vegetação, rendimento de grãos e seus componentes em soja, em área com avaliação de agentes de controle biológico |
author |
Oliveira Neto, Deoclides de |
author_facet |
Oliveira Neto, Deoclides de |
author_role |
author |
dc.contributor.advisor1.fl_str_mv |
Amaral, Lúcio de Paula |
dc.contributor.advisor1Lattes.fl_str_mv |
http://lattes.cnpq.br/6612592358172016 |
dc.contributor.referee1.fl_str_mv |
Bredemeier, Christian |
dc.contributor.referee2.fl_str_mv |
Zamberlan, João Fernando |
dc.contributor.authorLattes.fl_str_mv |
http://lattes.cnpq.br/6884507489333712 |
dc.contributor.author.fl_str_mv |
Oliveira Neto, Deoclides de |
contributor_str_mv |
Amaral, Lúcio de Paula Bredemeier, Christian Zamberlan, João Fernando |
dc.subject.por.fl_str_mv |
Agricultura de precisão Insumos biológicos Aeronave remotamente pilotada Sensor multiespectral |
topic |
Agricultura de precisão Insumos biológicos Aeronave remotamente pilotada Sensor multiespectral Precision agriculture Biological supplies Remotely piloted aircraft Multispectral sensor in ARP CNPQ::CIENCIAS AGRARIAS::AGRONOMIA |
dc.subject.eng.fl_str_mv |
Precision agriculture Biological supplies Remotely piloted aircraft Multispectral sensor in ARP |
dc.subject.cnpq.fl_str_mv |
CNPQ::CIENCIAS AGRARIAS::AGRONOMIA |
description |
The use of Remote Sensing (RS), especially in Precision Agriculture (PA) is in full expansion. Sensors embedded in the remotely piloted aircraft (RPA), complement the orbital and proximal RS, combined with the techniques of spatial statistical analysis and data modeling generate numerous information, which can assist in the evolution of agriculture. In a similar way, but with research carried out for a longer time, but not jointly, the use of biological control agents can fill the need to reduce the use of chemical inputs in crops, so that the activity approaches more a balance between production and sustainability, mainly in soybean (Glicine max (L.)), currently the main commodities produced in Brazil and in the world. This research presents a study on the effects of biological control on the vegetation indexes (VI) and on the of soybean yield components (YC). The research starts from the reality of conventional agriculture and seeks, through the use of biotechnology and disruptive technologies of Precision Agriculture, as is the case of RS through multispectral sensors embedded in RPA, as a way of non-destructive monitoring, to assist in making decision-making. The general objective of this research was to test biological control agents in the soybean culture, and to use vegetation indexes obtained with a multispectral sensor embedded in a RPA to identify the spatial variability produced by the use of these products and to estimate the yield of grains and their components. An experiment was implemented in the municipality of Tupanciretã, in the central region of RS, with treatments carried out during the sowing of a soybean crop through the application of biological control agents in the sowing furrow. Three treatments and a control were used (without application of the products). The first, using the fungus Trihcoderma harzianun, the second, using the same fungus mixed with the bacterium Bacillus amyloliquefaciens, and the third, with only that bacterium. The crop was imaged using RPA, of the fixed wing type, and an embedded multispectral sensor. Six images were taken, three in the vegetative stages of the crop (V4, V6 and V9) and three in the reproductive stages (R1, R2 and R6). From the images, five VI were generated, NDVI, NDRE, MPRI and SAVI, the latter with two soil adjustment constants (0.25 and 0.5). The data of the YC were obtained, with the measurement of crop characteristics, at the time of harvest, in which the vegetable samples were collected, in two planting lines x 0.80 m, making 120 sample units of 0.720 m2, 30 for each treatment and witness. From each sample, the number of plants, viable and non-viable pods (manually), the number of grains (electronic grain counter) were counted and, after determining the humidity, the dry matter masses of a thousand grains and total were measured, and their results were converted into m2. For statistical analysis of the data, descriptive statistics were used, which gave a general idea of the data, Pearson's Correlation analysis between the generated IV and the YC, analysis of variance or average rank, to compare the effect of treatments on the IV and YC, and regression analysis, to estimate productivity from the VI (with zonal statistic data). The results showed that the biological control agents applied in the experiment provided the treatments with a larger population of plants, when compared to the control, with possible vigor and health superior to the same, and statistically different values were detected in the IV, YC and productivity. The IV that best estimated productivity was NDRE, at the R1 stage, with a correlation of 0.718 and a coefficient of determination with productivity of 0.804. It is concluded that the best treatment was obtained with the mixture of T. harzianun and B. amyloliquefaciens (greater number of plants, viable pods, grains and greater productivity), than the multispectral sensor embedded in RPA, proved to be useful for monitoring the culture development in various phenological stages and that the best IV were NDRE, NDVI and SAVI, in the phenological stages R1 and R2. |
publishDate |
2020 |
dc.date.issued.fl_str_mv |
2020-06-01 |
dc.date.accessioned.fl_str_mv |
2022-02-16T18:54:06Z |
dc.date.available.fl_str_mv |
2022-02-16T18:54:06Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/masterThesis |
format |
masterThesis |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://repositorio.ufsm.br/handle/1/23672 |
url |
http://repositorio.ufsm.br/handle/1/23672 |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.relation.cnpq.fl_str_mv |
500100000009 |
dc.relation.confidence.fl_str_mv |
600 600 600 |
dc.relation.authority.fl_str_mv |
abc8d702-44d0-410b-907e-d93c28b7fb00 b83c4c79-c823-446e-82a2-40daaca088d6 e52de4a9-eedb-4207-9793-d43dab5b6cbc 8d262e83-4e24-44c1-8fb9-17fdb4754e94 |
dc.rights.driver.fl_str_mv |
Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/ info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/ |
eu_rights_str_mv |
openAccess |
dc.publisher.none.fl_str_mv |
Universidade Federal de Santa Maria Colégio Politécnico da UFSM |
dc.publisher.program.fl_str_mv |
Programa de Pós-Graduação em Agricultura de Precisão |
dc.publisher.initials.fl_str_mv |
UFSM |
dc.publisher.country.fl_str_mv |
Brasil |
dc.publisher.department.fl_str_mv |
Agronomia |
publisher.none.fl_str_mv |
Universidade Federal de Santa Maria Colégio Politécnico da UFSM |
dc.source.none.fl_str_mv |
reponame:Biblioteca Digital de Teses e Dissertações do UFSM instname:Universidade Federal de Santa Maria (UFSM) instacron:UFSM |
instname_str |
Universidade Federal de Santa Maria (UFSM) |
instacron_str |
UFSM |
institution |
UFSM |
reponame_str |
Biblioteca Digital de Teses e Dissertações do UFSM |
collection |
Biblioteca Digital de Teses e Dissertações do UFSM |
bitstream.url.fl_str_mv |
http://repositorio.ufsm.br/bitstream/1/23672/1/DIS_PPGAP_2021_OLIVEIRA%20NETO_DEOCLIDES.pdf http://repositorio.ufsm.br/bitstream/1/23672/2/license_rdf http://repositorio.ufsm.br/bitstream/1/23672/3/license.txt |
bitstream.checksum.fl_str_mv |
8eaf20babfb91bb5c3a31b80e3368b99 4460e5956bc1d1639be9ae6146a50347 2f0571ecee68693bd5cd3f17c1e075df |
bitstream.checksumAlgorithm.fl_str_mv |
MD5 MD5 MD5 |
repository.name.fl_str_mv |
Biblioteca Digital de Teses e Dissertações do UFSM - Universidade Federal de Santa Maria (UFSM) |
repository.mail.fl_str_mv |
atendimento.sib@ufsm.br||tedebc@gmail.com |
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1793239975936917504 |