Uso de análise multivariada para identificação de zonas de potenciais produtivos agrícolas
Ano de defesa: | 2023 |
---|---|
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/1/29437 |
Resumo: | The highlight in the Brazilian economy is agribusiness, specifically grain production, which takes place from north to south of the country. Farmers aim to increase their production efficiency with minimal input usage on the same cultivated area. Precision Agriculture aligns with this objective as an essential tool for detecting problems within the property and aiding in decision-making processes. However, some productivity variations cannot be explained solely by traditional mapping of variables; it is necessary to perform analyses with multiple variables to find indicators that contribute to understanding this scenario. The objective of this study was to define zones of productive potential through multivariate analysis for an agricultural cultivation area using data from various sources. The study was conducted in a commercial soybean field covering 35.40 hectares with a sampling grid of one point every two hectares, generating a database with altitude, clay content, soil fertility for the year 2018, fertilization, corrections, and productivity (basic data). These data, combined with remote sensing data (NDVI, NDRE, NDWI, and Surface Brightness Temperature) for the four subsequent harvests following the sampling, increased the degree of data utilization to intermediate and advanced levels. The mesh file was used to create Voronoi polygons, and the data was tabulated and subjected to cluster analysis. The polygons were grouped according to the dendrograms of each analysis based on the data acquisition degree. The productivity data from the four harvests were used to calculate the historical average productivity. The results formed the maps of zones of potential productivity for each dendrogram of each cut and were compared to identify the stabilization of zone formation. To choose the final map representing the management areas, the maps were subjected to map algebra to find the map of zones of potential productivity. Thus, the map that best represented the arrangement of zones of potential productivity belonged to the intermediate category for the 2nd cut of the dendrogram, comprising five zones of potential productivity ranging from very low to very high productivity. The results were satisfactory, and the objective of the study was achieved. The two methodologies for finding management zones and potential productivity converged to the same arrangement in this study, incorporating not only traditional Precision Agriculture data but also remote sensing data. However, in other analyses, it is possible to include data on variable seed rates, rainfall, electrical conductivity, and other variables. |
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2023-06-15T14:24:49Z2023-06-15T14:24:49Z2023-03-09http://repositorio.ufsm.br/handle/1/29437The highlight in the Brazilian economy is agribusiness, specifically grain production, which takes place from north to south of the country. Farmers aim to increase their production efficiency with minimal input usage on the same cultivated area. Precision Agriculture aligns with this objective as an essential tool for detecting problems within the property and aiding in decision-making processes. However, some productivity variations cannot be explained solely by traditional mapping of variables; it is necessary to perform analyses with multiple variables to find indicators that contribute to understanding this scenario. The objective of this study was to define zones of productive potential through multivariate analysis for an agricultural cultivation area using data from various sources. The study was conducted in a commercial soybean field covering 35.40 hectares with a sampling grid of one point every two hectares, generating a database with altitude, clay content, soil fertility for the year 2018, fertilization, corrections, and productivity (basic data). These data, combined with remote sensing data (NDVI, NDRE, NDWI, and Surface Brightness Temperature) for the four subsequent harvests following the sampling, increased the degree of data utilization to intermediate and advanced levels. The mesh file was used to create Voronoi polygons, and the data was tabulated and subjected to cluster analysis. The polygons were grouped according to the dendrograms of each analysis based on the data acquisition degree. The productivity data from the four harvests were used to calculate the historical average productivity. The results formed the maps of zones of potential productivity for each dendrogram of each cut and were compared to identify the stabilization of zone formation. To choose the final map representing the management areas, the maps were subjected to map algebra to find the map of zones of potential productivity. Thus, the map that best represented the arrangement of zones of potential productivity belonged to the intermediate category for the 2nd cut of the dendrogram, comprising five zones of potential productivity ranging from very low to very high productivity. The results were satisfactory, and the objective of the study was achieved. The two methodologies for finding management zones and potential productivity converged to the same arrangement in this study, incorporating not only traditional Precision Agriculture data but also remote sensing data. However, in other analyses, it is possible to include data on variable seed rates, rainfall, electrical conductivity, and other variables.O destaque na economia brasileira é o agronegócio, em específico, a produção de grãos que é realizada de norte a sul do país. Os agricultores objetivam cada vez mais aumentar sua eficiência na produção com baixo uso de insumo na mesma porção de área cultivada. A Agricultura de Precisão vem de encontro com esse objetivo, pois é uma ferramenta essencial para detectar problemas dentro da propriedade e ajudar no momento de tomadas de decisão. Porém, alguns problemas de variação na produtividade não se explicam apenas com o mapeamento tradicional das variáveis, é necessário realizar análises com múltiplas variáveis para encontrar os indicativos que colaborem no entendimento deste cenário. O objetivo deste trabalho foi realizar uma definição de zonas de potencial produtivo através de análise multivariada para uma área de cultivo agrícola com dados de diversas fontes. O trabalho foi realizado em uma lavoura comercial de 35,40 ha de soja com grid amostral de um ponto a cada dois hectares, gerando um banco de dados com altitude, argila, fertilidade do solo do ano de 2018, adubação, correções, produtividade (dados básicos) que aliados a dados de sensoriamento remoto (NDVI, NDRE, NDWI e Temperatura de Brilho da Superfície) para as quatro safras subsequentes à amostragem, elevaram o grau de utilização de dados a pré-intermediário e avançado. O arquivo da malha foi utilizado para criar os polígonos de voronoi e os dados foram tabulados e submetidos à análise de cluster. Os polígonos foram agrupados conforme os dendrogramas de cada análise de acordo com o grau de obtenção de dados. Os dados de produtividade das quatro safras foram utilizados para realizar o cálculo da produtividade média histórica. Os resultados compuseram os mapas de zonas de potencial produtividade para cada dendrograma de cada corte e foram comparados para identificar a estabilização da formação de zonas. Para escolha do mapa final para representar as áreas de manejo, os mapas foram submetidos a álgebra de mapas para encontrar o mapa de zonas de potencial produtivo. Com isso o mapa que melhor configurou o arranjo de zonas de potencial produtivo foi da categoria intermediário para 2º corte do dendrograma, cinco zonas de potencial produtivo, variando de muito baixa produtividade a muito alta produtividade. Os resultados foram satisfatórios e o objetivo do trabalho foi alcançado, as duas metodologias para encontrar as zonas de manejo e potencial produtivo conseguiram convergir para o mesmo arranjo, neste estudo buscou trabalhar com, além dos dados tradicionais da AP com dados de sensoriamento remoto. Porém, em outras análises é possível agregar dados de taxa variada de semente, pluviosidade, condutividade elétrica e outros IV.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/openAccessSensoriamento remotoAnálise de clustersMapas de colheitaRemote sensingCluster analysisHarvest mapsCNPQ::CIENCIAS AGRARIAS::AGRONOMIAUso de análise multivariada para identificação de zonas de potenciais produtivos agrícolasUsing multivariate analysis for the identification of agricultural productive potential zonesinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisAmaral, Lúcio de Paulahttp://lattes.cnpq.br/6612592358172016Kayser, Luiz PatricSebem, ElódioMoraes, Bibiana Silveirahttp://lattes.cnpq.br/7131856457004430Melos, Natália Duarte500100000009600600600600600600abc8d702-44d0-410b-907e-d93c28b7fb00e3e87aec-73e9-497d-848a-cd7fb5b9f12bd0cf1fc6-58f6-4055-84b7-1d55b089589be8f79531-e15e-49b4-afda-71d0aa6ef45586d9ce33-be97-4070-a075-497bf41de991reponame:Biblioteca Digital de Teses e Dissertações do UFSMinstname:Universidade Federal de Santa Maria (UFSM)instacron:UFSMLICENSElicense.txtlicense.txttext/plain; charset=utf-81956http://repositorio.ufsm.br/bitstream/1/29437/3/license.txt2f0571ecee68693bd5cd3f17c1e075dfMD53ORIGINALDIS_PPGAP_2023_MELOS_NATALIA.pdfDIS_PPGAP_2023_MELOS_NATALIA.pdfDissertação de Mestradoapplication/pdf5375856http://repositorio.ufsm.br/bitstream/1/29437/1/DIS_PPGAP_2023_MELOS_NATALIA.pdffe15af7b0f8388d7f153792bc15029a6MD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8805http://repositorio.ufsm.br/bitstream/1/29437/2/license_rdf4460e5956bc1d1639be9ae6146a50347MD521/294372023-06-15 11:24:49.443oai:repositorio.ufsm.br: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 Digital de Teses e Dissertaçõeshttps://repositorio.ufsm.br/ONGhttps://repositorio.ufsm.br/oai/requestatendimento.sib@ufsm.br||tedebc@gmail.comopendoar:2023-06-15T14:24:49Biblioteca Digital de Teses e Dissertações do UFSM - Universidade Federal de Santa Maria (UFSM)false |
dc.title.por.fl_str_mv |
Uso de análise multivariada para identificação de zonas de potenciais produtivos agrícolas |
dc.title.alternative.eng.fl_str_mv |
Using multivariate analysis for the identification of agricultural productive potential zones |
title |
Uso de análise multivariada para identificação de zonas de potenciais produtivos agrícolas |
spellingShingle |
Uso de análise multivariada para identificação de zonas de potenciais produtivos agrícolas Melos, Natália Duarte Sensoriamento remoto Análise de clusters Mapas de colheita Remote sensing Cluster analysis Harvest maps CNPQ::CIENCIAS AGRARIAS::AGRONOMIA |
title_short |
Uso de análise multivariada para identificação de zonas de potenciais produtivos agrícolas |
title_full |
Uso de análise multivariada para identificação de zonas de potenciais produtivos agrícolas |
title_fullStr |
Uso de análise multivariada para identificação de zonas de potenciais produtivos agrícolas |
title_full_unstemmed |
Uso de análise multivariada para identificação de zonas de potenciais produtivos agrícolas |
title_sort |
Uso de análise multivariada para identificação de zonas de potenciais produtivos agrícolas |
author |
Melos, Natália Duarte |
author_facet |
Melos, Natália Duarte |
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.advisor-co1.fl_str_mv |
Kayser, Luiz Patric |
dc.contributor.referee1.fl_str_mv |
Sebem, Elódio |
dc.contributor.referee2.fl_str_mv |
Moraes, Bibiana Silveira |
dc.contributor.authorLattes.fl_str_mv |
http://lattes.cnpq.br/7131856457004430 |
dc.contributor.author.fl_str_mv |
Melos, Natália Duarte |
contributor_str_mv |
Amaral, Lúcio de Paula Kayser, Luiz Patric Sebem, Elódio Moraes, Bibiana Silveira |
dc.subject.por.fl_str_mv |
Sensoriamento remoto Análise de clusters Mapas de colheita |
topic |
Sensoriamento remoto Análise de clusters Mapas de colheita Remote sensing Cluster analysis Harvest maps CNPQ::CIENCIAS AGRARIAS::AGRONOMIA |
dc.subject.eng.fl_str_mv |
Remote sensing Cluster analysis Harvest maps |
dc.subject.cnpq.fl_str_mv |
CNPQ::CIENCIAS AGRARIAS::AGRONOMIA |
description |
The highlight in the Brazilian economy is agribusiness, specifically grain production, which takes place from north to south of the country. Farmers aim to increase their production efficiency with minimal input usage on the same cultivated area. Precision Agriculture aligns with this objective as an essential tool for detecting problems within the property and aiding in decision-making processes. However, some productivity variations cannot be explained solely by traditional mapping of variables; it is necessary to perform analyses with multiple variables to find indicators that contribute to understanding this scenario. The objective of this study was to define zones of productive potential through multivariate analysis for an agricultural cultivation area using data from various sources. The study was conducted in a commercial soybean field covering 35.40 hectares with a sampling grid of one point every two hectares, generating a database with altitude, clay content, soil fertility for the year 2018, fertilization, corrections, and productivity (basic data). These data, combined with remote sensing data (NDVI, NDRE, NDWI, and Surface Brightness Temperature) for the four subsequent harvests following the sampling, increased the degree of data utilization to intermediate and advanced levels. The mesh file was used to create Voronoi polygons, and the data was tabulated and subjected to cluster analysis. The polygons were grouped according to the dendrograms of each analysis based on the data acquisition degree. The productivity data from the four harvests were used to calculate the historical average productivity. The results formed the maps of zones of potential productivity for each dendrogram of each cut and were compared to identify the stabilization of zone formation. To choose the final map representing the management areas, the maps were subjected to map algebra to find the map of zones of potential productivity. Thus, the map that best represented the arrangement of zones of potential productivity belonged to the intermediate category for the 2nd cut of the dendrogram, comprising five zones of potential productivity ranging from very low to very high productivity. The results were satisfactory, and the objective of the study was achieved. The two methodologies for finding management zones and potential productivity converged to the same arrangement in this study, incorporating not only traditional Precision Agriculture data but also remote sensing data. However, in other analyses, it is possible to include data on variable seed rates, rainfall, electrical conductivity, and other variables. |
publishDate |
2023 |
dc.date.accessioned.fl_str_mv |
2023-06-15T14:24:49Z |
dc.date.available.fl_str_mv |
2023-06-15T14:24:49Z |
dc.date.issued.fl_str_mv |
2023-03-09 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/masterThesis |
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masterThesis |
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http://repositorio.ufsm.br/handle/1/29437 |
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http://repositorio.ufsm.br/handle/1/29437 |
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por |
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500100000009 |
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Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/ |
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openAccess |
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Universidade Federal de Santa Maria Colégio Politécnico da UFSM |
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Programa de Pós-Graduação em Agricultura de Precisão |
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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 |
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