Métodos de amostragem para a modelagem espacial de fósforo disponível no solo
Autor(a) principal: | |
---|---|
Data de Publicação: | 2021 |
Tipo de documento: | Dissertação |
Idioma: | por |
Título da fonte: | Biblioteca Digital de Teses e Dissertações do UFSM |
Texto Completo: | http://repositorio.ufsm.br/handle/1/23098 |
Resumo: | Sampling represents a crucial step for digital soil mapping because it directly interferes with the operational costs of the project and in the following steps of data processing, up to the quality of the generated map. Given the need to obtain information related to different data collection methods, the aim of this study was to compare the sampling design and two scientific modeling methods in the spatial prediction of P available on soil. The study was conducted in a 160 ha rural property located in the municipality of Tupanciretã - RS. In this area there are intense agricultural activities, the addition of inputs (fertilizers), and irrigation using a central pivot system. Three sampling methods were tested - simple regular grid (RG) with fixed distance between points, spatial coverage sampling (SCS) containing points over short distances and simulated annealing sampling considering the marginal distribution of environmental covariates (DIST) - as a basis for prediction of the available phosphorus content in the soil, at a depth of 0 - 10 cm. The sampling density was prioritized in the three sampling methods. The results were validated with an external and independent set containing 50 points. Thus, each calibration set contains 160 (with the exception of the regular grid, which has 162), which were used to learn two predictive models: kriging with external drift (KED), considered a mixed model because it encompasses the geostatistical approach and deterministic; and ordinary kriging (OK). In addition, for prior knowledge of the soil classes that occur in the area, 8 representative profiles had their morphology analyzed. The quality of the visualization maps was assessed by calculating the error. The best prediction result was found by combining the DIST sampling with the KED model, which has a lower mean absolut error (MAE) = 14.62, mean error (ME) = -3.12 and root mean squared error (RMSE) = 23.44 mg dm-3 and a higher Nash-Sutcliffe efficiency (NSE) = 0.13. The results found in the present study confirmed the hypothesis that sample strokes that consider environmental covariables contribute to the increase in the quality of the predicted soil attribute maps. |
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2021-12-02T11:11:06Z2021-12-02T11:11:06Z2021-03-30http://repositorio.ufsm.br/handle/1/23098Sampling represents a crucial step for digital soil mapping because it directly interferes with the operational costs of the project and in the following steps of data processing, up to the quality of the generated map. Given the need to obtain information related to different data collection methods, the aim of this study was to compare the sampling design and two scientific modeling methods in the spatial prediction of P available on soil. The study was conducted in a 160 ha rural property located in the municipality of Tupanciretã - RS. In this area there are intense agricultural activities, the addition of inputs (fertilizers), and irrigation using a central pivot system. Three sampling methods were tested - simple regular grid (RG) with fixed distance between points, spatial coverage sampling (SCS) containing points over short distances and simulated annealing sampling considering the marginal distribution of environmental covariates (DIST) - as a basis for prediction of the available phosphorus content in the soil, at a depth of 0 - 10 cm. The sampling density was prioritized in the three sampling methods. The results were validated with an external and independent set containing 50 points. Thus, each calibration set contains 160 (with the exception of the regular grid, which has 162), which were used to learn two predictive models: kriging with external drift (KED), considered a mixed model because it encompasses the geostatistical approach and deterministic; and ordinary kriging (OK). In addition, for prior knowledge of the soil classes that occur in the area, 8 representative profiles had their morphology analyzed. The quality of the visualization maps was assessed by calculating the error. The best prediction result was found by combining the DIST sampling with the KED model, which has a lower mean absolut error (MAE) = 14.62, mean error (ME) = -3.12 and root mean squared error (RMSE) = 23.44 mg dm-3 and a higher Nash-Sutcliffe efficiency (NSE) = 0.13. The results found in the present study confirmed the hypothesis that sample strokes that consider environmental covariables contribute to the increase in the quality of the predicted soil attribute maps.A amostragem representa uma etapa crucial para o mapeamento digital de solos, pois interfere diretamente nos custos operacionais do projeto e nas etapas seguintes do processamento dos dados, até a qualidade do mapa gerado. Dada a necessidade de obtenção de informações relacionadas a diferentes métodos de coleta de dados, o objetivo deste estudo foi comparar o desenho amostral e dois métodos de modelagem científica na predição espacial do P disponível no solo. O estudo foi realizado em uma propriedade rural de 160 ha localizada no município de Tupanciretã - RS. Nesta área ocorrem intensas atividades agrícolas, adição de insumos (fertilizantes) e irrigação por sistema de pivô central. Três métodos de amostragem foram testados - grade regular simples (RG) com distância fixa entre pontos, amostragem de cobertura espacial (SCS) contendo pontos em distâncias curtas e amostragem de recozimento simulado considerando a distribuição marginal de covariáveis ambientais (DIST) - como base para a previsão de o conteúdo de fósforo disponível no solo, a uma profundidade de 0 - 10 cm. A densidade amostral foi priorizada nos três métodos de amostragem. Os resultados foram validados com um conjunto externo e independente contendo 50 pontos. Assim, cada conjunto de calibração contém 160 (com exceção da grade regular, que tem 162), que foram usados para aprender dois modelos preditivos: krigagem com deriva externa (KED), considerado um modelo misto por englobar a abordagem geoestatística e determinística ; e krigagem comum (OK). Além disso, para conhecimento prévio das classes de solo que ocorrem na área, 8 perfis representativos tiveram sua morfologia analisada. A qualidade dos mapas de visualização foi avaliada pelo cálculo do erro. O melhor resultado de previsão foi encontrado combinando a amostragem DIST com o modelo KED, que tem um erro absoluto médio inferior (MAE) = 14,62, erro médio (ME) = -3,12 e erro quadrático médio da raiz (RMSE) = 23,44 mg dm- 3 e uma maior eficiência Nash-Sutcliffe (NSE) = 0,13. Os resultados encontrados no presente estudo confirmaram a hipótese de que golpes amostrais que consideram covariáveis ambientais contribuem para o aumento da qualidade dos mapas de atributos preditos do solo.Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPESporUniversidade Federal de Santa MariaCentro de Ciências RuraisPrograma de Pós-Graduação em Ciência do SoloUFSMBrasilAgronomiaAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessMapeamento digital de solosGeoestatísticaDesign de amostragemPedometriaAgricultura de precisãoDigital soil mappingGeostatisticSampling designPedometricPrecision agricultureCNPQ::CIENCIAS AGRARIAS::AGRONOMIA::CIENCIA DO SOLOMétodos de amostragem para a modelagem espacial de fósforo disponível no soloSampling methods for phosphorus available spatial modeling in the soilinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisPedron, Fabrício de Araújohttp://lattes.cnpq.br/6868334304493274Schenato, Ricardo BergamoGubiani, Paulo IvonirTen Caten, Alexandrehttp://lattes.cnpq.br/9331102168675462Soligo, Matheus Flesch500100100005600600600600600600f8f6a495-68e2-4652-9f6b-c78d1dab67f597209e71-44e4-4b5c-ba10-78c6e7f7ea8d448fda12-08dd-41b5-891e-a2aa66495c77f3ae1e44-884a-45c4-9bbc-faf9b95094219c541621-6245-451c-aa44-7fda439507e0reponame:Biblioteca Digital de Teses e Dissertações do UFSMinstname:Universidade Federal de Santa Maria (UFSM)instacron:UFSMORIGINALDIS_PPGCS_2020_SOLIGO_MATHEUS.pdfDIS_PPGCS_2020_SOLIGO_MATHEUS.pdfDissertaçãoapplication/pdf3950096http://repositorio.ufsm.br/bitstream/1/23098/1/DIS_PPGCS_2020_SOLIGO_MATHEUS.pdf1e8f4637361506f4bb0938edf4772ccbMD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; 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dc.title.por.fl_str_mv |
Métodos de amostragem para a modelagem espacial de fósforo disponível no solo |
dc.title.alternative.eng.fl_str_mv |
Sampling methods for phosphorus available spatial modeling in the soil |
title |
Métodos de amostragem para a modelagem espacial de fósforo disponível no solo |
spellingShingle |
Métodos de amostragem para a modelagem espacial de fósforo disponível no solo Soligo, Matheus Flesch Mapeamento digital de solos Geoestatística Design de amostragem Pedometria Agricultura de precisão Digital soil mapping Geostatistic Sampling design Pedometric Precision agriculture CNPQ::CIENCIAS AGRARIAS::AGRONOMIA::CIENCIA DO SOLO |
title_short |
Métodos de amostragem para a modelagem espacial de fósforo disponível no solo |
title_full |
Métodos de amostragem para a modelagem espacial de fósforo disponível no solo |
title_fullStr |
Métodos de amostragem para a modelagem espacial de fósforo disponível no solo |
title_full_unstemmed |
Métodos de amostragem para a modelagem espacial de fósforo disponível no solo |
title_sort |
Métodos de amostragem para a modelagem espacial de fósforo disponível no solo |
author |
Soligo, Matheus Flesch |
author_facet |
Soligo, Matheus Flesch |
author_role |
author |
dc.contributor.advisor1.fl_str_mv |
Pedron, Fabrício de Araújo |
dc.contributor.advisor1Lattes.fl_str_mv |
http://lattes.cnpq.br/6868334304493274 |
dc.contributor.advisor-co1.fl_str_mv |
Schenato, Ricardo Bergamo |
dc.contributor.referee1.fl_str_mv |
Gubiani, Paulo Ivonir |
dc.contributor.referee2.fl_str_mv |
Ten Caten, Alexandre |
dc.contributor.authorLattes.fl_str_mv |
http://lattes.cnpq.br/9331102168675462 |
dc.contributor.author.fl_str_mv |
Soligo, Matheus Flesch |
contributor_str_mv |
Pedron, Fabrício de Araújo Schenato, Ricardo Bergamo Gubiani, Paulo Ivonir Ten Caten, Alexandre |
dc.subject.por.fl_str_mv |
Mapeamento digital de solos Geoestatística Design de amostragem Pedometria Agricultura de precisão |
topic |
Mapeamento digital de solos Geoestatística Design de amostragem Pedometria Agricultura de precisão Digital soil mapping Geostatistic Sampling design Pedometric Precision agriculture CNPQ::CIENCIAS AGRARIAS::AGRONOMIA::CIENCIA DO SOLO |
dc.subject.eng.fl_str_mv |
Digital soil mapping Geostatistic Sampling design Pedometric Precision agriculture |
dc.subject.cnpq.fl_str_mv |
CNPQ::CIENCIAS AGRARIAS::AGRONOMIA::CIENCIA DO SOLO |
description |
Sampling represents a crucial step for digital soil mapping because it directly interferes with the operational costs of the project and in the following steps of data processing, up to the quality of the generated map. Given the need to obtain information related to different data collection methods, the aim of this study was to compare the sampling design and two scientific modeling methods in the spatial prediction of P available on soil. The study was conducted in a 160 ha rural property located in the municipality of Tupanciretã - RS. In this area there are intense agricultural activities, the addition of inputs (fertilizers), and irrigation using a central pivot system. Three sampling methods were tested - simple regular grid (RG) with fixed distance between points, spatial coverage sampling (SCS) containing points over short distances and simulated annealing sampling considering the marginal distribution of environmental covariates (DIST) - as a basis for prediction of the available phosphorus content in the soil, at a depth of 0 - 10 cm. The sampling density was prioritized in the three sampling methods. The results were validated with an external and independent set containing 50 points. Thus, each calibration set contains 160 (with the exception of the regular grid, which has 162), which were used to learn two predictive models: kriging with external drift (KED), considered a mixed model because it encompasses the geostatistical approach and deterministic; and ordinary kriging (OK). In addition, for prior knowledge of the soil classes that occur in the area, 8 representative profiles had their morphology analyzed. The quality of the visualization maps was assessed by calculating the error. The best prediction result was found by combining the DIST sampling with the KED model, which has a lower mean absolut error (MAE) = 14.62, mean error (ME) = -3.12 and root mean squared error (RMSE) = 23.44 mg dm-3 and a higher Nash-Sutcliffe efficiency (NSE) = 0.13. The results found in the present study confirmed the hypothesis that sample strokes that consider environmental covariables contribute to the increase in the quality of the predicted soil attribute maps. |
publishDate |
2021 |
dc.date.accessioned.fl_str_mv |
2021-12-02T11:11:06Z |
dc.date.available.fl_str_mv |
2021-12-02T11:11:06Z |
dc.date.issued.fl_str_mv |
2021-03-30 |
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/1/23098 |
url |
http://repositorio.ufsm.br/handle/1/23098 |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.relation.cnpq.fl_str_mv |
500100100005 |
dc.relation.confidence.fl_str_mv |
600 600 600 600 600 600 |
dc.relation.authority.fl_str_mv |
f8f6a495-68e2-4652-9f6b-c78d1dab67f5 97209e71-44e4-4b5c-ba10-78c6e7f7ea8d 448fda12-08dd-41b5-891e-a2aa66495c77 f3ae1e44-884a-45c4-9bbc-faf9b9509421 9c541621-6245-451c-aa44-7fda439507e0 |
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 Centro de Ciências Rurais |
dc.publisher.program.fl_str_mv |
Programa de Pós-Graduação em Ciência do Solo |
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 Centro de Ciências Rurais |
dc.source.none.fl_str_mv |
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