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Registro Completo |
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Biblioteca(s): |
Embrapa Amazônia Ocidental. |
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Data corrente: |
19/03/2004 |
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Data da última atualização: |
16/10/2014 |
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Tipo da produção científica: |
Artigo em Anais de Congresso |
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Autoria: |
DINKELMEYER, H.; LEHMANN, J.; KAISER, K.; TEIXEIRA, W.; RENCK, A.; ZECH, W. |
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Afiliação: |
University of Bayreuth; Embrapa Amazônia Ocidental. |
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Título: |
Fate of applied N fertilizer in mixed cropping systems in the Central Amazon. |
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Ano de publicação: |
2002 |
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Fonte/Imprenta: |
In: GERMAN-BRAZILIAN WORKSHOP ON NEOTROPICAL ECOSYSTEMS. ACHIEVEMENTS AND PROSPECTS OF COOPERATIVE RESEARCH, 2000, Hamburg. Proceedings... Hamburg: SHIFT: MADAM: WAVES, 2002. p. 859-861. |
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Idioma: |
Inglês |
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Conteúdo: |
Aim of the study was to investigate the nitrogen dynamics and the fate of applied nitrogen fertilizer in the soil-plant system of a mixed cropping system consisting of Bactris gasipaes Kunth. (peach palm, Arecaceae), Theobroma grandiflorum (cupuaçu, Sterculiaceae), Bertholletia excelsa Humb. & Bonpl. (Brazil nut, Lecythidaceae) and Bixa orellana L. (annatto, Bixaceae). |
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Palavras-Chave: |
Agrofloresta; Amazonas; Brasil; Manaus; Urucu. |
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Thesagro: |
Absorção; Adubação; Bactris Gasipaes; Bertholletia Excelsa; Bixa Orellana; Castanha do Para; Conservação do Solo; Cultivo Multiplo; Floresta Tropical Úmida; Nitrogênio; Pupunha; Raiz; Relação Solo-Planta; Theobroma Grandiflorum. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/110091/1/p0859-dinkelmeyer.pdf
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Marc: |
LEADER 01552nam a2200397 a 4500
001 1673222
005 2014-10-16
008 2002 bl uuuu u00u1 u #d
100 1 $aDINKELMEYER, H.
245 $aFate of applied N fertilizer in mixed cropping systems in the Central Amazon.
260 $aIn: GERMAN-BRAZILIAN WORKSHOP ON NEOTROPICAL ECOSYSTEMS. ACHIEVEMENTS AND PROSPECTS OF COOPERATIVE RESEARCH, 2000, Hamburg. Proceedings... Hamburg: SHIFT: MADAM: WAVES, 2002. p. 859-861.$c2002
520 $aAim of the study was to investigate the nitrogen dynamics and the fate of applied nitrogen fertilizer in the soil-plant system of a mixed cropping system consisting of Bactris gasipaes Kunth. (peach palm, Arecaceae), Theobroma grandiflorum (cupuaçu, Sterculiaceae), Bertholletia excelsa Humb. & Bonpl. (Brazil nut, Lecythidaceae) and Bixa orellana L. (annatto, Bixaceae).
650 $aAbsorção
650 $aAdubação
650 $aBactris Gasipaes
650 $aBertholletia Excelsa
650 $aBixa Orellana
650 $aCastanha do Para
650 $aConservação do Solo
650 $aCultivo Multiplo
650 $aFloresta Tropical Úmida
650 $aNitrogênio
650 $aPupunha
650 $aRaiz
650 $aRelação Solo-Planta
650 $aTheobroma Grandiflorum
653 $aAgrofloresta
653 $aAmazonas
653 $aBrasil
653 $aManaus
653 $aUrucu
700 1 $aLEHMANN, J.
700 1 $aKAISER, K.
700 1 $aTEIXEIRA, W.
700 1 $aRENCK, A.
700 1 $aZECH, W.
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Registro original: |
Embrapa Amazônia Ocidental (CPAA) |
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 | Acesso ao texto completo restrito à biblioteca da Embrapa Solos. Para informações adicionais entre em contato com cnps.biblioteca@embrapa.br. |
Registro Completo
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Biblioteca(s): |
Embrapa Solos. |
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Data corrente: |
09/07/2015 |
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Data da última atualização: |
11/11/2021 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
A - 1 |
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Autoria: |
SAMUEL-ROSA, A.; HEUVELINK, G. B. M.; VASQUES, G. M.; ANJOS, L. H. C. |
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Afiliação: |
ALESSANDRO SAMUEL-ROSA, CAPES/UFRRJ/ISRIC; GERARD B. M. HEUVELINK, ISRIC; GUSTAVO DE MATTOS VASQUES, CNPS; LUCIA HELENA CUNHA DOS ANJOS, UFRRJ. |
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Título: |
Do more detailed environmental covariates deliver more accurate soil maps? |
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Ano de publicação: |
2015 |
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Fonte/Imprenta: |
Geoderma, v. 243/244, p. 214-227, Apr. 2015. |
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DOI: |
https://doi.org/10.1016/j.geoderma.2014.12.017 |
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Idioma: |
Inglês |
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Conteúdo: |
In this study we evaluated whether investing in more spatially detailed environmental covariates improves the accuracy of digital soil maps. We used a case study from Southern Brazil to map clay content (CLAY), organic carbon content (SOC), and effective cation exchange capacity (ECEC) of the topsoil for a ~ 2000 ha area located on the edge of the plateau of the Paraná Sedimentary Basin. Five covariates, each with two levels of spatial detail were used: area-class soil maps, digital elevation models (DEM), geologic maps, land use maps, and satellite images. Thirty-two multiple linear regression models were calibrated for each soil property using all spatial detail combinations of the covariates. For each combination, stepwise regression was used to select predictor variables incorporated in the model. Model evaluation was done using the adjusted R-square of the regression. The baseline model, calibrated with the less detailed version of each covariate, and the best performing model were used to calibrate two linear mixed models for each soil property. Model parameters were estimated using restricted maximum likelihood. Spatial prediction was performed using the empirical best linear unbiased predictor. Validation of baseline and best performing linear multiple regression and linear mixed models was done using cross-validation. Results show that for CLAY the prediction accuracy did not considerably improve by using more detailed covariates. The amount of variance explained increased only ~ 2 percentage points (pp), less than that obtained by including the kriging step, which explained 4 pp. On the other hand, prediction of SOC and ECEC improved by ~ 13 pp when the baseline model was replaced by the best performing model. Overall, the increase in prediction performance was modest and may not outweigh the extra costs of using more detailed covariates. It may be more efficient to spend extra resources on collecting more soil observations, or increasing the detail of only those covariates that have the strongest improvement effect. In our case study, the latter would only work for SOC and ECEC, by investing in a more detailed land use map and possibly also a more detailed geologic map and DEM. MenosIn this study we evaluated whether investing in more spatially detailed environmental covariates improves the accuracy of digital soil maps. We used a case study from Southern Brazil to map clay content (CLAY), organic carbon content (SOC), and effective cation exchange capacity (ECEC) of the topsoil for a ~ 2000 ha area located on the edge of the plateau of the Paraná Sedimentary Basin. Five covariates, each with two levels of spatial detail were used: area-class soil maps, digital elevation models (DEM), geologic maps, land use maps, and satellite images. Thirty-two multiple linear regression models were calibrated for each soil property using all spatial detail combinations of the covariates. For each combination, stepwise regression was used to select predictor variables incorporated in the model. Model evaluation was done using the adjusted R-square of the regression. The baseline model, calibrated with the less detailed version of each covariate, and the best performing model were used to calibrate two linear mixed models for each soil property. Model parameters were estimated using restricted maximum likelihood. Spatial prediction was performed using the empirical best linear unbiased predictor. Validation of baseline and best performing linear multiple regression and linear mixed models was done using cross-validation. Results show that for CLAY the prediction accuracy did not considerably improve by using more detailed covariates. The amount of variance explained in... Mostrar Tudo |
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Palavras-Chave: |
Custo do mapeamento de solo; Informações auxiliares; Mapeamento digital do solo; Modelo de precisão; Modelo linear misto; Seleção de variáveis. |
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Categoria do assunto: |
P Recursos Naturais, Ciências Ambientais e da Terra |
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Marc: |
LEADER 03002naa a2200241 a 4500
001 2019572
005 2021-11-11
008 2015 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.1016/j.geoderma.2014.12.017$2DOI
100 1 $aSAMUEL-ROSA, A.
245 $aDo more detailed environmental covariates deliver more accurate soil maps?$h[electronic resource]
260 $c2015
520 $aIn this study we evaluated whether investing in more spatially detailed environmental covariates improves the accuracy of digital soil maps. We used a case study from Southern Brazil to map clay content (CLAY), organic carbon content (SOC), and effective cation exchange capacity (ECEC) of the topsoil for a ~ 2000 ha area located on the edge of the plateau of the Paraná Sedimentary Basin. Five covariates, each with two levels of spatial detail were used: area-class soil maps, digital elevation models (DEM), geologic maps, land use maps, and satellite images. Thirty-two multiple linear regression models were calibrated for each soil property using all spatial detail combinations of the covariates. For each combination, stepwise regression was used to select predictor variables incorporated in the model. Model evaluation was done using the adjusted R-square of the regression. The baseline model, calibrated with the less detailed version of each covariate, and the best performing model were used to calibrate two linear mixed models for each soil property. Model parameters were estimated using restricted maximum likelihood. Spatial prediction was performed using the empirical best linear unbiased predictor. Validation of baseline and best performing linear multiple regression and linear mixed models was done using cross-validation. Results show that for CLAY the prediction accuracy did not considerably improve by using more detailed covariates. The amount of variance explained increased only ~ 2 percentage points (pp), less than that obtained by including the kriging step, which explained 4 pp. On the other hand, prediction of SOC and ECEC improved by ~ 13 pp when the baseline model was replaced by the best performing model. Overall, the increase in prediction performance was modest and may not outweigh the extra costs of using more detailed covariates. It may be more efficient to spend extra resources on collecting more soil observations, or increasing the detail of only those covariates that have the strongest improvement effect. In our case study, the latter would only work for SOC and ECEC, by investing in a more detailed land use map and possibly also a more detailed geologic map and DEM.
653 $aCusto do mapeamento de solo
653 $aInformações auxiliares
653 $aMapeamento digital do solo
653 $aModelo de precisão
653 $aModelo linear misto
653 $aSeleção de variáveis
700 1 $aHEUVELINK, G. B. M.
700 1 $aVASQUES, G. M.
700 1 $aANJOS, L. H. C.
773 $tGeoderma$gv. 243/244, p. 214-227, Apr. 2015.
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