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| Registros recuperados : 6 | |
| 3. |  | RODRIGUES, H. M.; CEDDIA, M. B.; VASQUES, G. M.; MULDER, V. L.; HEUVELINK, G. B. M.; OLIVEIRA, R. P. de; BRANDAO, Z. N.; MORAIS, J. P. S.; NEVES, M. L.; TAVARES, S. R. de L. Remote sensing and kriging with external drift to improve sparse proximal soil sensing data and define management zones in precision agriculture. AgriEngineering, v. 5, n. 4, p. 2326-2348, 2023.| Biblioteca(s): Embrapa Algodão; Embrapa Solos; Embrapa Solos / UEP-Recife. |
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| 4. | | ARROUAYS, D.; GRUNDY, M. G.; HARTEMINK, A. E.; HEMPEL, J. W.; HEUVELINK, G. B. M.; HONG, S. Y.; LAGACHERIE, P.; LELYK, G.; MCBRATNEY, A. B.; MCKENZIE, N. J.; MENDONCA-SANTOS, M. D. L.; MINASNY, B.; MONTANARELLA, L.; ODEH, I. O. A.; SANCHEZ, P. A.; THOMPSON, J. A.; ZHANG, G.-L. GlobalSoilMap: toward a fine-resolution global grid of soil properties. Advances in Agronomy, v. 125, p. 93-134, 2014.| Biblioteca(s): Embrapa Solos. |
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| 5. | | ARROUAYS, D.; LEENAARS, J. G. B.; RICHER-DE-FORGES, A. C.; ADHIKARI, K.; BALLABIO, C.; GREVE, M.; GRUNDY, M.; GUERRERO, E.; HEMPEL, J.; HENGL, T.; HEUVELINK, G.; BATJES, N.; CARVALHO, E.; HARTEMINK, A.; HEWITT, A.; HONG, S.-Y.; KRASILNIKOV, P.; LAGACHERIE, P.; LELYK, G.; LIBOHOVA, Z.; LILLY, A.; MCBRATNEY, A.; MCKENZIE, N.; VASQUES, G. de M. Soil legacy data rescue via GlobalSoilMap and other international and national initiatives. GeoResJ, v. 14, p. 1-19, Dec. 2017.| Biblioteca(s): Embrapa Solos. |
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| 6. | | BRAY, A. W.; KIM, J. H.; SCHRUMPF, M.; PEACOCK, C.; BANWART, S.; SCHIPPER, L.; ANGERS, D.; CHIRINDA, N.; ZINN, Y. L.; ALBRECHT, A.; KUIKMAN, P.; JOUQUET, P.; DEMENOIS, J.; FARRELL, M.; SOUSSANA, J.-F.; KUHNERT, M.; MILNE, E.; FONTAINE, S.; TAGHIZADEH-TOOSI, A.; CERRI, C. E. P.; CORBEELS, M.; CARDINAEL, R.; CERVANTES, V. A.; OLESEN, J. E.; BATJES, N.; HEUVELINK, G.; MAIA, S. M. F.; KEESSTRA, S.; CLAESSEN, L.; MADARI, B. E.; VERCHOT, L.; NIE, W.; BRUNELLE, T.; MORAN, D.; FRANK, S.; BODLE, R.; FRELIH-LARSEN, A.; DOUGILL, A.; MONTANARELLA, L.; STRINGER, L.; CHENU, C.; HIEDERER, R.; SMITH, P.; ARIAS-NAVARRO, C. The science base of a strategic research agenda: executive summary. Wageningen: CIRCASA, 2019. 15 p. European Union's Horizon 2020 Research and Innovation Programme Grant Agreement No 774378. Coordination of International Research Cooperation on Soil Carbon Sequestration in Agriculture.| Biblioteca(s): Embrapa Arroz e Feijão. |
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| Registros recuperados : 6 | |
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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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