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Registro Completo |
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Biblioteca(s): |
Embrapa Solos. |
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Data corrente: |
02/04/2018 |
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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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Autoria: |
PINHEIRO, H. S. K.; CARVALHO JUNIOR, W. de; CHAGAS, C. da S.; ANJOS, L. H. C. dos; OWENS, P. R. |
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Afiliação: |
HELENA SARAIVA KOENOW PINHEIRO, UFRRJ; WALDIR DE CARVALHO JUNIOR, CNPS; CESAR DA SILVA CHAGAS, CNPS; LÚCIA HELENA CUNHA DOS ANJOS, UFRRJ; PHILLIP RAY OWENS, USDA. |
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Título: |
Prediction of topsoil texture through regression trees and multiple linear regressions. |
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Ano de publicação: |
2018 |
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Fonte/Imprenta: |
Revista Brasileira de Ciência do Solo, v. 42, article e0170167, 2018. |
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DOI: |
https://doi.org/10.1590/18069657rbcs20170167 |
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Idioma: |
Inglês |
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Conteúdo: |
Users of soil survey products are mostly interested in understanding how soil properties vary in space and time. The aim of digital soil mapping (DSM) is to represent the spatial variability of soil properties quantitatively to support decision-making. The goal of this study is to evaluate DSM techniques (Regression Trees - RT and Multiple Linear Regressions - MLR) and the ability of these tools to predict mineral fraction content under a wide variability of landscapes. The study site was the entire Guapi-Macacu watershed (1,250.78 km²) in the state of Rio de Janeiro in the Southeast region of Brazil. Terrain attributes and remote sensing data (with 30 m of spatial resolution) were used to represent landscape co-variables selected as an input in predictive models in order to develop the explanatory variables. The selection of sampling sites was based on the Latin Hypercube algorithm. A representative set of one hundred points with feasible field access was chosen. Different input databases were tested for prediction of mineral fraction content (harmonized and original data). The Spline algorithm was used to harmonize data according to the GlobalSoil.Net consortium standards. The results showed better performance from the RT models, using input from an average of six covariates; the simplest MLR model used twice as many input variables, creating more complex models without gaining precision. Furthermore, better R² values were obtained using RT models, irrespective of harmonization of soil data. The harmonized dataset from the 0.00-0.05 and 0.05-0.15 m layers, in general, presented better results for the clay and silt, with R2 values of 0.52 (0.00-0.05 m) and 0.69 (0.05-0.15 m), respectively. Prediction of sand content showed better results when the original depth data was used as an input, although all regression tree models had R2 values greater than 0.52. The RT models provided a better statistical index than MLR for all predicted properties; however, the variance between models suggests similarity of performance. Regarding harmonization of soil data, both input databases (harmonized or not) can be used to predict soil properties, since the variance of model performance was low and generalization of the soil maps showed similar trends. The products obtained from the digital soil mapping approach make it possible to integrate the factor of uncertainties, providing easier interpretation for soil management and land use decisions. MenosUsers of soil survey products are mostly interested in understanding how soil properties vary in space and time. The aim of digital soil mapping (DSM) is to represent the spatial variability of soil properties quantitatively to support decision-making. The goal of this study is to evaluate DSM techniques (Regression Trees - RT and Multiple Linear Regressions - MLR) and the ability of these tools to predict mineral fraction content under a wide variability of landscapes. The study site was the entire Guapi-Macacu watershed (1,250.78 km²) in the state of Rio de Janeiro in the Southeast region of Brazil. Terrain attributes and remote sensing data (with 30 m of spatial resolution) were used to represent landscape co-variables selected as an input in predictive models in order to develop the explanatory variables. The selection of sampling sites was based on the Latin Hypercube algorithm. A representative set of one hundred points with feasible field access was chosen. Different input databases were tested for prediction of mineral fraction content (harmonized and original data). The Spline algorithm was used to harmonize data according to the GlobalSoil.Net consortium standards. The results showed better performance from the RT models, using input from an average of six covariates; the simplest MLR model used twice as many input variables, creating more complex models without gaining precision. Furthermore, better R² values were obtained using RT models, irrespective of harmoniz... Mostrar Tudo |
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Palavras-Chave: |
Atributos do terreno; Funções de profundidade do solo; Mapeamento digital do solo; Modelos de regressão. |
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Categoria do assunto: |
P Recursos Naturais, Ciências Ambientais e da Terra |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/174786/1/2018-010.pdf
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Marc: |
LEADER 03254naa a2200229 a 4500
001 2089994
005 2021-11-11
008 2018 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.1590/18069657rbcs20170167$2DOI
100 1 $aPINHEIRO, H. S. K.
245 $aPrediction of topsoil texture through regression trees and multiple linear regressions.$h[electronic resource]
260 $c2018
520 $aUsers of soil survey products are mostly interested in understanding how soil properties vary in space and time. The aim of digital soil mapping (DSM) is to represent the spatial variability of soil properties quantitatively to support decision-making. The goal of this study is to evaluate DSM techniques (Regression Trees - RT and Multiple Linear Regressions - MLR) and the ability of these tools to predict mineral fraction content under a wide variability of landscapes. The study site was the entire Guapi-Macacu watershed (1,250.78 km²) in the state of Rio de Janeiro in the Southeast region of Brazil. Terrain attributes and remote sensing data (with 30 m of spatial resolution) were used to represent landscape co-variables selected as an input in predictive models in order to develop the explanatory variables. The selection of sampling sites was based on the Latin Hypercube algorithm. A representative set of one hundred points with feasible field access was chosen. Different input databases were tested for prediction of mineral fraction content (harmonized and original data). The Spline algorithm was used to harmonize data according to the GlobalSoil.Net consortium standards. The results showed better performance from the RT models, using input from an average of six covariates; the simplest MLR model used twice as many input variables, creating more complex models without gaining precision. Furthermore, better R² values were obtained using RT models, irrespective of harmonization of soil data. The harmonized dataset from the 0.00-0.05 and 0.05-0.15 m layers, in general, presented better results for the clay and silt, with R2 values of 0.52 (0.00-0.05 m) and 0.69 (0.05-0.15 m), respectively. Prediction of sand content showed better results when the original depth data was used as an input, although all regression tree models had R2 values greater than 0.52. The RT models provided a better statistical index than MLR for all predicted properties; however, the variance between models suggests similarity of performance. Regarding harmonization of soil data, both input databases (harmonized or not) can be used to predict soil properties, since the variance of model performance was low and generalization of the soil maps showed similar trends. The products obtained from the digital soil mapping approach make it possible to integrate the factor of uncertainties, providing easier interpretation for soil management and land use decisions.
653 $aAtributos do terreno
653 $aFunções de profundidade do solo
653 $aMapeamento digital do solo
653 $aModelos de regressão
700 1 $aCARVALHO JUNIOR, W. de
700 1 $aCHAGAS, C. da S.
700 1 $aANJOS, L. H. C. dos
700 1 $aOWENS, P. R.
773 $tRevista Brasileira de Ciência do Solo$gv. 42, article e0170167, 2018.
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Registro original: |
Embrapa Solos (CNPS) |
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Biblioteca |
ID |
Origem |
Tipo/Formato |
Classificação |
Cutter |
Registro |
Volume |
Status |
URL |
Voltar
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Registro Completo
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Biblioteca(s): |
Embrapa Meio Ambiente. |
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Data corrente: |
20/12/2013 |
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Data da última atualização: |
01/02/2021 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
B - 1 |
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Autoria: |
PINTO, Z. V.; MORANDI, M. A. B.; BETTIOL, W. |
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Afiliação: |
ZAYAME V. PINTO, CNPMA; MARCELO AUGUSTO BOECHAT MORANDI, CNPMA; WAGNER BETTIOL, CNPMA. |
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Título: |
Induction of suppressiveness to Fusarium wilt of chrysanthemum with composted sewage sludge. |
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Ano de publicação: |
2013 |
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Fonte/Imprenta: |
Tropical Plant Pathology, Brasília, DF, v. 38, n. 5, p. 414-422, 2013. |
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Idioma: |
Inglês |
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Conteúdo: |
Abstract: The effectiveness of composted sewage sludge incorporated into Pinus bark-based substrate with or without biofertilizer, fish hydrolyzate, chitosan and Trichoderma asperellum was evaluated for the control of Fusarium wilt in chrysanthemum. The substrate was obtained from pots containing chrysanthemum plants killed by the pathogen. Half of the substrate was sterilized prior to the incorporationof sewage sludge (0, 10%, 20% and 30% v/v). These substrates were or were not supplemented with the following: biofertilizer, fish hydrolyzate and Trichoderma. The mixtures were transferred to pots, and the chrysanthemum was transplanted. For all treatments, half of the plants were sprayed weekly with chitosan. Assessment of severity was performed on the 8th, 12th, 15th and 20th week after transplanting. In the 12th week, microbiological and chemical analysis of the substrate was performed. The incorporation of composted sewage sludge into the Pinus bark-based substrate significantly reduced Fusarium wilt, which was progressively decreased as the concentration of sewage sludge increased. The addition of biofertilizer, fish hydrolyzate, chitosan and Trichoderma had no effect on the disease. The microbial community was greater in non-disinfested substrates. The results indicate that suppressiveness is related to the interaction of chemical and microbiological factors. |
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Palavras-Chave: |
Biosolid; Container media; Soil-borne pathogens. |
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Thesaurus NAL: |
Chrysanthemum morifolium; organic matter. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/94427/1/2013AP36.pdf
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Marc: |
LEADER 02023naa a2200205 a 4500
001 1974582
005 2021-02-01
008 2013 bl uuuu u00u1 u #d
100 1 $aPINTO, Z. V.
245 $aInduction of suppressiveness to Fusarium wilt of chrysanthemum with composted sewage sludge.$h[electronic resource]
260 $c2013
520 $aAbstract: The effectiveness of composted sewage sludge incorporated into Pinus bark-based substrate with or without biofertilizer, fish hydrolyzate, chitosan and Trichoderma asperellum was evaluated for the control of Fusarium wilt in chrysanthemum. The substrate was obtained from pots containing chrysanthemum plants killed by the pathogen. Half of the substrate was sterilized prior to the incorporationof sewage sludge (0, 10%, 20% and 30% v/v). These substrates were or were not supplemented with the following: biofertilizer, fish hydrolyzate and Trichoderma. The mixtures were transferred to pots, and the chrysanthemum was transplanted. For all treatments, half of the plants were sprayed weekly with chitosan. Assessment of severity was performed on the 8th, 12th, 15th and 20th week after transplanting. In the 12th week, microbiological and chemical analysis of the substrate was performed. The incorporation of composted sewage sludge into the Pinus bark-based substrate significantly reduced Fusarium wilt, which was progressively decreased as the concentration of sewage sludge increased. The addition of biofertilizer, fish hydrolyzate, chitosan and Trichoderma had no effect on the disease. The microbial community was greater in non-disinfested substrates. The results indicate that suppressiveness is related to the interaction of chemical and microbiological factors.
650 $aChrysanthemum morifolium
650 $aorganic matter
653 $aBiosolid
653 $aContainer media
653 $aSoil-borne pathogens
700 1 $aMORANDI, M. A. B.
700 1 $aBETTIOL, W.
773 $tTropical Plant Pathology, Brasília, DF$gv. 38, n. 5, p. 414-422, 2013.
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Embrapa Meio Ambiente (CNPMA) |
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