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Registro Completo |
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Biblioteca(s): |
Embrapa Solos. |
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Data corrente: |
23/08/2016 |
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Data da última atualização: |
12/08/2020 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
CHAGAS, C. da S.; CARVALHO JUNIOR, W. de; BHERING, S. B.; CALDERANO FILHO, B. |
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Afiliação: |
CESAR DA SILVA CHAGAS, CNPS; WALDIR DE CARVALHO JUNIOR, CNPS; SILVIO BARGE BHERING, CNPS; BRAZ CALDERANO FILHO, CNPS. |
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Título: |
Spatial prediction of soil surface texture in a semiarid region using random forest and multiple linear regressions. |
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Ano de publicação: |
2016 |
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Fonte/Imprenta: |
Catena, v. 139, p. 232-240, Apr. 2016. |
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Idioma: |
Inglês |
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Conteúdo: |
Soil texture is an essential and extremely variable physical property that strongly influences many other soil properties that are highly relevant for agricultural production, e.g., fertility and water retention capacity. In plain areas, terrain properties derived from a digital elevation model are not effective for digital soil mapping, and the variation in the properties of such areas remains a challenge. In this regard, remote sensing can facilitate the mapping of soil properties. The purpose of this study was to evaluate the efficiency of using of data obtained fromthe ThematicMapper (TM) sensor of Landsat 5 for digital soil mapping in a semi-arid region, based on multiple linear regression (MLR) and a random forest model (RFM). To this end, 399 samples of the soil surface layer (0?20 cm)were used to predict the sand, silt and clay contents, using the bands 1, 2, 3, 4, 5 and 7, the Normalized Difference Vegetation Index (NDVI), the grain size index (GSI), and the relationships between bands 3 and 2, bands 3 and 7, and bands 5 and 7 (clay index) of the Landsat 5 TM sensor as covariates. Among these covariates, only band 1 (b1), the relationship between bands 5 and 7 (b5/b7) for sand, silt and clay, and band 4 (b4) for silt were not significantly correlated according to Pearson's correlation analysis. The validation of the models showed that the properties were best estimated using the RFM, which explained 63% and 56% of the spatial variability of sand and clay, respectively, whereas the MLR explained 30% of the spatial variation of silt. An analysis of the relevance of the variables predicted by the RFMshowed that the covariates b3/b7, b5, NDVI and b2 explained most of the variability of the considered properties. The RFM proved to be more advantageous than the MLR with respect to insensitivity to overfitting and the presence of noise in the data. In addition, the RFMproduced more realistic distribution maps of the soil properties than did the MLR, taking into account that the estimated values of the soil attributes were in the same range as the calibration data, while theMLR model estimates were out of the range of the calibration data. MenosSoil texture is an essential and extremely variable physical property that strongly influences many other soil properties that are highly relevant for agricultural production, e.g., fertility and water retention capacity. In plain areas, terrain properties derived from a digital elevation model are not effective for digital soil mapping, and the variation in the properties of such areas remains a challenge. In this regard, remote sensing can facilitate the mapping of soil properties. The purpose of this study was to evaluate the efficiency of using of data obtained fromthe ThematicMapper (TM) sensor of Landsat 5 for digital soil mapping in a semi-arid region, based on multiple linear regression (MLR) and a random forest model (RFM). To this end, 399 samples of the soil surface layer (0?20 cm)were used to predict the sand, silt and clay contents, using the bands 1, 2, 3, 4, 5 and 7, the Normalized Difference Vegetation Index (NDVI), the grain size index (GSI), and the relationships between bands 3 and 2, bands 3 and 7, and bands 5 and 7 (clay index) of the Landsat 5 TM sensor as covariates. Among these covariates, only band 1 (b1), the relationship between bands 5 and 7 (b5/b7) for sand, silt and clay, and band 4 (b4) for silt were not significantly correlated according to Pearson's correlation analysis. The validation of the models showed that the properties were best estimated using the RFM, which explained 63% and 56% of the spatial variability of sand and clay, respective... Mostrar Tudo |
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Palavras-Chave: |
Composição granulométrica; Conjunto de dados Landsat; Mapeamento digital de solos; Pedometria; Tamanho do grão. |
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Categoria do assunto: |
P Recursos Naturais, Ciências Ambientais e da Terra |
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Marc: |
LEADER 02879naa a2200217 a 4500
001 2051511
005 2020-08-12
008 2016 bl uuuu u00u1 u #d
100 1 $aCHAGAS, C. da S.
245 $aSpatial prediction of soil surface texture in a semiarid region using random forest and multiple linear regressions.$h[electronic resource]
260 $c2016
520 $aSoil texture is an essential and extremely variable physical property that strongly influences many other soil properties that are highly relevant for agricultural production, e.g., fertility and water retention capacity. In plain areas, terrain properties derived from a digital elevation model are not effective for digital soil mapping, and the variation in the properties of such areas remains a challenge. In this regard, remote sensing can facilitate the mapping of soil properties. The purpose of this study was to evaluate the efficiency of using of data obtained fromthe ThematicMapper (TM) sensor of Landsat 5 for digital soil mapping in a semi-arid region, based on multiple linear regression (MLR) and a random forest model (RFM). To this end, 399 samples of the soil surface layer (0?20 cm)were used to predict the sand, silt and clay contents, using the bands 1, 2, 3, 4, 5 and 7, the Normalized Difference Vegetation Index (NDVI), the grain size index (GSI), and the relationships between bands 3 and 2, bands 3 and 7, and bands 5 and 7 (clay index) of the Landsat 5 TM sensor as covariates. Among these covariates, only band 1 (b1), the relationship between bands 5 and 7 (b5/b7) for sand, silt and clay, and band 4 (b4) for silt were not significantly correlated according to Pearson's correlation analysis. The validation of the models showed that the properties were best estimated using the RFM, which explained 63% and 56% of the spatial variability of sand and clay, respectively, whereas the MLR explained 30% of the spatial variation of silt. An analysis of the relevance of the variables predicted by the RFMshowed that the covariates b3/b7, b5, NDVI and b2 explained most of the variability of the considered properties. The RFM proved to be more advantageous than the MLR with respect to insensitivity to overfitting and the presence of noise in the data. In addition, the RFMproduced more realistic distribution maps of the soil properties than did the MLR, taking into account that the estimated values of the soil attributes were in the same range as the calibration data, while theMLR model estimates were out of the range of the calibration data.
653 $aComposição granulométrica
653 $aConjunto de dados Landsat
653 $aMapeamento digital de solos
653 $aPedometria
653 $aTamanho do grão
700 1 $aCARVALHO JUNIOR, W. de
700 1 $aBHERING, S. B.
700 1 $aCALDERANO FILHO, B.
773 $tCatena$gv. 139, p. 232-240, Apr. 2016.
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Embrapa Solos (CNPS) |
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Registro |
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Biblioteca(s): |
Embrapa Pantanal. |
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Data corrente: |
16/10/2009 |
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Data da última atualização: |
17/08/2020 |
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Tipo da produção científica: |
Artigo de Divulgação na Mídia |
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Autoria: |
SANTOS, C. E. P. dos; JULIANO, R. S. |
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Afiliação: |
Carlos Eduardo Pereira dos Santos, Universidade Federal de Mato Grosso; RAQUEL SOARES JULIANO, CPAP. |
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Título: |
Criações extensivas de cavalos na região do pantanal: mortalidade aguda por cólica. |
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Ano de publicação: |
2020 |
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Fonte/Imprenta: |
Portal do Agronegócio, Minas Gerais, 23 jun. 2020. |
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Idioma: |
Português |
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Conteúdo: |
Em geral, a síndrome cólica ocorre mais frequentemente em equinos estabulados, alimentados com ração e com pouca oferta de alimento volumoso (fenos ou pastagem verde). Em criações extensivas, a alimentação desses animais é baseada na ingestão de fibras vegetais e quadros de cólica embora ocorram, são incomuns. Entretanto, uma enfermidade que acomete equinos e muares tem chamado atenção pela manifestação de quadros agudos de cólica envolvendo animais a campo, com a ocorrência de casos fatais se não tratados de forma precoce. O quadro clínico caracteriza-se por dilatação gástrica, distensão intestinal e paralização dos movimentos intestinais. É mais comum em equídeos adultos, sendo que os muares são aparentemente menos sensíveis do que os equinos, com a maior concentração de mortes ocorrendo em cavalos. |
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Thesagro: |
Cavalo; Cólica; Doença Animal. |
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Thesaurus NAL: |
Animal diseases; Colic; Horses. |
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Categoria do assunto: |
L Ciência Animal e Produtos de Origem Animal |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/214503/1/Criacoes-extensivas-cavalos-2020.pdf
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Marc: |
LEADER 01407nam a2200193 a 4500
001 1784024
005 2020-08-17
008 2020 bl uuuu u00u1 u #d
100 1 $aSANTOS, C. E. P. dos
245 $aCriações extensivas de cavalos na região do pantanal$bmortalidade aguda por cólica.$h[electronic resource]
260 $aPortal do Agronegócio, Minas Gerais, 23 jun. 2020.$c2020
520 $aEm geral, a síndrome cólica ocorre mais frequentemente em equinos estabulados, alimentados com ração e com pouca oferta de alimento volumoso (fenos ou pastagem verde). Em criações extensivas, a alimentação desses animais é baseada na ingestão de fibras vegetais e quadros de cólica embora ocorram, são incomuns. Entretanto, uma enfermidade que acomete equinos e muares tem chamado atenção pela manifestação de quadros agudos de cólica envolvendo animais a campo, com a ocorrência de casos fatais se não tratados de forma precoce. O quadro clínico caracteriza-se por dilatação gástrica, distensão intestinal e paralização dos movimentos intestinais. É mais comum em equídeos adultos, sendo que os muares são aparentemente menos sensíveis do que os equinos, com a maior concentração de mortes ocorrendo em cavalos.
650 $aAnimal diseases
650 $aColic
650 $aHorses
650 $aCavalo
650 $aCólica
650 $aDoença Animal
700 1 $aJULIANO, R. S.
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