|
|
| Acesso ao texto completo restrito à biblioteca da Embrapa Suínos e Aves. Para informações adicionais entre em contato com cnpsa.biblioteca@embrapa.br. |
Registro Completo |
Biblioteca(s): |
Embrapa Suínos e Aves. |
Data corrente: |
14/01/2019 |
Data da última atualização: |
14/01/2019 |
Tipo da produção científica: |
Artigo em Periódico Indexado |
Autoria: |
MOREIRA, G. C. M.; BOSCHIERO, C.; CESAR, A. S. M.; REECY, J. M.; GODOY, T. F.; PÉRTILLE, F.; LEDUR, M. C.; MOURA, A. S. A. M. T. M.; GARRICK, D. J.; COUTINHO, L. L. |
Afiliação: |
USP; USP; USP; Yowa State University; USP; USP; MONICA CORREA LEDUR, CNPSA; UNESP; Massey University; USP. |
Título: |
Integration of genome wide association studies and whole genome sequencing provides novel insights into fat deposition in chicken. |
Ano de publicação: |
2018 |
Fonte/Imprenta: |
Scientific Reports, v. 8, n. 16222, 2018. |
DOI: |
10.1038/s41598-018-34364-0 |
Idioma: |
Francês |
Thesagro: |
Melhoramento Genético Animal. |
Categoria do assunto: |
-- |
Marc: |
LEADER 00782naa a2200241 a 4500 001 2104078 005 2019-01-14 008 2018 bl uuuu u00u1 u #d 024 7 $a10.1038/s41598-018-34364-0$2DOI 100 1 $aMOREIRA, G. C. M. 245 $aIntegration of genome wide association studies and whole genome sequencing provides novel insights into fat deposition in chicken.$h[electronic resource] 260 $c2018 650 $aMelhoramento Genético Animal 700 1 $aBOSCHIERO, C. 700 1 $aCESAR, A. S. M. 700 1 $aREECY, J. M. 700 1 $aGODOY, T. F. 700 1 $aPÉRTILLE, F. 700 1 $aLEDUR, M. C. 700 1 $aMOURA, A. S. A. M. T. M. 700 1 $aGARRICK, D. J. 700 1 $aCOUTINHO, L. L. 773 $tScientific Reports$gv. 8, n. 16222, 2018.
Download
Esconder MarcMostrar Marc Completo |
Registro original: |
Embrapa Suínos e Aves (CNPSA) |
|
Biblioteca |
ID |
Origem |
Tipo/Formato |
Classificação |
Cutter |
Registro |
Volume |
Status |
URL |
Voltar
|
|
Registro Completo
Biblioteca(s): |
Embrapa Solos. |
Data corrente: |
23/08/2020 |
Data da última atualização: |
08/09/2020 |
Tipo da produção científica: |
Artigo em Periódico Indexado |
Circulação/Nível: |
B - 4 |
Autoria: |
VASQUES, G. de M.; RODRIGUES, H. M.; COELHO, M. R.; BACA, J. F. M.; DART, R. de O.; OLIVEIRA, R. P. de; TEIXEIRA, W. G.; CEDDIA, M. B. |
Afiliação: |
GUSTAVO DE MATTOS VASQUES, CNPS; HUGO MACHADO RODRIGUES, UFRRJ; MAURICIO RIZZATO COELHO, CNPS; JESUS FERNANDO MANSILLA BACA, CNPS; RICARDO DE OLIVEIRA DART, CNPS; RONALDO PEREIRA DE OLIVEIRA, CNPS; WENCESLAU GERALDES TEIXEIRA, CNPS; MARCOS BACIS CEDDIA, UFRRJ. |
Título: |
Field proximal soil sensor fusion for improving high-resolution soil property maps. |
Ano de publicação: |
2020 |
Fonte/Imprenta: |
Soil Systems, v. 4, n. 3, 52, 2020. |
DOI: |
https://doi.org/10.3390/soilsystems4030052 |
Idioma: |
Inglês |
Conteúdo: |
Mapping soil properties, using geostatistical methods in support of precision agriculture and related activities, requires a large number of samples. To reduce soil sampling and measurement time and cost, a combination of field proximal soil sensors was used to predict and map laboratory-measured soil properties in a 3.4-ha pasture field in southeastern Brazil. Sensor soil properties were measured in situ on a 10 × 10-m dense grid (377 samples) using apparent electrical conductivity meters, apparent magnetic susceptibility meter, gamma-ray spectrometer, water content reflectometer, cone penetrometer, and portable X-ray fluorescence spectrometer (pXRF). Soil samples were collected on a 20 × 20-m thin grid (105 samples) and analyzed in the laboratory for organic C, sum of bases, cation exchange capacity, clay content, soil volumetric moisture, and bulk density. Another 25 samples collected throughout the area were also analyzed for the same soil properties and used for independent validation of models and maps. To test whether the combination of sensors enhances soil property predictions, stepwise multiple linear regression (MLR) models of the laboratory soil properties were derived using individual sensor covariate data versus combined sensor data-except for the pXRF data, which were evaluated separately. Then, to test whether a denser grid sample boosted by sensor-based soil property predictions enhances soil property maps, ordinary kriging of the laboratory-measured soil properties from the thin grid was compared to ordinary kriging of the sensor-based predictions from the dense grid, and ordinary cokriging of the laboratory properties aided by sensor covariate data. The combination of multiple soil sensors improved the MLR predictions for all soil properties relative to single sensors. The pXRF data produced the best MLR predictions for organic C content, clay content, and bulk density, standing out as the best single sensor for soil property prediction, whereas the other sensors combined outperformed the pXRF sensor for the sum of bases, cation exchange capacity, and soil volumetric moisture, based on independent validation. Ordinary kriging of sensor-based predictions outperformed the other interpolation approaches for all soil properties, except organic C content, based on validation results. Thus, combining soil sensors, and using sensor-based soil property predictions to increase the sample size and spatial coverage, leads to more detailed and accurate soil property maps. MenosMapping soil properties, using geostatistical methods in support of precision agriculture and related activities, requires a large number of samples. To reduce soil sampling and measurement time and cost, a combination of field proximal soil sensors was used to predict and map laboratory-measured soil properties in a 3.4-ha pasture field in southeastern Brazil. Sensor soil properties were measured in situ on a 10 × 10-m dense grid (377 samples) using apparent electrical conductivity meters, apparent magnetic susceptibility meter, gamma-ray spectrometer, water content reflectometer, cone penetrometer, and portable X-ray fluorescence spectrometer (pXRF). Soil samples were collected on a 20 × 20-m thin grid (105 samples) and analyzed in the laboratory for organic C, sum of bases, cation exchange capacity, clay content, soil volumetric moisture, and bulk density. Another 25 samples collected throughout the area were also analyzed for the same soil properties and used for independent validation of models and maps. To test whether the combination of sensors enhances soil property predictions, stepwise multiple linear regression (MLR) models of the laboratory soil properties were derived using individual sensor covariate data versus combined sensor data-except for the pXRF data, which were evaluated separately. Then, to test whether a denser grid sample boosted by sensor-based soil property predictions enhances soil property maps, ordinary kriging of the laboratory-measured soil pr... Mostrar Tudo |
Palavras-Chave: |
Fluorescência de raios X; Fusão de sensor proximal; Geoestatística; Radiometria gama; Susceptibilidade magnética. |
Thesagro: |
Condutividade Eletrica; Sensoriamento Remoto. |
Thesaurus NAL: |
Electrical conductivity; Geostatistics; Remote sensing. |
Categoria do assunto: |
P Recursos Naturais, Ciências Ambientais e da Terra |
URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/215532/1/Field-proximal-soil-sensor-fusion-for-improving-high-resolution-soil-property-maps-2020.pdf
|
Marc: |
LEADER 03550naa a2200337 a 4500 001 2124518 005 2020-09-08 008 2020 bl uuuu u00u1 u #d 024 7 $ahttps://doi.org/10.3390/soilsystems4030052$2DOI 100 1 $aVASQUES, G. de M. 245 $aField proximal soil sensor fusion for improving high-resolution soil property maps.$h[electronic resource] 260 $c2020 520 $aMapping soil properties, using geostatistical methods in support of precision agriculture and related activities, requires a large number of samples. To reduce soil sampling and measurement time and cost, a combination of field proximal soil sensors was used to predict and map laboratory-measured soil properties in a 3.4-ha pasture field in southeastern Brazil. Sensor soil properties were measured in situ on a 10 × 10-m dense grid (377 samples) using apparent electrical conductivity meters, apparent magnetic susceptibility meter, gamma-ray spectrometer, water content reflectometer, cone penetrometer, and portable X-ray fluorescence spectrometer (pXRF). Soil samples were collected on a 20 × 20-m thin grid (105 samples) and analyzed in the laboratory for organic C, sum of bases, cation exchange capacity, clay content, soil volumetric moisture, and bulk density. Another 25 samples collected throughout the area were also analyzed for the same soil properties and used for independent validation of models and maps. To test whether the combination of sensors enhances soil property predictions, stepwise multiple linear regression (MLR) models of the laboratory soil properties were derived using individual sensor covariate data versus combined sensor data-except for the pXRF data, which were evaluated separately. Then, to test whether a denser grid sample boosted by sensor-based soil property predictions enhances soil property maps, ordinary kriging of the laboratory-measured soil properties from the thin grid was compared to ordinary kriging of the sensor-based predictions from the dense grid, and ordinary cokriging of the laboratory properties aided by sensor covariate data. The combination of multiple soil sensors improved the MLR predictions for all soil properties relative to single sensors. The pXRF data produced the best MLR predictions for organic C content, clay content, and bulk density, standing out as the best single sensor for soil property prediction, whereas the other sensors combined outperformed the pXRF sensor for the sum of bases, cation exchange capacity, and soil volumetric moisture, based on independent validation. Ordinary kriging of sensor-based predictions outperformed the other interpolation approaches for all soil properties, except organic C content, based on validation results. Thus, combining soil sensors, and using sensor-based soil property predictions to increase the sample size and spatial coverage, leads to more detailed and accurate soil property maps. 650 $aElectrical conductivity 650 $aGeostatistics 650 $aRemote sensing 650 $aCondutividade Eletrica 650 $aSensoriamento Remoto 653 $aFluorescência de raios X 653 $aFusão de sensor proximal 653 $aGeoestatística 653 $aRadiometria gama 653 $aSusceptibilidade magnética 700 1 $aRODRIGUES, H. M. 700 1 $aCOELHO, M. R. 700 1 $aBACA, J. F. M. 700 1 $aDART, R. de O. 700 1 $aOLIVEIRA, R. P. de 700 1 $aTEIXEIRA, W. G. 700 1 $aCEDDIA, M. B. 773 $tSoil Systems$gv. 4, n. 3, 52, 2020.
Download
Esconder MarcMostrar Marc Completo |
Registro original: |
Embrapa Solos (CNPS) |
|
Biblioteca |
ID |
Origem |
Tipo/Formato |
Classificação |
Cutter |
Registro |
Volume |
Status |
Fechar
|
Nenhum registro encontrado para a expressão de busca informada. |
|
|