|
|
Registro Completo |
Biblioteca(s): |
Embrapa Amapá; Embrapa Hortaliças; Embrapa Roraima; Embrapa Semiárido. |
Data corrente: |
16/02/1993 |
Data da última atualização: |
12/11/2010 |
Autoria: |
SILVA, L. A. da; SILVA, F. L. da; SEABRA FILHO, M.; MENEZES SOBRINHO, J. A. de. |
Afiliação: |
EMBRAPA-CNPH, Brasilia, DF. |
Título: |
Cultivares e epocas de plantio de alho (Allium sativum L.) para a regiao de Ibiapaba, Ceara. |
Ano de publicação: |
1991 |
Fonte/Imprenta: |
Fortaleza: EPACE, 1991. |
Páginas: |
16p. |
Série: |
(EPACE. Boletim de Pesquisa, 17). |
Idioma: |
Português |
Conteúdo: |
Determina as melhores cultivares e epocas de plantio de alho para a regiao da Ibiapaba. Foram realizados dez experimentos durante o periodo de 1980 e 1986. Foram utilizadas cultivares precoce (Branco Mineiro ou Jureia), media (Chines) e tardia (Centenario) plantadas em oito epocas espacadas de 45 dias. |
Palavras-Chave: |
Brasil; Ceara; Cultivar; Cultivate; Epoca; Ibiapaba; Regiao de Ibiapaba; Variety. |
Thesagro: |
Alho; Allium Sativum; Época de Plantio; Plantio; Variedade. |
Thesaurus Nal: |
Allium; Brazil; Garlic; planting date; varieties. |
Categoria do assunto: |
-- A Sistemas de Cultivo |
Marc: |
LEADER 01264nam a2200385 a 4500 001 1757788 005 2010-11-12 008 1991 bl uuuu u0uu1 u #d 100 1 $aSILVA, L. A. da 245 $aCultivares e epocas de plantio de alho (Allium sativum L.) para a regiao de Ibiapaba, Ceara. 260 $aFortaleza: EPACE$c1991 300 $a16p. 490 $a(EPACE. Boletim de Pesquisa, 17). 520 $aDetermina as melhores cultivares e epocas de plantio de alho para a regiao da Ibiapaba. Foram realizados dez experimentos durante o periodo de 1980 e 1986. Foram utilizadas cultivares precoce (Branco Mineiro ou Jureia), media (Chines) e tardia (Centenario) plantadas em oito epocas espacadas de 45 dias. 650 $aAllium 650 $aBrazil 650 $aGarlic 650 $aplanting date 650 $avarieties 650 $aAlho 650 $aAllium Sativum 650 $aÉpoca de Plantio 650 $aPlantio 650 $aVariedade 653 $aBrasil 653 $aCeara 653 $aCultivar 653 $aCultivate 653 $aEpoca 653 $aIbiapaba 653 $aRegiao de Ibiapaba 653 $aVariety 700 1 $aSILVA, F. L. da 700 1 $aSEABRA FILHO, M. 700 1 $aMENEZES SOBRINHO, J. A. de
Download
Esconder MarcMostrar Marc Completo |
Registro original: |
Embrapa Hortaliças (CNPH) |
|
Biblioteca |
ID |
Origem |
Tipo/Formato |
Classificação |
Cutter |
Registro |
Volume |
Status |
URL |
Voltar
|
|
| Acesso ao texto completo restrito à biblioteca da Embrapa Meio Ambiente. Para informações adicionais entre em contato com cnpma.biblioteca@embrapa.br. |
Registro Completo
Biblioteca(s): |
Embrapa Meio Ambiente. |
Data corrente: |
15/10/2020 |
Data da última atualização: |
25/08/2021 |
Tipo da produção científica: |
Artigo em Periódico Indexado |
Circulação/Nível: |
B - 1 |
Autoria: |
PADILHA, M. C. de C.; VICENTE, L. E.; DEMATTÊ, J. A. M.; LOEBMANN, D. G. dos S. W.; VICENTE, A. K.; URBINA SALAZAR, D. F.; GUIMARÃES, C. C. B. |
Afiliação: |
MANUELA CORRÊA DE CASTRO PADILHA, ESALQ-USP; LUIZ EDUARDO VICENTE, CNPMA; JOSÉ ALEXANDRE MELO DEMATTÊ, ESALQ-USP; DANIEL GOMES DOS SANTOS W LOEBMANN, CNPMA; ANDREA KOGA VICENTE; DIEGO FERNANDO URBINA SALAZAR, ESALQ-USP; CLÉCIA CRISTINA BARBOSA GUIMARÃES, ESALQ-USP. |
Título: |
Using Landsat and soil clay content to map soil organic carbon of oxisols and Ultisols near São Paulo, Brazil. |
Ano de publicação: |
2020 |
Fonte/Imprenta: |
Geoderma Regional, v. 21, e00253, 2020. |
ISSN: |
2352-0094 |
DOI: |
https://doi.org/10.1016/j.geodrs.2020.e00253 |
Idioma: |
Inglês |
Conteúdo: |
Abstract: Quantification of soil organic carbon (SOC) is a low-cost and necessary practice to meet increasing agricultural demands. Studies show that remote sensing (RS) is important for SOC prediction and its use has become crucial in agricultural management. In this study, a Multiple Linear Regression (MLR) model was constructed to predict SOC in a site in Piracicaba, São Paulo, Brazil. As predictor variables, we used the optical-satellite data of OLI/Landsat-8 sensor (bands 5 and 7, specifically), clay concentration, and the Normalized Difference Vegetation Index (NDVI). We collected 218 samples at the sampling points in the field to quantify clay and SOC in the laboratory as a calibration procedure. An Exposed Soil Mask (ESM) was created using the method GEOS3 technology, which showed pixels with greater variability of bare soil. The pixels were evaluated with their respective surface reflectance values obtained by the satellite sensor and their respective NDVI index values. We evaluated the model predictive performance based on the adjusted coefficient of determination (R2), the Root Mean-Squared Error (RMSE), and the Ratio of Performance to Interquartile Range (RPIQ) obtained in data validation. The MLR model presented R2 values 0.79 and 0.81 for calibration and validation, respectively. We obtained important RMSE and RPIQ values, 0.14 and 2.32, respectively. The high RPIQ indicated significative sampling distribution around the trendline. After construction, the model was applied to the C spatial distribution using the predictive variables as layers, predominant concentrations of 0.65 to 0.79 g. Kg-1 in 51 (23.4%) soil samples. The analysis presented here offer possibilities for SOC prediction using Geographic Information Systems (GIS) tools. MenosAbstract: Quantification of soil organic carbon (SOC) is a low-cost and necessary practice to meet increasing agricultural demands. Studies show that remote sensing (RS) is important for SOC prediction and its use has become crucial in agricultural management. In this study, a Multiple Linear Regression (MLR) model was constructed to predict SOC in a site in Piracicaba, São Paulo, Brazil. As predictor variables, we used the optical-satellite data of OLI/Landsat-8 sensor (bands 5 and 7, specifically), clay concentration, and the Normalized Difference Vegetation Index (NDVI). We collected 218 samples at the sampling points in the field to quantify clay and SOC in the laboratory as a calibration procedure. An Exposed Soil Mask (ESM) was created using the method GEOS3 technology, which showed pixels with greater variability of bare soil. The pixels were evaluated with their respective surface reflectance values obtained by the satellite sensor and their respective NDVI index values. We evaluated the model predictive performance based on the adjusted coefficient of determination (R2), the Root Mean-Squared Error (RMSE), and the Ratio of Performance to Interquartile Range (RPIQ) obtained in data validation. The MLR model presented R2 values 0.79 and 0.81 for calibration and validation, respectively. We obtained important RMSE and RPIQ values, 0.14 and 2.32, respectively. The high RPIQ indicated significative sampling distribution around the trendline. After construction, the model... Mostrar Tudo |
Palavras-Chave: |
Digital soil mapping; Multiple linear regression. |
Thesagro: |
Argissolos; Carbono; Latossolo; Oxisol; Satélite; Sensoriamento Remoto. |
Thesaurus NAL: |
Landsat; Oxisols; Soil organic carbon; Soil properties. |
Categoria do assunto: |
P Recursos Naturais, Ciências Ambientais e da Terra |
Marc: |
LEADER 02849naa a2200361 a 4500 001 2125532 005 2021-08-25 008 2020 bl uuuu u00u1 u #d 022 $a2352-0094 024 7 $ahttps://doi.org/10.1016/j.geodrs.2020.e00253$2DOI 100 1 $aPADILHA, M. C. de C. 245 $aUsing Landsat and soil clay content to map soil organic carbon of oxisols and Ultisols near São Paulo, Brazil.$h[electronic resource] 260 $c2020 520 $aAbstract: Quantification of soil organic carbon (SOC) is a low-cost and necessary practice to meet increasing agricultural demands. Studies show that remote sensing (RS) is important for SOC prediction and its use has become crucial in agricultural management. In this study, a Multiple Linear Regression (MLR) model was constructed to predict SOC in a site in Piracicaba, São Paulo, Brazil. As predictor variables, we used the optical-satellite data of OLI/Landsat-8 sensor (bands 5 and 7, specifically), clay concentration, and the Normalized Difference Vegetation Index (NDVI). We collected 218 samples at the sampling points in the field to quantify clay and SOC in the laboratory as a calibration procedure. An Exposed Soil Mask (ESM) was created using the method GEOS3 technology, which showed pixels with greater variability of bare soil. The pixels were evaluated with their respective surface reflectance values obtained by the satellite sensor and their respective NDVI index values. We evaluated the model predictive performance based on the adjusted coefficient of determination (R2), the Root Mean-Squared Error (RMSE), and the Ratio of Performance to Interquartile Range (RPIQ) obtained in data validation. The MLR model presented R2 values 0.79 and 0.81 for calibration and validation, respectively. We obtained important RMSE and RPIQ values, 0.14 and 2.32, respectively. The high RPIQ indicated significative sampling distribution around the trendline. After construction, the model was applied to the C spatial distribution using the predictive variables as layers, predominant concentrations of 0.65 to 0.79 g. Kg-1 in 51 (23.4%) soil samples. The analysis presented here offer possibilities for SOC prediction using Geographic Information Systems (GIS) tools. 650 $aLandsat 650 $aOxisols 650 $aSoil organic carbon 650 $aSoil properties 650 $aArgissolos 650 $aCarbono 650 $aLatossolo 650 $aOxisol 650 $aSatélite 650 $aSensoriamento Remoto 653 $aDigital soil mapping 653 $aMultiple linear regression 700 1 $aVICENTE, L. E. 700 1 $aDEMATTÊ, J. A. M. 700 1 $aLOEBMANN, D. G. dos S. W. 700 1 $aVICENTE, A. K. 700 1 $aURBINA SALAZAR, D. F. 700 1 $aGUIMARÃES, C. C. B. 773 $tGeoderma Regional$gv. 21, e00253, 2020.
Download
Esconder MarcMostrar Marc Completo |
Registro original: |
Embrapa Meio Ambiente (CNPMA) |
|
Biblioteca |
ID |
Origem |
Tipo/Formato |
Classificação |
Cutter |
Registro |
Volume |
Status |
Fechar
|
Expressão de busca inválida. Verifique!!! |
|
|