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Registro Completo |
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Biblioteca(s): |
Embrapa Solos. |
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Data corrente: |
31/08/2018 |
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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: |
GUEVARA, M.; OLMEDO, G. F.; STELL, E.; YIGINI, Y.; AGUILAR DUARTE, Y.; ARELLANO HERNÁNDEZ, C.; ARÉVALO, G. E.; ARROYO-CRUZ, C. E.; BOLIVAR, A.; BUNNING, S.; BUSTAMANTE CAÑAS, N.; CRUZ-GAISTARDO, C. O.; DAVILLA, F.; DELL ACQUA, M.; ENCINA, A.; FIGUEREDO TACONA, H.; FONTES, F.; HERNÁNDEZ HERRERA, J. A.; IBELLES NAVARRO, A. R.; LOAYZA, V.; MANUELES, A. M.; MENDOZA JARA, F.; OLIVERA, C.; OSORIO HERMOSILLA, R.; PEREIRA, G.; PIETRO, P.; RAMOS, I. A.; REY BRINA, J. C.; RIVERA, R.; RODRÍGUEZ-RODRÍGUEZ, J.; ROOPNARINE, R.; ROSALES IBARRA, A.; ROSALES RIVEIRO, K. A.; SCHULZ, G. A.; SPENCE, A.; VASQUES, G. de M.; VARGAS, R. R.; VARGAS, R. |
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Afiliação: |
MARIO GUEVARA, University of Delaware; GUILLERMO FEDERICO OLMEDO, INTA EEA Mendoza/FAO; EMMA STELL, University of Delaware; YUSUF YIGINI, FAO; YAMELI AGUILAR DUARTE, Instituto Nacional de Investigaciones Forestales, Agrícolas y Pecuarias, Mérida, Mexico; CARLOS ARELLANO HERNÁNDEZ, Instituto Nacional de Estadísitica y Geografía, Aguascalientes, Mexico; GLORIA E. ARÉVALO, Zamorano University of Honduras and Asociación Hondureña de la Ciencia del Suelo, Tegucigalpa, Honduras; CARLOS EDUARDO ARROYO-CRUZ, National Commission for the Knowledge and Use of Biodiversity, Mexico City, Mexico; ADRIANA BOLIVAR, Subdirección Agrología, Instituto Geográfico Agustín Codazzi, Bogotá, Colombia; SALLY BUNNING, Oficina Regional de la FAO para América Latina y el Caribe, Santiago de Chile, Chile; NELSON BUSTAMANTE CAÑAS, Servicio Agrícola y Ganadero, Santiago de Chile, Chile; CARLOS OMAR CRUZ-GAISTARDO, Instituto Nacional de Estadísitica y Geografía, Aguascalientes, Mexico; FABIAN DAVILLA, Ministerio de Ganaderia, Agricultura y Pesca, Montevideo, Uruguay; MARTIN DELL ACQUA, Ministerio de Ganaderia, Agricultura y Pesca, Montevideo, Uruguay; ARNULFO ENCINA, Facultad de Ciencias Agrarias de la Universidad Nacional de Asunción, Asunción, Paraguay; HERNÁN FIGUEREDO TACONA, Land Viceministry, Ministry of Rural Development and Land, La Paz, Bolivia; FERNANDO FONTES, Ministerio de Ganaderia, Agricultura y Pesca, Montevideo, Uruguay; JOSÉ ANTONIO HERNÁNDEZ HERRERA, Universidad Autónoma Agraria Antonio Narro Unidad Laguna, Torreón, Mexico; ALEJANDRO ROBERTO IBELLES NAVARRO, Instituto Nacional de Estadísitica y Geografía, Aguascalientes, Mexico; VERONICA LOAYZA, Ministerio de Agricultura y Ganaderia, Quito, Ecuador; ALEXANDRA M. MANUELES, Zamorano University of Honduras and Asociación Hondureña de la Ciencia del Suelo, Tegucigalpa, Honduras; FERNANDO MENDOZA JARA, Universidad Nacional Agraria, Managua, Nicaragua; CAROLINA OLIVERA, Oficina Regional de la FAO para América Latina y el Caribe, Bogotá, Colombia; RODRIGO OSORIO HERMOSILLA, Servicio Agrícola y Ganadero, Santiago de Chile, Chile; GONZALO PEREIRA, Ministerio de Ganaderia, Agricultura y Pesca, Montevideo, Uruguay; PABLO PIETRO, Ministerio de Ganaderia, Agricultura y Pesca, Montevideo, Uruguay; IVÁN ALEXIS RAMOS, Instituto de Investigación Agropecuaria de Panamá, Panamá; JUAN CARLOS REY BRINA, Sociedad Venezolana de la Ciencia del Suelo, Caracas, Venezuela; RAFAEL RIVERA, Ministerio de Medio Ambiente, Santo Domingo, Dominican Republic; JAVIER RODRÍGUEZ-RODRÍGUEZ, National Commission for the Knowledge and Use of Biodiversity, Mexico City, Mexico; RONALD ROOPNARINE, Department of Natural and Life Sciences, COSTAATT, Port of Spain, Trinidad an Tobago/University of the West Indies, St. Augustine Campus, Trinidad and Tobago; ALBÁN ROSALES IBARRA, Instituto de Innovación en Transferencia y Tecnología Agropecuaria, San José, Costa Rica; KENSET AMAURY ROSALES RIVEIRO, Ministerio de Ambiente y Recursos Naturales de Guatemala, Ciudad Guatemala, Guatemala; GUILLERMO ANDRÉS SCHULZ, INTA CNIA, Buenos Aires, Argentina; ADRIAN SPENCE, International Centre for Environmental and Nuclear Sciences, University of the West Indies, Kingston, Jamaica; GUSTAVO DE MATTOS VASQUES, CNPS; RONALD R. VARGAS, FAO, Vialle de Terme di Caracalla, Rome, Italy; RODRIGO VARGAS, University of Delaware, Department of Plant and Soil Sciences, Newark, DE, USA. |
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Título: |
No silver bullet for digital soil mapping: country-specific soil organic carbon estimates across Latin America. |
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Ano de publicação: |
2018 |
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Fonte/Imprenta: |
Soil, v. 4, n. 1, p. 173-193, 2018. |
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DOI: |
https://doi.org/10.5194/soil-4-173-2018 |
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Idioma: |
Inglês |
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Conteúdo: |
Country-specific soil organic carbon (SOC) estimates are the baseline for the Global SOC Map of the Global Soil Partnership (GSOCmap-GSP). This endeavor is key to explaining the uncertainty of global SOC estimates but requires harmonizing heterogeneous datasets and building country-specific capacities for digital soil mapping (DSM).We identified country-specific predictors for SOC and tested the performance of five predictive algorithms for mapping SOC across Latin America. The algorithms included support vector machines (SVMs), random forest (RF), kernel-weighted nearest neighbors (KK), partial least squares regression (PL), and regression kriging based on stepwise multiple linear models (RK). Country-specific training data and SOC predictors (5 x 5 km pixel resolution) were obtained from ISRIC - World Soil Information. Temperature, soil type, vegetation indices, and topographic constraints were the best predictors for SOC, but country-specific predictors and their respective weights varied across Latin America. We compared a large diversity of country-specific datasets and models, and were able to explain SOC variability in a range between ~ 1 and ~ 60 %, with no universal predictive algorithm among countries. A regional (n = 11 268 SOC estimates) ensemble of these five algorithms was able to explain ~ 39% of SOC variability from repeated 5-fold cross-validation.We report a combined SOC stock of 77.8 +- 43.6 Pg (uncertainty represented by the full conditional response of independent model residuals) across Latin America. SOC stocks were higher in tropical forests (30 +- 16.5 Pg) and croplands (13 +- 8.1 Pg). Country-specific and regional ensembles revealed spatial discrepancies across geopolitical borders, higher elevations, and coastal plains, but provided similar regional stocks (77.8 +- 42.2 and 76.8 +- 45.1 Pg, respectively). These results are conservative compared to global estimates (e.g., SoilGrids250m 185.8 Pg, the Harmonized World Soil Database 138.4 Pg, or the GSOCmap-GSP 99.7 Pg). Countries with large area (i.e., Brazil, Bolivia, Mexico, Peru) and large spatial SOC heterogeneity had lower SOC stocks per unit area and larger uncertainty in their predictions. We highlight that expert opinion is needed to set boundary prediction limits to avoid unrealistically high modeling estimates. For maximizing explained variance while minimizing prediction bias, the selection of predictive algorithms for SOC mapping should consider density of available data and variability of country-specific environmental gradients. This study highlights the large degree of spatial uncertainty in SOC estimates across Latin America. We provide a framework for improving country-specific mapping efforts and reducing current discrepancy of global, regional, and country-specific SOC estimates. MenosCountry-specific soil organic carbon (SOC) estimates are the baseline for the Global SOC Map of the Global Soil Partnership (GSOCmap-GSP). This endeavor is key to explaining the uncertainty of global SOC estimates but requires harmonizing heterogeneous datasets and building country-specific capacities for digital soil mapping (DSM).We identified country-specific predictors for SOC and tested the performance of five predictive algorithms for mapping SOC across Latin America. The algorithms included support vector machines (SVMs), random forest (RF), kernel-weighted nearest neighbors (KK), partial least squares regression (PL), and regression kriging based on stepwise multiple linear models (RK). Country-specific training data and SOC predictors (5 x 5 km pixel resolution) were obtained from ISRIC - World Soil Information. Temperature, soil type, vegetation indices, and topographic constraints were the best predictors for SOC, but country-specific predictors and their respective weights varied across Latin America. We compared a large diversity of country-specific datasets and models, and were able to explain SOC variability in a range between ~ 1 and ~ 60 %, with no universal predictive algorithm among countries. A regional (n = 11 268 SOC estimates) ensemble of these five algorithms was able to explain ~ 39% of SOC variability from repeated 5-fold cross-validation.We report a combined SOC stock of 77.8 +- 43.6 Pg (uncertainty represented by the full conditional response of i... Mostrar Tudo |
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Palavras-Chave: |
Carbono orgânico do solo; Mapeamento digital do solo. |
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Thesagro: |
Carbono. |
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Thesaurus Nal: |
Soil map; Soil organic carbon. |
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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/182258/1/2018-032.pdf
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Marc: |
LEADER 04629naa a2200637 a 4500
001 2094880
005 2021-11-11
008 2018 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.5194/soil-4-173-2018$2DOI
100 1 $aGUEVARA, M.
245 $aNo silver bullet for digital soil mapping$bcountry-specific soil organic carbon estimates across Latin America.$h[electronic resource]
260 $c2018
520 $aCountry-specific soil organic carbon (SOC) estimates are the baseline for the Global SOC Map of the Global Soil Partnership (GSOCmap-GSP). This endeavor is key to explaining the uncertainty of global SOC estimates but requires harmonizing heterogeneous datasets and building country-specific capacities for digital soil mapping (DSM).We identified country-specific predictors for SOC and tested the performance of five predictive algorithms for mapping SOC across Latin America. The algorithms included support vector machines (SVMs), random forest (RF), kernel-weighted nearest neighbors (KK), partial least squares regression (PL), and regression kriging based on stepwise multiple linear models (RK). Country-specific training data and SOC predictors (5 x 5 km pixel resolution) were obtained from ISRIC - World Soil Information. Temperature, soil type, vegetation indices, and topographic constraints were the best predictors for SOC, but country-specific predictors and their respective weights varied across Latin America. We compared a large diversity of country-specific datasets and models, and were able to explain SOC variability in a range between ~ 1 and ~ 60 %, with no universal predictive algorithm among countries. A regional (n = 11 268 SOC estimates) ensemble of these five algorithms was able to explain ~ 39% of SOC variability from repeated 5-fold cross-validation.We report a combined SOC stock of 77.8 +- 43.6 Pg (uncertainty represented by the full conditional response of independent model residuals) across Latin America. SOC stocks were higher in tropical forests (30 +- 16.5 Pg) and croplands (13 +- 8.1 Pg). Country-specific and regional ensembles revealed spatial discrepancies across geopolitical borders, higher elevations, and coastal plains, but provided similar regional stocks (77.8 +- 42.2 and 76.8 +- 45.1 Pg, respectively). These results are conservative compared to global estimates (e.g., SoilGrids250m 185.8 Pg, the Harmonized World Soil Database 138.4 Pg, or the GSOCmap-GSP 99.7 Pg). Countries with large area (i.e., Brazil, Bolivia, Mexico, Peru) and large spatial SOC heterogeneity had lower SOC stocks per unit area and larger uncertainty in their predictions. We highlight that expert opinion is needed to set boundary prediction limits to avoid unrealistically high modeling estimates. For maximizing explained variance while minimizing prediction bias, the selection of predictive algorithms for SOC mapping should consider density of available data and variability of country-specific environmental gradients. This study highlights the large degree of spatial uncertainty in SOC estimates across Latin America. We provide a framework for improving country-specific mapping efforts and reducing current discrepancy of global, regional, and country-specific SOC estimates.
650 $aSoil map
650 $aSoil organic carbon
650 $aCarbono
653 $aCarbono orgânico do solo
653 $aMapeamento digital do solo
700 1 $aOLMEDO, G. F.
700 1 $aSTELL, E.
700 1 $aYIGINI, Y.
700 1 $aAGUILAR DUARTE, Y.
700 1 $aARELLANO HERNÁNDEZ, C.
700 1 $aARÉVALO, G. E.
700 1 $aARROYO-CRUZ, C. E.
700 1 $aBOLIVAR, A.
700 1 $aBUNNING, S.
700 1 $aBUSTAMANTE CAÑAS, N.
700 1 $aCRUZ-GAISTARDO, C. O.
700 1 $aDAVILLA, F.
700 1 $aDELL ACQUA, M.
700 1 $aENCINA, A.
700 1 $aFIGUEREDO TACONA, H.
700 1 $aFONTES, F.
700 1 $aHERNÁNDEZ HERRERA, J. A.
700 1 $aIBELLES NAVARRO, A. R.
700 1 $aLOAYZA, V.
700 1 $aMANUELES, A. M.
700 1 $aMENDOZA JARA, F.
700 1 $aOLIVERA, C.
700 1 $aOSORIO HERMOSILLA, R.
700 1 $aPEREIRA, G.
700 1 $aPIETRO, P.
700 1 $aRAMOS, I. A.
700 1 $aREY BRINA, J. C.
700 1 $aRIVERA, R.
700 1 $aRODRÍGUEZ-RODRÍGUEZ, J.
700 1 $aROOPNARINE, R.
700 1 $aROSALES IBARRA, A.
700 1 $aROSALES RIVEIRO, K. A.
700 1 $aSCHULZ, G. A.
700 1 $aSPENCE, A.
700 1 $aVASQUES, G. de M.
700 1 $aVARGAS, R. R.
700 1 $aVARGAS, R.
773 $tSoil$gv. 4, n. 1, p. 173-193, 2018.
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Esconder MarcMostrar Marc Completo |
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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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|
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Registro Completo
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Biblioteca(s): |
Embrapa Uva e Vinho. |
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Data corrente: |
25/10/2017 |
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Data da última atualização: |
06/05/2019 |
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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: |
MOURA, C. J. M. de; FAJARDO, T. V. M.; EIRAS, M.; SILVA, F. N. da; NICKEL, O. |
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Afiliação: |
Cátia Jacira Martins de Moura, 1Biological Institute ? Dept. of Plant Pathology ? Lab. of Plant Virology, Av. Conselheiro Rodrigues Alves, 1252 ? 04014-900 ? São Paulo, SP ? Brazil.; THOR VINICIUS MARTINS FAJARDO, CNPUV; Marcelo Eiras, 1Biological Institute ? Dept. of Plant Pathology ? Lab. of Plant Virology, Av. Conselheiro Rodrigues Alves, 1252 ? 04014-900 ? São Paulo, SP ? Brazil.; Fábio Nascimento da Silva, 3Santa Catarina State University/Centre of Agroveterinary Sciences, Av. Luiz de Camões, 2090 ? 88520-000 ? Lages, SC ? Brazil.; OSMAR NICKEL, CNPUV. |
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Título: |
Molecular characterization of GSyV-1 and GLRaV-3 and prevalence of grapevine viruses in a grape-growing area. |
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Ano de publicação: |
2018 |
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Fonte/Imprenta: |
Scientia Agricola, Brasília,DF, v. 75, n. 1, p.43-51, jan./feb. 2018. |
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DOI: |
10.1590/1678-992X-2016-0328 |
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Idioma: |
Inglês |
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Conteúdo: |
The aims of this study were to determine the prevalence of viruses in 119 samples from 32 grapevine cultivars, collected from nine vineyards in a specific grape-growing area in southeastern Brazil, perform a partial molecular characterization of 14 isolates of Grapevine Syrah virus 1 (GSyV-1) and Grapevine leafroll-associated virus 3 (GLRaV-3) and assess the coat protein genetic variability of these viruses. The detection of viruses was implemented by realtime RT-PCR (reverse transcription polymerase chain reaction) aiming to detect seven viruses and one viroid. With the exception of the Grapevine Cabernet Sauvignon reovirus (GCSV), the viruses and viroid that were evaluated were widespread in the sampled areas, often in high prevalence and multiple infections, ranging from 15 % up to 76 %. Eight isolates of GSyV-1 and six of GLRaV-3, partially characterized by complete coat protein gene nucleotide sequencing and a variability study showed nucleotide identities ranging from 91 % to 99 % (GSyV-1) and from 98 % to 100 % (GLRaV-3) among themselves, respectively. Comparisons between conventional and real-time RT-PCR detections were implemented for GSyV-1 and GLRaV-3 infections. Analysis of genetic variability indicated molecular differences between GSyV-1 and GLRaV-3 isolates and negative selection acting on the coat protein gene of both viruses. This is the first report of GSyV-1 in commercial vineyards in Brazil. The survey revealed widespread infections of seven important pathogens in one prominent Brazilian grape-producing region implying contaminated grapevine cuttings in the spread of disease. Keywords: Vitis, diagnosis, variability, incidence, leafroll MenosThe aims of this study were to determine the prevalence of viruses in 119 samples from 32 grapevine cultivars, collected from nine vineyards in a specific grape-growing area in southeastern Brazil, perform a partial molecular characterization of 14 isolates of Grapevine Syrah virus 1 (GSyV-1) and Grapevine leafroll-associated virus 3 (GLRaV-3) and assess the coat protein genetic variability of these viruses. The detection of viruses was implemented by realtime RT-PCR (reverse transcription polymerase chain reaction) aiming to detect seven viruses and one viroid. With the exception of the Grapevine Cabernet Sauvignon reovirus (GCSV), the viruses and viroid that were evaluated were widespread in the sampled areas, often in high prevalence and multiple infections, ranging from 15 % up to 76 %. Eight isolates of GSyV-1 and six of GLRaV-3, partially characterized by complete coat protein gene nucleotide sequencing and a variability study showed nucleotide identities ranging from 91 % to 99 % (GSyV-1) and from 98 % to 100 % (GLRaV-3) among themselves, respectively. Comparisons between conventional and real-time RT-PCR detections were implemented for GSyV-1 and GLRaV-3 infections. Analysis of genetic variability indicated molecular differences between GSyV-1 and GLRaV-3 isolates and negative selection acting on the coat protein gene of both viruses. This is the first report of GSyV-1 in commercial vineyards in Brazil. The survey revealed widespread infections of seven important pat... Mostrar Tudo |
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Palavras-Chave: |
Caracterização molecular; Cultivo de uva; Diagnosis; Leafroll; Variabilitty; Videira; Vírus em videira. |
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Thesagro: |
DOença de planta; Uva. |
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Thesaurus NAL: |
incidence; Vitis. |
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Categoria do assunto: |
A Sistemas de Cultivo |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/165554/1/SA-Molecular-characterization-GSyV1-and-GLRaV3-2018.pdf
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Marc: |
LEADER 02596naa a2200313 a 4500
001 2078147
005 2019-05-06
008 2018 bl uuuu u00u1 u #d
024 7 $a10.1590/1678-992X-2016-0328$2DOI
100 1 $aMOURA, C. J. M. de
245 $aMolecular characterization of GSyV-1 and GLRaV-3 and prevalence of grapevine viruses in a grape-growing area.$h[electronic resource]
260 $c2018
520 $aThe aims of this study were to determine the prevalence of viruses in 119 samples from 32 grapevine cultivars, collected from nine vineyards in a specific grape-growing area in southeastern Brazil, perform a partial molecular characterization of 14 isolates of Grapevine Syrah virus 1 (GSyV-1) and Grapevine leafroll-associated virus 3 (GLRaV-3) and assess the coat protein genetic variability of these viruses. The detection of viruses was implemented by realtime RT-PCR (reverse transcription polymerase chain reaction) aiming to detect seven viruses and one viroid. With the exception of the Grapevine Cabernet Sauvignon reovirus (GCSV), the viruses and viroid that were evaluated were widespread in the sampled areas, often in high prevalence and multiple infections, ranging from 15 % up to 76 %. Eight isolates of GSyV-1 and six of GLRaV-3, partially characterized by complete coat protein gene nucleotide sequencing and a variability study showed nucleotide identities ranging from 91 % to 99 % (GSyV-1) and from 98 % to 100 % (GLRaV-3) among themselves, respectively. Comparisons between conventional and real-time RT-PCR detections were implemented for GSyV-1 and GLRaV-3 infections. Analysis of genetic variability indicated molecular differences between GSyV-1 and GLRaV-3 isolates and negative selection acting on the coat protein gene of both viruses. This is the first report of GSyV-1 in commercial vineyards in Brazil. The survey revealed widespread infections of seven important pathogens in one prominent Brazilian grape-producing region implying contaminated grapevine cuttings in the spread of disease. Keywords: Vitis, diagnosis, variability, incidence, leafroll
650 $aincidence
650 $aVitis
650 $aDOença de planta
650 $aUva
653 $aCaracterização molecular
653 $aCultivo de uva
653 $aDiagnosis
653 $aLeafroll
653 $aVariabilitty
653 $aVideira
653 $aVírus em videira
700 1 $aFAJARDO, T. V. M.
700 1 $aEIRAS, M.
700 1 $aSILVA, F. N. da
700 1 $aNICKEL, O.
773 $tScientia Agricola, Brasília,DF$gv. 75, n. 1, p.43-51, jan./feb. 2018.
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