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Registro Completo |
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Biblioteca(s): |
Embrapa Agrobiologia; Embrapa Agropecuária Oeste; Embrapa Meio-Norte; Embrapa Rondônia; Embrapa Roraima; Embrapa Semiárido; Embrapa Soja; Embrapa Unidades Centrais. |
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Data corrente: |
01/10/2008 |
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Data da última atualização: |
25/11/2008 |
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Tipo da produção científica: |
Circular Técnica |
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Autoria: |
OLIVEIRA, F. A. de; CASTRO, C. de; SFREDO, G. J.; KLEPKER, D.; OLIVEIRA JUNIOR, A. de. |
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Afiliação: |
FÁbio Álvares de Oliveira, CNPSo; César de Castro, CNPSo; Gedi Jorge Sfredo, CNPSo; Dirceu Klepker, CNPSo; Adilson de Oliveira Júnior, CNPSo. |
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Título: |
Fertilidade do solo e nutrição mineral da soja. |
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Ano de publicação: |
2008 |
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Fonte/Imprenta: |
Londrina: Embrapa Soja, 2008. |
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Páginas: |
8 p. |
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Série: |
(Embrapa Soja. Circular técnica, 62). |
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Idioma: |
Português |
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Conteúdo: |
Avaliação da fertilidade do solo; Correção da acidez; Correção da acidez subsuperficial; Exigências minerais e avaliação do estado nutricional; Adubação. |
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Thesagro: |
Adubação; Análise do Solo; Fertilidade; Fertilidade do Solo; Mineral; Nutrição; Nutriente; Soja; Solo. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/CNPSO-2009-09/28581/1/circtec62.pdf
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Marc: |
LEADER 00895nam a2200289 a 4500
001 1470944
005 2008-11-25
008 2008 bl uuuu u0uu1 u #d
100 1 $aOLIVEIRA, F. A. de
245 $aFertilidade do solo e nutrição mineral da soja.
260 $aLondrina: Embrapa Soja$c2008
300 $a8 p.
490 $a(Embrapa Soja. Circular técnica, 62).
520 $aAvaliação da fertilidade do solo; Correção da acidez; Correção da acidez subsuperficial; Exigências minerais e avaliação do estado nutricional; Adubação.
650 $aAdubação
650 $aAnálise do Solo
650 $aFertilidade
650 $aFertilidade do Solo
650 $aMineral
650 $aNutrição
650 $aNutriente
650 $aSoja
650 $aSolo
700 1 $aCASTRO, C. de
700 1 $aSFREDO, G. J.
700 1 $aKLEPKER, D.
700 1 $aOLIVEIRA JUNIOR, A. de
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Registro original: |
Embrapa Soja (CNPSO) |
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Registro |
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Registro Completo
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Biblioteca(s): |
Embrapa Meio Ambiente. |
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Data corrente: |
08/10/2015 |
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Data da última atualização: |
08/10/2015 |
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Tipo da produção científica: |
Artigo em Anais de Congresso |
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Autoria: |
MAIA, A. de H. N.; MEINKE, H.; BAETHGEN, W. |
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Afiliação: |
ALINE DE HOLANDA NUNES MAIA, CNPMA; H. MEINKE, Department of Primary Industries and Fisheries, Austrália; W. BAETHGEN, Columbia University. |
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Título: |
Better statistics to assess the quality of analogue-based forecast systems. |
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Ano de publicação: |
2006 |
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Fonte/Imprenta: |
In: INTERNATIONAL CONFERENCE ON SOUTHERN HEMISPHERE METEOROLOGY AND OCEANOGRAPHY, 8., 2006, Foz do Iguaçu,PR. [Anais?]. Foz do Iguaçu,PR: ICSHMO, 2006. p.575-582. |
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Idioma: |
Português |
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Conteúdo: |
Seasonal probabilistic forecast systems (SPFS) based on the analogue years approach (AYA) are used worldwide and provide valuable information for decision makers managing climate-sensitive systems (Sivakumar et al. 2000; Ferreyra et al. 2001; Selvaraju et al. 2004; Meinke and Stone 2005). Providing such categorisations are based on scientifically well understood mechanisms, such forecasts (or, more appropriately, scenarios) allow climate time series to be partitioned into ?year- or season-types? (analogue years) based on prevailing ocean and atmospheric conditions (i.e. Southern Oscillation Index, SOI and/or Sea Surface Temperatures SST anomalies), resulting in SOI or ENSO phases. These time series are usually represented by their respective cumulative distribution functions (CDFs) or their complement, probability of exceeding functions (POEs): a conditional CDFK for each class K and an unconditional CDF (CDFALL). Current oceanic and atmospheric conditions can then be assigned to a particular category K and the correspondent CDFK is then adopted for probabilistic assessments. To take action, decisions makers need to know: a) whether or not probabilistic forecasts provided by conditional distributions are sufficiently different from their respective from `climatology?; b) if so, what is the magnitude of change in the prognostic variable that might lead to a change in the decision; c) is there sufficient improvement in accuracy over the ?climatology? and d) if so, what is the improvement in accuracy of this forecast over the unconditional case (Maia et al. 2006). From a methodological perspective, the assessment of questions (a) and (c) requires inferential tools such as statistical tests for the hypothesis of `no class effect´. The assessment of questions (b) and (d) requires intuitive, descriptive statistics that are relevant for the question at hand. We propose using descriptive measures coupled with inferential methods to evaluate such SPFS. Detailed discussion about forecast qualit yassessments can be found in Potgieter et al. 2004). We illustrate these approaches by quantifying signal of a SOI-based forecast system across Australia and an ENSO-based forecast system across Southeast of South America. MenosSeasonal probabilistic forecast systems (SPFS) based on the analogue years approach (AYA) are used worldwide and provide valuable information for decision makers managing climate-sensitive systems (Sivakumar et al. 2000; Ferreyra et al. 2001; Selvaraju et al. 2004; Meinke and Stone 2005). Providing such categorisations are based on scientifically well understood mechanisms, such forecasts (or, more appropriately, scenarios) allow climate time series to be partitioned into ?year- or season-types? (analogue years) based on prevailing ocean and atmospheric conditions (i.e. Southern Oscillation Index, SOI and/or Sea Surface Temperatures SST anomalies), resulting in SOI or ENSO phases. These time series are usually represented by their respective cumulative distribution functions (CDFs) or their complement, probability of exceeding functions (POEs): a conditional CDFK for each class K and an unconditional CDF (CDFALL). Current oceanic and atmospheric conditions can then be assigned to a particular category K and the correspondent CDFK is then adopted for probabilistic assessments. To take action, decisions makers need to know: a) whether or not probabilistic forecasts provided by conditional distributions are sufficiently different from their respective from `climatology?; b) if so, what is the magnitude of change in the prognostic variable that might lead to a change in the decision; c) is there sufficient improvement in accuracy over the ?climatology? and d) if so, what is the ... Mostrar Tudo |
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Thesagro: |
Estatística; Previsão do tempo. |
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Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/130865/1/2006AA-010.pdf
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Marc: |
LEADER 02838nam a2200157 a 4500
001 2026083
005 2015-10-08
008 2006 bl uuuu u00u1 u #d
100 1 $aMAIA, A. de H. N.
245 $aBetter statistics to assess the quality of analogue-based forecast systems.$h[electronic resource]
260 $aIn: INTERNATIONAL CONFERENCE ON SOUTHERN HEMISPHERE METEOROLOGY AND OCEANOGRAPHY, 8., 2006, Foz do Iguaçu,PR. [Anais?]. Foz do Iguaçu,PR: ICSHMO, 2006. p.575-582.$c2006
520 $aSeasonal probabilistic forecast systems (SPFS) based on the analogue years approach (AYA) are used worldwide and provide valuable information for decision makers managing climate-sensitive systems (Sivakumar et al. 2000; Ferreyra et al. 2001; Selvaraju et al. 2004; Meinke and Stone 2005). Providing such categorisations are based on scientifically well understood mechanisms, such forecasts (or, more appropriately, scenarios) allow climate time series to be partitioned into ?year- or season-types? (analogue years) based on prevailing ocean and atmospheric conditions (i.e. Southern Oscillation Index, SOI and/or Sea Surface Temperatures SST anomalies), resulting in SOI or ENSO phases. These time series are usually represented by their respective cumulative distribution functions (CDFs) or their complement, probability of exceeding functions (POEs): a conditional CDFK for each class K and an unconditional CDF (CDFALL). Current oceanic and atmospheric conditions can then be assigned to a particular category K and the correspondent CDFK is then adopted for probabilistic assessments. To take action, decisions makers need to know: a) whether or not probabilistic forecasts provided by conditional distributions are sufficiently different from their respective from `climatology?; b) if so, what is the magnitude of change in the prognostic variable that might lead to a change in the decision; c) is there sufficient improvement in accuracy over the ?climatology? and d) if so, what is the improvement in accuracy of this forecast over the unconditional case (Maia et al. 2006). From a methodological perspective, the assessment of questions (a) and (c) requires inferential tools such as statistical tests for the hypothesis of `no class effect´. The assessment of questions (b) and (d) requires intuitive, descriptive statistics that are relevant for the question at hand. We propose using descriptive measures coupled with inferential methods to evaluate such SPFS. Detailed discussion about forecast qualit yassessments can be found in Potgieter et al. 2004). We illustrate these approaches by quantifying signal of a SOI-based forecast system across Australia and an ENSO-based forecast system across Southeast of South America.
650 $aEstatística
650 $aPrevisão do tempo
700 1 $aMEINKE, H.
700 1 $aBAETHGEN, W.
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Registro original: |
Embrapa Meio Ambiente (CNPMA) |
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