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Registro Completo |
Biblioteca(s): |
Embrapa Agroenergia. |
Data corrente: |
02/03/2020 |
Data da última atualização: |
20/04/2020 |
Tipo da produção científica: |
Documentos |
Autoria: |
GUIDUCCI, R. do C. N.; LAVIOLA, B. G.; COLLARES, D. G. |
Afiliação: |
ROSANA DO CARMO NASCIMENTO GUIDUCCI, CNPAE; BRUNO GALVEAS LAVIOLA, CNPAE; DANIELA GARCIA COLLARES, CPPSUL. |
Título: |
Análise e perspectivas de atuação no ciclo de políticas públicas: o caso da Embrapa Agroenergia. |
Ano de publicação: |
2019 |
Fonte/Imprenta: |
Embrapa Agroenergia: Brasília, DF, 2019. |
Páginas: |
58 p. |
Série: |
(Embrapa Agroenergia. Documentos, 32) |
Idioma: |
Português |
Palavras-Chave: |
Objetivo estratégico; Programas públicos. |
Thesagro: |
Gestão. |
Categoria do assunto: |
-- |
URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/211352/1/DOC-32.pdf
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Marc: |
LEADER 00595nam a2200181 a 4500 001 2120703 005 2020-04-20 008 2019 bl uuuu 00u1 u #d 100 1 $aGUIDUCCI, R. do C. N. 245 $aAnálise e perspectivas de atuação no ciclo de políticas públicas$bo caso da Embrapa Agroenergia.$h[electronic resource] 260 $aEmbrapa Agroenergia: Brasília, DF$c2019 300 $a58 p. 490 $a(Embrapa Agroenergia. Documentos, 32) 650 $aGestão 653 $aObjetivo estratégico 653 $aProgramas públicos 700 1 $aLAVIOLA, B. G. 700 1 $aCOLLARES, D. G.
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Registro original: |
Embrapa Agroenergia (CNPAE) |
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Registro Completo
Biblioteca(s): |
Embrapa Meio-Norte. |
Data corrente: |
23/12/2021 |
Data da última atualização: |
13/02/2023 |
Tipo da produção científica: |
Artigo em Periódico Indexado |
Circulação/Nível: |
A - 2 |
Autoria: |
ANDRADE, T. G.; ANDRADE JUNIOR, A. S. de; SOUZA, M. O.; LOPES, J. W. B.; VIEIRA, P. F. de M. J. |
Afiliação: |
THATIANE GOMES ANDRADE, UFPI, Bom Jesus, PI.; ADERSON SOARES DE ANDRADE JUNIOR, CPAMN; MELISSA ODA SOUZA, UESPI, Teresina, PI.; JOSE WELLINGTON BATISTA LOPES, UFPI, Bom Jesus, PI.; PAULO FERNANDO DE MELO JORGE VIEIRA, CPAMN. |
Título: |
Soybean yield prediction using remote sensing in Southwestern Piauí State, Brazil. |
Ano de publicação: |
2022 |
Fonte/Imprenta: |
Revista Caatinga, v. 35, n. 1, p. 105-116, jan./mar. 2022. |
ISSN: |
0100-316X (impresso); 1983-2125 (online) |
DOI: |
10.1590/1983-21252022v35n111rc |
Idioma: |
Inglês |
Conteúdo: |
Recent researches have shown promising results for the use of orbital data using the Normalized Difference Vegetation Index (NDVI) to monitor and predict soybean grain yield. The objective of this work was to evaluate propositions of multiple linear regression models to predict soybean grain yield using NDVI. The research was carried out at the Celeiro Farm, in Monte Alegre do Piauí, PI, Brazil, in an area of 200 ha. Five images were collected during the soybean crop cycle: one from the Landsat 8 and four from the Sentinel 2. Regression analyses were carried out between grain yield data (predicted variable) extracted from harvest maps and spectral data (predictor variables) from NDVI of soybean crops at different developmental stages. The promising models were selected by the Akaike Information Criterion (AIC). The models were validated using Root Mean Square Error (RMSE) and Normalized Root Mean Square Error (nRMSE), considering the mean of soybean yield of the plot. The linear regression models developed with NDVI for the V5-V6 and R2 developmental stages showed promising results for the prediction of soybean grain yield, with mean error of predictions of 153.9 kg ha-1, representing 4.2% when compared to the data from field measures. |
Palavras-Chave: |
NDVI; Regressão múltipla. |
Thesagro: |
Previsão de Safra. |
Thesaurus NAL: |
Agricultural forecasts; Regression analysis. |
Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/229625/1/SoybeanYieldPredictionRemoteSensing.pdf
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Marc: |
LEADER 02063naa a2200253 a 4500 001 2138334 005 2023-02-13 008 2022 bl uuuu u00u1 u #d 022 $a0100-316X (impresso); 1983-2125 (online) 024 7 $a10.1590/1983-21252022v35n111rc$2DOI 100 1 $aANDRADE, T. G. 245 $aSoybean yield prediction using remote sensing in Southwestern Piauí State, Brazil.$h[electronic resource] 260 $c2022 520 $aRecent researches have shown promising results for the use of orbital data using the Normalized Difference Vegetation Index (NDVI) to monitor and predict soybean grain yield. The objective of this work was to evaluate propositions of multiple linear regression models to predict soybean grain yield using NDVI. The research was carried out at the Celeiro Farm, in Monte Alegre do Piauí, PI, Brazil, in an area of 200 ha. Five images were collected during the soybean crop cycle: one from the Landsat 8 and four from the Sentinel 2. Regression analyses were carried out between grain yield data (predicted variable) extracted from harvest maps and spectral data (predictor variables) from NDVI of soybean crops at different developmental stages. The promising models were selected by the Akaike Information Criterion (AIC). The models were validated using Root Mean Square Error (RMSE) and Normalized Root Mean Square Error (nRMSE), considering the mean of soybean yield of the plot. The linear regression models developed with NDVI for the V5-V6 and R2 developmental stages showed promising results for the prediction of soybean grain yield, with mean error of predictions of 153.9 kg ha-1, representing 4.2% when compared to the data from field measures. 650 $aAgricultural forecasts 650 $aRegression analysis 650 $aPrevisão de Safra 653 $aNDVI 653 $aRegressão múltipla 700 1 $aANDRADE JUNIOR, A. S. de 700 1 $aSOUZA, M. O. 700 1 $aLOPES, J. W. B. 700 1 $aVIEIRA, P. F. de M. J. 773 $tRevista Caatinga$gv. 35, n. 1, p. 105-116, jan./mar. 2022.
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Embrapa Meio-Norte (CPAMN) |
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