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Registros recuperados : 5 | |
1. | | LOPES, M. N.; CÂNDIDO, M. J. D.; POMPEU, R. C. F. F.; SILVA, R. G. da; LOPES, J. W. B.; BEZERRA, F. M. L. Biomass components in Massai grass during establishment and regrowth under five nitrogen fertilization levels. Revista Brasileira de Zootecnia, v. 40, n. 8, p. 1629-1637, 2011. Biblioteca(s): Embrapa Caprinos e Ovinos. |
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2. | | GOMES, F. H. T.; CÂNDIDO, M. J. D.; PEREIRA, E. S.; LOPES, J. W. B.; FEITOSA, J. V.; POMPEU, R. C. F. F. Composição bromatológica e degradação in situ de subprodutos da cadeia do biodiesel. Revista Científica de Produção Animal, v. 11, n. 2, p. 144-156, 2009. Biblioteca(s): Embrapa Caprinos e Ovinos. |
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3. | | ANDRADE, T. G.; ANDRADE JUNIOR, A. S. de; SOUZA, M. O.; LOPES, J. W. B.; VIEIRA, P. F. de M. J. Soybean yield prediction using remote sensing in Southwestern Piauí State, Brazil. Revista Caatinga, v. 35, n. 1, p. 105-116, jan./mar. 2022. Biblioteca(s): Embrapa Meio-Norte. |
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4. | | LOPES, M. N.; CÂNDIDO, M. J. D.; POMPEU, R. C. F. F.; SILVA, R. G. da; LOPES, J. W. B.; FERNANDES, F. R. B.; LACERDA, C. F. de; BEZERRA, F. M. L. Fluxo de biomassa em capim-massai durante o estabelecimento e rebrotação com e sem adubação nitrogenada. Revista Ceres, Viçosa, MG, v. 60, n. 3, p. 363-371, maio/jun. 2013. Biblioteca(s): Embrapa Caprinos e Ovinos. |
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5. | | LOPES, M. N.; LACERDA, C. F. de; CÂNDIDO, M. J. D.; POMPEU, R. C. F. F.; SILVA, R. G. da; LOPES, J. W. B.; FERNANDES, F. R. B.; BEZERRA, F. M. L. Gas exchange in massai grass under five nitrogen fertilization levels during establishment and regrowth. Revista Brasileira de Zootecnia, v. 40, n. 9, p. 1862-1869, 2011. Biblioteca(s): Embrapa Caprinos e Ovinos. |
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Registros recuperados : 5 | |
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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
|
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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