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| 1. |  | AMARASINGHE, G. K.; AYLLÓN, M. A.; BÀO, Y.; BASLER, C. F.; BAVARI, S.; BLASDELL, K. R.; BRIESE, T.; BROWN, P. A.; BUKREYEV, A.; BUSCHMANN, A. B.; BUCHHOLZ, U. J.; JESUS, C. C.; CHANDRAN, K.; CHIAPPONI, C.; CROZIER, I.; SWART, R. L. de; DIETZGEN, R. G.; DOLNIK, O.; DREXLER, J. F.; DÜRRWALD, R.; DUNDON, W. G.; DUPREX, W. P.; DYE, J. M.; EASTON, A. J.; FOOKS, A. R.; FORMENTY, P. B. H; FOUCHIER, R. A. M; ASTUA, J. de F.; GRIFFITHS, A.; HEWSON, R.; HORIE, M.; HYNDMAN, T. H.; JIÃNG, D.; KITAJIMA, E. W.; KOBINGER. G. P.; KONDÕ, H.; KURATH, G.; KUZMIN, I. V.; LAMB, R. A.; LAVAZZA, A.; LEE, B.; LELLI, D.; LEROY, E. M.; LI, J.; MAES, P.; MARZANO, S. L.; MORENO, A.; MÜHLBERGER, E.; NETESOV, S. V.; NOWOTNY, N.; NYLUND, A.; ØKLAND, A. L.; PALACIOS, G.; Pályi, B.; PAW?SKA, J. T.; PAYNE, S. L.; PROSPERI, A.; GONZALEZ, P. L. R.; RIMA, B. K.; ROTA, P.; RUBBENSTROTH, D.; SH?, M.; SIMMONDS, P.; SMITHER, S. J.; SOZZI, E.; SPANN, K.; STENGLEIN, M. D.; STONE, D. M.; TAKADA, A.; TESH, R. B.; TOMONAGA, K.; TORDO, N.; TOWNER, J. S.; HOOGEN, B. V. D.; VASILAKIS, N.; WAHL, V.; WALKER, P. J.; WANG, L.; WHITFIELD, A. E.; WILLIAMS, J. V.; ZERBINI, F. M.; ZH?NG, T.; ZHANG, Y. Z.; KUHN, J. H. Taxonomy of the order Mononegavirales: update 2019. Archives of Virology, v.164, p.1967-1980, 2019.| Biblioteca(s): Embrapa Mandioca e Fruticultura. |
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 | Acesso ao texto completo restrito à biblioteca da Embrapa Arroz e Feijão. Para informações adicionais entre em contato com cnpaf.biblioteca@embrapa.br. |
Registro Completo
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Biblioteca(s): |
Embrapa Arroz e Feijão. |
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Data corrente: |
01/10/2021 |
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Data da última atualização: |
27/09/2023 |
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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: |
SANTOS, M. P. dos; HEINEMANN, A. B.; STONE, L. F.; MATTA, D. H. da; CASTRO, J. R. de; SANTOS, A. B. dos. |
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Afiliação: |
MARCOS PAULO DOS SANTOS, UFG; ALEXANDRE BRYAN HEINEMANN, CNPAF; LUIS FERNANDO STONE, CNPAF; DAVID HENRIQUES DA MATTA, UFG; JOÃO RODRIGO DE CASTRO, UFG; ALBERTO BAETA DOS SANTOS, CNPAF. |
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Título: |
Nitrogen determination in irrigated rice using spectral reflectance. |
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Ano de publicação: |
2021 |
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Fonte/Imprenta: |
Agronomy Journal, v. 113, n. 6, p. 5087-5101, Nov./Dec. 2021. |
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ISSN: |
1435-0645 |
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DOI: |
https://doi.org/10.1002/agj2.20892 |
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Idioma: |
Inglês |
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Conteúdo: |
Precision N management using optical radiation sensors is a promising management strategy. Using a combination of three spectral reflectance bands, 22 vegetation indices (VI) were calculated and evaluated for their efficiency in estimating N status in irrigated rice during growth stages. The results obtained here have showed a promising strategy to include crop stages as covariate in generalist models to predict the N status parameters for irrigated rice. This approach is interesting because it reduces the need for specific models, with different structures, for each crop stage. In addition, including crop stages as a covariate in the prediction models allows knowing the rice N status according to the crop stage, which is essential for efficient nitrogen management in commercial crops. The results obtained here show the beginning of vegetative stage (V1 to V9) significantly affects the prediction of all N status parameters. The dry leaf biomass (DLB), leaf area index (LAI), leaf N uptake (LNU) and N nutrition index (NNI) can be adequately predicted with combinations of just two vegetative indices (VI). These results show the importance of using active sensors with more than two fixed bands, preferably including a red-edge band, for effective crop N status estimation. |
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Thesagro: |
Arroz Irrigado; Nitrogênio; Oryza Sativa; Sensoriamento Remoto. |
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Thesaurus NAL: |
Ecophysiology; Nitrogen; Reflectance; Remote sensing; Rice. |
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Categoria do assunto: |
F Plantas e Produtos de Origem Vegetal |
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Marc: |
LEADER 02170naa a2200313 a 4500
001 2134978
005 2023-09-27
008 2021 bl uuuu u00u1 u #d
022 $a1435-0645
024 7 $ahttps://doi.org/10.1002/agj2.20892$2DOI
100 1 $aSANTOS, M. P. dos
245 $aNitrogen determination in irrigated rice using spectral reflectance.$h[electronic resource]
260 $c2021
520 $aPrecision N management using optical radiation sensors is a promising management strategy. Using a combination of three spectral reflectance bands, 22 vegetation indices (VI) were calculated and evaluated for their efficiency in estimating N status in irrigated rice during growth stages. The results obtained here have showed a promising strategy to include crop stages as covariate in generalist models to predict the N status parameters for irrigated rice. This approach is interesting because it reduces the need for specific models, with different structures, for each crop stage. In addition, including crop stages as a covariate in the prediction models allows knowing the rice N status according to the crop stage, which is essential for efficient nitrogen management in commercial crops. The results obtained here show the beginning of vegetative stage (V1 to V9) significantly affects the prediction of all N status parameters. The dry leaf biomass (DLB), leaf area index (LAI), leaf N uptake (LNU) and N nutrition index (NNI) can be adequately predicted with combinations of just two vegetative indices (VI). These results show the importance of using active sensors with more than two fixed bands, preferably including a red-edge band, for effective crop N status estimation.
650 $aEcophysiology
650 $aNitrogen
650 $aReflectance
650 $aRemote sensing
650 $aRice
650 $aArroz Irrigado
650 $aNitrogênio
650 $aOryza Sativa
650 $aSensoriamento Remoto
700 1 $aHEINEMANN, A. B.
700 1 $aSTONE, L. F.
700 1 $aMATTA, D. H. da
700 1 $aCASTRO, J. R. de
700 1 $aSANTOS, A. B. dos
773 $tAgronomy Journal$gv. 113, n. 6, p. 5087-5101, Nov./Dec. 2021.
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Embrapa Arroz e Feijão (CNPAF) |
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