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| 141. |  | LEITE, R. M. V. B. de C.; OLIVEIRA, M. C. N. de. Reação de genótipos de girassol à mancha de alternaria (Alternariaster helianthi) em condições de campo, nas safras 2019/2020 e 2020/2021. In: REUNIÃO NACIONAL DE PESQUISA DE GIRASSOL, 24.; SIMPÓSIO NACIONAL SOBRE A CULTURA DO GIRASSOL, 12., 2023, Campo Verde. Anais... Londrina: Embrapa Soja, 2023. (Embrapa Soja. Eventos técnicos & científicos, 2). RNPG, 2023. Editado por Regina Maria Villas Bôas de Campos Leite, Hugo Soares Kern. p. 29-33. Título em inglês: Reaction of sunflower genotypes to alternaria leaf spot (alternariaster helianthi) under field conditions during 2019/2020 and 2020/2021 Growing seasons.| Biblioteca(s): Embrapa Soja. |
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| 144. | | LEITE, R. M. V. B. de C.; OLIVEIRA, M. C. N. de. Reação de genótipos de girassol à podridão-branca (Sclerotinia sclerotiorum) em condições de campo, na safrinha 2022. In: REUNIÃO NACIONAL DE PESQUISA DE GIRASSOL, 24.; SIMPÓSIO NACIONAL SOBRE A CULTURA DO GIRASSOL, 12., 2023, Campo Verde. Anais... Londrina: Embrapa Soja, 2023. (Embrapa Soja. Eventos técnicos & científicos, 2). RNPG, 2023. Editado por Regina Maria Villas Bôas de Campos Leite, Hugo Soares Kern. p. 34-37. Título em inglês: Reaction of sunflower genotypes to sclerotinia stalk and head rot (Sclerotinia sclerotiorum) under field conditions during 2022 growing season.| Biblioteca(s): Embrapa Soja. |
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| 151. | | GAUDENCIO, C. de A.; WOBETO, C.; OLIVEIRA, M. C. N. de; REICHERT, P. Comportamento de especies para graos em sistemas de rotacao de culturas, em Latossolo bruno alico. In: REUNIAO DE PESQUISA DE SOJA DA REGIAO CENTRAL DO BRASIL, 22., 2000, Cuiaba. Resumos... Londrina: Embrapa Soja, 2000. p.35. (Embrapa Soja. Documentos, 144).| Biblioteca(s): Embrapa Soja. |
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| 152. | | GAUDÊNCIO, C. de A.; WOBETO, C.; OLIVEIRA, M. C. N. de; REICHERT, P. Comportamento da cevada e acompanhamento da agregação de latossolos brunos álicos, sob diferentes sistemas de rotação de culturas, em Guarapuava, PR. In: REUNIÃO ANUAL DE PESQUISA DE CEVADA, 21., 2001, Passo Fundo. Anais e ata. Passo Fundo: Embrapa Trigo, 2001. v.1 p. 143-149.| Biblioteca(s): Embrapa Soja. |
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| 159. | | LEITE, R. M. V. B. de C.; OLIVEIRA, M. C. N. de. Grouping sunflower genotypes for yield, oil content, and reaction to Alternaria leaf spot using GGE biplot. Pesquisa Agropecuária Brasileira, Brasília, DF, v. 50, n. 8, p. 649-657, ago. 2015.| Biblioteca(s): Embrapa Soja; Embrapa Unidades Centrais. |
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| Registros recuperados : 472 | |
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Registro Completo
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Biblioteca(s): |
Embrapa Territorial. |
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Data corrente: |
07/05/2021 |
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Data da última atualização: |
05/04/2022 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
B - 5 |
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Autoria: |
TEIXEIRA, A. H. de C. T.; PACHECO, E. P.; SILVA, C.; DOMPIERI, M. H. G.; LEIVAS, J. F. |
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Afiliação: |
ANTONIO HERIBERTO DE C TEIXEIRA, UFS; EDSON PATTO PACHECO, CPATC; CÉSAR SILVA, UNICAMP; MARCIA HELENA GALINA DOMPIERI, CNPM; JANICE FREITAS LEIVAS, CNPM. |
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Título: |
SAFER applications for water productivity assessments with aerial camera onboard a remotely piloted aircraft (RPA). A rainfed corn study in Northeast Brazil. |
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Ano de publicação: |
2021 |
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Fonte/Imprenta: |
Remote Sensing Applications: Society and Environment, v. 22, n. 100514, abr. 2021. |
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Páginas: |
15 p. |
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Idioma: |
Inglês |
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Conteúdo: |
Aerial images taken with a camera onboard a remotely piloted aircraft (ARP) at 4-cm resolution were used for rainfed corn water productivity (WP) assessments by applying the Simple Algorithm for Evapotranspiration Retrieving (SAFER) and the radiation use efficiency (RUE) Monteith’s model in Northeast Brazil. We present a methodology based on the use of the visible and near infrared images from a Sequoia camera together with weather and actual yield data (Ya), to model actual evapotranspiration (ET) and biomass production (BIO), retrieving water productivity based on both BIO (WPBIO) and actual yield (WPYa ). Different nitrogen (N) fertilizing cover levels (0–250 kg ha 1) and sources (nitrate – Nt and urea – Ur) were analyzed for supporting precision agriculture, aiming a fertilizing recommendation for good corn yield while reducing water use and N leaching problems. According to our statistical analyzes, there were no significant difference on ET values for the N treatments inside the analyzed phenological stages (PS) due to the proportional partition into soil evaporation and transpiration from seeding to harvest stages, but as BIO values is related to transpiration which in turn varied with PS until a certain limit, they affected WP till N cover fertilizing level of 150 kg ha 1 for both Nt and Ur N sources. Regarding the growing season (GS) WP components, no significant differences were verified between Nt and Ur N sources, being the average values for ET, BIO, and Ya, of 394 mm GS 1, 17.3 t GS 1, and 8.8 t GS 1, which yielded mean WPBIO andWPYa of 4.4 kg m 3 and 2.2 kg m 3, corresponding to a harvest index (HI) around 0.50 considering all N cover fertilizing treatments. The most important finding of the current research is that the SAFER algorithm can be applied to estimate ET with high resolution aerial cameras without the thermal bands, and together with modelled BIO or Ya data, WP assessments can be carried out following the principles of precision agriculture. The slightly lower WP values for Ur N source and the advantage of its lower both price and N leaching problems, Ur applications at 150 kg ha 1 is recommended to save money while avoiding N leaching to the ground water, when compared with Nt source. For replication of the tested methods in other regions, simple calibrations of the modelling equations may be done with field or/and remote sensing measurements, to infer the specific environmental conditions. MenosAerial images taken with a camera onboard a remotely piloted aircraft (ARP) at 4-cm resolution were used for rainfed corn water productivity (WP) assessments by applying the Simple Algorithm for Evapotranspiration Retrieving (SAFER) and the radiation use efficiency (RUE) Monteith’s model in Northeast Brazil. We present a methodology based on the use of the visible and near infrared images from a Sequoia camera together with weather and actual yield data (Ya), to model actual evapotranspiration (ET) and biomass production (BIO), retrieving water productivity based on both BIO (WPBIO) and actual yield (WPYa ). Different nitrogen (N) fertilizing cover levels (0–250 kg ha 1) and sources (nitrate – Nt and urea – Ur) were analyzed for supporting precision agriculture, aiming a fertilizing recommendation for good corn yield while reducing water use and N leaching problems. According to our statistical analyzes, there were no significant difference on ET values for the N treatments inside the analyzed phenological stages (PS) due to the proportional partition into soil evaporation and transpiration from seeding to harvest stages, but as BIO values is related to transpiration which in turn varied with PS until a certain limit, they affected WP till N cover fertilizing level of 150 kg ha 1 for both Nt and Ur N sources. Regarding the growing season (GS) WP components, no significant differences were verified between Nt and Ur N sources, being the average values for ET, BIO, and Ya,... Mostrar Tudo |
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Palavras-Chave: |
Actual evapotranspiration; Aerial images; Zea mays L. |
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Thesaurus NAL: |
Biomass production. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/doc/1131730/1/5900.pdf
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Marc: |
LEADER 03275naa a2200229 a 4500
001 2131730
005 2022-04-05
008 2021 bl uuuu u00u1 u #d
100 1 $aTEIXEIRA, A. H. de C. T.
245 $aSAFER applications for water productivity assessments with aerial camera onboard a remotely piloted aircraft (RPA). A rainfed corn study in Northeast Brazil.$h[electronic resource]
260 $c2021
300 $a15 p.
520 $aAerial images taken with a camera onboard a remotely piloted aircraft (ARP) at 4-cm resolution were used for rainfed corn water productivity (WP) assessments by applying the Simple Algorithm for Evapotranspiration Retrieving (SAFER) and the radiation use efficiency (RUE) Monteith’s model in Northeast Brazil. We present a methodology based on the use of the visible and near infrared images from a Sequoia camera together with weather and actual yield data (Ya), to model actual evapotranspiration (ET) and biomass production (BIO), retrieving water productivity based on both BIO (WPBIO) and actual yield (WPYa ). Different nitrogen (N) fertilizing cover levels (0–250 kg ha 1) and sources (nitrate – Nt and urea – Ur) were analyzed for supporting precision agriculture, aiming a fertilizing recommendation for good corn yield while reducing water use and N leaching problems. According to our statistical analyzes, there were no significant difference on ET values for the N treatments inside the analyzed phenological stages (PS) due to the proportional partition into soil evaporation and transpiration from seeding to harvest stages, but as BIO values is related to transpiration which in turn varied with PS until a certain limit, they affected WP till N cover fertilizing level of 150 kg ha 1 for both Nt and Ur N sources. Regarding the growing season (GS) WP components, no significant differences were verified between Nt and Ur N sources, being the average values for ET, BIO, and Ya, of 394 mm GS 1, 17.3 t GS 1, and 8.8 t GS 1, which yielded mean WPBIO andWPYa of 4.4 kg m 3 and 2.2 kg m 3, corresponding to a harvest index (HI) around 0.50 considering all N cover fertilizing treatments. The most important finding of the current research is that the SAFER algorithm can be applied to estimate ET with high resolution aerial cameras without the thermal bands, and together with modelled BIO or Ya data, WP assessments can be carried out following the principles of precision agriculture. The slightly lower WP values for Ur N source and the advantage of its lower both price and N leaching problems, Ur applications at 150 kg ha 1 is recommended to save money while avoiding N leaching to the ground water, when compared with Nt source. For replication of the tested methods in other regions, simple calibrations of the modelling equations may be done with field or/and remote sensing measurements, to infer the specific environmental conditions.
650 $aBiomass production
653 $aActual evapotranspiration
653 $aAerial images
653 $aZea mays L
700 1 $aPACHECO, E. P.
700 1 $aSILVA, C.
700 1 $aDOMPIERI, M. H. G.
700 1 $aLEIVAS, J. F.
773 $tRemote Sensing Applications: Society and Environment$gv. 22, n. 100514, abr. 2021.
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