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 | Acesso ao texto completo restrito à biblioteca da Embrapa Florestas. Para informações adicionais entre em contato com cnpf.biblioteca@embrapa.br. |
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Registro Completo |
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Biblioteca(s): |
Embrapa Florestas. |
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Data corrente: |
03/05/2016 |
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Data da última atualização: |
03/05/2016 |
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Tipo da produção científica: |
Artigo em Anais de Congresso |
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Autoria: |
CORREA, A. P. A.; WREGE, M. S. |
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Afiliação: |
ANA PAULA ARAUJO CORREA, UNIVERSIDADE FEDERAL DO PARANA; MARCOS SILVEIRA WREGE, CNPF. |
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Título: |
Modelagem de distribuição de Ocotea catharinensis, Ocotea odorifera e Tibouchina sellowiana como subsídio para estratégia de conservação e sistemas de manejo sustentável. |
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Ano de publicação: |
2013 |
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Fonte/Imprenta: |
In: CONGRESSO BRASILEIRO DE AGROMETEOROLOGIA, 18., 2013, Belém, PA. Belém, PA: Sociedade Brasileira de Agrometeorologia, 2013. |
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Idioma: |
Português |
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Conteúdo: |
Neste trabalho foram delimitadas as zonas de ocorrência de canela-preta, canelasassafrás e quaresmeira, espécies exclusivas da Floresta Ombrófila Densa (FOD), com o intuito de corroborar estratégias de conservação e manejo sustentável para estas espécies, através de modelagem de distribuição potencial. As delimitações foram feitas através da modelagem de nicho ecológico, onde foram relacionados matematicamente os locais de ocorrência das espécies com camadas de dados climáticos. Os pontos de ocorrência das espécies foram levantados no banco de dados do Centro de Referência em Informação Ambiental (CRIA) e na literatura, e os dados climáticos do Brasil fornecidos por diversas instituições de pesquisa. Os cenários climáticas e os mapas finais foram elaborados usando o software ArcGIS 10, e a modelagem espacial foi desenvolvida com o software OpenModeller. O modelos de distribuição potencial demonstraram uma correlação significativa entre os parâmetros climáticos e a distribuição das espécies. Embora todas as espécies tenham apresentado restrições intensas a FOD, o potencial de ocorrência em outros locais pode ser uma importante ferramenta para o estabelecimento de programas de conservação e manejo. |
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Palavras-Chave: |
Modelagem de nicho; Variável ambiental. |
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Thesagro: |
Conservação; Manejo. |
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Categoria do assunto: |
-- |
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Marc: |
LEADER 01945nam a2200169 a 4500
001 2044365
005 2016-05-03
008 2013 bl uuuu u00u1 u #d
100 1 $aCORREA, A. P. A.
245 $aModelagem de distribuição de Ocotea catharinensis, Ocotea odorifera e Tibouchina sellowiana como subsídio para estratégia de conservação e sistemas de manejo sustentável.$h[electronic resource]
260 $aIn: CONGRESSO BRASILEIRO DE AGROMETEOROLOGIA, 18., 2013, Belém, PA. Belém, PA: Sociedade Brasileira de Agrometeorologia$c2013
520 $aNeste trabalho foram delimitadas as zonas de ocorrência de canela-preta, canelasassafrás e quaresmeira, espécies exclusivas da Floresta Ombrófila Densa (FOD), com o intuito de corroborar estratégias de conservação e manejo sustentável para estas espécies, através de modelagem de distribuição potencial. As delimitações foram feitas através da modelagem de nicho ecológico, onde foram relacionados matematicamente os locais de ocorrência das espécies com camadas de dados climáticos. Os pontos de ocorrência das espécies foram levantados no banco de dados do Centro de Referência em Informação Ambiental (CRIA) e na literatura, e os dados climáticos do Brasil fornecidos por diversas instituições de pesquisa. Os cenários climáticas e os mapas finais foram elaborados usando o software ArcGIS 10, e a modelagem espacial foi desenvolvida com o software OpenModeller. O modelos de distribuição potencial demonstraram uma correlação significativa entre os parâmetros climáticos e a distribuição das espécies. Embora todas as espécies tenham apresentado restrições intensas a FOD, o potencial de ocorrência em outros locais pode ser uma importante ferramenta para o estabelecimento de programas de conservação e manejo.
650 $aConservação
650 $aManejo
653 $aModelagem de nicho
653 $aVariável ambiental
700 1 $aWREGE, M. S.
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Registro original: |
Embrapa Florestas (CNPF) |
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Biblioteca(s): |
Embrapa Acre. |
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Data corrente: |
04/02/2022 |
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Data da última atualização: |
07/02/2022 |
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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: |
BISWAS, A.; ANDRADE, M. H. M. L.; ACHARYA, J. P.; SOUZA, C. L. de; LOPEZ, Y.; ASSIS, G. M. L. de; SHIRBHATE, S.; SINGH, A.; MUNOZ, P.; RIOS, E. F. |
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Afiliação: |
ANJU BISWAS, Department of Agronomy, University of Florida, Gainesville, FL, United States; MARIO HENRIQUE MURAD LEITE ANDRADE, Department of Agronomy, University of Florida, Gainesville, FL, United States; JANAM P. ACHARYA, Department of Agronomy, University of Florida, Gainesville, FL, United States; CLEBER LOPES DE SOUZA, Department of Agronomy, University of Florida, Gainesville, FL, United States; YOLANDA LOPEZ, Department of Agronomy, University of Florida, Gainesville, FL, United States; GISELLE MARIANO LESSA DE ASSIS, CPAF-AC; SHUBHAM SHIRBHATE, Department of Agricultural and Biological Engineering, University of Florida, Gainesville, FL, United States; ADITYA SINGH, Department of Agricultural and Biological Engineering, University of Florida, Gainesville, FL, United States; PATRICIO MUNOZ, Department of Horticultural Sciences, University of Florida, Gainesville, FL, United States; ESTEBAN F. RIOS, Department of Agronomy, University of Florida, Gainesville, FL, United States. |
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Título: |
Phenomics-assisted selection for herbage accumulation in alfalfa (Medicago sativa L.). |
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Ano de publicação: |
2021 |
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Fonte/Imprenta: |
Frontiers in Plant Science, v. 12, 756768, Dec. 2021. |
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DOI: |
https://doi.org/10.3389/fpls.2021.756768 |
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Idioma: |
Inglês |
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Conteúdo: |
The application of remote sensing in plant breeding is becoming a routine method for fast and non-destructive high-throughput phenotyping (HTP) using unmanned aerial vehicles (UAVs) equipped with sensors. Alfalfa (Medicago sativa L.) is a perennial forage legume grown in more than 30 million hectares worldwide. Breeding alfalfa for herbage accumulation (HA) requires frequent and multiple phenotyping efforts, which is laborious and costly. The objective of this study was to assess the efficiency of UAV-based imagery and spatial analysis in the selection of alfalfa for HA. The alfalfa breeding population was composed of 145 full-sib and 34 half-sib families, and the experimental design was a row-column with augmented representation of controls. The experiment was established in November 2017, and HA was harvested four times between August 2018 and January 2019. A UAV equipped with a multispectral camera was used for HTP before each harvest. Four vegetation indices (VIs) were calculated from the UAVbased images: NDVI, NDRE, GNDVI, and GRVI. All VIs showed a high correlation with HA, and VIs predicted HA with moderate accuracy. HA and NDVI were used for further analyses to calculate the genetic parameters using linear mixed models. The spatial analysis had a significant effect in both dimensions (rows and columns) for HA and NDVI, resulting in improvements in the estimation of genetic parameters. Univariate models for NDVI and HA, and bivariate models, were fit to predict family performance for scenarios with various levels of HA data (simulated in silico by assigning missing values to full dataset). The bivariate models provided higher correlation among predicted values, higher coincidence for selection, and higher genetic gain even for scenarios with only 30% of HA data. Hence, HTP is a reliable and efficient method to aid alfalfa phenotyping to improve HA. Additionally, the use of spatial analysis can also improve the accuracy of selection in breeding trials. MenosThe application of remote sensing in plant breeding is becoming a routine method for fast and non-destructive high-throughput phenotyping (HTP) using unmanned aerial vehicles (UAVs) equipped with sensors. Alfalfa (Medicago sativa L.) is a perennial forage legume grown in more than 30 million hectares worldwide. Breeding alfalfa for herbage accumulation (HA) requires frequent and multiple phenotyping efforts, which is laborious and costly. The objective of this study was to assess the efficiency of UAV-based imagery and spatial analysis in the selection of alfalfa for HA. The alfalfa breeding population was composed of 145 full-sib and 34 half-sib families, and the experimental design was a row-column with augmented representation of controls. The experiment was established in November 2017, and HA was harvested four times between August 2018 and January 2019. A UAV equipped with a multispectral camera was used for HTP before each harvest. Four vegetation indices (VIs) were calculated from the UAVbased images: NDVI, NDRE, GNDVI, and GRVI. All VIs showed a high correlation with HA, and VIs predicted HA with moderate accuracy. HA and NDVI were used for further analyses to calculate the genetic parameters using linear mixed models. The spatial analysis had a significant effect in both dimensions (rows and columns) for HA and NDVI, resulting in improvements in the estimation of genetic parameters. Univariate models for NDVI and HA, and bivariate models, were fit to predict family... Mostrar Tudo |
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Palavras-Chave: |
Fitomejoramiento; Genetic gain; High-throughput phenotyping (HTP); Leguminosas forrajeras; Normalized difference vegetation index (NDVI); Teledetección; Variación espacial. |
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Thesagro: |
Alfafa; Leguminosa Forrageira; Medicago Sativa; Melhoramento Genético Vegetal; Sensoriamento Remoto. |
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Thesaurus NAL: |
Forage legumes; Phenotype; Plant breeding; Remote sensing; Spatial variation. |
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Categoria do assunto: |
G Melhoramento Genético |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/230884/1/27295.pdf
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Marc: |
LEADER 03318naa a2200445 a 4500
001 2139673
005 2022-02-07
008 2021 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.3389/fpls.2021.756768$2DOI
100 1 $aBISWAS, A.
245 $aPhenomics-assisted selection for herbage accumulation in alfalfa (Medicago sativa L.).$h[electronic resource]
260 $c2021
520 $aThe application of remote sensing in plant breeding is becoming a routine method for fast and non-destructive high-throughput phenotyping (HTP) using unmanned aerial vehicles (UAVs) equipped with sensors. Alfalfa (Medicago sativa L.) is a perennial forage legume grown in more than 30 million hectares worldwide. Breeding alfalfa for herbage accumulation (HA) requires frequent and multiple phenotyping efforts, which is laborious and costly. The objective of this study was to assess the efficiency of UAV-based imagery and spatial analysis in the selection of alfalfa for HA. The alfalfa breeding population was composed of 145 full-sib and 34 half-sib families, and the experimental design was a row-column with augmented representation of controls. The experiment was established in November 2017, and HA was harvested four times between August 2018 and January 2019. A UAV equipped with a multispectral camera was used for HTP before each harvest. Four vegetation indices (VIs) were calculated from the UAVbased images: NDVI, NDRE, GNDVI, and GRVI. All VIs showed a high correlation with HA, and VIs predicted HA with moderate accuracy. HA and NDVI were used for further analyses to calculate the genetic parameters using linear mixed models. The spatial analysis had a significant effect in both dimensions (rows and columns) for HA and NDVI, resulting in improvements in the estimation of genetic parameters. Univariate models for NDVI and HA, and bivariate models, were fit to predict family performance for scenarios with various levels of HA data (simulated in silico by assigning missing values to full dataset). The bivariate models provided higher correlation among predicted values, higher coincidence for selection, and higher genetic gain even for scenarios with only 30% of HA data. Hence, HTP is a reliable and efficient method to aid alfalfa phenotyping to improve HA. Additionally, the use of spatial analysis can also improve the accuracy of selection in breeding trials.
650 $aForage legumes
650 $aPhenotype
650 $aPlant breeding
650 $aRemote sensing
650 $aSpatial variation
650 $aAlfafa
650 $aLeguminosa Forrageira
650 $aMedicago Sativa
650 $aMelhoramento Genético Vegetal
650 $aSensoriamento Remoto
653 $aFitomejoramiento
653 $aGenetic gain
653 $aHigh-throughput phenotyping (HTP)
653 $aLeguminosas forrajeras
653 $aNormalized difference vegetation index (NDVI)
653 $aTeledetección
653 $aVariación espacial
700 1 $aANDRADE, M. H. M. L.
700 1 $aACHARYA, J. P.
700 1 $aSOUZA, C. L. de
700 1 $aLOPEZ, Y.
700 1 $aASSIS, G. M. L. de
700 1 $aSHIRBHATE, S.
700 1 $aSINGH, A.
700 1 $aMUNOZ, P.
700 1 $aRIOS, E. F.
773 $tFrontiers in Plant Science$gv. 12, 756768, Dec. 2021.
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