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Registro Completo |
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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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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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Registro original: |
Embrapa Acre (CPAF-AC) |
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Biblioteca(s): |
Embrapa Trigo. |
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Data corrente: |
10/05/2021 |
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Data da última atualização: |
30/11/2021 |
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Tipo da produção científica: |
Documentos |
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Autoria: |
DENARDIN, J. E.; MARCON, G. P.; FAGANELLO, A.; LEMAINSKI, J.; BACK, A. J.; JUNQUEIRA, B. R.; OLIVEIRA, V. B. de. |
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Afiliação: |
JOSE ELOIR DENARDIN, CNPT; GRACIOSO PIGNATEL MARCON, Técnico Agropecuário, Especialista em Manejo de Solo, pesquisador aposentado da Souza Cruz, Rua Argentina, 38, Vila Moema, 88705-340 Tubarão, SC.; ANTONIO FAGANELLO, Engenheiro mecânico, M.Sc. em Engenharia Agrícola, pesquisador aposentado da Embrapa Trigo, Passo Fundo, RS.; JORGE LEMAINSKI, CNPT; ÁLVARO JOSÉ BACK, Engenheiro Agrônomo, D.Sc. em Engenharia, pesquisador da Empresa de Pesquisa Agropecuária e Extensão Rural de Santa Catarina - Epagri, Rua Presidente Vargas, 116, 88840-000 Urussanga, SC.; BARBARA RODRIGUES JUNQUEIRA, Engenheira Agrônoma, M.Sc. em Agronomia/Entomologia Agrícola, pesquisadora científica da Souza Cruz, Rodovia BR 471, km 132,4, Distrito Industrial, 96835-642 Santa Cruz do Sul, RS.; VANDO BRAZ DE OLIVEIRA, Técnico Agrícola, Bacharel em Administração, Especialista em Manejo de Solo e Nutrição de Plantas, Gerente Global de Melhoramento e Agronomia na Souza Cruz, Avenida General Plínio Tourinho, 3200, Bairro Bom Jesus, 83880-000 Rio Negro, PR. |
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Título: |
Hydrological validation of High Wide Ridges as a soil conservation technology applied to tobacco crop. |
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Ano de publicação: |
2021 |
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Fonte/Imprenta: |
Passo Fundo: Embrapa Trigo, March 2021. |
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Páginas: |
24 p. |
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Série: |
(Embrapa Trigo. Documentos online, 194) |
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ISSN: |
1518-6512 |
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Idioma: |
Inglês |
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Conteúdo: |
High Wide Ridge Soil Management is a conservation practice associated with the no-tillage, reduced tillage, and conventional tillage systems, usual in small farms in southern Brazil. Integrated with winter grain or ground cover crops such as wheat (Triticum aestivum), rye (Secale cereale), black oats (Avena strigosa) and white oats (Avena sativa), millet (Pennisetum glaucum L.), Sudan grass (Sorghum sudanense), sorghum (Sorghum spp.), mucuna (Mucuna spp.), and brachiaria (Brachiaria spp.), high wide ridge is being used for tobacco (Nicotiana tabacum), soybean (Glycine max L.), maize (Zea mays), and beans (Phaseolus vulgaris) during the spring and summer seasons. The benefits expected from this technology, related to the performance of the tobacco crop, have already been proven experimentally and on a farm scale. However, the benefit in regard to the effectiveness in controlling runoff resulting from intense rainfall, has not yet been quantified. The aim of this study was to validate the effectiveness of the high wide ridge in containing runoff from intense rainfall, with return periods equal to, or greater than, 10 years. The study was carried out on 11 farms, located in nine municipalities (four in Rio Grande do Sul state, three in Santa Catarina state, and four in Paraná state), where tobacco was grown following winter cereal as a ground cover crop, on three types of topography (gently undulating topography, undulating topography, and highly undulating topography) with 11 soil types that were texturally and taxonomically different. It was concluded that high wide ridge is capable of containing the flooding generated by rainfall with return periods of more than 10 years, allowing the science of soil conservation to promote it as a conservation soil management practice. MenosHigh Wide Ridge Soil Management is a conservation practice associated with the no-tillage, reduced tillage, and conventional tillage systems, usual in small farms in southern Brazil. Integrated with winter grain or ground cover crops such as wheat (Triticum aestivum), rye (Secale cereale), black oats (Avena strigosa) and white oats (Avena sativa), millet (Pennisetum glaucum L.), Sudan grass (Sorghum sudanense), sorghum (Sorghum spp.), mucuna (Mucuna spp.), and brachiaria (Brachiaria spp.), high wide ridge is being used for tobacco (Nicotiana tabacum), soybean (Glycine max L.), maize (Zea mays), and beans (Phaseolus vulgaris) during the spring and summer seasons. The benefits expected from this technology, related to the performance of the tobacco crop, have already been proven experimentally and on a farm scale. However, the benefit in regard to the effectiveness in controlling runoff resulting from intense rainfall, has not yet been quantified. The aim of this study was to validate the effectiveness of the high wide ridge in containing runoff from intense rainfall, with return periods equal to, or greater than, 10 years. The study was carried out on 11 farms, located in nine municipalities (four in Rio Grande do Sul state, three in Santa Catarina state, and four in Paraná state), where tobacco was grown following winter cereal as a ground cover crop, on three types of topography (gently undulating topography, undulating topography, and highly undulating topography) with 11 ... Mostrar Tudo |
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Palavras-Chave: |
Direct planting system; High wide ridges; Infiltration of water in the soil; Intense rain; Return period. |
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Thesagro: |
Camalhão. |
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Thesaurus NAL: |
Soil; Soil conservation; Tobacco. |
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Categoria do assunto: |
F Plantas e Produtos de Origem Vegetal |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/223174/1/Documentos-194-online-ingles-2021.pdf
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Marc: |
LEADER 02752nam a2200325 a 4500
001 2131755
005 2021-11-30
008 2021 bl uuuu u0uu1 u #d
022 $a1518-6512
100 1 $aDENARDIN, J. E.
245 $aHydrological validation of High Wide Ridges as a soil conservation technology applied to tobacco crop.$h[electronic resource]
260 $aPasso Fundo: Embrapa Trigo, March 2021.$c2021
300 $a24 p.
490 $a(Embrapa Trigo. Documentos online, 194)
520 $aHigh Wide Ridge Soil Management is a conservation practice associated with the no-tillage, reduced tillage, and conventional tillage systems, usual in small farms in southern Brazil. Integrated with winter grain or ground cover crops such as wheat (Triticum aestivum), rye (Secale cereale), black oats (Avena strigosa) and white oats (Avena sativa), millet (Pennisetum glaucum L.), Sudan grass (Sorghum sudanense), sorghum (Sorghum spp.), mucuna (Mucuna spp.), and brachiaria (Brachiaria spp.), high wide ridge is being used for tobacco (Nicotiana tabacum), soybean (Glycine max L.), maize (Zea mays), and beans (Phaseolus vulgaris) during the spring and summer seasons. The benefits expected from this technology, related to the performance of the tobacco crop, have already been proven experimentally and on a farm scale. However, the benefit in regard to the effectiveness in controlling runoff resulting from intense rainfall, has not yet been quantified. The aim of this study was to validate the effectiveness of the high wide ridge in containing runoff from intense rainfall, with return periods equal to, or greater than, 10 years. The study was carried out on 11 farms, located in nine municipalities (four in Rio Grande do Sul state, three in Santa Catarina state, and four in Paraná state), where tobacco was grown following winter cereal as a ground cover crop, on three types of topography (gently undulating topography, undulating topography, and highly undulating topography) with 11 soil types that were texturally and taxonomically different. It was concluded that high wide ridge is capable of containing the flooding generated by rainfall with return periods of more than 10 years, allowing the science of soil conservation to promote it as a conservation soil management practice.
650 $aSoil
650 $aSoil conservation
650 $aTobacco
650 $aCamalhão
653 $aDirect planting system
653 $aHigh wide ridges
653 $aInfiltration of water in the soil
653 $aIntense rain
653 $aReturn period
700 1 $aMARCON, G. P.
700 1 $aFAGANELLO, A.
700 1 $aLEMAINSKI, J.
700 1 $aBACK, A. J.
700 1 $aJUNQUEIRA, B. R.
700 1 $aOLIVEIRA, V. B. de
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Embrapa Trigo (CNPT) |
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