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Registro Completo |
Biblioteca(s): |
Embrapa Arroz e Feijão. |
Data corrente: |
18/06/2014 |
Data da última atualização: |
18/06/2014 |
Tipo da produção científica: |
Artigo em Periódico Indexado |
Autoria: |
FAGERIA, N. K.; FERREIRA, E. P. B.; MELO, L. C.; KNUPP, A. M. |
Afiliação: |
NAND KUMAR FAGERIA, CNPAF; ENDERSON PETRONIO DE BRITO FERREIRA, CNPAF; LEONARDO CUNHA MELO, CNPAF; ADRIANO MOREIRA KNUPP, CNPAF. |
Título: |
Genotypic differences in dry bean yield and yield components as influenced by nitrogen fertilization and rhizobia. |
Ano de publicação: |
2014 |
Fonte/Imprenta: |
Communications in Soil Science and Plant Analysis, New York, v. 45, n. 12, p. 1583-1604, 2014. |
ISSN: |
0010-3624 |
DOI: |
10.1080/00103624.2013.875204 |
Idioma: |
Inglês |
Conteúdo: |
Dry bean (Phaseolus vulgaris L.) is an important legume worldwide and nitrogen (N) is most yield limiting nutrients. A field experiment was conducted for two consecutive years to evaluate response of 15 dry bean genotypes to nitrogen and rhizobial inoculation. The N and rhizobia treatments were (i) control (0 kg N ha−1), (ii) seed inoculation with rhizobia strains, (iii) seed inoculation with rhizobia strains + 50 kg N ha−1, and (iv) 120 kg N ha−1. Straw yield, grain yield, and yield components were significantly influenced by N and rhizobial treatments. Grain yield, straw yield, number of pods m−2, and grain harvest index were significantly influenced by year, nitrogen + rhizobium, and genotype treatments. Year × Nitrogen + rhizobium × genotype interactions were also significant for these traits. Hence, these traits varied among genotypes with the variation in year and nitrogen + rhizobium treatments. Inoculation with rhizobium alone did not produce maximum yield and fertilizer N is required in combination with inoculation. Based on grain yield efficiency index, genotypes were classified as efficient, moderately efficient, and inefficient in nitrogen use efficiency (NUE). NUE defined as grain produced per unit N applied decreased with increasing N rate. Overall, NUE was 23.17 kg grain yield kg−1 N applied at 50 kg N ha−1 and 13.33 kg grain per kg N applied at 120 kg N ha−1. |
Thesagro: |
Feijão; Fertilizante nitrogenado; Oxisol; Phaseolus vulgaris; Rhizobium. |
Categoria do assunto: |
F Plantas e Produtos de Origem Vegetal |
Marc: |
LEADER 02214naa a2200241 a 4500 001 1988623 005 2014-06-18 008 2014 bl uuuu u00u1 u #d 022 $a0010-3624 024 7 $a10.1080/00103624.2013.875204$2DOI 100 1 $aFAGERIA, N. K. 245 $aGenotypic differences in dry bean yield and yield components as influenced by nitrogen fertilization and rhizobia.$h[electronic resource] 260 $c2014 520 $aDry bean (Phaseolus vulgaris L.) is an important legume worldwide and nitrogen (N) is most yield limiting nutrients. A field experiment was conducted for two consecutive years to evaluate response of 15 dry bean genotypes to nitrogen and rhizobial inoculation. The N and rhizobia treatments were (i) control (0 kg N ha−1), (ii) seed inoculation with rhizobia strains, (iii) seed inoculation with rhizobia strains + 50 kg N ha−1, and (iv) 120 kg N ha−1. Straw yield, grain yield, and yield components were significantly influenced by N and rhizobial treatments. Grain yield, straw yield, number of pods m−2, and grain harvest index were significantly influenced by year, nitrogen + rhizobium, and genotype treatments. Year × Nitrogen + rhizobium × genotype interactions were also significant for these traits. Hence, these traits varied among genotypes with the variation in year and nitrogen + rhizobium treatments. Inoculation with rhizobium alone did not produce maximum yield and fertilizer N is required in combination with inoculation. Based on grain yield efficiency index, genotypes were classified as efficient, moderately efficient, and inefficient in nitrogen use efficiency (NUE). NUE defined as grain produced per unit N applied decreased with increasing N rate. Overall, NUE was 23.17 kg grain yield kg−1 N applied at 50 kg N ha−1 and 13.33 kg grain per kg N applied at 120 kg N ha−1. 650 $aFeijão 650 $aFertilizante nitrogenado 650 $aOxisol 650 $aPhaseolus vulgaris 650 $aRhizobium 700 1 $aFERREIRA, E. P. B. 700 1 $aMELO, L. C. 700 1 $aKNUPP, A. M. 773 $tCommunications in Soil Science and Plant Analysis, New York$gv. 45, n. 12, p. 1583-1604, 2014.
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Embrapa Arroz e Feijão (CNPAF) |
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Biblioteca(s): |
Embrapa Milho e Sorgo. |
Data corrente: |
05/10/2023 |
Data da última atualização: |
09/10/2023 |
Tipo da produção científica: |
Artigo em Periódico Indexado |
Circulação/Nível: |
A - 1 |
Autoria: |
CHANDNANI, R.; QIN, T.; YE, H.; HU, H.; PANJVANI, K.; TOKIZAWA, M.; MACIAS, J. M.; MEDINA, A. A.; BERNARDINO, K. C.; PRADIER, P.-L.; BANIK, P.; MOONEY, A.; MAGALHAES, J. V. de; NGUYEN, H. T.; KOCHIAN, L. V. |
Afiliação: |
RAHUL CHANDNANI, UNIVERSITY OF SASKATCHEWAN; TONGFEI QIN, UNIVERSITY OF SASKATCHEWAN; HENG YE, UNIVERSITY OF MISSOURI; HAIFEI HU, UNIVERSITY OF WESTERN AUSTRALIA; KARIM PANJVANI, UNIVERSITY OF SASKATCHEWAN; MUTSUTOMO TOKIZAWA, UNIVERSITY OF SASKATCHEWAN; JAVIER MORA MACIAS, UNIVERSITY OF SASKATCHEWAN; ALMA ARMENTA MEDINA, UNIVERSITY OF SASKATCHEWAN; KARINE C. BERNARDINO; PIERRE-LUC PRADIER UNIVERSITY OF SASKATCHEWAN, UNIVERSITY OF SASKATCHEWAN; PANKAJ BANIK, UNIVERSITY OF SASKATCHEWAN; ASHLYN MOONEY, UNIVERSITY OF SASKATCHEWAN; JURANDIR VIEIRA DE MAGALHAES, CNPMS; HENRY T. NGUYEN, UNIVERSITY OF MISSOURI; LEON V. KOCHIAN, UNIVERSITY OF SASKATCHEWAN. |
Título: |
Application of an improved 2-dimensional high-throughput soybean root phenotyping platform to identify novel genetic variants regulating root architecture traits. |
Ano de publicação: |
2023 |
Fonte/Imprenta: |
Plant Phenomics, v. 5, article 97, 2023. |
DOI: |
https://doi.org/10.34133/plantphenomics.0097 |
Idioma: |
Inglês |
Conteúdo: |
Nutrient-efficient root system architecture (RSA) is becoming an important breeding objective for generating crop varieties with improved nutrient and water acquisition efficiency. Genetic variants shaping soybean RSA is key in improving nutrient and water acquisition. Here, we report on the use of an improved 2-dimensional high-throughput root phenotyping platform that minimizes background noise by imaging pouch-grown root systems submerged in water. We also developed a background image cleaning Python pipeline that computationally removes images of small pieces of debris and filter paper fibers, which can be erroneously quantified as root tips. This platform was used to phenotype root traits in 286 soybean lines genotyped with 5.4 million single-nucleotide polymorphisms. There was a substantially higher correlation in manually counted number of root tips with computationally quantified root tips (95% correlation), when the background was cleaned of nonroot materials compared to root images without the background corrected (79%). Improvements in our RSA phenotyping pipeline significantly reduced overestimation of the root traits influenced by the number of root tips. Genome-wide association studies conducted on the root phenotypic data and quantitative gene expression analysis of candidate genes resulted in the identification of 3 putative positive regulators of root system depth, total root length and surface area, and root system volume and surface area of thicker roots (DOF1-like zinc finger transcription factor, protein of unknown function, and C2H2 zinc finger protein). We also identified a putative negative regulator (gibberellin 20 oxidase 3) of the total number of lateral roots. MenosNutrient-efficient root system architecture (RSA) is becoming an important breeding objective for generating crop varieties with improved nutrient and water acquisition efficiency. Genetic variants shaping soybean RSA is key in improving nutrient and water acquisition. Here, we report on the use of an improved 2-dimensional high-throughput root phenotyping platform that minimizes background noise by imaging pouch-grown root systems submerged in water. We also developed a background image cleaning Python pipeline that computationally removes images of small pieces of debris and filter paper fibers, which can be erroneously quantified as root tips. This platform was used to phenotype root traits in 286 soybean lines genotyped with 5.4 million single-nucleotide polymorphisms. There was a substantially higher correlation in manually counted number of root tips with computationally quantified root tips (95% correlation), when the background was cleaned of nonroot materials compared to root images without the background corrected (79%). Improvements in our RSA phenotyping pipeline significantly reduced overestimation of the root traits influenced by the number of root tips. Genome-wide association studies conducted on the root phenotypic data and quantitative gene expression analysis of candidate genes resulted in the identification of 3 putative positive regulators of root system depth, total root length and surface area, and root system volume and surface area of thicker roots (... Mostrar Tudo |
Thesagro: |
Genética Vegetal; Raiz; Seleção Fenótipa; Soja. |
Categoria do assunto: |
G Melhoramento Genético |
URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/doc/1157100/1/Application-of-an-improved-2-dimensional-high-throughput-soybean-root.pdf
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Marc: |
LEADER 02746naa a2200349 a 4500 001 2157100 005 2023-10-09 008 2023 bl uuuu u00u1 u #d 024 7 $ahttps://doi.org/10.34133/plantphenomics.0097$2DOI 100 1 $aCHANDNANI, R. 245 $aApplication of an improved 2-dimensional high-throughput soybean root phenotyping platform to identify novel genetic variants regulating root architecture traits.$h[electronic resource] 260 $c2023 520 $aNutrient-efficient root system architecture (RSA) is becoming an important breeding objective for generating crop varieties with improved nutrient and water acquisition efficiency. Genetic variants shaping soybean RSA is key in improving nutrient and water acquisition. Here, we report on the use of an improved 2-dimensional high-throughput root phenotyping platform that minimizes background noise by imaging pouch-grown root systems submerged in water. We also developed a background image cleaning Python pipeline that computationally removes images of small pieces of debris and filter paper fibers, which can be erroneously quantified as root tips. This platform was used to phenotype root traits in 286 soybean lines genotyped with 5.4 million single-nucleotide polymorphisms. There was a substantially higher correlation in manually counted number of root tips with computationally quantified root tips (95% correlation), when the background was cleaned of nonroot materials compared to root images without the background corrected (79%). Improvements in our RSA phenotyping pipeline significantly reduced overestimation of the root traits influenced by the number of root tips. Genome-wide association studies conducted on the root phenotypic data and quantitative gene expression analysis of candidate genes resulted in the identification of 3 putative positive regulators of root system depth, total root length and surface area, and root system volume and surface area of thicker roots (DOF1-like zinc finger transcription factor, protein of unknown function, and C2H2 zinc finger protein). We also identified a putative negative regulator (gibberellin 20 oxidase 3) of the total number of lateral roots. 650 $aGenética Vegetal 650 $aRaiz 650 $aSeleção Fenótipa 650 $aSoja 700 1 $aQIN, T. 700 1 $aYE, H. 700 1 $aHU, H. 700 1 $aPANJVANI, K. 700 1 $aTOKIZAWA, M. 700 1 $aMACIAS, J. M. 700 1 $aMEDINA, A. A. 700 1 $aBERNARDINO, K. C. 700 1 $aPRADIER, P.-L. 700 1 $aBANIK, P. 700 1 $aMOONEY, A. 700 1 $aMAGALHAES, J. V. de 700 1 $aNGUYEN, H. T. 700 1 $aKOCHIAN, L. V. 773 $tPlant Phenomics$gv. 5, article 97, 2023.
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