Detecting sources of resistance to multiple diseases in Argentine maize (Zea mays L.) germplasm

Kistner, María Belén; Nazar, Lázaro; Montenegro, Lucía Daniela; Cervigni, Gerardo Domingo Lucio; Galdeano, Ernestina; Iglesias, Juliana

doi:10.1007/s10681-022-03000-4

Cited by 2 publications

(5 citation statements)

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Section: Multi-trait Index Approach (Fai-blups)mentioning

confidence: 65%

“…All gains were favorable towards the selection criteria, with negative gains for IN (−0.8), 50%IN (−1.97) FLO (−1.07), and 50%FLO (−0.77), and an interesting positive gain for GY (18%), which fits the objective of producing early and high-yielding genotypes. Satisfactory results using this method were reported by several authors [38][39][40][41]. In total, four genotypes, namely G13, G18, G17, and G11, showed balanced and desirable genetic gains with equal efficiency to simultaneously improve all the traits [30].…”

Section: Multi-trait Index Approach (Fai-blups)mentioning

confidence: 81%

“…Consequently, early-flowering Pearl millet genotypes would score higher in arid regions, whereas late-flowering millet would adapt much better in humid ones [23,71,72]. These findings emphasize the need to select varieties within different precocity classes that are appropriate for different agroecological conditions [32,33,41,43]. Based on the heatmap analysis [68], the third subgroup of the second cluster was formed solely of genotypes with a relatively short cycle and high performance, namely, G3, G11, G13, G12, G17 and G18, which could be an interesting future prospect for Pearl millet breeders.…”

Section: Discussionmentioning

confidence: 92%

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Characterization and Trait Association Analysis of 27 Pearl Millet Landraces in Southern Tunisia

et al. 2023

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Pearl millet (Pennisetum glaucum (L.) R. Br., 2n = 2x = 14, Poaceae), is a cross-pollinated, warm-season crop grown worldwide. To select genotypes for breeding pearl millet cultivars that adapt to drought condition in southern Tunisia, we evaluated the grain yield (GY) and yield-related traits using a set of 27 landraces at two locations in southern Tunisia for two grown seasons (2019 and 2020). The genetic variability, phenotypic and genotypic association, and path coefficient (PC), based on grain yield (GY) and different yield-related agronomic traits, were evaluated. Analysis of variance and BLUPs value revealed a wide range of variability and the possibility of genetic selection for traits that are advantageous. Broad sense heritability (H) for all the traits ranged from 24.10% for grain yield (GY) to 57.11% for spike length (SL), indicating low to moderate inheritability. Genetic advance as a percentage of the mean (GAM) ranged from high (29.56%) for principal panicle weight (PPW) to moderate for all the traits except from plant high (PH) (7.31%). For all the traits, the phenotypic coefficient of variation (PCV) was higher than genotypic coefficient of variation (GCV), indicating the magnitude of environmental conditions. GY was significantly correlated with all the traits at the genotypic and phenotypic level. According to the path coefficient, the traits PPW and SL displayed the highest direct effects on GY. Heatmap analysis demonstrated a clear segregation between the early and late genotypes based on their geographic origin. Based on the cluster analysis and FAI-BLUPS analysis, genotypes G11, G13, G12, G17 and G18 were selected as the best-performing genotypes with the shortest cycle.

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Section: Multi-trait Index Approach (Fai-blups)mentioning

confidence: 65%

Section: Multi-trait Index Approach (Fai-blups)mentioning

confidence: 81%

Section: Discussionmentioning

confidence: 92%

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Characterization and Trait Association Analysis of 27 Pearl Millet Landraces in Southern Tunisia

et al. 2023

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“…O uso dos índices de seleção é uma estratégia particularmente vantajosa, já que permite a seleção de várias características de interesse agronômico de forma simultânea, contribuindo para o sucesso dos programas de melhoramento Woyann et al, 2020). Índices de seleção como o FAI-BLUP (Factor analysis and ideotype-design), proposto por e o MGIDI (Multitrait Genotype-Ideotype Distance Index) proposto por Olivoto e Nardino (2021) tem ganhado espaço nas pesquisas científicas Woyann et al, 2019;Kistner et al, 2022;Nardino et al, 2022;Silva Junior et al, 2022). Estes índices, além de realizar a seleção para múltiplas características de forma simultânea, lida bem com problemas relacionados à multicolinearidade.…”

Section: Introdução Geralunclassified

“…Posteriormente, é realizado a estimação da distância ideótipo-genótipo, possibilitando o ranqueamento dos genótipos. Muitos trabalhos têm sido realizados envolvendo este índice para seleção de genótipos em diversas culturas, como tomate , Soja (Woyann et al, 2019), Sorgo , milho (Kistner et al, 2022) e arroz (Silva Junior et al, 2022).…”

Section: Introductionunclassified

Estratégias e seleção de cultivares de soja em estádios iniciais para tolerância ao déficit hídrico

Duarte

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A soja é uma importante oleaginosa em todo o mundo e apresenta sensibilidade ao deficit hídrico, especialmente na fase inicial de desenvolvimento. Por isso, avaliar diferentes estratégias buscando selecionar cultivares de soja no início do desenvolvimento é extremamente necessário. Nesse sentido, são objetivos deste estudo: i) selecionar cultivares de soja tolerantes ao deficit hídrico utilizando os índices de seleção FAI-BLUP (Factor analysis and ideotype- design - Best Linear Unbiased Prediction) e o MGIDI (multi-trait genotype–ideotype distance index), além de indicar cultivares tolerantes ao deficit hídrico na fase inicial de desenvolvimento; ii) predizer o conteúdo de água nas folhas de soja e classificá-las quanto a condição hídrica, por meio de dados de espectroscopia NIR (near infrared) e diferentes modelos de machine learning. Dois experimentos foram avaliados envolvendo duas formas de imposição de estresse por deficit hídrico (em solo e em areia) em dois estádios diferentes (germinação e V1) e repetidos por duas épocas. Os experimentos envolveram 100 cultivares de soja, as quais foram submetidas a duas condições de disponibilidade hídrica (condição controle e condição estresse). Em ambos os experimentos, o estresse permaneceu por 20 dias. Os índices de seleção FAI-BLUP e MGIDI possibilitaram a seleção de 15 cultivares de soja. Foram selecionadas 12 cultivares comuns aos dois índices. As cultivares M 9144 RR, BMX TITAN RR foram as que mais se aproximaram do ideótipo. Em relação aos modelos de machine learning, todos os quatro modelos utilizados apresentaram boas performances ao realizar as tarefas de classificação e regressão. Os modelos PLS (Partial Least Squares) e SVM (Support Vector Machine) apresentaram os melhores resultados para classificar folhas de soja quanto à condição hídrica. Já para a tarefa de regressão, os modelos PLS e PCR (principal component regression) apresentaram os melhores desempenhos. Palavras-chave: Índices de seleção. Machine learning. Espectroscopia NIR. Seca.

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Detecting sources of resistance to multiple diseases in Argentine maize (Zea mays L.) germplasm

Cited by 2 publications

References 61 publications

Characterization and Trait Association Analysis of 27 Pearl Millet Landraces in Southern Tunisia

Characterization and Trait Association Analysis of 27 Pearl Millet Landraces in Southern Tunisia

Estratégias e seleção de cultivares de soja em estádios iniciais para tolerância ao déficit hídrico

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