Seleção de genótipos resistentes de amendoinzeiro a Anticarsia gemmatalis Hübner (Lepidoptera: Noctuidae) com base em análises multivariadas

Pitta, Rafael Major; Boiça, Arlindo Leal; Jesus, Flávio Gonçalves de; Tagliari, Sônia Regina Alves

doi:10.1590/s1519-566x2010000200018

Cited by 9 publications

(9 citation statements)

References 3 publications

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“…The combination of the linear relationship between the traits and the distribution of genotypes according to the projection of the vectors of the traits in the same analysis can be considered comprehensive and thorough analysis. Dallastra et al (2014), in a study on the selection of soybean genotypes carriers of the RR gene, and Pitta et al (2010), in a study on the selection of genotypes of groundnut resistant to Anticarsia gemmatalis, noted that principal componente analysis was efficient in the process of selection, leading to genetic gains for agronomic traits of interest, which is an advantageous and appropriate situation to identify superior genotypes. DS = differential of selection; MSG = Average of selected genotypes; MG 0 = overall average of genotypes; PHF = plant height at flowering (cm); PHM = plant height at maturity (cm); HIP = height at insertion of the first pod (cm); GP = grain productivity (PG) in kg ha -1 ; NN = number of nodes; NP = number of pods per plant.…”

Section: Resultsmentioning

confidence: 99%

“…Several multivariate methods can be applied to selection of superior genotypes, allowing also to observe the influence of agronomic traits in the selection of genotypes and the relationships between them. We emphasize the use of principal component analysis can be used in experimental data based on the average of genotypes for each trait, as used by Dallastra et al (2014); Viana et al (2013); Pitta et al (2010).…”

Section: Introductionmentioning

confidence: 99%

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Multivariate Exploratory Approach and Influence of Six Agronomic Traits on Soybean Genotypes Selection

Leite

Pavan²,

Neto

et al. 2016

NATIVA

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A comprehensive analysis for selection of superior genotypes can be useful and necessary. The aim of this study was to select soybean genotypes with superior agronomic traits through the use of multivariate exploratory analysis and to identify those traits that are most influent over grain productivity and selection. We evaluated 29 soybean genotypes in a randomized block design with three replications. Agronomic traits analyzed were: plant height at flowering (PHF), plant height at maturity (PHM), height at insertion of the first pod (HIP), grain productivity (GP), number of nodes (NN) and number of pods (NP). Data were submitted to principal component analysis. Two eigenvalues explained 68.17% of the variance contained in the original information, generating two components with relevant amount of information. These were characterized by the traits PHF, PHM, HIP, NP and GP which allowed to discriminate and select 6 genotypes with good agronomic traits with emphasis on grain productivity. Direct (positive) relationships were observed between GP and the traits PHF, PHM, HIP and NP, as these were observed to influence the distribution of genotypes with greater GP in the two-dimensional plane. The traits that were more related and favorably influencing GP of selected genotypes were plant height at maturity and number of pods. Keywords:Glycine max, principal components, genetic improvement, trait selection, grain productivity.Abordagem exploratória multivariada e influência de seis caracteres agronômicos na seleção de genótipos de soja RESUMO: Uma análise mais abrangente para seleção de genótipos superiores pode ser útil e necessária. O objetivo do presente estudo foi selecionar genótipos de soja com caracteres agronômicos superiores por meio de análise exploratória multivariada, e identificar aqueles caracteres que mais influenciam na produtividade de grãos e na seleção. Para tanto, foram avaliados 29 genótipos de soja, conduzidos no delineamento de blocos ao acaso, com três repetições. Os caracteres agronômicos avaliados foram: altura da planta no florescimento (APF) e na maturidade (APM), altura de inserção da primeira vagem (AIV), produtividade de grãos (PG), número de nós (NN) e número de vagens (NV). Os dados foram submetidos à análise de componentes principais. Na análise de componentes principais dois autovalores explicaram 68,17% da variância contida nas informações originais, gerando dois componentes com quantidades de informações relevantes, sendo caracterizados pelos caracteres APF, APM, AIV, NV e PG que permitiram discriminar e selecionar 6 genótipos com bons atributos agronômicos com ênfase em produtividade de grãos. Relações diretas (positivas) foram observadas entre PG com os caracteres APF, APM, AIV e NV, pois estes tiveram influência na distribuição dos genótipos com PG superior no plano bidimensional. Os caracteres que mais relacionaram e influenciaram favoravelmente a PG dos genótipos selecionados foram altura da planta na maturidade e número de vagens. Palavras-chave:Glycine max, compo...

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multivariate Exploratory Approach and Influence of Six Agronomic Traits on Soybean Genotypes Selection

Leite

Pavan²,

Neto

et al. 2016

NATIVA

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“…In a study with multivariate analysis for resistant peanut genotypes selection, Pitta et al (2010) concluded that Ward and K-means clustering methods were efficient and complementary to the principal components analysis, also presented in this study. Due to the presented variability within the accessions, the generated dendrogram did not reveal a pattern with similar geographic regions, and it is explained by the fact that each group brings together different accessions within it.…”

Section: Resultsmentioning

confidence: 65%

Phenotypic diversity in physic nut (Jatropha curcas L.) in vivo germplasm bank for superior parent selection

Moraes¹,

Silva²,

CorrÃaa³

et al. 2017

Afr. J. Biotechnol.

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Jatropha curcas is an interesting alternative for biodiesel production due to the high oil content in its seeds, its ability to grow in a wide range of climate and soil conditions as well as low cost of production. However, the species is considered to be in domestication and there are no defined cultivars. Therefore, it is extremely important to understand the genetic diversity of the species for selection and characterization of promising genotypes to initiate breeding programs. The objective of this study is to evaluate the phenotypic diversity of physic nut in order to select the most divergent and superior genotypes to compose future breeding programs, using multivariate analysis. Eleven agronomic characters were evaluated in 165 J. curcas genotypes from the in vivo germplasm bank, which were: Plant height, stem diameter, number of primary branches, fruit length, width, weight and shape, seed length, width and weight plus the oil content. The data were analyzed by principal component analysis (PCA), cluster analysis by Ward and k-means methods. The character fruit shape was removed from the multivariate analysis as the only one with qualitative character. The PCA resulted in 4 main components (PC), which explained 71.62% of total variance. The characters selected in PC1 were seed weight, fruit width, fruit length and fruit weight. There were 22 promising genotypes highlighted, with potential to be exploited in breeding programs. Cluster analysis by Ward and k-means methods generated 9 groups influenced by all analyzed characters, of which five groups of genotypes had advantageous characters. Regarding fruit shape, 13 genotypes had an ellipsoid lanceolate shape and the others had an ellipsoid spherical shape. Multivariate analyses allowed genotype characterization, indicating good strategies used for the selection in genetic breeding programs.

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“…These methods are more suitable for assessing genetic divergence because it allow a more holistic interpretation of data, once consider the potential of the random trait sets, setting them at the same level of importance (Cruz and Regazzi, 2014). Among them, clustering and graphic dispersion techniques have been widely adopted by breeders (Pitta et al, 2010;Cruz and Regazzi, 2014).…”

Section: Introductionmentioning

confidence: 99%

“…The methods of graphic dispersion more often used are Principal Components (PC) and Canonical Variables (CV) analyses, that are linear combinations of the original quantitative measurements that contain the highest possible multiple correlation with each group and that best summarize among-class variation (Pitta et al, 2010;Cruz and Regazzi, 2014). To plant breeder, CV are more contributive because it allows the interpretation of the data with repetition, taking into account the residual covariance between the means of genotypes (Oliveira et al, 2003).…”

Section: Introductionmentioning

confidence: 99%

Assessment of genetic divergence in runner peanut genotypes grown in the Brazilian Northeast environments

Ricardo¹,

Jean²,

Lucas³

et al. 2016

Afr. J. Agric. Res.

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Runner cultivars are widely demanded by peanut farmers because of their high oil and grain productions. As those are late cycles, the management is limited in environments with water restriction. For improvement of runner genotypes to these environments is necessary to identify genotypes adapted and the knowledge of genetic divergence is quite relevant to assist the breeding procedures. In this work, ten morphological and agronomic traits were measured on 13 runner peanut genotypes grown in sandy loam textured soils during the rainy season in three environments on Brazilian Northeast region. These measurements included harvest index (%), main stem height (cm), 100 seed weight, 100 pod weight, pod length (mm), number of pod/plant, blooming (days after emergence, dae), number of seed/plant, physiological maturation of pods (dae) and oil content in seeds (%). The genetic divergence of genotypes was estimated by multivariate methods. Data set was analyzed by canonical variable (CV) in combination with UPGMA-clustering analyses. The statistical analysis was performed using the GENES program. We found that the first two CV were significant and accounted for 82.13% of the total variation. Three groups were clearly formed, separated by earliness and pod production. This arrangement was further attested by the dendrogram generated by UPGMA. The CV indicated that physiological maturation of pods, main stem height, number of pods/plant and 100 seed weight were the most differentiating traits among the genotypes. These two last allowed high magnitude associations and were contributive to selection procedures in improvement works. We provide indications of the best genotypes with potential to generate robust progenies, in a peanut breeding program.

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Seleção de genótipos resistentes de amendoinzeiro a Anticarsia gemmatalis Hübner (Lepidoptera: Noctuidae) com base em análises multivariadas

Cited by 9 publications

References 3 publications

Multivariate Exploratory Approach and Influence of Six Agronomic Traits on Soybean Genotypes Selection

Multivariate Exploratory Approach and Influence of Six Agronomic Traits on Soybean Genotypes Selection

Phenotypic diversity in physic nut (Jatropha curcas L.) in vivo germplasm bank for superior parent selection

Assessment of genetic divergence in runner peanut genotypes grown in the Brazilian Northeast environments

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