Yield traits as selection indices in seedling populations of cassava

Ojulong, Henry; Labuschagne, Maryke; Herselman, L.; Fregene, Martin A.

doi:10.1590/s1984-70332010000300002

Cited by 23 publications

(20 citation statements)

References 10 publications

(21 reference statements)

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“…The high genetic correlation between foliar CBSD3s and CBSD6s in the current study implies the possibility of single and effective assessment of CBSD foliar symptoms at 6 MAP, permitting more efficient use of resources. phenotypic (r p = 0.56) and genetic correlation (r g = 0.41) between HI and root weight with the large number of clones evaluated in the present study agree with the results of previous studies conducted in other breeding populations (Ojulong et al, 2010;Akinbo et al, 2012), suggesting that HI could be used as a complementary trait for RTWT to select for fresh root yield, particularly at early stages of selection, when a large number of clones are evaluated in smaller plots. phenotypic (r p = 0.56) and genetic correlation (r g = 0.41) between HI and root weight with the large number of clones evaluated in the present study agree with the results of previous studies conducted in other breeding populations (Ojulong et al, 2010;Akinbo et al, 2012), suggesting that HI could be used as a complementary trait for RTWT to select for fresh root yield, particularly at early stages of selection, when a large number of clones are evaluated in smaller plots.…”

Section: Estimates Of Phenotypic and Genetic Correlations Among Traitssupporting

confidence: 91%

Genetic Variation and Trait Correlations in an East African Cassava Breeding Population for Genomic Selection

et al. 2019

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Cassava (Manihot esculenta Crantz) is a major source of dietary carbohydrates for >700 million people globally. However, its long breeding cycle has slowed the rate of genetic gain for target traits. This study aimed to asses genetic variation, the level of inbreeding, and trait correlations in genomic selection breeding cycles. We used phenotypic and genotypic data from the National Crops Resources Research Institute (NaCRRI) foundation population (Cycle 0, C0) and the progeny (Cycle 1, C1) derived from crosses of 100 selected C0 clones as progenitors, both to evaluate and optimize genomic selection. The highest broad‐sense heritability (H2 = 0.95) and narrow‐sense heritability (h2 = 0.81) were recorded for cassava mosaic disease severity and the lowest for root weight per plot (H2 = 0.06 and h2 = 0.00). We observed the highest genetic correlation (rg= 0.80) between cassava brown streak disease root incidence measured at seedling and clonal stages of evaluation, suggesting the usefulness of seedling data in predicting clonal performance for cassava brown streak root necrosis. Similarly, high genetic correlations were observed between cassava brown streak disease severity (rg= 0.83) scored at 3 and 6 mo after planting (MAP) and cassava mosaic disease, scored at 3 and 6 MAP (rg= 0.95), indicating that data obtained on these two diseases at 6 MAP would suffice. Population differentiation between C0 and C1 was not well defined, implying that the 100 selected progenitors of C1 captured the diversity in the C0. Overall, genetic gain for most traits were observed from C0 to C1.

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Section: Estimates Of Phenotypic and Genetic Correlations Among Traitssupporting

confidence: 91%

Genetic Variation and Trait Correlations in an East African Cassava Breeding Population for Genomic Selection

et al. 2019

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“…An important aspect of this trait is that even with accessions with high shoot production, group 7 also has a high YiComRoo, evidenced by its high harvest index (average of 65.47%). Using phenotypic means, Ojulong et al (2010) also observed high amplitude for this trait (5 to 90%). Moreover, considering the high inheritability of the HI trait, as well as its correlation with fresh root production, some authors have used the HI as an indirect measure of root yield in the early breeding stages (KAWANO et al, 1998).…”

Section: Resultsmentioning

confidence: 84%

“…Recently, Ojulong et al (2010) reported averages for DMC ranging from 16.3% to 69.1%. However, these values are related to phenotypic averages of segregating populations in the early improvement stages and must; therefore, be confirmed in further trials with the greatest number of plants per plot.…”

Section: Resultsmentioning

confidence: 99%

Non-hierarchical clustering ofManihot esculentaCrantz germplasm based on quantitative traits

Oliveira

Aud

Morales

et al. 2016

Revista Ciência Agronômica

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-The knowledge of the phenotypic variation of cassava (Manihot esculenta Crantz) germplasm allows the estimative of the genetic variability to support the selection of contrasting genitors. Therefore, the aim of this work was to define homogeneous groups of cassava germplasm based on yield traits, disease resistance and root quality using K-means as a nonhierarchical method. Breeding values estimated by Best Linear Unbiased Predictor (BLUP) were used for the cluster analysis. The number of groups was defined according to the stabilization of the smallest within-group sum of squares. Seventeen clusters were defined to represent the diversity of the germplasm, whose number of accessions ranged from 7 (Group 15) to 69 (Group 9). In general, accessions belonging to Groups 1, 4, 7, 12, 15 and 16 showed good agronomic traits, such as high fresh root yield and starch yield (> 60.7 t ha -1 and 18.6 t ha -1 , respectively). In contrast, only Group 15 presented low bacterial blight severity. The groups obtained showed strong differences, as evidenced by the within-groups sums of squares values, which ranged from 215.1 (Group 15) to 2,338.3 (Group 8). The K-means algorithm allowed the formation of consistent groups based on yield traits, disease resistance and root quality. Therefore, the K-means algorithm was efficient in the formation of groups with low within genotypic variation, especially concerning large amounts of data, such as in cassava germplasm banks.

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“…1988), using the supplementary genotype and environment proposed by Duarte and Vencovsky (1999), Pacheco et al (2005), which in this study will be called genotype and ideal environment. Multivariate procedures have been studied in cassava by Kvistichal et al (2006), Carvalho et al (2008 and Ojulong et al (2010).…”

Section: Methodsmentioning

confidence: 99%

Phenotypic stability in cassava estimated by the AMMI analysis with supplementary genotypes

Morais

Santiago

Cavalcante³

2017

Crop Breed. Appl. Biotechnol.

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Yield traits as selection indices in seedling populations of cassava

Abstract: -

Cited by 23 publications

References 10 publications

Genetic Variation and Trait Correlations in an East African Cassava Breeding Population for Genomic Selection

Genetic Variation and Trait Correlations in an East African Cassava Breeding Population for Genomic Selection

Non-hierarchical clustering ofManihot esculentaCrantz germplasm based on quantitative traits

Phenotypic stability in cassava estimated by the AMMI analysis with supplementary genotypes

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