Heterosis and Combing Ability Studies for Yield and Yield  Component Traits in Sunflower (Helianthus annuus)

Application of machine learning in plant breeding is a recent concept, that has to be optimized for precise utilization in the breeding program of high yielding crop plants. Identification and efficient utilization of heterotic grouping pattern aided with machine learning approaches is of utmost importance in hybrid cultivar breeding as it can save time and resources required to breed a new plant hybrid/variety. In the present study, 109 genotypes of sunflower were investigated at morphological, biochemical (SDS-PAGE) and molecular levels (through micro-satellites (SSR) markers) for heterotic grouping. All the three datasets were combined, scaled, and subjected to unsupervised machine learning algorithms, i.e., Hierarchical clustering, K-means clustering and hybrid clustering algorithm (hierarchical + K-means) for assessment of efficiency and resolution power of these algorithms in practical plant breeding for heterotic grouping identification. Following the application of machine learning unsupervised clustering approach, two major groups were identified in the studied sunflower germplasm, and further classification revealed six smaller classes in each major group through hierarchical and hybrid clustering approach. Due to high resolution, obtained in hierarchical clustering, classification achieved through this algorithm was further used for selection of potential parents. One genotype from each smaller group was selected based on the maximum seed yield potential and hybridized in a line × tester mating design producing 36 F1 cross combinations. These F1s along with their parents were studied in open field conditions for validating the efficacy of identified heterotic groups in sunflowers genetic material under study. Data for 11 agronomic and qualitative traits were recorded. These 36 F1 combinations were tested for their combining ability (General/Specific), heterosis, genotypic and phenotypic correlation and path analysis. Results suggested that F1 hybrids performed better for all the traits under investigation than their respective parents. Findings of the study validated the use of machine learning approaches in practical plant breeding; however, more accurate and robust clustering algorithms need to be developed to handle the data noisiness of open field experiments.

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Heterosis in sunflower using cytoplasmic male sterility

Abdulhamed¹,

Abood

Noaman³

2021

IOP Conf. Ser.: Earth Environ. Sci.

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A field trial was carried in the University of Anbar Alternation location in Iraq, the longitude 440 and latitude 330. Eight cytoplasmic male sterile lines CMS, Female parents vis., A1, A3, A5, A11, and A21 and 3 tester restorers (Male parents which contains genes of fertility restoration and had mute- head (R-line) viz., (R1, R2, and R3) of sunflower were used in a Line x Tester mating system. In spring season 2018, to produce 15 hybrids. The genotypes seed 8 parents and 15 hybrids were planted in fall season 2018 using R.C.B.D. with three replicates. Analysis of variance revealed significant differences among the genotypes, parents, and hybrids in all studied characters. Parent A3 was disk area 194.31cm2, seed number of disk 934.40 seed and seed yield per disk 67.73 gm, while the parent A11 gave high value for weight 1000 seed 74.2 gm, while the tester R1 gave high value for most of the characters. Hybrid A3 x R3 gave high value for head area 249.13 cm2, for seed number of disk 1356.04 seed and seed yield per disk 94.71 gm, the hybrid A5 x R3 gave high heterosis for seed yield ratio 57.15%.

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Heterosis and Combing Ability Studies for Yield and Yield Component Traits in Sunflower (Helianthus annuus)

Cited by 5 publications

References 7 publications

Genetic and Molecular Technologies for Achieving High Productivity and Improved Quality in Sunflower

Genetic and Molecular Technologies for Achieving High Productivity and Improved Quality in Sunflower

Application of machine learning for identification of heterotic groups in sunflower through combined approach of phenotyping, genotyping and protein profiling

Heterosis in sunflower using cytoplasmic male sterility

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