Browsing by Author "Gwali, S."

Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    Effect of Soaking Treatment on Germination of Hard Coated Tropical Forest Tree Seeds
    (Uganda Journal of Agricultural Sciences, 2019) Odoi, J. B.; Mugeni, D.; Kiiza, R.; Apolot, B.; Gwali, S.
    Seed germination and seedling growth performance of Maesopsis eminii and Terminalia catappa under different water soaking treatments were evaluated for 120 days under nursery conditions. A total of 1400 seeds were pre-treated with hot (95oC) and cold water (ambient temperature) by soaking for 12, 24 and 48 hours with a control of no soaking. The seeds were sown directly into polythene pots filled with uniform growth medium (top forest soil, sand and clay soil mixed in a ratio of 5:3:2) to avoid disturbance of the root system after germination. The seeds were sown in a randomized block design with seven treatments and three replicates. Data were analysed using ANOVA in GenStat v18. Results indicated that soaking enhanced seed germination. Soaking of seeds in cold water for 12 hours resulted into higher germination (90% for Terminalia catappa and 85% for Maesopsis eminii) than the control (48%). Soaking period and water temperature significantly influenced seedling vigour (F value = 0.962; p = 0.038). Soaking seeds in cold water for 24 hours enhanced Maesopsis eminii seedling growth by 8.0 cm Terminalia catappa seedlings by 7.4 cm. Seed dormancy, germination percentage and growth performance in hard coated seeds such as Maesopsis eminii and Terminalia catappa can be broken by soaking in cold water for 12-24 hours. Pre-germination treatments significantly influences the germination and seedling growth.
  • Thumbnail Image
    Item
    Evaluation of Genomic Prediction Algorithms for Reducing Selection and Breeding Cycles in Shea Tree (Vitellaria Paradoxa)
    (Uganda Journal of Agricultural Sciences, 2021) Odoi, J. B.; Prasad, H.; Arfang, B.; Kitiyo, R.; Ozimati, A.; Gibson, P.; Edema, R.; Gwali, S.; Odong, T. L.
    The focus of this study was to determine the genomic prediction (GP) algorithms with the highest prediction accuracies for reducing the breeding and selection cycles in Vitellaria paradoxa. The efficiency of the GP algorithms were compared to evaluate five Shea tree growth traits in 708 genotypes with 30734 Single Nucleotide Polymorphic (SNPs) markers, which were reduced to 27063 after removing duplicates. Five hundred forty-nine (77.54%) Shea tree training population and 159 (22.46%) training population were genotyped for 30734 single nucleotide polymorphisms (SNPs) and phenotyped for five Shea tree growth traits. We built a model using phenotype and marker data from a training population by optimizing its genomic prediction accuracy for effectiveness of GS. The phenotype and marker data were used for cross validation of the prediction accuracies of the different models. Prediction accuracies varied among the genomic prediction algorithms based on the five phenotypic traits. We determined the best genomic algorithm that is more suitable for reduction of selection and breeding cycles in Vitellaria paradoxa. The GP algorithms were evaluated and we conclude that rrBLUP is the best for improving the prediction accuracy for reducing the breeding cycle in Shea tree.