Genetic progress of yield and its components of Uganda sorghum varieties through breeding
Rono, Evalyne Chepkoech
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Genetic progress and diversity of released sorghum varieties over the last 50 years in Uganda have not been assessed. This has led to limited information on the diversity, rate of genetic progress for grain yield and its components over time. Estimating the rate of genetic progress is necessary to determine whether the improvement rate will meet production demand. At the same time, molecular diversity would help to understand variation among the genotypes, which can help genotype improvement to buffer against seasonal fluctuations, emerging pests and diseases. A total of 38 SSR markers were used for diversity studies of the genotypes. Estimating the rate of genetic progress was done by the direct method, whereby thirty sorghum genotypes cultivated from 1960 to 2017 in Uganda by sorghum Breeding Programs were also used to study genetic progress in five environments. The experiments were replicated three times using an alpha lattice block design during the two cropping seasons of 2019. Molecular data from SSR markers were analysed using Power Marker V3.2, Mega-X, DARwin 6, and GenAlEx 6.5, while R Statistical software, XLSTAT 2020, and Genstat V18 were used for phenotypic analysis. Molecular diversity showed a PIC mean of 0.39 with the average allele number ranging from 2 to 5 and a mean gene diversity of 0.48, signifying the effectiveness of the markers in assessing the dissimilarity among the genotypes. In identifying dispersion of released varieties, principal co-ordinate analysis (PCoA) grouped the genotypes into 6 distinct groups while 8 clusters were formed using a dendrogram. Genetic progress for released varieties showed an increase of 23.61 kgha-1 per year for grain yield with a relative genetic gain of 32 %, implying substantial progress in improving yield traits over the period. ANOVA for 10 Agro-morphological traits showed G x E was significant (P<0.001), justifying the need to test GGE components using GGE biplot analysis to understand the effects. Genotype by environment interaction (37.6 %) contributed the largest variability observed while genotype and environment contributed 21.6 % and 16.7 %, respectively, implying environmental effects on the differences observed among the genotypes. Test environments formed 2 mega environments and the comparison biplot shows Apac 2019A as the ideal environment while AMMI2 showed that Narosorg2, Narosorg4, and MCK/UG/042 were the most stable genotypes. Pearson correlation showed that yield was positively correlated with panicle weight and 100 seed weight. PC1 to PC4 accounted for 79.7 % of the variation observed with the highest variation in seed weight and yield with eigenvalues of 0.67 and 0.65, respectively, suggesting seed weight trait can be used in yield improvement. From the studies, it is recommended that Narosorg2, Narosorg4 and Sila varieties which are high yielding and stable across the environments, can be used as parents for yield improvement studies. In contrast, MCK/UG/128 can be used as a check material for disease studies as it was susceptible to rust, turcicum and anthracnose diseases.