Assessment of 60Co gamma radiation on early phenological stages of two generations of OFADA rice

Traditional Ofada rice varieties from South-West, Nigeria is preferred for its unique taste, aroma and massive potential for export but has low yield. Based on this background, two Ofada rice varieties, FUNAABOR 1 and FUNAABOR 2 were irradiated to create genetic variability as it affects vegetative traits. Seeds from the varieties were exposed to nine levels of 60Co gamma irradiation (0, 50, 100, 150, 200, 250, 300, 350 and 400 Gy). The seeds were nursed for 30 days before M1 seedlings were transplanted into a well tilled soil in a two factorial RCBD with three replicates. Selections from M1 plants were used to establish M2 plants generation. The results revealed diverse effects of 60Co gamma irradiation treatments on different plant vegetative traits. The establishment rates of M1 Ofada rice population were unaffected (p > 0.01) by increasing gamma irradiation from 0 to 300 Gy but decreased at 350 Gy. Above 300 Gy, tiller numbers, plant height, lodging incidence, leaf number, leaf length and leaf angle decreased significantly when compared with control (p < 0.01) in both generations (M1 and M2). Moderately tillered (10 tillers), tall plant (116.9 cm) obtained from 350 dosage rate recorded highest grain weight of 7.8 g per panicle. High phenotypic coefficient of variation (PCV) and genotypic coefficient of variation (GCV) promoted by the irradiation dosages in M1 selection indicate the extent of environmental influence. High broad sense heritability observed from leaf number, leaf angle, leaf length, leaf blade colour, basal leaf sheath colour and grain weight per panicle shows possibility of rapid genetic improvement of these characters through selection

RELATIVE DISCRIMINATING POWERS OF GGE AND AMMI MODELS IN THE SELECTION OF TROPICAL SOYBEAN GENOTYPES

Selection of crops is preceded by multi-locational testing in plant breeding; however, it becomes difficult for breeders to determine which genotypes should be selected in the presence of genotype by environment (GEI). Six genotypes of soybean ( Glycine max (L.) Merr.) were evaluated at ten locations in Nigeria for grain yield and stability. The analysis of variance revealed significant (P 640.05) GEI effect. Mean grain yield of the soybean genotypes ranged from 1148 kg ha-1 for genotype M351 to 1584 kg ha-1 for TGx 1448-2E. Ilorin in the southern guinea savanna of Nigeria was the most variable with high interaction principal component axes (IPCA); while Bauch in the northern guinea savanna was identified as more stable location in evaluating the soybean genotype. Mega-environments and the best yielding soybean genotypes in each mega-environment were revealed by the GGE biplot analysis. Furthermore, TGx 1448-2E and TGx 1440-1E, were established as the most promising, and stable genotypes across the test locations. Stability model of GGE biplot was superior, effective and informative in mega-environment analysis compared to AMMI analysis.La s\ue9lection des cultures est pr\ue9c\ue9d\ue9e de tests multilocaux en am\ue9lioration des plantes; cependant, il appara\ueet difficile pour les am\ue9liorateurs de d\ue9terminer quels types de g\ue9notypes s\ue9lectionner en pr\ue9sence du g\ue9notype x environnement (GEI). Six g\ue9notypes du Soja ( Glycine max (L.) Merr.) \ue9taient \ue9valu\ue9s dans dix milieux au Nigeria pour le rendement en grains et la stabilit\ue9. L\u2019analyse de la variance a r\ue9v\ue9l\ue9 un effet significatif (P 64 0.05) du GEI. Le rendement moyen en grains des g\ue9notypes du soja variait de 1148 kg ha-1 pour le g\ue9notype M351 \ue0 1584 kg ha-1 pour TGx 1448-2E. Ilorin au sud de la savanne guin\ue9enne au Nigeria \ue9tait le plus variable avec une interaction \ue9lev\ue9e des axes de la composante principale (IPCA); pendant que Bauch dans le nord de la savanne guinn\ue9enne \ue9tait identifi\ue9 comme milieu le plus stable dans l\u2019\ue9valuation du g\ue9notype du soja. Les Mega-environments et le meilleur g\ue9notype du soja du point de vue rendement dans chaque mega-environment \ue9taient r\ue9v\ue9l\ue9s par l\u2019analyse du biplot. En plus, TGx 1448-2E et TGx 1440-1E, \ue9taient jug\ue9s les plus promettants et g\ue9notypes stables \ue0 travers le test de milieu. Le mod\ue8le de stabilit\ue9 du biplot GGE \ue9tait sup\ue9rieur, effective et informative dans l\u2019analyse m\ue9ga-environmentale en comparaison avec l\u2019analyse du AMMI

The evolving SARS-CoV-2 epidemic in Africa: Insights from rapidly expanding genomic surveillance

INTRODUCTION Investment in Africa over the past year with regard to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) sequencing has led to a massive increase in the number of sequences, which, to date, exceeds 100,000 sequences generated to track the pandemic on the continent. These sequences have profoundly affected how public health officials in Africa have navigated the COVID-19 pandemic. RATIONALE We demonstrate how the first 100,000 SARS-CoV-2 sequences from Africa have helped monitor the epidemic on the continent, how genomic surveillance expanded over the course of the pandemic, and how we adapted our sequencing methods to deal with an evolving virus. Finally, we also examine how viral lineages have spread across the continent in a phylogeographic framework to gain insights into the underlying temporal and spatial transmission dynamics for several variants of concern (VOCs). RESULTS Our results indicate that the number of countries in Africa that can sequence the virus within their own borders is growing and that this is coupled with a shorter turnaround time from the time of sampling to sequence submission. Ongoing evolution necessitated the continual updating of primer sets, and, as a result, eight primer sets were designed in tandem with viral evolution and used to ensure effective sequencing of the virus. The pandemic unfolded through multiple waves of infection that were each driven by distinct genetic lineages, with B.1-like ancestral strains associated with the first pandemic wave of infections in 2020. Successive waves on the continent were fueled by different VOCs, with Alpha and Beta cocirculating in distinct spatial patterns during the second wave and Delta and Omicron affecting the whole continent during the third and fourth waves, respectively. Phylogeographic reconstruction points toward distinct differences in viral importation and exportation patterns associated with the Alpha, Beta, Delta, and Omicron variants and subvariants, when considering both Africa versus the rest of the world and viral dissemination within the continent. Our epidemiological and phylogenetic inferences therefore underscore the heterogeneous nature of the pandemic on the continent and highlight key insights and challenges, for instance, recognizing the limitations of low testing proportions. We also highlight the early warning capacity that genomic surveillance in Africa has had for the rest of the world with the detection of new lineages and variants, the most recent being the characterization of various Omicron subvariants. CONCLUSION Sustained investment for diagnostics and genomic surveillance in Africa is needed as the virus continues to evolve. This is important not only to help combat SARS-CoV-2 on the continent but also because it can be used as a platform to help address the many emerging and reemerging infectious disease threats in Africa. In particular, capacity building for local sequencing within countries or within the continent should be prioritized because this is generally associated with shorter turnaround times, providing the most benefit to local public health authorities tasked with pandemic response and mitigation and allowing for the fastest reaction to localized outbreaks. These investments are crucial for pandemic preparedness and response and will serve the health of the continent well into the 21st century

RELATIVE DISCRIMINATING POWERS OF GGE AND AMMI MODELS IN THE SELECTION OF TROPICAL SOYBEAN GENOTYPES

Selection of crops is preceded by multi-locational testing in plant breeding; however, it becomes difficult for breeders to determine which genotypes should be selected in the presence of genotype by environment (GEI). Six genotypes of soybean ( Glycine max (L.) Merr.) were evaluated at ten locations in Nigeria for grain yield and stability. The analysis of variance revealed significant (P ≤0.05) GEI effect. Mean grain yield of the soybean genotypes ranged from 1148 kg ha-1 for genotype M351 to 1584 kg ha-1 for TGx 1448-2E. Ilorin in the southern guinea savanna of Nigeria was the most variable with high interaction principal component axes (IPCA); while Bauch in the northern guinea savanna was identified as more stable location in evaluating the soybean genotype. Mega-environments and the best yielding soybean genotypes in each mega-environment were revealed by the GGE biplot analysis. Furthermore, TGx 1448-2E and TGx 1440-1E, were established as the most promising, and stable genotypes across the test locations. Stability model of GGE biplot was superior, effective and informative in mega-environment analysis compared to AMMI analysis.La sélection des cultures est précédée de tests multilocaux en amélioration des plantes; cependant, il apparaît difficile pour les améliorateurs de déterminer quels types de génotypes sélectionner en présence du génotype x environnement (GEI). Six génotypes du Soja ( Glycine max (L.) Merr.) étaient évalués dans dix milieux au Nigeria pour le rendement en grains et la stabilité. L’analyse de la variance a révélé un effet significatif (P ≤ 0.05) du GEI. Le rendement moyen en grains des génotypes du soja variait de 1148 kg ha-1 pour le génotype M351 à 1584 kg ha-1 pour TGx 1448-2E. Ilorin au sud de la savanne guinéenne au Nigeria était le plus variable avec une interaction élevée des axes de la composante principale (IPCA); pendant que Bauch dans le nord de la savanne guinnéenne était identifié comme milieu le plus stable dans l’évaluation du génotype du soja. Les Mega-environments et le meilleur génotype du soja du point de vue rendement dans chaque mega-environment étaient révélés par l’analyse du biplot. En plus, TGx 1448-2E et TGx 1440-1E, étaient jugés les plus promettants et génotypes stables à travers le test de milieu. Le modèle de stabilité du biplot GGE était supérieur, effective et informative dans l’analyse méga-environmentale en comparaison avec l’analyse du AMMI