Browsing by Author "Asare, P.A."

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Identifying key contributing root system traits to genetic diversity in field-grown cowpea (Vigna unguiculata L. Walp.) genotypes
(Field Crops Research, 2019-02) Adu, M.O.; Asare, P.A.; Yawson, D.O.; Dzidzienyo, D.K.; Nyadanu, D.; Asare-Bediako, E.; Afutu, E.; Tachie-Menson, J.W.; Amoah, M.N.
Cowpea is a grain legume that is grown extensively as an alternate protein and income source for many smallholder farmers. Characterising cowpea root phenotypes could provide the basis for developing genotypes with root system architecture (RSA) traits that increase soil resource acquisition. Measuring RSA traits of any field crop is a demanding task, in terms of expediency, time, cost, and competence. Targeted root phenotyping strategies focusing on a few relevant traits might aid in side-stepping some of the challenges associated with phenotyping roots of field crops. The objectives of this study were to (i) measure genotypic variation for RSA and shoot traits of cowpea genotypes; and (ii) identify candidate variables and genotypes that contribute the largest share of variance. Sixty cowpea accession were grown in field trials at the University of Cape Coast, Ghana. Seventy variables, mostly quantitative RSA traits were measured. Multivariate analysis was used to determine major traits contributing to variation. There were significant differences (P < 0.05) for the majority of traits evaluated. Fifty-nine traits had coefficients of variation of ≥0.3 among genotypes and were selected for further examination. Broad-sense heritability (H2) estimates were generally intermediate to high and ranged from 0.11 to 0.96. The Shannon-Weaver diversity index (H’) was variable among traits and ranged from 0.00 to 0.88. Shoot and root biomass correlated closely and positively with count- and length- and diameter-related traits. Cluster analysis identified three homogeneous genotype groups and identified groups of cowpea genotypes that could be exploited in breeding programs to improve the genetic basis of root traits. The first nine principal components explained over 74% of total genotypic variation for the twenty-nine traits included in the PCA. Sixteen traits contributed more than other traits to the variability in PC1 and PC2. Soil and root tissue angle-related traits, shoot and root diameter-related traits, root biomass, hypocotyl root length, root count and lateral root density -related traits were among the top 50% of the most important traits contributing to variation and thus warrant consideration in efforts to breed for improved genotypes in cowpea. The workflow presented offers a robust, cost-effective and simple approach to identifying focal root traits that contribute to diversity in grain legumes. The results could potentially facilitate the characterization of specific traits suitable for targeted genotype selection and breeding of new cowpea varieties for efficient RSAs.
The Impact of Energy Sector Debt on Banks’ Total Liabilities to Total Asset Ratio in Ghana
(University of Ghana, 2019-07) Asare, P.A.
The energy sector has experienced tremendous growth in the past year in line with rapid population growth in the country. The increase in demand sometimes leads to the inability of the State Owned Enterprises (SOEs) to meet the user's needs and therefore increases its operational cost. They, therefore, borrow from both internal and external sources to finance its operations. Unfortunately, there are less empirical studies that show the impact of energy sector debts on the balance sheet of commercial banks. This study sought to address these challenges by examining if borrowings from these energy sector SOEs contribute significantly to the challenges being faced by the banks. In addition, the study investigates the factors that increase the liability ratio of banks and the relationship between energy sector loans from commercial banks and the liability ratio of the banks. The researcher used secondary data from annual financial reports of twelve (12) commercial banks. The study also gathered energy sector borrowings from the SOEs using their annual reports. The paper used Panel Data Regression analysis to determine the impact of energy sector debt on banks’ liability ratio and other factors that affected it. The major findings attained from the regression analysis indicate that energy sector loans do not have any significant effect on solvency ratio whereas the capital adequacy ratio had a positive effect on the solvency ratio of banks. Moreover, the empirical analysis further suggest that the size of banks was found to substantially affect the solvency ratio positively despite the fact that non-performing loans were found not to significantly affect the solvency rati