Browsing by Author "Raji, A. O."

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Effects of particle size, stem component and genotype on absorbency of kenaf (hibiscus cannabinus L.) grown in Nigeria for oil spill clean-up
(Scientific Research Publishing Inc., 2016) Balogun, M. O.; Raji, A. O.
The efficacies of different products made from different parts of the stem of different varieties of kenaf grown in Nigeria as oil absorbents were tested. Particle sizes, genotypes and whether mixed or sole stem core or bast differed among the treatments. The fibre component sank in water, and so was not buoyant; the core was partially buoyant while the mixed fibre and core was completely buoyant. Sorption capacity was the highest in the ground whole stem (8.16 g oil/g sorbent), which also retained (6.31 g oil/g sorbent) the highest quantity of oil. Sorption and retention of oil were the lowest in the fibre (1.66 and 0.69 g oil/g sorbent, respectively). After the first use, only the ground stem can be used to re-absorb up to 1.97 g oil/g sorbent of used sorbent. A mixture of genotypes was more absorbent than a single genotype. The implications of these findings in absorbency research are discussed.
Evaluation of three methods for estimating leaf area index of cowpea (Vigna unguiculata)
(Kuwait University, Faculty of Engineering and Petroleum, 2011-03) Ewemoje, T. A.; Raji, A. O.
Leaf Area Index (LAI) is a concept that cuts across agricultural sciences and agricultural engineering with an encapsulating feature in environmental engineering. It is one of the most difficult to quantify properly owing to large spatial and temporal variability. This paper discusses briefly LAI and the use of three methods which are non-destructive in determination of its value for cowpea, namely: the empirical formulae, the graphical and the image processing methods. Cowpea seeds were planted and samples were marked for determination of LAI by the three methods. The results showed that image processing as a more accurate and promising method compared to the other two
Hydrological responses to climate and land use changes: the paradox of regional and local climate effect in the Pra River Basin of Ghana
(Elsevier Ltd., 2020) Bessah, E.; Raji, A. O.; Taiwo, O. J.; Agodzo, S. K.; Ololade, O. O.; Strapasson, A.
Study Region: Pra River Basin, Ghana. Study Focus: The study modelled the changes in water yield using regional, sub-regional and local climate conditions from modelling outputs at spatial resolutions of 44 km, 12 km and 0.002 km respectively to drive the Integrated Valuation of Ecosystem Services and Trade-offs model at three time periods of land use land cover (LULC). Changes in historical water yield (simulated for 1986, 2002 & 2018 LULC using the mean climatic parameters from 1981-2010) and future scenario (simulated for 2018 LULC using the mean climatic parameters from 2020-2049) for annual, seasonal and monthly periods were assessed. New Hydrological Insights for the Region: The results show that future annual water yield could change by -46%, -48%, +44% and -35% under the regional, sub-regional, local and ensemble mean of the climate scenarios respectively. Seasonal water yield from the ensemble mean of the future climate scenario was projected to decrease between 2-16 mm, with a mean decrease of 33.39% during the December–February season. There was no directional effect of spatial resolution on water yield. The future period could be impacted by both drought and flood. We recommend that re/afforestation should be encouraged to improve infiltration and reduce deforestation which was 2.27% per annum in the assessed period to prevent flood causing runoffs, while irrigation technology will help to improve resilience to drought.
The impact of varying spatial resolution of climate models on future rainfall simulations in the Pra River Basin (Ghana)
(IWA Publishing, 2020) Bessah, E.; Raji, A. O.; Taiwo, O. J.; Agodzo, S. K.; Ololade, O. O.
This work compares future projections of rainfall over the Pra River Basin (Ghana) using data from five climate models for the period 2020–2049, as referenced to the control period 1981–2010. Bias-correction methods were applied where necessary and models’ performances were evaluated with Nash–Sutcliffe Efficiency, root-mean-square error and coefficient of determination. Standardised Anomaly Index (SAI) was used to determine variability. The onset and cessation dates and length of the rainy season were determined by modifying the Walter–Olaniran method. The ensemble means of the models projected a 1.77% decrease in rainfall. The SAI showed that there would be drier than normal years with the likelihood of drought occurrence in 2021, 2023, 2031 and 2036. The findings showed that high-resolution models ( 25 km) were more capable of simulating rainfall at the basin scale than mid-resolution models (26–150 km) and projected a 20.13% increase. Therefore, the rainfall amount is expected to increase in the future. However, the projected increase in the length of the dry season by the ensemble of the models suggested that alternative sources of water would be necessary to supplement rainfed crop production for food security.
Variable resolution modeling of near future mean temperature changes in the dry sub-humid region of Ghana
(Springer, 2018-05) Bessah, E.; Raji, A. O.; Taiwo, O. J.; Agodzo, S. K.; Ololade, O. O.
The study used two models from Rossby Centre Regional Atmospheric Model (RCA4) and two from Weather Research and Forecasting Model (WRF) plus the Statistical Downscaling Model—Decision Centric (SDSM-DC) at 44 km, 12 km and 2 m resolution respectively to project the impact of climate change on mean temperature in the Pra River Basin for the period 2020–2049. Results showed that the minimum temperature increased (+ 1.47 °C) faster than the increase (+ 1.11 °C) in maximum temperature for observed period 1981–2010. An evaluation of the performance of the models with time-series based metrics showed that SDSM-DC and RCA4 are better for projecting mean temperature in the study area compared to WRF despite its resolution. Analysis of variance (p < 0.05) indicated significant difference between the projected mean temperature of the five models but there was no significant difference between SDSM-DC and RCA4 models. Correlation between models was highest at R = 0.727 between SDSM-DC and RCA4. The years 2041, 2042 and 2047 were projected as hottest by minimum two different models. The mean temperature change was projected at + 1.36, + 1.42 and + 1.12 °C by SDSM-DC, RCA4 and WRF respectively. The ensemble of projection depicted same trend of February—April as the high mean temperature and July—September as the lowest as was for the observed period. However, January is projected to have the highest change in mean temperature of + 1.51 °C. The maximum temperature for observed period was found to be the mean temperature in the period 2020–2049. Future study will focus on the impact of projected temperature change on ecosystem services delivery in the region.