FUNGAL AND BACTERIAL PATHOGENS ASSOCIATED WITH SOFT ROT DISEASE OF SWEET POTATO (Ipomoea batatas, L. Lam)

This study focused on screening for fungal and bacterial pathogens associated with soft rot disease of sweetpotato tuber. Standard microbiological techniques were used to characterize and identify the fungi and bacteriaisolated. The fungal and bacterial isolates were subjected to pathogenicity test to ascertain their degree ofpathogenicity. Aspergillus flavus, Botryodiplodia theobromae, Fusarium oxysporium and Erwinia spp inducedrots in the healthy sweet potato tubers at significant levels, with rot length ranging from 12.40 ±0.125mm to15.25+0.135mm; an indication of being pathogenic. Rhizopus stolonifer, Bacillus spp and Corynebacteriumspp with rot length 7.63+0.250mm, 4.63+0.145mm, and 4.50+0.157mm respectively, were not pathogenic. Thefungal isolates pre-dominated the bacterial isolates as pathogens associated with soft rot disease in the sweetpotato tubers. This study therefore provide a benchmark upon which future research can be carried out in thequest to reduce post-harvest loss of sweet potato tubers

Comparative Analysis of Predictive Data-Mining Techniques

This thesis compares five different predictive data-mining techniques (four linear techniques and one nonlinear technique) on four different and unique data sets: the Boston Housing data sets, a collinear data set (called “the COL” data set in this thesis), an airliner data set (called “the Airliner” data in this thesis) and a simulated data set (called “the Simulated” data in this thesis). These data are unique, having a combination of the following characteristics: few predictor variables, many predictor variables, highly collinear variables, very redundant variables and presence of outliers. The natures of these data sets are explored and their unique qualities defined. This is called data pre-processing and preparation. To a large extent, this data processing helps the miner/analyst to make a choice of the predictive technique to apply. The big problem is how to reduce these variables to a minimal number that can completely predict the response variable. Different data-mining techniques, including multiple linear regression MLR, based on the ordinary least-square approach; principal component regression (PCR), an unsupervised technique based on the principal component analysis; ridge regression, which uses the regularization coefficient (a smoothing technique); the Partial Least Squares (PLS, a supervised technique), and the Nonlinear Partial Least Squares (NLPLS), which uses some neural network functions to map nonlinearity into models, were applied to each of the data sets. Each technique has different methods of usage; these different methods were used on each data set first and the best method in each technique was noted and used for global comparison with other techniques for the same data set. Based on the five model adequacy measuring criteria used, the PLS outperformed all the other techniques for the Boston housing data set. It used only the first nine factors and gave an MSE of 21.1395, a condition number less than 29, and a modified coefficient of efficiency, E-mod, of 0.4408. The closest models to this are the models built with all the variables in MLR, all PCs in PCR, and all factors in PLS. Using only the mean absolute error (MAE), the ridge regression with a regularization parameter of 1 outperformed all other models, but the condition number (CN) of the PLS (nine factors) was better. With the COL data, which is highly collinear data set, the best model, based on the condition number (\u3c100) and MSE (57.8274) was the PLS with two factors. If the selection is based on the MSE only, the ridge regression with an alpha value of 3.08 would be the best because it gave an MSE of 31.8292. The NLPLS was not considered even though it gave an MSE of 22.7552 because NLPLS mapped nonlinearity into the model and in this case, the solution was not stable. With the Airliner data set, which is also a highly ill-conditioned data set with redundant input variables, the ridge regression with regularization coefficient of 6.65 outperformed all the other models (with an MSE of 2.874 and condition number of 61.8195). This gave a good compromise between smoothing and bias. The lease MSE and MAE were recorded in PLS (all factors), PCR (all PCs), and MLR (all variables), but the condition numbers were far above 100. For the Simulated data set, the best model was the optimal PLS (eight factors) model with an MSE of 0.0601, an MAE of 0.1942 and a condition number of 12.2668. The MSE and MAE were the same for the PCR model built with PCs that accounted for 90% of the variation in the data, but the condition numbers were all more than 1000. The PLS, in most cases, gave better models both in the case of ill-conditioned data sets and also for data sets with redundant input variables. The principal component regression and the ridge regression, which are methods that basically deal with the highly ill-conditioned data matrix, performed well also in those data sets that were ill-conditioned

Appraisal of the Effect of Savings on Stock Market Development in Nigeria

Mobilization of savings has been regarded as a veritable factor in stock market development and a crucial variable for development economists.  The attainment of efficient developed stock market as a stimulus to economic growth in Nigeria may lack adequate viability without desired level of savings. The objective of this study was to empirically appraise the impact of savings on the Nigerian stock market development from 2001 to 2010. Data for the study were gathered from central bank of Nigeria statistical bulletin. From Ordinary Least Square (OLS) regression model, findings show that savings has significant and positive impact on stock market development in Nigeria. Keywords: savings, stock market, developmen

Conventional Methods of Controlling Microbial Contaminants in Meristematic Tissue Cultures: A Review

Microbial contaminants in meristematic tissue cultures remain a big problem in the quest to grow plants in vitro in the laboratory prior to commercial scale roll out. The ubiquitous nature and the ability to compete favourably with explants for the same nutrient in the growth medium make these contaminants a serious threat in meristematic tissue cultures. The common microbial contaminants frequently reported in in vitro meristematic tissue cultures are endophytes such as bacteria, fungi and sometimes viruses. Most of the epiphytic microbes are usually removed by surface sterilization but the endophytes may persist to contaminate the culture. Endophytic microorganisms; usually bacteria, actinomycetes and fungi colonize almost every plant species. This review therefore focuses on the present conventional methods of controlling microbial contaminants in meristematic tissue cultures from the list of relevant available articles

Preliminary investigation of amylase-producing bacteria from some cassava farms in Umudike, Abia State, South-East Nigeria

Amylase producing microorganisms are richly distributed in environment where cassava is processed or where it is being cultivated. Isolation and identification of these microorganisms may be a step forward in providing ways of converting cassava into value added products for the benefit of man and farm animals. In this research, soil, water and air samples of three cassava farms located in Umudike community were examined for the presence of amylase producing bacteria. Isolation of these bacteria was carried out by culture plate method. The pure isolates were identified on the bases of their colonial morphology, microscopy and biochemical tests. The isolates obtained were mainly environmental contaminants though the gram positive isolates may possibly be used as starter cultures in cassava processing due to the possession of amylase but the gram negative isolate may not possibly be used due to the absence of amylase. Psuedomonas sp. was the only gram negative isolate identified while Bacillus sp., Corynebacterium sp. and Micrococcus sp. were the gram positive isolates identified. Bacillus sp. was the most prevalent isolate in all the samples investigated compared to the other isolates identified. We suggest that effort be made to validate these preliminary findings of the potentiality of these gram positive isolates identified as amylase producing bacteria in future studies. Keywords: Amylase producing bacteria, and Cassava farm

The Effect of Kitchen Residue Ash, Poultry Manure and Goat Dung on Soil Properties and Yield of Cowpea (Vigna unguiculata) in a Typic Kandiudult of Southeast Nigeria

A pot experiment was conducted at Michael Okpara University of Agriculture Umudike to determine the effect of kitchen residue ash, poultry manure and goat dung on soil properties and yield of Cowpea (Vigna unguiculata) on Typic Kandiudult of Southeastern Nigeria. Six levels of the treatments; 0t/ha (control), 2t/ha of kitchen residue ash, 2t/ha of goat dung, 2t/ha of poultry manure, 1t/ha of kitchen residue ash + 1t/ha of poultry manure and 1 t/ha kitchen residue + 1t/ha goat dung were applied, replicated three times in a Completely Randomized Design (CRD). At the end of the experiment, plants agronomic parameters and soil chemical properties were measured. The result obtained showed that the application of kitchen residue ash, poultry manure and goat dung significantly (P<0.05) increased soil pH, available phosphorus, total nitrogen, soil carbon, exchangeable calcium, exchangeable potassium and magnesium over the control. The applied manure and ash increased the cowpea height, root length, root branching  and grain yield more than the control. The combination of 1t/ha kitchen residue ash and 1t/ha goat dung gave the overall best performance in terms of increasing plant height, root length and number of root branching. Among the amendments tested, 1t/ha kitchen residue ash + 1t/ha poultry manure significantly (P<0.05) increased the soil pH, exchangeable calcium, exchangeable potassium, percentage base saturation and reduced exchangeable acidity more than the other treatment. Further research is recommended for the field application of the treatment on cowpea production.Key Words: Kitchen residue ash, goat dung, poultry manure, cowpe

Plant Growth Promoting Microbes in Plant Tissue Culture

Plant Growth Promoting Microbes (PGPMs) are key players in major ecological processes like atmospheric nitrogen fixation, water uptake, solubilization, and transport of minerals from the soil to the plant. A broad spectrum of PGPMs has been proposed as biofertilizers, biocontrol agents and biostimulants to enhance plant growth, agricultural sustainability and food security. However, little information exists with regard to the application of PGPMs in plant tissue culture. This review therefore presents an insight into the importance of PGPMs in plant tissue culture from relevant available articles. In addition, exploiting the potential benefits of PGPMs will lead to a significant reduction in the cost production of in vitro plantlets during plant tissue culture

Assessment of land use and land cover changes and urban expansion using remote sensing and GIS in Gboko, Benue State, Nigeria

There has been a rapid growth of urban areas across the globe since 1950s with the majority of world population living in urban areas rather than rural areas, in search of better job opportunities and higher quality of services. This trend of transition from rural to urban is expected to continue to rise and government in developing countries are likely going to face more challenges in different sectors, necessitating the need of understanding the spatial pattern of the growth for effective urban planning. The objectives of this study were to map and determine the nature, extent and rate of land use and land cover changes, to analyze the spatio-temporal land use and land cover change patterns and assess urban expansion in Gboko Local Government Area of Benue State, Nigeria. The emphasis was on determining the extent and rate of urban expansion in the area. The study focused on a period of 30 years; from 1987 to 2017. Satellite imageries used included Landsat TM (1987); Landsat ETM+ (2007); and Operational Land Imager (OLI) (2017). The Landsat imagery dataset was sourced from the Earthexplorer platform from United States Geological Surveys (USGS), Global Land Cover Facility (GLCF) and GloVis. The three images of 1987, 2007 and 2017 were classified using maximum likelihood classifier in Idrisi Selva to detect the land cover changes. The study resulted in an overall classification accuracy of 80.77% ,85.84% and 86.24% for 1987, 2007 and 2017 respectively. The result of the classification revealed that between 1987 and 2017, urban area increased from 3232ha (1.68%) in 1987 to 8542ha (4.45%) in 2007 and rose up to 16614ha (8.65%) in 2017. Forest land on the other hand declined from 52108ha (27.13%) to 46523ha (24.23%) down to 16723ha (8.71%) in the same period. Grassland was the dominant land cover occupying 69074ha (35.97%) in 1987 increasing to 79874ha (41.59%) and 129715ha (67.54%) in 2007 and 2017 respectively. The overall trend (1987-2017) revealed that urban area has increased up to 13382ha (9.01%) at an annual rate as high as 2.7% higher than the rate in the first period. Forest declined throughout the period with a loss of 5585ha(12.57%) in the first period at the annual rate of -2.51% and 29800ha (25.7%) in the second period at the annual rate of -2.57%. The overall trend shows that forest lost 35385ha (23.82%) at the rate of -7.15%. Farmland also decreased during the period losing 16006ha (36.03%) in the first period at an annual rate of -7.21% and 22317ha (19.25%) in the second with an annual rate of change of -1.93%. This high rate is an indication that in no distant future the area may be completely devoid of forest vegetation. From the result, it is evident that the rate of urban growth will continue and would certainly threaten forest areas in Gboko LGA. Finally, this study provides a guide to planners for successive urban planning in exploring the rate and pattern of urban growth in Gboko LGA.Keywords: Urban growth; LULC change; Landsat TM; Landsat ETM+; and Operational Land Imager (OLI), spatio-temporal, maximum likelihood classifier, Idrisi Selva , Gbok

IMPACT OF FOREIGN DIRECT INVESTMENT ON ECONOMIC GROWTH: EMPIRICAL EVIDENCE FROM NIGERIA, 1985-2016

This study analyses the impact of foreign direct investment on economic growth in Nigeria using data for 32 years, from 1985-2016. The OLS estimation and the Johansen cointegration test were the key techniques of analysis employed. The results indicate that foreign direct investment has no positive impact on the Nigerian economic growth. Trade openness and exchange rate, however, have positive but insignificant influence on economic growth. The cointegration test result revealed that there is evidence of a long-run relationship between foreign direct investment and economic growth. The paper thus recommends that there is need for in-depth investigation of economic and institutional forces that determine the composition of FDI inflows to developing countries and to work towards improving such forces. Moreover, government should also take measures in order to stabilize the exchange rate system that may attract foreign investors in the country, and also liberalize the trade policy to attract foreign investors to the country. JEL: E22, F21, G11  Article visualizations

PHYTOCHEMICALS ANALYSIS AND TOXICITY STUDIES OF ETHANOL LEAVES EXTRACT OF AZADIRACHTA INDICA, ANACARDIUM OCCIDENTALE, AND MORINGA OLEIFERA IN RATS

Medicinal plants have importance medicinal properties and pharmacological activities. Medicinal value and pharmacological activities of plants are attributed to their bioactive constituents. The efficacy of medicinal plants could not only be considered but also their safety for consumption. The phytochemicals analyses were performed using standard methods. Toxicity tests were conducted using Lorke’s method. The ethanol leaves extract of Azadirachta indica, Anacardium occidentale, and Moringa oleifera contain several phytochemicals. The LD50 value of ethanol leaves extract of Azadirachta indica, Anacardium occidentale, and Moringa oleifera was 3807.89 mg/kg, 4505.55 mg/kg, and above 5000 mg/kg, respectively. The ethanol leaves extract of Anacardium occidentale and Azadirachta indica demonstrated an increase in the levels of ALT, AST, total bilirubin, and globulin, while the Moringa oleifera exhibited a decrease in the levels of ALT, AST, and total bilirubin coupled with increase in total protein and albumin levels. The administration of ethanol leaves extract of Anacardium occidentale and Azadirachta indica increased the levels of urea, creatinine, sodium, potassium, calcium, chlorides, and HCO3- while decreased by the administration of the ethanol leaves extract of Moringa oleifera in rats. The ethanol leaves extract of Azadirachta indica and Anacardium occidentale demonstrated significant decrease in RBC count and PCV while Moringa oleifera exhibited significant increase in RBC count and PCV. Hemoglobin content was significantly decrease by the ethanol leaves extract of Azadirachta indica. The presence of these bioactive metabolites implies that the plants have medicinal properties. The ethanol leaves extract of Moringa oleifera at 5000 mg/kg dose is relatively non-toxic and safe for gavage administration while ethanol leaves extract of Azadirachta indica and Anacardium occidentale are slightly toxic for oral administration