Contamination status and source identification of heavy metals in the riverbank soils and sediments of Ona River, Ibadan, southwest Nigeria

The present study investigated the pollution levels, sources, and contamination status of trace metals (Pb, Cd, Fe, Zn, Cu and Mn) in riverbank soils and sediments at three different sections of Ona River bordering residential area. The relationship of studied metals with selected soil chemical properties was also examined. The average concentration of each of the studied metals in soils and sediments at each sampling site was less than 1 mg/kg having a decreasing trend of Zn > Cu > Fe > Cd > Pb > Mn and Zn > Cd > Fe > Cu > Pb > Mn, respectively in soils and sediments. Results of enrichment factors (EF) were generally less than 1.5 for all assessed metals; index of geoaccumulation (Igeo) in soils and sediments of the investigated sites were less than 1 while contamination factor (CF) of assessed metals were in the range: 1< CF < 3. The results of integrated pollution indicators support lithogenic sources and a low-to-restrained pollution of the considered soils/sediments by metals. The environmental risks indices of sampling sites that could be ascribed to the metals revealed low mean ecological risks potential in soils and sediments. The values of co-efficient of variation (CV) of analyzed metals were less than 50%, indicating absence of strong anthropogenic inputs, while Fe-Zn, Fe-Pb, and Pb-Zn pairs in soils and sediments exhibited strong positive correlations, an indication of common sources due to lithogenic processes. Inverse relation between analyzed metals and organic matter further confirmed little impact of anthropogenic inputs as sources of metals in soil/sediment. This study elucidated that the area was not heavily polluted by metals and revealed that the investigated riverbank areas were mildly contaminated by assessed metals, thus posing a low ecological risk

Interactive Effects of Genotype X Year on Disease Reactions, Grain Yield and other Agronomic Traits of Newly Developed Quality Protein Maize in Nigeria

Experiments were conducted on six newly developed open pollinated quality protein maize (QPM) genotypes and two check entries for three years (2009-2011). The objective was to assess their yield potentials and disease tolerance/ resistance in the southern Guinea savanna agro-ecology of Nigeria. Genotype and year of evaluation were significant for (P<0.01 and <0.05) for grain yield, harvest moisture and lodging characteristics. Genotypes x year interactive effect for grain yield revealed different genotypic performance of the genotypes tested with two checks (Oba-Super 1 and DMRLSR-Y) being responsible for the significant differences obtained in the three years of evaluation. Average grain yield was significantly higher in the year 2011 compared to 2009 and 2010. All the genotypes tested were moderately tolerant to the five diseases ranging from 1.5 (Streak virus) in Oba-Super 1 (check) to 2.9 (Southern leaf blight, Curvularia leaf spot and Leaf rust) in the ART98-SW6-OB and ART98-SW4-OB respectively. Ear rot mostly affected the leaves among diseases with a range of 2.3 to 2.8 in TZPB-OB and DMR-LSR-Y respectively.. Four QPM genotypes (ART98-SW5-OB, ART98-SW4-OB, TZPB-OB and ART98-SW6-OB) were superior for grain yield with yield advantage of 28% over the best OPV check. These QPM genotypes can therefore serve as useful replacement for existing cultivars and also as source of genes for future maize breeding activities in the development of superior maize varieties with high protein contents for the savanna agro-ecology. Keywords: Tolerance, streak virus, ear rot, leaf spot and blight

AGRICULTURAL MITIGATION AND ADAPTATION TO CLIMATE CHANGE IN NIGERIA: AN OVERVIEW

- In this paper we put forward a hybrid stacking ensemble approach for classifiers which is found to be a better choice than selecting the best base level classifier. This paper also describes and compares various data mining methodologies for the domain called employment prediction. The proposed application helps the prospective students to make wise career decisions. A student enters his Entrance Rank, Gender (M/F), Sector (rural/urban) and Reservation category. Based on the entered information the data mining model will return which branch of study is Excellent, Good, Average or poor for him/her. Various data mining models are prepared, compared and analyzed. Keywords- Confusion matrix, Data Mining, Decision tree, Neural Network, stacking ensemble, voted perceptro