Analysis of Income Diversification Strategies among Farm Households in Oyo State

Diversification of income sources is considered as a desirable option to augments income among small scale farmers. This study evaluates the income diversification among farm households in Oyo State of Nigeria. A multi-stage sampling technique was used to select 280 rural households. The data collected were analysed using diversity index and Tobit regression analysis. The results showed that all the respondents participated in arable farming and this accounts for 28.29 percent of the total income. 57.85 percent participated in tree crop income and this accounts for 11.95 percent of the total income, 60.36 percent of the households engaged in non-farm income and it accounts for 19.93 percent of the total income. The results of Tobit regression showed that education, household size, access to credit and extension contact were the factors increasing income diversification among the rural households in the study area. The study revealed that agriculture remains the major source of income among the respondents. Therefore, the study recommends improvement of agricultural activities through the distribution of agricultural inputs such as improved seeds, fertilizers and better extension services delivery in order to boost agricultural production. Keywords: Income diversification, Diversity index, Rural Household, Tobit regression

Estimation of Garch Models for Nigerian Exchange Rates Under Non-Gaussian Innovations

Financial series often displays evidence of leptokurticity and in that case, the empirical distribution often fails normality.  GARCH models were initially based on normality assumption but estimated model based on this assumption cannot capture all the degree of leptokurticity in the return series. In this paper, we applied variants of GARCH models under non-normal innovations-t-distribution and Generalized Error Distribution (GED) on selected Nigeria exchange rates. The Berndt, Hall, Hall, Hausman (BHHH) numerical derivatives applied in the estimation of models converged faster and the time varied significantly across models. Asymmetric GARCH model with t-distribution (GARCH-t) was selected in most of the cases whereas for Nigeria-US Dollar exchange rate, GARCH-GED was specified. Both distributions showed evidence of leptokurticity in Naira exchange rate return series. The result is of practical importance to practitioners. Key Words: GARCH, Exchange rate, Model specification, Non-Gaussian distribution.

Bioflavor: a review

The research work was partly supported by RFBR grant 18-29-12129mk

APPLICATION OF RESPONSE SURFACE METHODOLOGY (RSM) AND ARTIFICIAL NEURAL NETWORK (ANN) FOR ACHIEVING DESIRE BA IN THE BIOTRANSFORMATION OF BENZALDEHYDE USING FREE CELLS OF SACCHAROMYCES CEREVISAE AND THE EFFECT OF Β-CYCLODEXTRIN

This work dwells on the production of benzene alcohol (BA) from the biotransformation of benzaldehyde using free cells of Saccharomyces cerevisae and effects of β-Cyclodextrin. Meanwhile, the properties of BA produced was evaluated. The effects of five variables considered in this research work were evaluated using RSM and ANN. The root mean square error, the coefficient of determination, the adjusted coefficient of determination and the predicted values were used to compare the performance of the RSM and ANN models. The RMSE and R2 of RSM and ANN were 2.00 and 0.0739; 0.9898 and 0.99206, respectively. The R2 adj. and the predicted values of RSM and ANN were found to be 0.98416 and 0.9889 and 327.259 mg/100 ml and 351.50 mg/100 ml. The quality of BA showed that at room temperature, BA was colourless liquid with density 1.030 kg/dm3, the boiling point and refractive index was found to be 204 ± 2 0C and 1.5453, respectively. The results indicated the ANN model to have higher predictive capability than RSM model. Thus, the ANN methodology presents a better alternative than the RSM model. The quality of produced BA was found to be in line with Analytic grade values

Nutritional potential of underutilized gum arabic tree seeds (acacia nilotica) and locust bean seeds (Parkia biglobosa)

Acacia nilotica seed (ANS) and Parkia biglobosa seed (PBS) are underutilised legume found to have health benefits and functional properties. Th is study determined nutrient composition of A . nilotica and P. biglobosa seed s . ANS and PBS were collected and processed properly for c hemical analysis. The proximate, minerals, vitamins, essential amin o - acids, and antinutri ent composition were analyzed to ascertain nutritional attributes and its potential in promoting dietary diversity . The raw and fermented A . nilotica se eds contained in g/100 g , protein (12.88 to 15.3 8), fat (3.29 to 4.91), ash (5.24 to 6.84), dietary fibre (1.98 to 2.66) and available carbohydrate (69.63 to 71.73), whil e the FPB contained in g/100 g, protein (18.30), fat (9.20), ash (8.69), dietary fibre (2.61), and available carbohydrate (56.27). The fermented A . nilotica (FAN) seed contained all the nine (9) essential amino acids. The raw and fermented ANS contained in mg/100 g , iron (9.67 to 12.23), zinc (0.69 to 1.13), calcium (0.17 to 0.22), sodium (0.14 - 0.21 ) while the F PB seed contained 14.86, 1.59, 0.25, and 0.24 , respecti vely. FAN and FPB contained in μg/100 g, vitamin A (148.79 and 197.81), vitamin E (15.90 and 24.69) and vitamin K (1.41 and 1.63) , respectively. The levels of antinutrient factors in all the samples were not significant. Fermented A . nilotica seed contained adequate level of some micronutrients and essential amino acids. Consumption of the seed should therefore be promoted

HISTOGRAM NORMALIZATION TECHNIQUE FOR PREPROCESSING OF DIGITAL MAMMOGRAPHIC IMAGES

Digital mammogram has become the most efficient tool for early breast cancer detection modalities and pre-processing these images requires high computational capabilities. Pre-processing is one of the most important step in the mammogram analysis due to poor captured mammographic image qualities. Pre-processing is basically used to correct and adjust the mammogram image for further study and classification.  Many image pre-processing techniques have been developed over the past decades to help radiologists in diagnosing breast cancer. Most studies conducted have proven that a pre-processed image is easier for radiologist to accurately detect breast cancer especially for dense breast. Different types of techniques are available for pre-processing of mammograms, which are used to improve image quality, remove noise, adjust contrast, enhance the image and preserve the edges within the image. This paper acquired 20 digital mammograms from Mammographic Image Analysis Society (MIAS) database and uses Histogram Normalization algorithm for pre-processing of the mammograms. A percentage of 95% was obtained. It was observed that the pre-processed mammographic images displayed breast abnormalities clearer with little or no noise

Exploring the Effect of Operational Factors and Characterization Imperative to the Synthesis of Silver Nanoparticles

The synthesis and application of silver nanoparticles are increasingly becoming attractive. Hence, a critical examination of the various factors needed for the synthesis of silver nanoparticles as well as the characterization is imperative. In light of this, we addressed in this chapter, the nitty-gritty on the operational parameters (factors) and characterization relevant to synthesis of silver nanoparticle. The following characterization protocols were discussed in the context of silver nanoparticle synthesis. These protocols include spectroscopic techniques such as ultraviolet visible spectroscopy (UV–Vis), Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), transmission electron microscopy (TEM), energy-dispersive X-ray spectroscopy (EDX), X-ray fluorescence (XRF), X-ray diffraction (XRD), thermogravimetric analysis (TGA) and X-ray photoelectron spectroscopy (XPS)

Effect of Storage Temperature on Some Ogi Properties

Abstract: The study aimed at investigating the effect of storage temperatures on some quality properties of Ogi putting into consideration the peculiar situation of power supply in Nigeria. Ogi was processed using traditional method and stored at different temperatures (27±3, 5 ±2, -10±3 and -20±3ºC) for a period of 12 weeks. Proximate, pH, total titrable acidity, pasting characteristics and sensory evaluation were carried out. The total titratable acidity (Lactic acid based) began to decrease as from week 2 and throughout the period of storage in ogi samples stored at ambient temperature of 27±3ºC. A similar observation was noticed in the ogi stored at 5 ±2ºC, while ogi stored at -10±3 and -20±3ºC maintained the total titrable acidity when compared with the fresh ogi. The mean values of pH were significantly different (p<0.05) in all the storage conditions, while high pH values of 3.61±0.25 and 3.65±0.05 were recorded at week 12 of ogi stored at ambient temperature and 5±2ºC respectively. There was significant difference (p<0.05) in moisture content throughout the period of storage. There was significant difference in proximate composition (p<0.05) in all the storage conditions and throughout the storage period. The peak viscosity and final viscosity of ogi stored under the ambient temperature witnessed a noticeable reduction throughout the period of storage compared with the fresh sample of ogi. Storage at 5±2,-10±3 and -20±3ºC conditions maintained the hold strength (hot paste viscosity). The range of pasting temperature for ogi samples throughout the period of storage was between 76 and 80ºC. There was no significant difference (p<0.05) in multiple comparison results of sensory evaluation and the values for consistency were 2.7 and 2.75 at weeks 8 and 10 respectively for ogi stored at ambient temperature. The acceptability results for consistency and colour showed a significant difference (p<0.05). Ogi stored at low temperatures (-10±3 and -20±3ºC) were preferred

Household Welfare Effects of Stress-Tolerant Varieties in Northern Uganda

This study assessed the adoption of stress-tolerant varieties and their effect on household welfare, measured by net crop income per capita in Nwoya District, Uganda. The stress-tolerant varieties were considered to be climate-smart because they stabilise and increase crop income in the presence of climatic shocks. However, the uptake of the stress-tolerant varieties was still low in northern Uganda, due to bad past experience in terms of the performance of other improved varieties. Using data from a random sample of 585 households, a logistic model was estimated to assess the drivers for adoption of stress-tolerant varieties. In addition, a propensity score matching model was employed to assess causal effects. The second model was estimated because it controls for unobserved heterogeneity caused by self-selection bias. Results showed that adoption of stress-tolerant varieties was positively influenced by household size, access to information from non-governmental organizations (NGOs), the perception of future climate change, the number of years an individual had lived in the village, and the number and type of assets owned as an indicator of household well-being. Average treatment effect from results showed that stress-tolerant varieties can increase crop income within a range of United States Dollars (USD) 500–864 per hectare per year, representing an 18–32% increase in crop income. The findings offer justification for scaling up stress tolerant varieties among smallholder farmers in northern Uganda to improve their welfare