Relationships of Learning Styles to Bloom’s Taxonomy of Gen Z

This research investigates a cohort of Gen Z students’ learning attributes to understand their learning styles. In this study, we applied Bloom’s Taxonomy as a framework to understand the cognitive learning strategies of Gen Z students and explore the development of corresponding and effective learning and appropriate assessment approaches for their academic success. A survey of Business students at a medium-sized southeastern (US) university was conducted. The results indicate a significant positive relationship between the dependent variable, taking information apart, and exploring relationships to some of the Bloom Taxonomy attributes as independent variables

Improving Technology Perception through Information and Education: A case of Biotechnology in Nigeria

A study was conducted in two states in Nigeria (Edo and Delta) as part of the International Institute of Tropical Agriculture Activities in the Niger Delta area of the south-western agro-ecological zone part of Nigeria. A workshop was organized for the sole purpose of presenting information on biotechnology as a discipline and as a necessary technology that can be safely adopted by even peasant farmers. Several areas of biotechnology such as biosafety,ethics, environmental and health safety where the audience can participate and explore were presented by speakers. Ninety-five participants at the workshop formed the respondents for the study and a questionnaire was designed to elicit information on the participants’ awareness, knowledge, perception and attitude about biotechnology and its products, before and after the workshop. The results showed that the age of the respondents ranged from 19 to 56 years with a mean of 41 years. Results also showed that all the participants, apart from 14.8 percent, had educational qualification higher than secondary school. Majority (63 percent) were civil servants including 30 percent from Ministry of Agriculture and 33 percent from Agricultural Research Institutes, 24 percent from the academia and others from private organisations. Through workshop as an education method, there was change in perception after training. Before the workshop 67.4 percent of the respondents said they would eat food made from genetically engineered crops however, at the end of the workshop 80 percent of the same group of respondents indicated they will eat food made from genetically engineered crops. Using a paired sample t-test statistics, the test of difference on disposition before and after the workshop gave a t-value of 4.569 which was significant at 0.05 level. The study concludes that information dissemination through training method such as workshop has contributed to change in perception of biotechnology in Nigeria

Detection and identification of public health important pathogens present in fruit salads sold on Lagos State University campus: Article Retracted by the Authors

A total of fifteen pre-packaged fruit salad samples containing pineapple, water melon, pawpaw and cucumber sold in the Lagos State University, Ojo Campus was analyzed using culture techniques for its microbial qualities. Five bacteria genera isolates obtained are Bacillus spp, Staphylococcus aureus, Pseudomonas spp, Escherichia coli, Streptococcus and the three fungi genera isolates are Aspergillus species, Penicillium species, and Saccharomyces cerevisiae. Escherichia coli had the highest frequency of (40%) followed by Streptococcus with (20%), Staphylococcus, Bacillus, Pseudomonas has the same frequency of (13%). The total viable count was in the range of 1.6 × 105 cfu/g to 5.65 × 105 cfu/g while the total coliform count ranged from 1.0 × 105 to 3.3 × 105 cfu/g. The fungal count ranged from 1.5 × 105 to 3.4 × 105 cfu/g. This study revealed that fruit salads in the studied area needs proper sanitation practice during processing in order to avoid risks associated with the consumption of contaminated fruits for the consumers

Welfare Status of Rice Farming Household in Office du Niger, Segou Region of Mali

The study examined the welfare status of rice farming household in Office du Niger, Segou region, Mali. Multi-stage sampling was used to select 137 rice technology adopters and 65 non-adopters in the study area. Frequency counts, percentages, PPPMC and t-test were used for data analysis. About 45.0% of non-adopters and 29.2% of adopters were aged 31-40. Non-adopters and adopters had an average of 6 and 10 years of experience, respectively. Most adopters (73.0 %) and non-adopters (50.8%) had a farm size between 1-5 hectares. Most adopted rice varieties were Kogoni 91-1 (94.2%) and IR 32 mille (81.0%). There was a high level of adoption among 59.1% of adopters. About 61.0% of adopters, but 53.8% of non-adopters earned less than 500,000 CFA annually from other activities as against between 500,000 and 1,000,000 CFA among 58.5% of non-adopters and above 2,000,000 CFA for 67.2 % of adopters. Majority (61.5%) of non-adopters had improved welfare status as against 80.3% of adopters in the same category. Household size (r = 0.192), income gained from rice production (r = 0.482, p = 0.000) significantly influenced respondents’ welfare level. There was a significant difference (t = - 12.089) in quantity of rice produced by adopters (38544.73±17721.69768Kg) and non- adopters (11394.77±5244.97546). There was a significant difference (t = - 2.917) in welfare status of the adopters (1281790.88±525400.62012) and non–adopters (1037750.00±614462.87743). Office du Niger should intensify efforts at up-scaling dissemination of improved rice technologies to cover more rice farmers

Welfare Status of Rice Farming Household in Office du Niger, Segou Region of Mali

The study examined the welfare status of rice farming household in Office du Niger, Segou region, Mali. Multi-stage sampling was used to select 137 rice technology adopters and 65 non-adopters in the study area. Frequency counts, percentages, PPPMC and t-test were used for data analysis. About 45.0% of non-adopters and 29.2% of adopters were aged 31-40. Non-adopters and adopters had an average of 6 and 10 years of experience, respectively. Most adopters (73.0 %) and non-adopters (50.8%) had a farm size between 1-5 hectares. Most adopted rice varieties were Kogoni 91-1 (94.2%) and IR 32 mille (81.0%). There was a high level of adoption among 59.1% of adopters. About 61.0% of adopters, but 53.8% of non-adopters earned less than 500,000 CFA annually from other activities as against between 500,000 and 1,000,000 CFA among 58.5% of non-adopters and above 2,000,000 CFA for 67.2 % of adopters. Majority (61.5%) of non-adopters had improved welfare status as against 80.3% of adopters in the same category. Household size (r = 0.192), income gained from rice production (r = 0.482, p = 0.000) significantly influenced respondents’ welfare level. There was a significant difference (t = - 12.089) in quantity of rice produced by adopters (38544.73±17721.69768Kg) and non- adopters (11394.77±5244.97546). There was a significant difference (t = - 2.917) in welfare status of the adopters (1281790.88±525400.62012) and non–adopters (1037750.00±614462.87743). Office du Niger should intensify efforts at up-scaling dissemination of improved rice technologies to cover more rice farmers

Factors Influencing Maize Production in Sikasso Region of Mali

In recent times, there has been decline in maize production in Mali due to several factors; one of which is that of access to inputs such as fertilizer and improved seeds. Hence, this study examined the contribution of subsidized inputs to farmers’ level of maize production in Sikasso region of Mali.  A multistage sampling procedure was used to select 200 beneficiaries of input subsidy for this study. Structured questionnaires were used to collect data which was analysed using descriptive and inferential statistics such as Chi-square, Pearson Product Moment Correction (PPMC), ANOVA and regression at 0.05 significant levels. Results reveal beneficiaries’ mean age to be 48.50±12.63 years. Almost all (99.0%) beneficiaries were males and married, 92.5% were Muslims, while 63.5% had informal education. Mean farm size and mean seasonal income was 3.18±2.72 ha and 259,250±286,592.90 Fcfa respectively. Subsidized inputs that are mostly used and accessed by beneficiaries are UREA and NPK fertilizers, while DAP, organic fertilizer and hybrid seeds were least accessed and used. Inadequate financial capability to purchase inputs despite being subsidized and insufficiency of inputs are the major constraint faced by beneficiaries. Farm size (r=0.57, p<0.01) and income (r=0.271, p<0.01) significantly related to respondents’ production level. Significant difference exists in the level of maize production among beneficiaries’ groups (f=8.646, p<0.01). Farm size and income were significant predictors of production level. The study concludes that farm size and farmers’ income contributed more to farmers’ level of maize production. This study recommends that credit should be made available to maize farmers by government, NGOs or other financial institutions with little or no collateral. Also, hybrid seeds that could be preserved till the next planting season should be developed so that farmers’ utilization of hybrid seeds will be encouraged. Keywords: Inputs, subsidy, maize production, farmer

Factors Influencing Maize Production in Sikasso Region of Mali

In recent times, there has been decline in maize production in Mali due to several factors; one of which is that of access to inputs such as fertilizer and improved seeds. Hence, this study examined the contribution of subsidized inputs to farmers’ level of maize production in Sikasso region of Mali.  A multistage sampling procedure was used to select 200 beneficiaries of input subsidy for this study. Structured questionnaires were used to collect data which was analysed using descriptive and inferential statistics such as Chi-square, Pearson Product Moment Correction (PPMC), ANOVA and regression at 0.05 significant levels. Results reveal beneficiaries’ mean age to be 48.50±12.63 years. Almost all (99.0%) beneficiaries were males and married, 92.5% were Muslims, while 63.5% had informal education. Mean farm size and mean seasonal income was 3.18±2.72 ha and 259,250±286,592.90 Fcfa respectively. Subsidized inputs that are mostly used and accessed by beneficiaries are UREA and NPK fertilizers, while DAP, organic fertilizer and hybrid seeds were least accessed and used. Inadequate financial capability to purchase inputs despite being subsidized and insufficiency of inputs are the major constraint faced by beneficiaries. Farm size (r=0.57, p<0.01) and income (r=0.271, p<0.01) significantly related to respondents’ production level. Significant difference exists in the level of maize production among beneficiaries’ groups (f=8.646, p<0.01). Farm size and income were significant predictors of production level. The study concludes that farm size and farmers’ income contributed more to farmers’ level of maize production. This study recommends that credit should be made available to maize farmers by government, NGOs or other financial institutions with little or no collateral. Also, hybrid seeds that could be preserved till the next planting season should be developed so that farmers’ utilization of hybrid seeds will be encouraged. Keywords: Inputs, subsidy, maize production, farmer

Factors behind the performance of green bond markets

The market for green bonds has grown dramatically over the past several years, necessitating an understanding of the variables that might forecast its performance. Studies on how the green bond market interacts with other markets are widely discussed in the literature, but little is known about the variables that improve predictions of green bond returns. In this study, we use data on commodity and financial asset prices, as well as speculative factors, to predict the returns on green bonds using the Feasible Quasi-Generalized Least Squares (FQGLS) and the causality-in-quantiles estimators. The findings demonstrate that most factors are significant predictors of the returns on green bonds, with speculative factors having a detrimental predictive influence, and commodity and financial asset prices having a mixed predictive impact. When asymmetries are taken into account, the asymmetric predictive model performs better at predicting the returns on green bonds than its symmetric counterpart in most instances. Finally, all the factors, except investors' sentiment, affect the returns on green bonds in a variety of market situations. The interdependence among the global financial and commodity markets, as well as economic uncertainties justify the established predictive influence, since green bonds are a component of the broader investment bonds