http://hdl.handle.net/123456789/1108 Thu, 09 Apr 2026 23:46:38 GMT 2026-04-09T23:46:38Z http://hdl.handle.net/123456789/1220 Relationship between Availability Status of the School Farm Facilities and the Level of Acquisition of Agricultural Skills among Secondary School Students in Kenya Recha, R. O., Kyule, M., & Kinuthia, L. One of the objectives of teaching Agriculture at the secondary school level is to equip learners with practical agricultural skills as this is considered one of the ultimate panacea to addressing unemployment and food insecurity. The school farm is considered a necessity in the teaching and learning of Agriculture for acquisition of practical skills. This study aimed at establishing the relationship between availability status of the school farm facilities and the acquisition of agricultural skills. Correlational research design was adopted. The study targeted 1532 secondary school teachers and 4327 form three students in Malava Sub-County. The accessible population comprised of the 171 teachers of Agriculture and 2532 form three Agriculture students. Based on Nassiuma formula, 15 schools were sampled. Using the Yamane formula, 150 form three students of Agriculture were sampled. One Agriculture teacher was selected from each of the sampled school. Questionnaires and an observation guide were used to gather data. A pilot study was carried out in Khwisero Sub-County to determine the instruments' reliability where Cronbach’s alpha of 0.89 and 0.72 were obtained for the agriculture teachers and students’ questionnaires respectively. Reliability of the observation guide was determined qualitatively by discussing the items with expert data analysts from Egerton University. Chi-square test of relationship was used to analyse the findings of this study aided by the Statistical Package for Social Sciences (SPSS) version 26. The study established that availability status of school farm facilities does not significantly contribute to students’ level of acquisition of agricultural skills. Based on the findings, the study recommended that the government of Kenya through the Ministry of Education and school managements should not only improve on availability status of the school farm facilities but also find ways of improving on other factors such as level of access, adequacy and utilization of the school farm to ensure practical teaching of Agriculture for skill acquisition. Keywords: Agriculture Teaching, secondary school level Wed, 20 Nov 2024 00:00:00 GMT http://hdl.handle.net/123456789/1220 2024-11-20T00:00:00Z http://hdl.handle.net/123456789/1219 nfluence of Farm Size on Adoption of Improved Coffee Varieties by Smallholder Farmers in Mathira East Sub-County, Nyeri County, Kenya Njeru, E.W., Munyua, C.N. & Kinuthia L. N Coffee productivity in Kenya has been on decline while the global demand has been on the rise. Mathira East Sub-County has not been left out with notable decline since the 1980s negatively affecting the local and national economy. The decline is attributed to rising costs of farming and drop in yield from traditional coffee varieties. Despite introduction of improved coffee varieties, productivity has remained low. Thus, this paper investigated the influence of farm size on the adoption of the improved coffee varieties by smallholder farmers in Mathira East Sub- County. The study adopted the cross-sectional research design involving key coffee farming household informants. The results showed that most farmers with larger farms had adopted improved varieties. The binary logistic regression results indicated that farm size significantly influences adoption of the improved coffee varieties at p value of 0.011. This finding is attributed to the ability of farmers to diversify part of their land into improved varieties. The study recommends that the county government of Nyeri should enhance extension services to educate and train farmers on how to graft traditional coffee bushes with improved varieties. This could help farmers with small farm sizes to diversify part of their coffee into improved varieties. Tue, 10 Jun 2025 00:00:00 GMT http://hdl.handle.net/123456789/1219 2025-06-10T00:00:00Z http://hdl.handle.net/123456789/1212 Exploring The Impact Of Machine Learning On Financial Markets: Opportunities, Risks, And Regulatory Challenges Anyango, W. O., & Gichaiya, M. W. The increasing use of machine learning in finance is creating the potential to transform the financial industry, offering opportunities for improved risk management, fraud detection, trading strategies, and customer experience. However, there are also significant risks associated with the use of machine learning in financial markets, including data privacy concerns, algorithmic bias, and the potential for unintended consequences or "black swan" events. Additionally, there are regulatory challenges in ensuring that the use of AI in finance complies with existing laws and regulations, as well as developing new rules and standards as needed to address emerging issues. This study involved a comprehensive analysis of financial market indices, namely the S&P 500, NASDAQ Composite, and FTSE 100. These indices were chosen as representative benchmarks for the U.S. and UK financial markets. Historical data for these indices was collected and examined, covering a period of five years to capture a significant timeframe for analysis. The research findings indicate several key implications of machine learning for financial markets: The application of machine learning algorithms has the potential to enhance market efficiency by processing vast amounts of data, identifying patterns, and generating insights in real-time; contribute to better risk management strategies by providing advanced risk models and early warning systems; and development of sophisticated trading strategies by analyzing market data, identifying trends, and generating trading signals. However, the findings also underscore the importance of addressing regulatory challenges. The adoption of machine learning in financial markets presents regulatory challenges that require careful consideration. Regulators need to address issues related to algorithmic bias, data privacy, model interpretability, and system stability to ensure the fair and safe implementation of machine learning techniques in finance. This study highlights the significant impact of machine learning on financial markets, showcasing its potential for improving market efficiency, enhancing risk management practices, and generating alpha through advanced trading strategies. By leveraging financial market indices as benchmarks, this research provides valuable insights into the opportunities, risks, and regulatory considerations associated with the adoption of machine learning in financial markets Thu, 14 Nov 2024 00:00:00 GMT http://hdl.handle.net/123456789/1212 2024-11-14T00:00:00Z http://hdl.handle.net/123456789/1178 Effect Of Mobile Payments On The Financial Performance Of SMES In Kirinyaga County Kimathi, S. M., Mwangi, G., & Mutiso, A The objective of the study is to establish the effect of mobile payments on the financial performance of SMEs in Kirinyaga County. The study was based on the Diffusion of Innovation theory. This study employed a descriptive study design. The targeted population was the 226 SMEs recorded by ministry of finance and revenue, revenue department, of Kirinyaga County as having renewed their licenses for 5 consecutive years. The sample size was 160 respondents, which were arrived at with the aid of the Yamane formula. Cluster sampling technique was used based on the fact that the data was collected from different towns within the same county; hence, there was equal geographical representation among the different towns within Kirinyaga County. Mixed questionnaires were used to collect primary data for the study. The questionnaires were administered through Google forms, filled and submitted through the same platform. Prior to giving out the questionnaires in the field to SME owners, a pre-test of 10 SME owners in Tharaka Nithi County was conducted to ensure that the questionnaire is reliable and valid. Some judgmental techniques tested the validity, followed by a second review from the experts to guarantee content validity. Raw data from the questionnaires was cleaned, coded and analyzed using SPSS V29. Descriptive statistics was used to summarize the findings by use of percentages, standard deviations frequencies and means, while inferential statistics used the Karl Pearson to establish how mobile banking affects the financial performance of SMEs in Kirinyaga County. The findings indicated that mobile payments have a significant and positive effect on the financial performance of SMEs in Kirinyaga County. The study recommended that in order to help SMEs increase cash flow management, reduce operational costs, and access capital for expanding their businesses, SMEs should adopt a holistic mobile payments services. Thu, 03 Apr 2025 00:00:00 GMT http://hdl.handle.net/123456789/1178 2025-04-03T00:00:00Z