Expanding the Agricultural Finance Frontier: A Kenyan Case

Agriculture is the mainstay of the Kenyan Economy. However, agriculture has experienced low productivity over the years. Poor access to agricultural finance has been identified as a contributing factor to low crop productivity. Kenyan agriculture has undergone some fundamental changes which have profoundly affected agricultural financial services. In addition, most financiers shy away from lending to the agricultural sector because of the covariant risks related to rain-fed agriculture. Given this background, we undertake a comparative analysis of emerging models of agricultural finance that have expanded the agricultural finance frontier to the smallholder farmers. Key findings indicate that demand for farming credit takes the highest proportion of the credit needs among the rural households, thus accentuating the importance of agricultural finance. The state run model of agricultural financing has the lowest financial sustainability. On the contrary, the community financing models are the most likely drivers of change in the rural finance landscape.Agribusiness, Agricultural Finance, Community/Rural/Urban Development, Demand and Price Analysis, Farm Management, Financial Economics, Food Consumption/Nutrition/Food Safety, Food Security and Poverty, Institutional and Behavioral Economics, International Relations/Trade, Labor and Human Capital, Land Economics/Use, Marketing, Production Economics, Productivity Analysis, Research and Development/Tech Change/Emerging Technologies,

Technical and economic analysis of parabolic trough concentrating solar thermal power plant

Includes abstract.Includes bibliographical references.This thesis reports on the technical and economic analysis of wet and dry cooling technologies of parabolic trough CSTP plant. This was done through modelling and simulation of a standalone and grid connected parabolic trough using the System Advisor Model (SAM)

Application of stochastic frontier approach model to assess technical efficiency in Kenya’s maize production

Kenya realised tremendous growth in maize production between 1964 and 1997, fueled by the introduction of high yielding hybrid maize. However, from 1997, there has been a decline in yield from 1.85 to 1.57 metric tones per hectare with observed supply shortages occasionally. Maize shortages result in famine among the poor urban and rural households. Since almost all the arable land is under cultivation, future increase in maize production will heavily depend on technical efficiency and yield improvement rather than expansion in area under production. The main objective of this study was to determine the technical efficiency of smallholder maize production in Kenya. The stochastic frontier model was used as the method of analysis to estimate several production function forms using crosssectional household data for the 2003/2004 main cropping season. Variations in technical efficiency index across smallholder farm units were explained through a number of socio-economic, farm characteristic and Agro-Ecological Zone variables. The results of the translog functional form revealed that the technical efficiency index across smallholder farm units ranged from 8 to 98 percent. Purchased hybridseeds, use of tractors for land preparation, number of school years of household head, male headed households, age of household head, access to credit and high potential zone dummy variables had a negative sign, and therefore decreased technical inefficiency (increased technical efficiency). Calculations of marginal effects showed that purchased inputs and primary education had the highest improvement of technical efficiency i.e. hybrid seed (36%), tractor services (26%) and an extra year of  household head primary schooling (0.84%). It is therefore concluded that improvement of maize input markets together with an emphasis onprimary school education would enhance maize productivity. Thus, if hybrid seeds, tractor services and agricultural credit are made available and affordable to farmers technical efficiency would increase.Key words: Socio-economic factors, farm characteristics, maiz

Trends in Kenyan Agricultural Productivity: 1997-2007

Egerton University Tegemeo Institute of Agricultural Policy and DevelopmentAfrica, Kenya, productivity, Productivity Analysis, Q10,

Trends and Patterns in Fertilizer Use by Smallholder Farmers in Kenya, 1997-2007

This study uses nationwide household panel survey data from 1996/97 to 2006/07 to examine trends in fertilizer use on maize by smallholder maize growers. The paper also compares these findings with fertilizer use rates according to other recent surveys in Kenya to assess comparability. We also examine the correlation between household fertilizer use and indicators of welfare such as wealth and landholding size. In addition, we use econometric techniques applied to household survey data to identify the main household and community characteristics associated with fertilizer purchases. Lastly, the study considers alternative policy strategies for maintaining smallholders’ access to fertilizer in the current context of substantially higher world fertilizer prices.Fertilizer, Africa, Malawi, Kenya, Small Holders, Crop Production/Industries, Q13,

Relationship between quality of learning and student inter-university transfer in private universities in Kenya

The study sought to investigate the relationship between the quality of learning and student inter-university transfer in private universities in Kenya. This study targeted 26 registered private universities (including private university constituents where mobility rate records are too high) in Nairobi County, Kenya. The research sample size was 180 private university students and nine registrars. Quantitative data was analysed using Statistical Package for Social Sciences (SPSS) version 22.0. Descriptive analysis, inferential statistics, and regression analysis were used to analyse the findings. Descriptive statistics such as mean scores, percentages, and standard deviation were computed appropriately. Binary logistic regression analysis was adopted to establish the extent of the impact on the dependent variables of independent variables. The study concludes that the quality of learning significantly influences students\u27 mobility in private universities in Nairobi County in Kenya. Students prefer being in institutions of learning that can guarantee them quality education to enable them to get opportunities to thrive in the labour market. The study recommends that private universities invest in their respective infrastructure to ensure superior learning possibilities. There is a need to invest in qualified lecturers, classrooms, libraries, laboratories and many other things that contribute to quality learning. Student\u27s mobility in private universities is in favour of institutions that are perceived to offer quality education

Does course completion time affect student inter-university transfer? An investigation from selected private universities in Kenya

The objective of this study was to establish the relationship between the course completion time and student mobility. This study employed a descriptive quantitative survey design. This study targeted 26 registered private universities (including private university constituents where mobility rate records are too high) in Nairobi County, Kenya. The research sample size was 180 private university students and nine registrars. Version 22.0 of Statistical Package for Social Sciences (SPSS) was used in the analyses of quantitative data. Descriptive analysis, inferential statistics, and regression analysis were used to analyse the findings. Descriptive statistics such as standard deviation, mean scores and percentages were computed appropriately. The Binary logistic regression analysis was employed to find out the extent of the effect on the dependent variables of independent variables. This study found that the mobility of students in Nairobi County, Kenya, is greatly influenced by course completion times. This study concluded that course completion time significantly influences students\u27 mobility in private universities in Nairobi County in Kenya. Students who enrol in an academic institution of higher learning are determined to complete the whole course. It is, therefore, the role of any such institution to ensure that all possible controls are made to avoid delays in completion rates. The study recommended that private universities make deliberate efforts to ensure that course completion time improves in their study programmes

Relationship between economic status of students and inter-university transfers among private universities in Nairobi County, Kenya

This paper aims to establish the relationship between students’ economic status and mobility in private universities in Nairobi County, Kenya. A descriptive survey design was employed to accomplish this objective by targeting 26 registered private universities (including private university constituents where mobility rate records are too high) in Nairobi County, Kenya. A sample of 180 private university students and nine registrars was obtained using a multi-stage sampling technique at three different stages. Statistical Package for Social Sciences (SPSS) version 22.0 was used in analysing the collected data, from which descriptive statistics such as mean scores, percentages, standard deviation, and linear regression were computed. This study found that economic status does not influence student mobility in private universities in Nairobi County, Kenya. This study recommends the involvement of government agencies, including the Ministry of Education (MOE), Kenya Universities and Colleges Placement Service (KUCCPS), Commission for University Education (CUE), and Higher Education Loans Board (HELB), to figure out the origin of this mobility and effectively control the alarming student mobility cases

Student mobility in Kenya’s private universities: An assessment of the effect of universities’ customer care services

The study sought to analyse the effect of customer care services on student mobility in private universities in Nairobi County, Kenya. The study employs a descriptive survey design targeting 26 registered private universities. A study sample of 180 private university students and 9 registrars was selected using multi-stage sampling whereby the private universities from Nairobi County, Kenya, were purposely selected. The universities were further sampled in stratus using stratified sampling. The registrars further applied random sampling to select the students who were interviewed. Data was collected using semi-structured interviews and survey questionnaires for registrars and students. Analysis was then done descriptively and through thematic means for quantitative and qualitative data. Linear regression analysis was also used to establish the extent of the effect on the dependent variables of independent variables. The study\u27s findings indicate that customer care services influence students’ mobility in private universities in Nairobi County in Kenya. This implies that students prefer being in institutions of learning that provide good customer care services. The study recommends that marketing departments of universities in the target areas invest more in customer care services to promote their respective universities to prospective students

The role of government policies on students\u27 inter-university transfer among selected private universities in Nairobi County, Kenya

This study assessed the influence of government policies on students\u27 mobility in private universities in Nairobi County, Kenya. This study employed a descriptive quantitative survey design. This study targeted 26 registered private universities (including private university constituents where mobility rate records are too high) in Nairobi County, Kenya. The research sample size was 180 private university students and nine registrars. Statistical Package for Social Sciences (SPSS) version 22.0 was used to analyse quantitative data. Descriptive analysis, inferential statistics, and regression analysis were used to analyse the findings. Descriptive statistics such as standard deviation, percentages, and mean scores were computed appropriately. Binary logistic regression analysis was adopted to find out the extent of the effect on the dependent variables of independent variables. The study concluded that government policies significantly influence the mobility of students in private universities in Nairobi County in Kenya. The Government agencies through the Commission for University Education (CUE), Ministry of Education (MOE), Kenya Universities and Colleges Placement Service (KUCCPS), and Higher Education Loans Board (HELB) that are concerned with the welfare of students and need to control the alarming cases of student mobility should take measures to review the existing policies that guide learning in institutions of higher education and introduce systematic measures that can enhance student engagement while undertaking their studies