A STUDY ON THE EFFECTS OF STUDENTS INFORMATION RETRIEVAL SKILLS ON SCHOLARLY INFORMATION MANAGEMENT AMONG POST GRADUATE STUDENTS IN KIAMBU COUNTY, KENYA

With the rapid growth of information, the ability of students to be information literate has become critically important. However students information retrieval skills required for scholarly information management are still wanting as most papers written by postgraduate learners contain irrelevant information that are not related to their study topics. This study investigated the effects of students’ information retrieval skills on scholarly information management among post graduate students in Kiambu County Kenya. The study adopted information management theory as it showed that information literacy practices influence scholarly information management. A mixed methodology with concurrent triangulation design was used to conduct the research as it allowed the researcher to collect and analyse qualitative and quantitative data concurrently. Generalization was made based on findings generated from the collected data. The study had a target of 2451 which included 40 librarians, 40 supervisors, and 249 postgraduate students. The study participants were selected through purposive and simple random procedures. The sample population included 245 postgraduate students from 4 selected Universities and 4 library staff from each of the selected universities. Data was collected using self-administered questionnaires for students and interview schedule for librarians. Pilot testing ensured certainty of instruments, the retest method helped estimate reliability (r=0.70) to ensure that data gathered were accurate and reliable and the instruments were validated by seeking constant guidance of the supervisor. To achieve all the study objectives, both qualitative and quantitative data were gathered and analyzed to generate descriptive, inferential as well as qualitatively. Quantitative data collected was analyzed using SPSS version 21. Both descriptive and inferential statistics were used in analyzing quantitative data while thematic analysis was used to analyze qualitative data. The analyzed quantitative data were presented using Tables. The study findings indicated that postgraduate students’ information retrieval skills normally had positive effects on the students’ scholarly information management. The results further revealed that there exists a statistical significant relationship between the independent variables and the dependent variable investigated.  Article visualizations

Socioeconomic Factors that Influence Smallholder Farmers’ Membership in a Dairy Cooperative Society in Embu County, Kenya

Smallholder dairy farmers produce the bulk of total marketed milk in Kenya. Dairy cooperatives are one of the avenues for these smallholder farmers to harness markets for their milk. The paper sought to find out the socioeconomic factors that would influence these farmers to join dairy cooperatives in Embu County, Kenya. Systematic random sampling and simple random sampling were used to select a total of 236 smallholder farmers. The data was analysed using descriptive statistics and the binomial logit model. The results show that age, gender, household size, herd size, distance to the nearest market, access to credit and milk sold influenced the decision to join cooperative societies. The study recommends further study whether cooperatives are improving the incomes of smallholder farmers. Keywords:Dairy cooperative society, smallholder farmers, Binomial logit mode

Application of Deep Learning to Emulate an Agent-Based Model

In light of the dynamic challenges facing agricultural land markets, the conventional analytical frameworks fall short in capturing the intricate interplay of strategic decisions and evolving complexities. This necessitates the development of a novel method, integrating deep learning into Agent-based Modelling, to provide a more realistic and nuanced understanding of land market dynamics, enabling informed policy assessments and contributing to a comprehensive discourse on agricultural structural change. In this paper, different deep learning models are tested and evaluated, as emulators of AgriPoliS (Agricultural Policy Simulator). AgriPoliS is an agent-based model used to model the evolution of structural change in agriculture resultant on the change in the policy environment. This study is part of preliminary works towards integrating deep learning methods and predictions with AgriPoliS to capture strategic decision making and actions of agents in land markets. The paper tests the models on their suitability, computational requirements and run-time complexities. The output from AgriPoliS serves as the input features for the deep learning models. Models are evaluated using a combination of coefficient of determination (R2 score), mean absolute error, visual displays and runtime. The models were able to replicate the variable of interest with a high degree of accuracy with R2 score of more than 90%. The CNN was the most suited for replicating the data. Through this work, we learned the required complexities, computational and training efforts needed to integrate deep learning and AgriPoliS to capture strategic decision-making

Making milk quality assurance work on an unlevel playing field : Lessons from the Happy Cow pilot

This report describes and assesses a milk quality assurance innovation, the milk quality tracking andtracing system (MQT&T) and Quality-Based Milk Payment System (QBMPS) project. The project was piloted by Happy Cow Ltd (HC), a medium-scale processor in Nakuru, Kenya, and its milk suppliers.The objective of the pilot project was to offer a proof of concept to track and trace milk quality within a smallholder-dominated supply chain and to develop and implement a payment system based on the quality of raw milk delivered. The assessment adapted the PPPLab Scaling Scan as the mainframework to enumerate the various project investments, interventions and achievements and toreflect on the success factors, shortcomings and preconditions required for QBMPS scalability