School of Agriculture, Veterinary Science and Technologyhttp://ir-library.mmust.ac.ke:8080/xmlui/handle/123456789/392024-03-29T09:53:07Z2024-03-29T09:53:07ZPERCEPTIONS OF SUGAR SUBSECTOR ACTORS ON THE IMPACT OF POLICY ISSUES ON REVIVAL OF SUGARCANE FARMIN IN THE WESTERN KENYA SUGARBELKOMBO, JOSEPHAT BARASAhttp://ir-library.mmust.ac.ke:8080/xmlui/handle/123456789/26182024-02-15T18:02:37Z2023-11-01T00:00:00ZPERCEPTIONS OF SUGAR SUBSECTOR ACTORS ON THE IMPACT OF POLICY ISSUES ON REVIVAL OF SUGARCANE FARMIN IN THE WESTERN KENYA SUGARBEL
KOMBO, JOSEPHAT BARASA
2023-11-01T00:00:00ZOCCURRENCE OF GROUNDNUT ROSETTE DISEASE AND DIVERSITY OF ITS CAUSAL AGENTS IN WESTERN KENYAMukoye, Benardhttp://ir-library.mmust.ac.ke:8080/xmlui/handle/123456789/25582024-01-16T12:54:21Z2018-01-01T00:00:00ZOCCURRENCE OF GROUNDNUT ROSETTE DISEASE AND DIVERSITY OF ITS CAUSAL AGENTS IN WESTERN KENYA
Mukoye, Benard
Groundnut (Arachis hypogaea L.) is an economically important edible oilseed legume in Sub-Saharan Africa (SSA). Nearly 75% to 80% of the world’s groundnut is grown by resource poor smallholder farmers in developing countries, who routinely obtain yields of 500-800kg/ha, as opposed to the potential yield of >2.5t/ha. Groundnut rosette disease (GRD) is a major constraint in SSA, which can cause 100% yield losses in a devastating severe epidemic situation, to the extent of abandoning the fields. The disease is caused by two synergistic viruses; groundnut rosette assistor virus (GRAV, genus Luteovirus) and groundnut rosette virus (GRV, genus Umbravirus) associated with a satellite-ribonucleic acid (Sat-RNA). The complex etiology and lack of sensitive and specific diagnostic tools, are major limitations in understanding the epidemiology of GRD viruses, and developing appropriate management strategies for the disease. Simultaneous detection of the GRD causal agents is possible by multiplex RT-PCR but this depends on the availability of specific primers to known agents that occur in a specific area. This information is limited for GRD causal agents in western Kenya. This requires a robust detection method which can single out all the GRD agents and their variants. To date, lack of sufficient research on the occurrence, distribution and diversity of GRD causal agents has resulted in continued and increased yield losses amongst groundnut farmers. Recent observations made in groundnut farms in western Kenya have shown that GRD is very severe and highly variable in symptoms appearance. The causes of this is not well documented. This study will determine the occurrence of GRD and characterize GRD causal agents in western Kenya. Disease diagnostic surveys will be conducted in six counties; Bungoma, Busia, Homabay, Kakamega, Siaya and Vihiga. Disease incidence and severity will be scored on the disease score sheet. Symptomatic and asymptomatic groundnut leafy samples will be collected and preserved for laboratory analysis. Total RNA will be extracted by RNeasy Mini Kit (Qiagen), and sequenced using next generation sequencing technologies (NGS). Biological characterization of GRD will be done through mechanical inoculation on leguminous hosts and further vector and seed transmission studied. The data collected on incidence and severity will be subjected to analysis of variance (ANOVA), using Statistical Analysis Software (SAS) program (SAS Institute lnc.). Pairwise comparison of means will be done using Least Significance Difference (LSD) at P
0.05 confidence level. Sequence reads will be analyzed using an in-house, customized version of the Galaxy project bioinformatics pipeline. The reads will be mapped to a custom database of plant virus sequences using Bowtie2 2.2.3+, and further analysis done to establish diversity and other molecular characteristics. This research will provide comprehensive knowledge of GRD viruses, rosette symptoms, better crop protection technologies and acceptable agronomic farming practices, for considerable increased groundnut production.
Doctor of Philosophy in Crop Protection
2018-01-01T00:00:00ZAPPLICATION OF SOCIAL NETWORK ANALYSIS TOOL TO INFORMATION FLOW AND ITS INFLUENCE ON THE ADOPTION OF SUSTAINABLE AGRICULTURAL INNOVATIONS IN BUSIA COUNTY, KENYAMBAKAHYA, GEORGE MICHAELShttp://ir-library.mmust.ac.ke:8080/xmlui/handle/123456789/25572024-02-15T17:59:06Z2018-10-01T00:00:00ZAPPLICATION OF SOCIAL NETWORK ANALYSIS TOOL TO INFORMATION FLOW AND ITS INFLUENCE ON THE ADOPTION OF SUSTAINABLE AGRICULTURAL INNOVATIONS IN BUSIA COUNTY, KENYA
MBAKAHYA, GEORGE MICHAELS
The growth of Agricultural productivity in Western Kenya has lagged behind largely due to low adoption of agricultural innovations. The low adoption is attributed to deficiencies in the existing agricultural extension system. The system for a long time has embraced the linear top-down model of information generation and dissemination. In this model, farmers are regarded as spectators of the innovation development process yet; a lot of information is shared through interpersonal channels within social networks. To help address the issue, Social Network Analysis (SNA) was used to map, measure and analyze social relationships among farmers, agricultural extension service providers and researchers who act as channels for the transfer of information. The study was conducted in 4 villages randomly selected in Nambale Sub-county namely; Elwanikha, Ibanda, Budokomi and Ekisumo. The specific objectives of the study were; to determine flow of agricultural information among the farmers through their social networks, to document relational and structural factors that influence flow of agricultural information within the social networks, to describe the formal and informal communication and their influence on adoption of agricultural innovations and to provide recommendations how extension service providers can make use of social networks to increase the of adoption of agricultural innovations. The study adopted ethnographic research design which comprised of social mapping and in-depth interviews. Initial respondents in each village were purposively identified followed by snowballing to generate subsequent respondents. Data was collected using sociometric technique, semi-structures interviews and in-depth interviews to investigate flow of agricultural information and adoption of three selected agricultural innovations within social networks; 1.) Use of Desmodium (Desmodium uncinatum) to smoother Striga (Striga hermonthica) 2.) Use of lime to control soil acidity 3.) Use of hermetic bags in post-harvest storage of maize. Socio-metric analysis was done using UCINET VI version 6.624. Net draw version 2. 160 an interphase program was used to create illustrative maps. The socio-metric analysis of the villages produced 716 nodes (actors) with 1,952 ties (relationships). The socio-grams showed a mixture of weak and strong and weak ties with a minimum and maximum clustering co-efficient of 0.214 and 0.612 respectively. The study established that the social networks of Nambale Sub-county are characterized by both weak and strong ties which are traits in network structure that are significant in sharing of information on sustainable agricultural innovations. However, agricultural extension workers have failed to take advantage of these existing social networks to disseminate agricultural information because the adoption of the selected innovations was low in all the three villages. By leveraging on the power of social networks, the extension service providers can use the method to map information networks which can be used to disseminate agricultural information that would stimulate adoption of innovations among farmers.
Doctor of Philosophy in Sustainable Agricultural Systems
2018-10-01T00:00:00ZA DATA-DRIVEN MODEL FOR SUSTAINABLE DEPLOYMENT AND ADOPTION OF CLIMATE SMART AGRICULTURE PRACTICES AMONG SMALLHOLDER FARMERS IN KAKAMEGA COUNTYNdung'u, Simon Ndogohttp://ir-library.mmust.ac.ke:8080/xmlui/handle/123456789/25112024-02-15T17:58:43Z2023-11-01T00:00:00ZA DATA-DRIVEN MODEL FOR SUSTAINABLE DEPLOYMENT AND ADOPTION OF CLIMATE SMART AGRICULTURE PRACTICES AMONG SMALLHOLDER FARMERS IN KAKAMEGA COUNTY
Ndung'u, Simon Ndogo
Kenya’s agriculture is dominated by 4.5 million smallholder farmers who produce over 75% of the national agricultural production. These farmers are the most vulnerable to climate change because of various socioeconomics, demography, and policy trends limiting their capacity to adapt to the change. To mitigate the negative effects of climate change on smallholder farmers, numerous interventions in the form of Climate Smart Agriculture (CSA) Technologies have been developed and promoted by various organizations. The current deployment of CSA practices, however, does not consider individual farm-level biophysical and socio-economic characteristics during the design and implementation of the interventions. This study, therefore, enhances smallholder farmers adaptation to climate change by development, prototyping and evaluating the suitability of a data-driven model for the sustainable deployment and adoption of CSA practices. Through a quantitative survey of 428 respondents, this study investigated the major socio-economic and biophysical characteristics of smallholder CSA farmers and developed a predictive tool for sustainable deployment of CSA practices. Supervised Machine Learning using the Scikit-Learn library of Python Programming language was used to build, pilot, and review Decision Tree and Random Forest Classifier models. The predictive tool was piloted among 15 smallholder CSA farmers and validated by key stakeholders in the CSA ecosystem through a Focus Group Discussion. While agroforestry, composting, and soil and water conservation structures were the most adopted, push-pull technology, conservation agriculture, and vermiculture were the least adopted CSA technologies. This study, further, established that smallholder farmers’ level of education, membership to a farmers’ group, interaction with extension officers and farming experience influenced adoption of CSA technologies. Factors that increase household productive resources, such as land ownership, household income, and access to agricultural credit also influenced adoption of CSA practices. The classifier model produced a Mean Squared Error of 0.16. The model predicted smallholder farmer adoption at an accuracy of 89.53% and 80.0% with test data and pilot data, respectively. Through the study, it was possible to predict which smallholder farmers would be CSA technology adopters using their farm specific characteristics. This study, therefore, develops a model for the optimal selection of Climate Smart Agriculture intervention beneficiaries.
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