Influence Of Disaster Risk Reduction Projects On Adaptive Capacity To Climate Variability In Kitui County, Kenya

ABSTRACT

Disaster risk reduction projects are some of the initiatives undertaken with an aim of addressing climate variability associated disasters such as droughts and floods and loss of agricultural production. The projects are normally aimed at reducing vulnerability and increasing resilience among rural communities towards climate variability and change. Results of adaptation measures undertaken by such projects to increase adaptive capacity of the communities remain largely under researched. By use of a descriptive survey design, this study focused on the influence of Caritas disaster risk reduction projects on the adaptive capacity to climate variability in Kitui County. Specifically, the study sought to analyze rainfall and temperature data (1979-2014) for climate variability in the study area. It also evaluated the effectiveness of adaptation measures to climate variability among project beneficiaries as well as examining the influence of socio-economic factors on the uptake of the adaptation measures. During the study, two sampling techniques were used; purposive sampling that was used to select The Kenya Livelihood Support Project and Arid and Semi-Arid Lands Resilience Project implemented by Caritas-Kitui and also select key informants. Random sampling aided in selection of 106 project beneficiary households who were administered with questionnaires. Data obtained was analyzed through Statistical Package of Social Sciences and Excel in relation to the set objectives. The study employed descriptive statistics such as frequencies, mean and standard deviation as well as inferential statistics of ANOVA to test any significant differences on decadal means of rainfall and temperature while Chi-Square was used to test for any significant difference in rating used in Likert scale responses. Data was displayed in tables and graphs. Analyzed rainfall and temperature data obtained from Katumani (9137089) Kenya Meteorological Department station indicated that there was statistically significant mean temperature variation (p