Awareness and Perceptions of Climate Variability Adaptation among Forest-adjacent Communities in Mau Forest, Kenya

Adequate knowledge of climate variability is essential for the success of any adaptation or mitigation efforts. Thus, a study was conducted to investigate the levels of awareness and perceptions on the adaptation of climate variability among forest-adjacent communities (FACs) in Mau Forest, Kenya. Data for the investigation was generated through the administration of 313 questionnaires across randomly selected forest-adjacent households. Of the entire questionnaire, 311 were analysed, while two were returned uncompleted and therefore discarded. Descriptive and inferential statistical analyses were conducted. Descriptive analysis showed that 96% of the respondents had knowledge of climate variability, while 4% claimed no knowledge of it. Nearly two-thirds of respondents (65%) reported that they receive weather information, with media (electronic and print) cited as the most common (63%) source of this information. As regards frequency of information, those respondents who get information about climate variability occasionally constituted 60%, while 29% of respondents received information frequently. However, 14% of respondents rarely received any climate-related information. Respondents who received climate-related information occasionally (sometimes) constituted 56%. Alternatively, respondents reported that they obtained weather information from agricultural extension officers (17.6%) and Kenya Forestry Service (KFS) officials (15.3%). The perceptions of the farmers that they had observed erratic weather patterns with a general decline in rainfall and an increase in temperatures were corroborated by scientific data as a trend analysis on rainfall and temperature data over a 20-year period mirrored the farmer’s perceptions. A logistic regression model was fitted to determine the socioeconomic factors that influence farmers’ choice of adaptation to climate variability. The ANOVA test results (F-test) show that overall, the logistic regression model was significant (df = 310, p = 0.002). Results of the model revealed that household head age (df = 310, p = 0.015), household head years of residency (df = 310, p = 0.034), and farming experience (df = 310, p = 0.024) were all significant factors that influence FACs’ decision to adopt to climate variability either positively or negatively. This study therefore recommends for more awareness creation and training of FACs of East Mau on how to identify and deal with changing climatic conditions.  