Determinants of small-scale farmers adaptation decision to climate variability and change in the North-West region of Cameroon

摘要

Small-scale farmers’ adaptation decision in the face of climate variability and change (CVC) depends largely on their ability to perceive the impacts of CVC as well as their degree of vulnerability to these impacts. This research looks at the factors that influence small-scale farmers’ adaptation decision faced with climate variability and change, with particular focus on Mbengwi Central Sub-Division, North-West Region of Cameroon. The study made use of household surveys to identify the impacts, determine vulnerability and assess the factors influencing small-scale farmers’ adaptation decision. Data obtained from household surveys was analyzed using descriptive statistics (bar charts, percentage indices) and inferential statistics (Mann-Whitney test, Chi-Square, and the Binomial Logistic (BNL) regression model). Data analysis was done on Microsoft Excel 2007 and the Statistical Package for Social Sciences (SPSS) 17.0. Results showed that, following small-scale farmers’ perceptions, crop productivity decline was the main impact of CVC (96.7%) and poverty the principal cause of vulnerability to CVC (98.3%). Mann-Whitney test results revealed that there was a significant difference between farmers’ adaptation decision and six hypothesized continuous explanatory variables (age, household size, farm size, number of farms, annual family income, farm experience) (p<0.01). Chi-square test results revealed that there was a significant difference between farmers adaptation decision and some hypothesized discontinuous explanatory variables (perception of extreme weather events, access to weather information, access to extension services, access to credit, membership in farming groups and distance to markets) (p<0.01). Results of the BNL regression model showed that the main determinants of small-scale farmers’ adaptation decision in the study area were age of household head, farm size and access to weather information (p<0.05).