The study was conducted in three villages namely Barjuna, Nakasini and Korolia under Kapasia Upazila of Gazipur District. The long term meteorological data (1961-2010) were collected from the nearby Meteorology Station and then analyzed to identify the climate variability and extreme events and to verify the climate variability and events with the farmer experiences and perceptions. Climatic parameters were analyzed by calculating LCL (Lower Confidence Level) and UCL (Upper Confidence Level) from the period of 1960-2000.
LCL= X -2.025 * {SD (1960-2000)/ SQ (n)}
UCL= X +2.025 * {SD (1960-2000)/ SQ (n)}
Here X= Average of 1960-2000 observation, SD= Standard deviation of 1960-2000 observation, SQ= Square root of 1960-2000 observation, n= Number of observation, 2.025= Coefficient factor
Ninety respondents from three villages were extensively interviewed through pre-tested questionnaire during November 2010 to February 2011. Focal Group Discussions (FDGs) were made to verify the information and to know the important issues. The major parameters which were included in the study were socio-economic, changing pattern of homestead production systems, perception of climate change and its impacts. The changing pattern of homestead production systems of the respondents on climate change were compared to current time to at least 10 years back. Major adaptation measures are being undertaken by the local community and possible steps to be undertaken to sustain the homestead farming were also studied. To indicate the importance and species richness of different plant species in study area, Relative Prevalence (RP) of species was calculated as follows:
RP = Population of the species per homestead * percent homesteads with the species.
The Shannon-Wiener Index (SWI) was also used to evaluate the species richness and abundance of trees in all three locations (Margurran, 1988). The proportion of species (i) relative to the total number of species (pi) was calculated and then multiplied by the natural logarithm of the same proportion (lnpi). The resulting product is summed across species, and multiplied by-1.
SWI = pi[ln(pi)]
The Standardized Precipitation Index (SPI) calculation for any location is based on the long-term precipitation records for a desired time period. This long-term record is fitted to probability distribution, which is then transformed into a normal distribution (Edwards and McKee, 1997). It reflects the number of standard deviations that an observed value deviates from the long-term mean.
SPI = Xi - X/σ
Where, SPI is Standardized Precipitation Index Xi - X; and σ are ith year precipitation, long term mean of precipitation and standard deviation of mean, respectively. After collection of data, all information contained in the interview schedule were edited. Statistical Package of Social Science (SPSS) computer software was used to analyze the data. Statistical measures, such as frequency counts, percentages, range, mean and standard deviation were used to describe the data.