J. Sultana
Agrotechnology Discipline, Khulna University, Khulna, Bangladesh
M.B. Ahmed
Agrotechnology Discipline, Khulna University, Khulna, Bangladesh
M.Y. Ali
Agrotechnology Discipline, Khulna University, Khulna, Bangladesh
Climate, Resilient, Correlation, Cropping Patterns, Farmers’ perception
Batiaghata upazila of Khulna district
Knowledge Management
Cropping pattern
The present study was a descriptive and diagnostic type of research, based on a collection of data by door-to-door interviewing of the respondents. In this study, two techniques were used: (i) a statistical survey to identify present climate-resilient cropping patterns in four villages (Shoilmari, Joikhali, Raingamari, and Dorgatola) of Jalma union under Batiaghata Upazila of Khulna district and to assess farmers’ perception regarding the causes of adopting climate-resilient cropping patterns, (ii) Focus Group Discussion (FGD) with the participation of local people to find the cropping patterns over the last 30 years. The sample was collected by following the multistage disproportionate random method. For data collection, 10 % Upazila (out of 9 Upazila) in Khulna district were selected and then 10 % union (out of 7 unions) from each Upazila and 10 % villages (out of 37 villages) in each union were selected. Form 4 villages (10 % of total), 80 farmers (20 from each village) were selected. Primary data were collected through face-to-face interviews using an interview schedule from January to March 2019. Some of the preferred characteristics of the respondents were considered as independent variables viz. age, educational qualification, family education, family size, farm size, annual income, farming experience, and exposure to mass media. The perception of the respondents regarding causes of the adopting climate-resilient cropping patterns was considered as the dependent variable in this study. For analysis purposes, all qualitative data were converted to quantitative form by using an appropriate technique of scoring. In several instances, indices and scales were constructed through the simple accumulation of scores assigned to individuals or patterns of attributes. Among the selected characteristics age was measured in ‘actual year’, educational qualification in ‘years of schooling, family size in ‘number’, family education in ‘years of schooling’, farm size in ‘hectare’, farming experience in ‘year’, annual income in ‘000 BDT’ and exposure to mass media in ‘score’. A 9-item statement was used to determine the respondents’ perception regarding the causes of adopting climate-resilient cropping patterns. To determine the perception of the respondents regarding causes of adopting climate-resilient cropping patterns Likert’s type scale such as agree, strongly agree, undecided, disagree, and strongly disagree were used against each of the 9 statements. A score of 5, 4, 3, 2, and 1 was assigned against rating scales respectively. The perception score regarding the causes of adopting climate-resilient cropping patterns was determined by summing up all scores obtained against each of the 9-statements. The perception score of a respondent could range from 9 to 45, where ‘9’ indicates less clear perception and ‘45’ indicates highly clear perception. On the basis of perception score, the respondents were categorized into three groups as less clear perception (≤15), moderately clear perception (16-30), and highly clear perception (>30). To compare statements, a perception index of causes (PIC) was calculated using following formula (Ahmed, 2011): PIC = Nsa× 5 + Nag × 4 + Nud× 3 + Nda× 2 + Nsd× 1. Where PIC = Perception Index of Causes of Adopting Climate Resilient Cropping Patterns; Nsa=Number of respondents indicated as strongly agree Nag = Number of respondents indicated as agree; Nud= Number of respondents indicated as undecided Nda= Number of respondents indicated as disagreeing; Nsd = Number of respondents indicated as strongly disagree. The PIC scores could vary from 80-400. The scores were converted to percentages for a clear understanding of the causes that enhance the adoption of climate-resilient cropping patterns by using the following formula (Ahmed, 2011): PI = Observed PIC Score/Highest Possible PIC Score X 100. Different statistical treatments such as number, mean, standard deviation, range, minimum, maximum, rank order, and percentage were used to describe the variables. To explore the relationship between any two variables, Pearson Product Correlation Coefficient (for interval and ratio type of data) was used. The data were analyzed by using Statistical Package for Social Science (SPSS) 20.
SAARC J. Agri., 18(2): 207-217 (2020)
Journal