Selection of the Study Area and Data Collection: The field study was performed in 2014 and included two villages, namely Salakandi (V1) and Binpara (V2) in the Mymensingh district of Bangladesh. These villages were selected due to the concentration of small-scale dairy farms in that area, since small farm owners constitute the population for the present study. Fifty households from each village were randomly selected for interviews. This study was based on field level primary data collected from the dairy farmers. The methods used were direct observation and interview with the respondents using interview questionnaires. Before the interview each respondent was provided a brief explanation of the purpose of the study.
Participation of Rural Women in Dairy Farming Activity: The participation of rural women in dairy farming activities was studied under the categories of feeding, breeding, livestock management, health care, purchasing, processing, and marketing, which were outlined by Lahoti et al. (2012), Arshad et al. (2013), and Rathod et al. (2011). The rural women were asked to indicate who participated in each activity on four-point Likert scale viz. participation by wife alone, participation by husband alone, participation by both husband and wife, and no participation.
Percentage of Women Participation Index: A percentage of women participation index was calculated to determine the participation rate of women in each
dairy farming activity. This was based on the following formula:
Percentage of Women Participation Index (WPI) = (Actual participation of women/Full participation of women)*100
Factors Influencing Rural Women in Participation in Dairy Farming: Factors were constructed to identify the reasons that influenced and motivated rural women to engage in smallscale dairy farming. These factors were to become self-dependent (personal factor), poverty (economic factor), and availability of loans (social factor). These variables were determined by using yes = 1 and no = 0. A multiple linear regression model was used to identify the factors influencing their participation in dairy farming. The model can be specified as:
Y = ?0 + ?1X1 + ?2X2 + ?3X3+ei (1)
Where, Y = women’s participation in dairy farming, X1 = personal factor, X2 = social factor, X3 = economic factor and ei = error term, ?1, ?2, ?3 are the coefficients
Poverty Reduction Indicators: Items related to reducing poverty were measured using a four-point Likert scale in which 1 = not at all, 2 = to some extent, 3 = to an average extent, 4 = to a great extent. These items were selected according to basic human needs and livelihood indicators.
Relationship between Poverty Reduction Indictors and Women’s Contributions through Participation in Dairy Farming: s correlation was used to test the relationship between poverty reduction indictors and women’s contributions through participation in dairy farming. Here, the variable of women’s contribution through participation in dairy farming uses the following formula:
Women’s contribution through participation in dairy farming = (Dairy farming return/Total household income) *100
- Dairy farming return = milk sales + cow dung sales + cattle sales
- Total household income = husband income+ dairy farming return+ other income
All variables, such as dairy farming returns, total household income, milk sales, cow dung sales, cattle sales, husband’s income and other income were considered as taka per year. One US dollar = 78 taka (Bangladeshi currency)
Impact of Participation in Dairy Farming on Poverty Reduction in Rural Areas: Some selected variables, such as emerging from a patriarchal social system, economic well-being and consciousness of rural women can be expected to reduce poverty in rural area through women’s participation in small-scale dairy farming.
Explanation of the Independent Variables
Emerging from the Patriarchal Social System: This variable has been considered, because after participation in dairy farming, rural women were able to increase their inner strength. They could express their own opinions, decisions, and feelings in front of their husband. Additionally, they feel protected against violence and their bargaining power was raised. Therefore, coming out from under a patriarchal social system can be expected to reduce poverty in rural areas. Three variables, i.e., increasing inner strength of women, control over household income and independent decision making were considered. Increasing inner strength was determined by using a five-point Likert scale ranging from 5 = strongly agree to 1 = strongly disagree. Control over household income was assessed using a three-point Likert scale ranging from 3 = full control, 2 = medium control and 1 = no control. Lastly the variable of independent decision making was calculated by using a four-point Likert scale from 4 = to a great extent to 1 = not at all.
Economic Well-being: This variable was measured by using two variables: improvement of quality of life of women and spending dairy farming money independently. A four-point Likert scale ranging from 4 = to a great extent to 1 = not at all was used to measure these variables.
Consciousness of Rural Women: After participating in dairy farming, the consciousness level of rural women increased, which is also expected to reduce poverty. This was determined by considering factors like reduced family size, which was measured using a five-point Likert scale ranging from 5 = strongly agree to 1 = strongly disagree. A multiple linear regression model was used to identify the effects of dairy farming on poverty reduction. The model can be specified as:
Y = ?0 + ?1X1 + ?2X2 + ?3X3+ei (2)
Y = poverty reduction, X1 = emerging from patriarchal social system, X2 = increasing consciousness of rural women, X3 = economic well-being of rural women and ei = error term, ?1, ?2, ?3 are the coefficients
Hypothesis of the Vicious Cycle of Poverty: It is expected that women participating in small-scale dairy farming would reduce poverty through increasing
family income, savings, improving educational facilities and living standards resulting from the breakdown of low productivity, low income, low savings and poor livelihood.
Data Analysis: Data were analyzed using a multiple linear regression model to measure the effects of different independent variables on dependent variables. Pearson’s correlation test was also used to determine the relationship between concerned independent variables and dependent variables. Results were considered at 1%, 5%, and 10% significance levels.