CONTRIBUTIONS OF WOMEN TO HOUSEHOLD FARMING DECISIONS AMONG COCOA-BASED AGROFORESTRY HOUSEHOLDS IN EKITI STATE, NIGERIA

Abstract
This study examined the contributions of women to household farming decisions among
cocoa-based agroforestry households in Ekiti state, Nigeria using cross-sectional data.
The study used purposive, multistage and random sampling techniques for the selection of
120 cocoa-based agroforestry farm units that constituted respondents for the study. The
analytical techniques involved descriptive statistics, exploratory factor analysis and
multinomial logistic regression model. With regards to food crop production activities, the
contributions of women to decision making were very high with mean values of between
2.48 - 3.19 on a 4-point scale, while that of the men were comparatively low with mean
values ranging from 1.85 - 2.66. However, in the cocoa production activities, the
contributions of women to decision making were relatively low with mean ranging between
1.42 - 3.23 compare to high contributions of men with mean values ranging from 2.82 -
3.94 on a 4-point scale.

The multinomial logistic regression result comparing high
contribution (3) as base outcome, revealed that years of formal education of the women,
financial contribution status of the women to farming activities, average number of hours
spent on cocoa farm per day were negatively related while years of farming experience of
the women and number of adult male farmers in a household were positively and
significantly related with the probability of women making low (1) or medium (2)
contributions to household farming decisions. The t-test of no significant difference
between the contributions of women and men to farming decisions in the production of the
integrated food crops and the cocoa revealed that, on the average, women had
significantly higher contributions to decision making in food crop production activities
while in cocoa production, men had significantly higher contributions. The identified
constraints militating against women farmers were classified into three major factors
using principal component factor analysis with varimax - rotated and factor loading of
0.30.