Bananas and Food Security - Bioversity International

Afrique / Africa : F. Bagamba et al.
inputs used. The same study by Bagamba (1994) shows that banana in pure stand in
central Uganda was more profitable than a banana/beans system (benefit/cost
ratio = 1.86) but less profitable than a cassava/beans system. A previous study had shown
bananas to be more profitable (704 U. Sh/man-day) than maize (650), beans (500) and
cassava (556) but less profitable than sweet potatoes (734). However, differences in
plantation management, levels of inputs used and their effect on crop profitability were
not clearly apparent.
This study was undertaken to assess the profitability of existing management systems
and of banana at the farm level. The role of banana in the production systems of the
study area was established. Factors affecting banana productivity and profitability were
examined. Major competing cropping systems identified in the study area were banana,
coffee, maize/beans, beans, sweet potatoes and cassava.
Methodology
Study area
The study was carried out in Kisekka sub-county, Masaka district. The district is located
approximately 130 km south of Kampala. The terrain is generally flat with shallow
elongated valleys and flat-topped hills.
Soils are mainly sandy loams. Rains are bimodal, ranging from 750-1200 mm annually.
The natural vegetation is mainly dry acacia savanna, with forest and savanna mosaics in
some areas. The dominant grass is Cymbopogon afronadus (“Eteete”). One parish,
Nakatete, and three villages in the subcounty were named after this grass, emphasising
the its abundance in the area. The sub-county falls under the banana/coffee farming
system with bananas as the major food crop and coffee the traditional cash crop. Other
crops grown include cassava, sweet potatoes, maize, beans, sorghum, millet, tomatoes,
onions, passion fruit, ginger, cabbage, pineapples, sugarcane, tobacco, pumpkins, yams,
egg plants and field peas. The main economic activity is agriculture with a bias towards
food crops.
Data collection
Primary data were collected using a pre-tested questionnaire. Kisekka sub-county was
selected because it is located in the major banana producing area, although production
is on the decline.
The study involved 65 farmers selected from four parishes at random. With the help
of local council officials, a list of villages for the selected parishes was obtained. One or
two villages were selected from each parish depending on the intensity of agricultural
activity in the parish. At the village level, a list of households was obtained and farmers
categorised into three wealth groups using indicators provided by farmers. Farmers were
randomly selected from each group, depending on the number of households in each
wealth group, village household population and location. Wealth indicators used included
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