The Future of Smallholder Farming in Eastern Africa - Uganda ...

The strongest variable by far, however, proved to be that of “vulnerability” which displays a
strongly negative coefficient. The vulnerability of a household was determined by the absence of
adult family members as potential income earners. These vulnerable households, which are
either child headed or headed by elderly persons beyond the normal income generating age, face
extreme problems of poverty and their food security is severely at risk.
The diverse determinants of Food Security in the Lake Kyoga catchment area, illustrates the
increasing integration of even this isolated area into the market, and the diversity of coping
strategies being implemented by rural households. Rural households spread risk by embarking
on a range of income generation strategies as confirmed by the community and household level
data on sources of income (Figures 2-5.) and the household level data on coping strategies during
times of food shortage. (Figure 1)
Wetland development has historically been primarily directed at agricultural and industrial
expansion to meet the needs of urban areas, rather than devising development strategies that
focus on the productivity of these ecosystems and respond to the needs of the poor rural
producers who depend on them. To rectify this neglect, development approaches building on the
productivity of the natural ecosystem and the essential interdependence of agricultural
development and sustainable environmental management are now being developed (McNeeley
and Scherr, 2001).
6.1.4 Determinants of Agricultural Productivity
The results of the productivity analysis show a considerable consistency. All of the equations
were well defined. The mix of crops produced in the various districts was quite varied, with very
few observations for some crops in some districts. As a result the dummy variable for certain
districts was dropped in some equations. Even so, location is clearly an important determinant of
productivity. In general, the coefficients on the dummy variables for district were highly
significant – indicating considerable variability in productivity by district. It is only with
groundnut and simsim production that location is a less significant determinant of productivity.
As expected, in nearly all cases, seasonality proved to be a significant determinant of
productivity. The exception is beans, which are really only grown in first season. Certain crops,
however, do better during first season, while others perform best in second season. This is
largely the result of differences in rainfall and disease incidence, which is correlated with
particular weather factors.
Increased investment in agricultural production inputs (ie. Purchase of improved seeds, fertilizer
and pest control chemicals) was significantly associated with improved productivity for all crops
except groundnuts. The exception for groundnuts is likely related to the fact that seed cost is
very high, even for local varieties, and that the new rosette resistant varieties of groundnuts were
not yet widely available at the time of the study. For this reason groundnut production remains
very risky and prone to complete loss in the event of a rosette attack that is not caught early
enough.
The use of simple bucket or trench irrigation was observed for five of the eight crops. Irrigation
was found to be positively associated with productivity. The association was significant for
cassava, sweet potatoes and maize, but insignificant for beans and sorghum (probably due to the
limited number of observations). Unfortunately it was not possible to conduct an analysis of the