4.0 RESULTS AND
DISCUSSION
In this chapter the
results of the study are presented and discussed. First, we characterize
smallholder maize farms in Gisagara district and later analyze their
productivity and technical efficiency.
4.1 Characterization of
smallholder maize farms in Gisagara district
A total of 241 household
heads from Gisagara district were retained as the sample (after excluding 39
outliers). The number of households managed by males was quite higher than the
number of households managed by females (Table 4.1). This however did not
tempt us to expect that sex would have a positive effect on technical
efficiency since its effect has been reported in the literature to be ambiguous
(Tchale and Sauer, 2007).
Table 4.1: Gender decomposition of households
|
Sex of the household head
|
Frequency
|
Percentage
|
|
Female
|
102
|
42
|
|
Male
|
139
|
58
|
|
Total
|
241
|
100
|
Source: Survey data, 2009
Generally, almost 80
percent of households in Gisagara district had 30 or more years (Table 4.2).
The implication is that if old age had a significant positive effect on
technical efficiency, then a majority of households would be efficient.
However, some literature considers age to have an ambiguous effect (Shuhao,
2005).
Table 4.2: Age frequency distribution of
household heads
|
Age of household head
|
Frequency
|
Percentage
|
|
18-30
|
49
|
20.3
|
|
30-42
|
76
|
31.5
|
|
42-54
|
60
|
24.9
|
|
54-66
|
40
|
16.6
|
|
66 and above
|
16
|
6.6
|
|
Total
|
241
|
100
|
Source: Survey data,
2009
In many studies, education has been hypothesized to positively
influence technical efficiency of farms (Amos, 2007; Kibaara, 2005). Education
levels of household heads of Gisagara district were low given that those who
attained either secondary or university education were only 9.13 and 90.87
percent either had no education or attained primary education. However, almost
66 percent attained some education (Table 4.3).
Table 4.3: Distribution of
household heads according to education level
|
Education level of household head
|
Frequency
|
Percentage
|
|
No education
|
82
|
34.02
|
|
Primary
|
137
|
56.85
|
|
Secondary
|
20
|
8.30
|
|
University
|
2
|
0.83
|
|
Total
|
241
|
100
|
Source: Survey data,
2009
Households with a dependency ratio of 0.5 or larger were
almost 71 percent (Table 4.4). Since higher dependency ratio has been reported
to reduce efficiency levels (Bagamba, 2007.
Table 4.4: Dependency ratio
of household heads
|
Dependency ratio
|
Frequency
|
Percentage
|
|
0
|
26
|
10.8
|
|
0.1-0.5
|
45
|
18.7
|
|
0.5-0.9
|
155
|
64.3
|
|
1
|
15
|
6.2
|
|
Total
|
241
|
100
|
Source: Survey data,
2009
Access to extension services has been reported to positively
influence technical efficiency of farmers especially because farmers acquire
information about better farming practices and agricultural technologies
(Bagamba, 2007; Shuhao, 2005). In Gisagara district, access to extension
services was low given that only 19 percent received 1 extension visit or more
during season A of 2008/09 (Table 4.5).
Table 4.5: Extension visits
received by households
|
Extension visits
|
Frequency
|
Percentage
|
|
0
|
195
|
81
|
|
1-5
|
40
|
16.6
|
|
6-10
|
5
|
2
|
|
11-15
|
1
|
0.4
|
|
Total
|
241
|
100
|
Source:
Survey data, 2009
Possession of land titles
helps to improve land tenure security and makes land owners feel confident to
make long-term investments in their land which in turn may enhance their
productivity and technical efficiency (Blarel, 2001; Musahara, 2006). In
Gisagara district, the number of households with land titles was higher than
those without titles (Table 4.6).
Table 4.6: Possession of land titles
|
Land title
|
Frequency
|
Percentage
|
|
Have title
|
138
|
57.3
|
|
Have no title
|
103
|
42.7
|
|
Total
|
241
|
100
|
Source: Survey data, 2009
Gisagara district is divided into 2 agro-climatic zones,
Bwanamukali (which receives more rainfall and is more fertile) and Mayaga
(which receives less rainfall and is less fertile). The number of households
who belonged to Bwanamukali was higher than that of Mayaga (Table 4.7).
Table 4.7: Households per agro-climatic
zone
|
Agro-climatic zone
|
Frequency
|
Percentage
|
|
Bwanamukali
|
139
|
57.7
|
|
Mayaga
|
102
|
42.3
|
|
Total
|
241
|
100
|
Source: Survey data, 2009
Access to the market has
been reported to positively influence the productivity and technical efficiency
of farms (Bagamba, 2007). At least 67 percent of total sampled households
travelled less than five kilometers (Table 4.8) to reach the market while at
least 33 percent of total sampled households travelled five kilometers and
above.
Table 4.8: Households and
distance to the market
|
Distance to the market (Km)
|
Frequency
|
Percentage
|
|
5
|
161
|
66.8
|
|
5 and above
|
80
|
33.2
|
|
Total
|
241
|
100
|
Source: Survey data, 2009
Distance from the households' residences to plots has been
reported to negatively affect the productivity and technical efficiency of
farms (Shuhao, 2005; Byiringiro and Reardon, 1996). In Gisagara district,
almost 94 percent of households travelled an average distance of less than two
kilometers to reach their plots (Table 4.9). Thus, distances were not so
constraining. Note that in the table 4.9, the single household with plot
distance of 19-20 km was treated as an outlier and dropped.
Table 4.9: Households and average plot
distance
|
Average plot distance (Km)
|
Frequency
|
Percentage
|
|
< 2
|
226
|
93.78
|
|
2-4
|
12
|
4.98
|
|
4
|
2
|
0.83
|
|
Total
|
240
|
99.59
|
Source: Survey data,
2009
It has been argued that a plot that is averagely less than one
hectare cannot be economically productive (Mosley, 2004). On average, at least
88 percent of households in Gisagara district had plots of less than one
hectare (Table 4.10).
Table 4.10:
Average plot size per Household
|
Average plot size category
|
Number of households
|
Percentage
|
|
0.25
|
133
|
55.2
|
|
0.26-0.5
|
53
|
22.0
|
|
0.51-0.75
|
28
|
11.6
|
|
0.76-1
|
7
|
2.9
|
|
above 1
|
20
|
8.3
|
|
Total
|
241
|
100
|
Source: Survey data,
2009
The number of plots can have positive effects (Shuhao, 2005;
Marara and Takeuchi, 2003) on technical efficiency. However, other studies have
reported that the higher the number of plots the lower the technical efficiency
levels of farmers (Raghbendra, 2005). Households with 2 or more plots were
54.36 percent of total sampled households (Table 4.11).
Table 4.11:
Number of plots per household
|
Number of plots
|
Number of households
|
Percentage
|
|
1
|
110
|
45.64
|
|
2
|
85
|
35.27
|
|
3
|
33
|
13.69
|
|
4
|
7
|
2.9
|
|
5
|
5
|
2.07
|
|
7
|
1
|
0.41
|
|
Total
|
241
|
100
|
Source: Survey data,
2009
| 
