Table 5.13 provides socio-economic characteristics of the heads of the households interviewed during the survey of producers of potential feedstocks for producing biofuels. The table shows that almost half of the interviewed households’ heads were aged between 30 and 50 years. Households’ heads aged less than 30 years constitutes 11.2% of the total number of respondents. It could be further noted that interviewees aged more than 50 years accounts for 37.5% of the total number of interviewees. The small proportion of respondents aged below 30 years could be attributed to several factors. One of the most likely reasons is the rural urban migration which in most cases involves the youth.
The present study also assessed the levels of education of the producers of potential feedstocks for biofuels production. The results of this assessment, presented in table 5.13, show that about 80% of the respondents reported to have attained some form of formal education. This is close to the national literacy level which is 78% (URT, 2006). Furthermore, the table shows that only 10.5% of those who reported to have acquired formal education had attained more than seven years of schooling. This implies that most of the respondents who reported to have got formal education had achieved a maximum of seven years of schooling. The large proportion of respondents who had attained only seven years of formal education could be attributed to the fact that the compulsory primary education in Tanzania lasts for seven years.
Furthermore, table 5.13 shows that female household heads constitute only 21% of the total number of farmers interviewed during the survey undertaken for the present study. The national average for female headed households in rural Tanzania is 17.5% (URT, 2006). Thus, the gender distribution of the interviewees of the present study reflects the rural households heads distribution of the entire country.
Moreover, the table shows that farmers whose farms were less than three hectares account for 41.6% of the total number of interviewees. The large proportion of respondents who had farms which were less than three hectares is not surprising. This is because the national average farm size is less than one hectare (URT, 2006).
Table 5.13: Socio-economic Characteristics of Household Heads A: Households heads’ age distribution:
Age Category Number Percent
Below 30 30 11.2 30-40 65 24.3 40-50 72 27.0 50-60 45 16.9 Above 60 55 20.6 Total 267 100.0
B: Households heads’ levels of education: Number Percent
No formal education 40 15.0
Adult education 12 4.5
Primary education 187 70.0
Secondary education 28 10.5
Total 267 100.0
C: Gender of the respondent: Number Percent
Male 211 79.0
Female 56 21.0
Total 267 100.0
D: Average household size 6.0
E: Average farm size and farm size distribution
Average farm size 4.80 ha
Farm Size Categories Number of Farms Percent
Below 3 ha 111 41.6 3-6 ha 82 30.7 6-9 ha 43 16.1 9-12 ha 12 4.5 Above 12 ha 19 7.1 Total 267 100.0
F: Annual off-farm income
Source of Income Number of Farmers Average Annual Income(TZS)
Formal employment 19 1,054,086 Local brewing 6 1,000,000 Carpentry 3 710,000 Charcoal making 4 234,000 Small business 58 933,103 Masonry 8 1,064,700
Average Annual Income 98 936032
G: Annual farm income by farm size category
Farm Size Category Average Farm Size (ha) Average Annual Income(TZS)
Below 3 ha 1.82 857925
3-6 ha 4.16 1197793
6-9 ha 7.35 2033936
9-12 ha 10.42 2378460
Above 12 ha 16.91 3848973
Total Sample Average 4.8 1432884
Source: Own computation
Table 5.13 shows that the average farm size among the respondents is 4.8 ha. This is very high if compared to the national average of 0.7 ha (URT, 2006). The relatively larger farms among the respondents could be attributed to the commercial orientation of the farmers targeted by the present study. Their commercial orientation implies that their production plans are not only determined by the need to fulfil subsistence
requirements, which is likely to be the major determinant of farm size for the majority of smallholders in the country, but also other objectives such as profit or revenue maximisation. These additional requirements are likely to be the main driving force behind the relatively larger farm sizes among producers of potential feedstocks for producing biofuels. Moreover, the respondents produce sugarcane, amongst other crops. Sugarcane is produced for sell to nearby sugar factories which also support the farmers in various ways. The support offered by the factories, which include provision of land preparation services, seed-cane, and in some few instances fertilisers on credit terms, might be among the reasons for the observed relatively larger farms in the study area.
Furthermore, table 5.13 shows that the average annual income from the various off-farm activities undertaken by the producers of potential feedstocks for producing biofuels ranges from TZS 234000 for charcoal making to TZS 1064700 for masonry. Also the table shows that operating small businesses is the most common source of off-farm income. Operators of small businesses constitute 21.7% of the total number of respondents and 59% of those who participate in the non-farm income generating activities. The average annual income from off-farm activities is TZS 936032. Moreover, the table shows that the average off-farm income is lower than the average farm income, which is TZS 1432884 per year. The difference between the two sources of income could be attributed to the fact that most farmers consider farming to be their primary activity and off-farm activities to be secondary. Thus, in most cases farmers concentrate most of their efforts on farming.
Furthermore, the table shows that off-farm income generating activities account for 39.5% of the total household income. Thus, it is plausible to argue that off-farm income generating activities constitute an important source of livelihoods for producers of potential feedstocks for producing biofuels. Since off-farm income sources have been found to have a significant contribution to the total household income, then poverty alleviation efforts in rural areas should not only focus on the improvement of the performance of farming activities, but should also seek to provide a conducive environment for the operation of non-farm income generating activities.