Rural aquaculture: Assessment of its contribution to household income and farmers’ perception in selected districts, Tanzania

Figures & data

Figure 1. Map of Tanzania with districts where the research was conducted.

Table 1. Some background information on fish pond farming in the surveyed districts (URT, 2016b).

District	Number of fish farmers	Number of fish ponds	Average pond size (m^2)
Kilombero	24	45	300
Mvomero	378	378	200
Igunga	71	56	150
Songea urban	1,051	3,571	200
Songea rural	1,327	3,254	200
Mbarali	293	380	450

Table 2. Gender, age, pond size, household size, education level, and experience among interviewed fish farmers in selected districts in Tanzania.

Characteristic	Description	n	(%)
Gender	Male	86	93
	Female	3	7
Age	≤26–35	16	18
	36–45	21	24
	46–55	17	19
	56–65	22	25
	>65	13	14
Pond size (mean ± SE)	399 ± 60.6	89
House hold size	<6	59	66.3
	7–9	24	27
	10–12	3	3.4
	>12	3	3.4
Time spent in school (level of education)	0 year (never went to school)	3	3
	7 years (standard seven)	51	57
	11 years (form four)	11	12
	13 years (form six)	2	2
	12 years (basic certificate)	5	6
	14 years (diploma level)	6	7
	16 years (bachelor degree)	7	8
	18 years (master’s degree)	4	4
Experience in fish farming	<5 years	40	45
	5–10 years	14	16
	11–15 years	9	10
	16–25 years	14	16
	>6 years	12	13

Table 3. Annual average household income (US$/year) from fish farming and other sources and their contribution to the household income.

Income	Average (mean ± SE)	Contribution (%)
Fish farming	222.4 ± 35.5	13.3
Non-fish farming	1458.8 ± 224.5	86.7
Total income	1681.2 ± 213

Table 4. Summary of regression model of the total household income as determined by fish and non-fish incomes.

Variable	B	SE	Β	T	Significance
Constant	0.349	0.065	—	5.371	0.004
Fish income	0.176	0.18	0.221	9.689	<0.001
Non-fish income	0.792	0.02	0.926	40.588	<0.001

Table 5. Average income (US$ per year) of household grouped by income sources and degree of livelihood diversification.

	Degree of diversification (number of activities)
	f + 1 (N = 16)	f + 2 (N = 36)	f + 3 (N = 20)
Fish income (mean ± SE)	152.66 ± 32.98a	270.84 ± 59.78a	191.04 ± 62a
Non-fish income (mean ± SE)	592.31 ± 107a	1,648.53 ± 349.32b	1,800.36 ± 398b
Total household income (mean ± SE)	744.99 ± 104.01a	1,919.4 ± 362.9b	1,991.4 ± 434b
Percentage contribution of fish to total income	20	14	9.6

f + 1 stands for fish farming with one additional source of income, f + 2 for fish farming with two additional sources of income, and f + 3 for fish farming with three or more additional sources of income. Rows with different letter differ significantly (ANOVA, p < 0.05). N stands for the sample size.

Figure 2. Farmers’ estimates on different utilization proportion of the cultured fish. Source: Field survey, April–July 2017.

Figure 3. Gender and age groups participating in household fish farming management practices. Source: Field survey, April–July 2017.

Table 6. Spearman correlation results for factors associated with income from fish farming.

Factor	Correlation coefficient	Significance
Age	0.150	p = 0.208
Education level	0.19	p = 0.873
Household income per person	0.585	p = 0.065
Experience	0.235	p = 0.047
Training	0.422	p < 0.01

Figure 4. Farmers’ future plan regarding fish farming. Source: Field survey, April–July 2017.

Table 7. Farmers’ perception on fish farming challenges and their severity.

	N	Severity
		Not at all	Minor	Moderate	Serious
Stunted growth	89	8%	10%	18%	64%
Water shortage	89	19%	19%	17%	45%
Lack of good quality fingerlings	89	39%	12%	10%	38%
High pond construction costs	89	22%	28%	16%	34%
High fingerling price	89	39%	16%	13%	31%
Lack of extension services	89	53%	15%	16%	17%
Lack of land	89	61%	13%	11%	15%
Conflict with other on water use	89	55%	15%	16%	15%
Lack of cold storage facilities	89	82%	3%	2%	12%
Lack of buyers	89	89%	4%	3%	3%
Lack of transportation	89	90%	4%	3%	2%

Figure 5. Farmers’ perception on the adoption rate of fish farming in the community. Source: Field survey, April–July 2017.

Figure 6. Reasons for poor community fish farming adoption status based on farmers’ perception. Source: Field survey, April–July 2017.

Figure 7. Farmers’ perception on measures to be taken to improve fish farming adoption rates in Tanzania. Source: Field survey, April–July 2017.

United Republic of Tanzania (URT). (2016b). Ministry of livestock and fisheries development, fisheries sector. Aquaculture Development Division. Aquaculture production statistics 2015/2016.

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