Aquaculture is widely recognised as a way to reduce malnutrition and poverty. So far, research has mainly focused on Asia, and the few studies available from sub-Saharan Africa are predominantly ex-post partial analyses. By constructing a village computable general equilibrium (CGE) model, we aim to investigate whether aquaculture improves local livelihoods and simultaneously has the potential to counteract local overfishing. We apply this to a rural case study region in Namibia where malnutrition, poverty and fish resource overexploitation are current problems. Our village CGE model shows that aquaculture would be a viable livelihood activity improving household incomes and utility through labour reallocations. Furthermore, aquaculture can counteract malnutrition through increased fish consumption. Higher opportunity costs lead to households leaving the fisheries and switching to aquaculture. These substitution effects offer the possibility of reducing the pressure on local freshwater fish stocks. Policy makers can use the results to introduce aquaculture interventions in rural areas. Our findings indicate that such interventions should take particular account of the poorest households, which are most dependent on fisheries. The derived opportunity costs provide information about payments that are necessary to make policy interventions acceptable for different household groups.
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For a detailed specification of the class of CGE model used, see Lofgren et al. (2002).
The key modelling power of complementarity is that it chooses which inequality to satisfy as equality. Complementary slackness means that for each choice variable we must find the optimal solution that either (1) the marginal condition holds with a strict equality; or (2) the choice variable in question must take a zero value; or (3) both of the above (Bishop et al. 2001). This implies: if there is slack in a constrained resource (leftovers), then additional quantities of that resource must have no value. Likewise, if there is slack in the (shadow) price non-negativity constraint requirement (i.e. the price is not zero) then there must be scarce supplies (no leftovers).
GAMS is designed for the construction and solution of large and complex mathematical programming models. It enables solving of various kinds of economic models including linear and nonlinear optimisation as well as equilibrium modelling (Brooke et al. 1992).
The consumer bundle of the case study region includes maize, beef, milk, fish, rice, wheat, noodle, oil, potato, orange, bread, chicken, egg and non-food items.
In contrast, the Armington (1969) assumption is based on imperfect transformability and substitutability. He used constant elasticity of substitution for imports and constant elasticity of transformation for exports, which mathematically avoid a total elimination of unproductive trade flows. Hence, he did not allow any regime shifts. No country ever shifts completely from importing or exporting a commodity (Ackermann and Gallagher 2008).
This is ensured by simultaneous adjustments in three (endogenous) components of absorption: household consumption, investment quantity and government consumption. Under other investment-driven closures (value of savings adjusts), the quantities of investment and government consumption are both fixed, only household consumption is flexible (Lofgren et al. 2002).
Marginal interventions cannot be estimated reliably within the modelling system.
A SAM is a comprehensive economy-wide data framework and represents the whole economic system by explaining all payments within the economy for a single year. It plays an important role in policy planning at the regional and village level (Morton et al. 2016).
Overall robustness: the CGE results remained robust when applying an alternative production function (Cobb–Douglas).
Livelihood diversification can be defined as the process by which rural households construct a diverse portfolio of activities in order to survive and to improve their standards of living (Ellis 1998).
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This article has been written in the context of the project ‘SASSCAL—Southern African Service Science Centre for Climate Change and Adaptive Land Management’ (http://www.sasscal.org/). The project is funded by the German Ministry of Education and Research (BMBF) [01LG1201H].
Appendix A: Stylised social accounting matrix of the Sikunga Conservancy
|(A4) Fish resources||64.13|
|(A5) Forest resources||765.29|
|(A6) Off-farm employment||89.76||44.10||4.40|
|(C4) Fish resources||12.67|
|(C5) Forest resources|
|(C6) Off-farm employment|
|(C7) Food items||21.36|
|(C8) Non-food items||3.90|
|(F1) Unskilled labour||8.80||42.85||49.18||19.33||35.99||63.40|
|(F2) Skilled labour||13.85||48.28|
|(H1) Subsistence farmers|
|(H2) Fish and forest users|
|(H3) Skilled off-farm|
|(H4) Senior members|
|(S1) Cash savings|
|(S2) Agricultural capital|
|(ROW) Rest of World||41.95||4.69||9.22||10.11||0.83||21.14||69.85||70.74|
|(A4) Fish resources||1.95||8.18||0.03||1.96|
|(A5) Forest resources||25.83||20.31||5,84||4.95|
|(A6) Off-farm employment|
|(C4) Fish resources||3.39||1.68||1.06||2.13||53.30|
|(C5) Forest resources||0.24||0.07||0.06||0.46||748.98||12.65||3.67|
|(C6) Off-farm employment||7.84||4.20||3.97||5.98||32.89||39.86||11.13||5.04|
|(C7) Food items||17.45||11.39||9.51||10.14||44.10|
|(C8) Non-food items||24.45||16.11||11.59||14.69||4.40|
|(F1) Unskilled labour||27.20|
|(F2) Skilled labour||58.97|
|(H1) Subsistence farmers||76.46||14.98||5.55||3.76||1.21||9.10||17.82||0.66||12.72|
|(H2) Fish and forest users||62.66||12.68||7.70||2.48||5.15||12.31||9.00||6.41|
|(H3) Skilled off-farm||69.46||92.62||3.15||− 0.57||0.13||1.31||2.52||2.06|
|(H4) Senior members||16.62||0.81||7.67||1.56||0.20||1.88||16.54||5.28||5.87|
|(S1) Cash savings||14.86||13.02||121.76||− 20.94||51.73||11.19||− 14.85|
|(S2) Agricultural capital||14.54||20.58||6.29||12.54|
|(ROW) Rest of World||21.55||2.03||1.92||0.72||4.59||176.78|
Appendix B: Aquaculture module input data
|Requirement of a 665 m2 fish pond||References|
|Fingerlings (1000 pieces per year at 0.1 US$ each)||Murphy and Lilungwe (2012)|
|Feed (nearly 500 kg maize bran per year at 0.175 US$ per kg)||Gronau et al. (2017), Hilundwa and Teweldemedhin (2016)|
|Fertiliser (almost 600 kg manure per year at 0.75 US$ per kg)||Nunoo et al. (2014), Quagrainie et al. (2005)|
|Labour costs (clearing, feeding, harvesting, monitoring, recording) at 0.51 US$ per m2||Nunoo et al. (2014)|
|Construction (approximately 5 labourers at 3 US$ per day and 20 days) at 300 US$||Hilundwa and Teweldemedhin (2016)|
|Equipment (e.g. shovels, hooks, pick-axes, tanks/buckets, boats, wheelbarrow, fishing gear) at 150 US$ (0.075 US$ per m2)||Nunoo et al. (2014)|
|Depreciation is based on a productive life of 10 years for a pond and 3 years for the equipment (i.e. 10 and 33% respectively per year must be replaced).||Nunoo et al. (2014)|
|Land at 98.72 US$ per pond (factor value).||Own estimation|
|Fish at 0.4 US$ per kg (factor value).||Morton et al. (2016)|
|Fish at 1 US$ per kg (home consumption) and 2 US$ per kg (market sales)||Murphy and Lilungwe (2012); NNF (2013)|
|Harvesting is done once or twice a year and produces a yield of 1020 kg.||Murphy and Lilungwe (2012)|
|Per 100 g||Energy (kcal)||Fat (g)||Iodine (µg)||Iron (mg)||Protein (g)||Vitamin A (µg)||Zinc (mg)|
Appendix D: Parameters used for production, utility and biological fish growth functions
|Fish pond production function||Aggregate intermediate input coefficient||24.6||Percent||Own calculation|
|Aggregate value-added (factor) coefficient||75.4||Percent||Own calculation|
|Feed (share parameter of intermediate input use)||31.6||Percent||Own calculation|
|Fertiliser (share parameter of intermediate input use)||3.3||Percent||Own calculation|
|Fingerlings and depreciation pond and equipment (share parameter of intermediate input use)||65.1||Percent||Own calculation|
|Land (share parameter of factor use)||40.1||Percent||Own calculation|
|Labour (share parameter of factor use)||11.7||Percent||Own calculation|
|Fish (share parameter of factor use)||48.2||Percent||Own calculation|
|Utility function||Technical coefficient (ν)||0.25||Coefficient||Winter et al. (2015)|
|Wealth state of household group 1 and 2 (α and β)||0.2||Exponent||Angelsen (1999) and Winter et al. (2015)|
|Wealth state of household group 3 and 4 (α and β)||0.9||Exponent||Angelsen (1999) and Winter et al. (2015)|
|Biological fish growth function||Annual growth rate (r)||10||Percent||FAO (2013)|
|Carrying capacity in Sikunga Conservancy (Zambezi Region); yield for tropical natural fish production systems (k)||1,600,000||kg||Hay et al. (2002) and Welcomme et al. (2010)|
|Fish stock in Sikunga Conservancy (Zambezi Region) (F)||1,000,000||kg||Hay et al. (2002)|
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Gronau, S., Winter, E. & Grote, U. Aquaculture, fish resources and rural livelihoods: a village CGE analysis from Namibia’s Zambezi Region. Environ Dev Sustain 22, 615–642 (2020). https://doi.org/10.1007/s10668-018-0212-1
- Fish resources
- Rural households
- Village computable general equilibrium model