Abstract
Using a dynamic Computable General Equilibrium (CGE ) model of Uganda, we simulate the effects of water shortages and their impact on agricultural production and the broader Ugandan economy. It is expected that Ugandan crop production will be hit hard over the next forty years by increasing temperatures and decreasing precipitation associated with climate change . We use forecasts from the literature for ten specific crop outputs to simulate the effects of weather-related agricultural disruption on the rest of the economy. We are particularly interested in the effects on food security in Uganda. We find that Uganda is far from being food secure, and hypothesize that if the pessimistic forecasts from the literature were to become true, then the situation would deteriorate significantly. The adaptation strategies for Uganda should focus on the following: the country should diversify agricultural production to include more hardy crops; build transport infrastructure to improve access to the available food to all citizens, but also to enable the expansion of the manufacturing industry, which is dependent on trade between regions; improve sanitation conditions significantly in both rural and urban areas, and develop water infrastructure so that households and agricultural industries can gain better access to an increasingly scarce resource.
The work described in this chapter was carried out at the Centre of Policy Studies and the University of Pretoria. The original UgAGE model was developed for the Ugandan Ministry of Finance, Planning and Economic Development. We kindly acknowledge their support. The model used in this chapter has been adapted to focus on agriculture and food security . The views expressed are those of the authors and should not be attributed to the Ministry of Finance, Planning and Economic Development.
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Notes
- 1.
In the early 1970s there was scarcity in the world grain market, soaring prices and famines spread over several countries of Asia and Africa. A world food conference was held in 1974 to address world hunger (FAO 2003).
- 2.
See https://www.wfp.org/node/359289 accessed 24 September 2018.
- 3.
See http://water.org/country/uganda/ accessed 24 September 2018.
- 4.
See https://www.zaragoza.es/contenidos/medioambiente/onu/825-eng-v14.pdf accessed 24 September 2018.
- 5.
See https://www.wsp.org/sites/wsp.org/files/publications/WSP-Uganda-CFD-Profile.pdf accessed 24 September 2018.
- 6.
See https://waterinstitute.unc.edu/files/2015/12/learning-series-uganda-learning-brief-2015-12.pdf accessed 24 September 2018.
- 7.
See https://pubs.usgs.gov/fs/2012/3062/FS2012-3062.pdf accessed 24 September 2018.
- 8.
See https://www.cia.gov/library/publications/the-world-factbook/geos/ug.html accessed 24 September 2018.
- 9.
Numerical models representing physical processes in the atmosphere, ocean, cryosphere and land surface, used to simulate the response of the global climate system to increasing greenhouse gas concentrations.
- 10.
Improved Global Agro- Ecological Zones method.
- 11.
Uganda coffee production consists of 57.2% Arabica and 42.8% Robusta, which we use as weights here.
- 12.
Other options are available, provided that there are data to support the non-default settings. For example, if there are data on economies or diseconomies of scale in certain industries, then this can be incorporated into the database and into the theoretical structure of the model. Similarly, if there are data suggesting imperfectly competitive market conditions, then this can also be incorporated into both the data and equation structure of the model.
- 13.
The model requires a core database with separate matrices for basic, tax and margin flows for both domestic and imported sources of commodities sold to domestic and foreign users. These separate matrices are not explicitly captured in the SUT. Instead they appear in an aggregate form e.g. the USE table shows the use of commodity c by user u valued at purchasers price. These values should be disaggregated into three separate matrices namely, the use of commodities valued at basic price, tax and margin flows. They then have to be disaggregated to show where these commodities are sourced from, either from the local market or imported from the ROW.
- 14.
See Roos et al. (2015) for a description of how such an IO database can be created from a SUT.
- 15.
Downstream manufactures such as textiles, meat processing, grain and bakery industries contribute little to GDP. Most agricultural commodities, including sorghum , cereals, pulses, rootcrops, cassava and bananas are sold mainly to private households.
- 16.
We imposed observed data for real investments, household and public spending and exports (UBOS 2017).
- 17.
The base year is 2010 where nominal and real value are similar. By multiplying the initial database value with the year-on-year percentage change in industry output, we are able to calculate the value of real output per year.
- 18.
In this simple BOTE there is only one domestic industry, namely grain, and one imported commodity, namely vehicles. Consumers buy both goods while investors use both as inputs into capital production.
- 19.
In our modelling we assume that following a shock capital shifts between industries to eventually eliminate the initial disturbances in rates of return. For example, if the shock increases profitability in an industry, investment will rise (relative to baseline levels). The increase in investment will lead to increased capital. Over time, capital will continue to move into the industry until the initial disturbance in rate of return is eliminated. Note, that in the simulations reported here, the shocks are not once-off, but continue through the projection period. Thus, rates of return almost, but never fully, return to their baseline levels.
- 20.
GDP comprises the cost of capital, the cost of labour, the cost of natural resources and indirect taxes net of subsidies. The combined share of natural resources and indirect taxes is 0.10. In this simulation, the stock of natural resources is held fixed and indirect taxes changes little relative to baseline levels.
- 21.
Tobacco and Cotton .
- 22.
This decomposition is referred to as the Fan decomposition, in recognition of the Chinese scholar who developed the idea while at the Centre of Policy Studies.
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van Heerden, J.H., Roos, E.L., Adams, P.D., Kilimani, N. (2019). Any Rain on Victoria Lake Is Only a Drop in the Bucket: A CGE Analysis of the Effects of Water Shortages on Food Security in Uganda. In: Wittwer, G. (eds) Economy-Wide Modeling of Water at Regional and Global Scales. Advances in Applied General Equilibrium Modeling. Springer, Singapore. https://doi.org/10.1007/978-981-13-6101-2_6
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