1. The document discusses macro-economic modeling of the food-energy-water nexus in Malawi using computable general equilibrium (CGE) models and a yield-water module. 2. It presents findings from a 2010 social accounting matrix for Malawi that was used as the baseline for CGE modeling, including data on household income sources. 3. The yield-water module calculates water needs for different crops in Malawi, finding that sugarcane requires more water than maize. Integration of this module with the CGE model and SAM is proposed to assess impacts of biofuel policy scenarios.

1. Macro-economic modeling
in the
Food-Energy-Water-Nexus
The 2010 Social Accounting Matrix for Malawi
&
a Yield-Water module
Franziska Schuenemann, M.Sc., M.A.
University of Hohenheim
Institute of Agricultural Economics and Social Sciences in the Tropics and Subtropics

2. Overview
1. What is the FEW Nexus
2. Policy: Expansion of biofuel (ethanol) production from sugarcane
3. Computable General Equlibrium Models (CGE)
- Circular flow of income
- Malawi 2010 macro SAM
- Malawi 2010 disaggregated micro SAM
- Findings
4. Yield-Water module
- Methodology
- Findings
5. Next steps and question for discussion
2

3. What is the Food-Energy-Water-Nexus?
• Three nexus sectors intrinsically tied
together and dependent on each other
 Production and consumption linkages
 Competition for the same resources
• Policy targeting one sector invariably
affects the other two
 Need policy approach to minimize
trade-offs in resource use and to build
synergies
 Need for economy-wide integrated
framework to determine impact of
different policy scenarios ex ante
Water
Supply
security
Available
resources
Energy
security
Food
Security
Urbanization Population Growth Climate Change
Global Trends
3
After Hoff, 2011

4. Policy: Expansion of biofuel (ethanol) production from
sugarcane
Policy Background
• Ethanol-gasoline blend in National Energy Policy
• Sugarcane cluster in National Export Strategy
Potential Effects
• Energy security:
• Positive if ethanol is blended into gasoline or if ethanol exports can be used to buy
petrol
• Negative regarding hydropower if water levels decrease due to extensive irrigation
• Food security:
• Negative if land is taken away from food crop production: lower food availability
• Positive if poorer farmers can take part in sugarcane production (Herrmann et al.,
2013): higher income and improved access to food
• Water security:
• Negative due to large amounts of irrigation water
 Development of simulation models to assess these complex effects 4

5. Computable General Equlibrium Models (CGE)
• Capture all links between markets and agents in an economy
• Combine economic theory with real world data to simulate different
policy scenarios ex ante
• Consists of two parts:
• Theoretical model: explains the behavior of household, producers, etc.
through mathematical equations
• The social accounting matrix (SAM): economy-wide representation of
a country’s economic structure
5

6. Factor
market
Producers Households
Product
market
Factor cost
Wages &
rents
Private
cons.
Intermediate
dem.
Save/invest
Household savings
Income taxes
Government
Government
expenditure
Gov. savings
Investment
demand
Domestic demand for final goods
(C + I + G)
Rest of
the world
Import payments
(M)
Export
receipts (X)
Foreign savings
Sales
revenue
6
Circular flow of income

7. The 2010 Malawi Social Accounting Matrix (SAM)
• Base-year equilibrium state for the CGE model from which
simulations can be conducted
• Captures all income and expenditure flows among households,
producers, the government, and the rest of the world during the
year 2010 in “accounts”
• Main data sources of 2010 Malawi SAM:
 National accounts (with help from NSO workshop)
 Annual Economic Survey
 Balance of payments
 Input–output tables (supply–use tables)
 Malawi Integrated Household Survey (IHS) 2010/2011
7

8. The 2010 Malawi “macro” SAM
8
Activities Commodities Factors Enterprises Households Government Investment
Rest of the
World
Total
Activities 1,705,532 1,705,532
Commodities 733,923 147,937 918,696 162,955 169,863 183,696 2,317,071
Factors 971,609 68 971,677
Enterprises 240,808 28,515 35,792 305,114
Households 705,762 241,952 27,894 2,519 978,127
Government 87,338 56,767 47,750 48,368 240,223
Savings 6,102 9,437 18,109 136,215 169,863
Rest of the World 376,264 25,107 293 2,244 2,750 406,657
Total 1,705,532 2,317,071 971,677 305,114 978,127 240,223 169,863 406,657
2010 Macro SAM for Malawi (Millions of Kwacha)

9. The disaggregated micro SAM
• 59 Sectors:
• 19 in agriculture, 20 in industry & 11 in services
• Factors of production
• Labor (6) by skill/education and rural/urban
• Land and livestock (7) by farm type (small-, medium-, large-scale
and estate)
• Capital (4) by sector (agriculture or non-agriculture) and farm
size
• 30 types of households:
• Rural/urban
• Farm/nonfarm
• Farm size
• Income quintiles
• Next step: Regional disaggregation
9

10. Micro SAM findings
Income
quintile
Government transfers
as percentage
of HH income
1 0.01
2 0.06
3 0.15
4 0.47
5 4.77
10
Household
Type
Non-agricultural
capital as percentage
of HH income
Rural/small 10.24
Rural/medium 10.97
Rural/large 13.65
Rural/nonfarm 28.91
Urban/farm 11.84
Urban/nonfarm 37.20

11. Yield water module
Calculates water needs for 14 different crops in Malawi including maize and sugarcane
11
Methodology
• Following FAO linear yield response to water (Doorenbos & Kassam, 1979)
1 −
푌푎
푌푚
= 퐾푦 1 −
퐸푇푎
퐸푇푚
1. Calculation of reference evapotranspiration ET0 of grass with the FAO Penman-
Monteith equation based on historic climatological data for 20 weather stations in
Malawi for the years 1983-2005 (Allen et al., 1998)
퐸푇0 =
0.408Δ 푅푛 − 퐺 + 훾
900
푇 + 273
푢2(푒푠 − 푒푎)
Δ + 훾(1 + 0.34 푢2)
2. Calculation of ETm for each crop by multiplying ET0 with the respective crop
coefficient: 퐸푇푚 = 푘푐 퐸푇0
3. Calculation of ETa through a daily soil-water-balance

12. Yield water module findings
12
350
300
250
200
150
100
50
0
1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005
ET in mm
Actual and potential evapotranspiration for maize in Southern Malawi
ETa
ETm

13. Yield water module findings
13
1000
900
800
700
600
500
400
300
200
100
0
1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005
ET in mm
Actual evapotranspiration for maize and sugarcane in Southern Malawi
Maize
Sugarcane

14. Next steps and questions for discussion
Next research steps:
• Combination of yield-water module with CGE model and SAM
• Extension of the SAM with sugarcane produced for biofuel as well as
ethanol processing sectors
• Link to microsimulation module for poverty and nutrition analysis based on
Integrated Household Survey (IHS) 2010/2011
Questions:
• Is there new data for the ethanol supply chain in Malawi?
• What is the role of water security in Malawian policy making and will
analyses such as ours raise more awareness?
14

15. 15
References
Allen, R.G., Pereira, L.S., Raes, D. & Smith, M. 1998. Crop evapotranspiration. Guidelines
for computing crop water requirements. In: FAO Irrigation and Drainage Paper No. 56.
Rome, FAO.
Hermann, R., Grote, U. & Brüntrup, M., 2013. Household Welfare Outcomes of large-scale
agricultural Investments: Insights from Sugarcane Outgrower Schemes and Estate
Employment in Malawi. Paper prepared for presentation at the “Annual World Bank
Conference on Land and Poverty”, Washington DC, April 8-11, 2013.
Hoff, H. (2011). Understanding the Nexus. Background Paper for the Bonn2011 Conference:
The Water, Energy and Food Security Nexus. Stockholm Environment Institute,
Stockholm.