1. •
Analysis of policy impact on the farming sector in
Africa. Selected activities at the EC-JRC-IPTS
Sergio Gomez y Paloma, Kamel Louhichi
1EU-
JRC- Institute for Prospective Technological Studies (IPTS), Seville, Spain
2CIHEAM-IAMM, 3191 route de Men 34090, Montpellier, France
Africa-Day, ZALF, Food Security in the light of Climate Change and
Bioenergy – Challenges for Research in Sub-Saharan Africa

2. Where does the Joint Research Centre (JRC) fit in
the European Commission (EC)
President Barroso
27 Commission Members
Commissioner Geoghegan-Quinn
Research, Innovation and Science
Joint Research Centre (JRC)
Research DG (RTD)
The JRC is a Directorate-General of the EC

3. 7 Institutes & Headquarters on 6 sites
 2700 staff
IE – Petten, The Netherlands
Institute for Energy
IRMM – Geel, Belgium
Institute for Reference Materials and
Measurements
ITU – Karlsruhe, Germany
Institute for Transuranium Elements
IES/ IHCP/ IPSC – Ispra, Italy
Institute for Environment and Sustainability
Institute for Health & Consumer Protection
Institute for the Protection & Security of the
Citizen
IPTS – Sevilla, Spain
Institute for Prospective Technological
Studies
JRC home page: http://ec.europa.eu/dgs/jrc/index.cfm
IPTS home page: http://ipts.jrc.ec.europa.eu/

4. JRC Mission is to help put EU policy-making onto a
scientifically robust foundation
• by providing customer-driven scientific and technical support
for the conception, development, implementation and
monitoring of EU policies
• “customers” are predominantly other Commission services
Institute for Prospective Technological Studies
(IPTS)
Focuses on quantitative economics
• i.e. economic modelling, econometrics, input/output
accounting, scenario analysis, sensitivity analysis, cost benefit
analysis, …
and economic analysis of (among others)
• Agriculture and rural development, international markets
(AGRILIFE unit)

5. JRC-IPTS AGRILIFE divisions
• Sustainable Agriculture and Rural Development (SUSTAG)
Action
• Support to Agricultural Trade and Market Policies
(AGRITRADE) Action
• New Technologies in Agriculture – their agronomic and socioeconomic impact (AGRITECH) Action
JRC-IPTS AGRILIFE main clients within EC
• DG AGRI
• DG DEVCO
• Other DGs: SANCO, ENV, CLIMA, TRADE, ENLARGEMENT
JRC-IPTS AGRILIFE main project partners
• AfDB, OECD, FAO, World Bank, worldwide universities, etc.

6. from data access… to policy impact analysis
• Crucial for all national, inter- and supranational organisations,
private business (farmers, enterprises)

7. …aims at strengthening research on agri-economic and
rural development in Africa
Analyses at the micro (farm) level
Direct survey: Sierra Leone (2010), Ivory Coast (2014)
Modelling: FSSIM-DEV
Based on FSSIM (Farmer System Simulator) developed
under the DG RTD FP by the SEAMLES consortium
•
•
•
Louhichi et al., 2010. Agricultural Systems, vol. 103, n° 8. pp. 585597.
Janssen et al., 2010. Environmental management, vol. 46, n° 6. pp.
862-877
Other refs: http://www.seamlessassociation.org/;
Africa-Day, ZALF, October 21, 2013

8. FSSIM-DEV
Modelling Farm-Household
(FH) with FSSIM-DEV
(Farm-System Simulator for Developing Countries)
 A quantitative tool to gain
knowledge on food security and
rural poverty alleviation in low
income economies
 A simulation model for impact
assessment of agrifood/environment and rural polices
at FH, regional & national levels
 Generic & modular set-up to be reusable, adaptable and easily
extendable
 Tested for a sample of 400 farm
households in Sierra Leone.
 Prospects: extension to selected
African Countries
 AA JRC-DEVCO 2013-2017 (under
signature)

9. Introduction
Modelling
Simulation
Conclusion
What is FSSIM-Dev?
• A bio-economic farm household model (based on
European Farm System Simulator – FSSIM) for use in
the context of Developing Countries (Dev) in order
to gain knowledge on food security and rural
poverty alleviation.
• A generic simulation model for ex-ante assessment
of agri-food/rural policies and technological
innovations at farm household and regional levels.
9

10. Introduction
Modelling
Application Conclusion
FSSIM-Dev specifications
• Farm Household model (i.e. production and
consumption decisions)
• Static & non-linear optimization model
• PMP (Positive Mathematical Programming) based
model
• Relevant for individual (real) & representative farms
(farm types)
• Generic & Modular setup to be re-usable, adaptable
and easily extendable to achieve different
10
modelling goals

11. Introduction
Modelling
Application Conclusion
FSSIM-Dev specifications
Detailed representation of:
• land heterogeneity: land availability is specified by
agri-environmental zone (i.e. climate & soil type) and
type of use (arable, grass..).
• commodities coverage: arable & perennial crops and
livestock
• farming practices: e.g. arable activities are defined as
crop rotations growing under specific agrienvironmental zone and under well-defined agromanagements
11

12. Introduction
Modelling
Simulation
Conclusion
FSSIM-Dev key issues
• Capture key features of Developing Countries agriculture
• non-separability of production and consumption decisions
• effects of transaction costs on market participation
• heterogeneity of farm households
• interaction among farm-households for factor markets
• seasonality of cropping activities and resource use
• Models technological change through alternative activities
(i.e. innovative varieties, crop rotations, managements…)
• Smoothly integrates results from biophysical models needed
to assess the environmental effects of production
12
activities.

13. Introduction
Modelling
Simulation
Conclusion
Modelling market imperfection in FSSIM-Dev
• Production and consumption decisions are non-
separables: household solve simultaneously its
production and consumption problems
• Endogenous market participation decision: depends
on farm supply and consumption function
• Transaction costs: FH prices  market prices
• Prices are endogenous within price bands
13

14. Introduction
Modelling
Simulation
Conclusion
FSSIM-Dev application: Rice Seed Policy (SP)* Sierra Leone
Aims: - increase rice production
- improve self-sufficiency
Instruments:
SP: delivering high quality rice seeds
SP-FR: SP + Reduction of Fallow period in upland
from 5 to 3 years
Indicators: household income, land use, production,
consumption and poverty level At farm/regional levels
Case study: SL Northern region – Bombali & Tonkolili
14
(400 sample farms)
* National Sustainable Agriculture Development Plan (2010-2030)

15. Introduction
Modelling
Simulation
Conclusion
FSSIM-Dev
Base year Vs. Baseline
Policy
Base year
2009
Exogenous assumptions
(yields & prices)
Impact of policy
& Innovation:
SP& SP-FR
Baseline
2020
15

16. Introduction
Modelling
Simulation
Conclusion
FSSIM-Dev results: Sierra Leone
Northern Region (2020) – land use –
100%
90%
3.4%
4.0%
3.0%
2.6%
1.9%
2.9%
11.9%
11.9%
11.9%
80%
increase of
rice area in
detriment of
fallow, cass
ava and
sweet
potatoes
% of total area
70%
60%
50%
54.2%
56.5%
40%
30%
20%
10%
33.3%
28.3%
24.2%
50.0%
0%
Baseline_2020
SP_2020
SP-FR_2020
16
Rice
Fallow
Palm oil
Cassava
Other crops

17. Introduction
Modelling
Simulation
Conclusion
Policy analysis: the Seed Policy would improve the viability
and profitability of smallholders in Sierra-Leona but not
sufficiently to fight poverty
Methodology: highlights the relevance of this type of model
for making fine analysis. Further methodological
improvements could be made such as:
- modelling factor market imperfections (labour,
land and capital)
- use of more flexible form for consumption function
- explicit modelling of market and climate risks
17

18. What next
Striving new Arrangement with EC DG DEVCO on "FNS4Africa:
Food and Nutrition Security for Sub Saharan Africa
incl. micro/regional/macro analysis of policy effects
Selected activities:
• Analyses at the micro (farm) level (2014-2016)
• Drivers of Food demands (2014-2016)
• Draft Countries List: : Senegal, Mali, Ivory Coast,
Burkina Faso, Ghana, Niger, Ethiopia
• … from 2015 (=> 2017)
• Agricultural Systems viability
• Governance best practices

19. Thank you for your attention
Contact:
sergio.gomez-y-paloma@ec.europa.eu

20. •
ANNEXES

22. Individual-Farm Level Model (IFM-CAP)
Analysing the CAP
AGRI New challenges
• Modelling EU Farmer level responses to the CAP
 A EU wide farm level model for ex-ante
assessment of CAP reform.
• Static, deterministic and non-linear
programming model.
• Run for the whole FADN sample (60.550
in constant sample for 2007-2009).
• The aim is to capture farm heterogeneity
and new CAP measures (e.g. greening).
• Provides disaggregated economic results
(farm income, land use, production, etc.)
at finer geographical scale.
• Linkable with market model to have price
feedback from the demand side

23. MORE
Modelling Rural Economies
• Ex ante assessment of Pillar 2 reforms at NUTS3 level, for
urban and rural areas
• Recursive dynamic bi-regional CGE model
• Current research towards more coverage across EU NUTS3