• Analysis of policy impact on the farming sector in Africa. Selected activities at the EC-JRC-IPTS

Africa-Day, ZALF, Food Security in the light of Climate Change and Bioenergy – Challenges for Research in Sub-Saharan Africa

Sergio Gomez y Paloma, Kamel Louhichi1EU- JRC- Institute for Prospective Technological Studies (IPTS), Seville, Spain

2CIHEAM-IAMM, 3191 route de Men 34090, Montpellier, France

Where does the Joint Research Centre (JRC) fit in the European Commission (EC)

Commissioner Geoghegan-Quinn

Research, Innovation and Science

Joint Research Centre (JRC)

President Barroso 27 Commission Members

Research DG (RTD)

The JRC is a Directorate-General of the EC

7 Institutes & Headquarters on 6 sites 2700 staffIE – 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, ItalyInstitute for Environment and Sustainability Institute for Health & Consumer ProtectionInstitute 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/

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)

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 socio-

economic 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.

from data access… to policy impact analysis

• Crucial for all national, inter- and supranational organisations, private business (farmers, enterprises)

Africa-Day, ZALF, October 21, 2013

…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-DEVBased 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. 585-597.

• Janssen et al., 2010. Environmental management, vol. 46, n° 6. pp. 862-877

• Other refs: http://www.seamlessassociation.org/;

• 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 agri-food/environment and rural polices at FH, regional & national levels

Generic & modular set-up to be re-usable, 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)

FSSIM-DEV

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• 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.

What is FSSIM-Dev?

Introduction Modelling Simulation Conclusion

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• 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 modelling goals

FSSIM-Dev specifications

Introduction Modelling Application Conclusion

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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 agri- environmental zone and under well-defined agro-managements

FSSIM-Dev specifications

Introduction Modelling Application Conclusion

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• 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

activities.

FSSIM-Dev key issues

Introduction Modelling Simulation Conclusion

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• 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

Modelling market imperfection in FSSIM-Dev

Introduction Modelling Simulation Conclusion

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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 (400 sample farms)

FSSIM-Dev application: Rice Seed Policy (SP)* Sierra Leone

Introduction Modelling Simulation Conclusion

* National Sustainable Agriculture Development Plan (2010-2030)

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Base year Baseline

2009 2020

Policy

Impact of policy & Innovation: SP& SP-FR

Exogenous assumptions (yields & prices)

FSSIM-DevBase year Vs. Baseline

Introduction Modelling Simulation Conclusion

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FSSIM-Dev results: Sierra LeoneNorthern Region (2020) – land use –

Introduction Modelling Simulation Conclusion

24.2% 28.3%33.3%

56.5%54.2%

50.0%

11.9% 11.9% 11.9%4.0% 2.6% 2.9%3.4% 3.0% 1.9%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Baseline_2020 SP_2020 SP-FR_2020

% o

f to

tal a

rea

Rice Fallow Palm oil Cassava Other crops

increase of rice area in detriment of fallow, cassava and sweet potatoes

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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

Striving new Arrangement with EC DG DEVCO on "FNS4Africa: Food and Nutrition Security for Sub Saharan Africaincl. 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

What next

Thank you for your attention

Contact:sergio.gomez-y-paloma@ec.europa.eu

• ANNEXES

• 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

Individual-Farm Level Model (IFM-CAP) Analysing the CAP AGRI New challenges

MOREModelling 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