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

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  • 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
  • 21. 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
  • 22. 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

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