Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

Hagar El-Didi (IFPRI Egypt) • 2018 IFPRI Egypt Seminar: Unleashing the potential of Egyptian farmers


Published on

The IFPRI-Egypt Seminar Series is part of the United States Agency for International Development (USAID) funded project called “Evaluating Impact and Building Capacity” (EIBC) that is implemented by IFPRI. The seminar supports USAID’s Agribusiness for Rural Development and Increasing Incomes (ARDII) project’s objectives.

Published in: Government & Nonprofit
  • Be the first to comment

  • Be the first to like this

Hagar El-Didi (IFPRI Egypt) • 2018 IFPRI Egypt Seminar: Unleashing the potential of Egyptian farmers

  1. 1. A typology of farming households in Egypt Alejandro Nin Pratt, Hagar ElDidi and Clemens Breisinger IFPRI March 29, 2018
  2. 2. Introduction and motivation • Well known policies/investments that can help farmers in Egypt to increase productivity and incomes: • Increasing water use efficiency and soil quality; • Improving input and output marketing; • Linking farmers to knowledge and technology, including climate smart solutions • However, implementation of policies and investments need to consider: • Macroeconomic environment • Volatility of markets • Institutions • Heterogeneity of farmers • The contribution of this paper is to: • Present a typology to identify main types of farm households in Egypt (beyond small vs. large) • Identify key constraints for different types of farm households • Way forward: Integrate farm types to sectoral model
  3. 3. Data collection, limitations and fixes • Household survey data: ERF • Egyptian Labor Market Panel Survey (ELMPS) (Agriculture module - 2012 round) • Disaggregated agriculture production data: CAPMAS ; MoALR • The Bulletin of Agricultural Statistics (2013/2014), the Statistical Bulletin of Cost of Production and Net Return (2013/2014), and the Annual Bulletin of Irrigation and Water Resources (2014), respectively (MoALR, 2015a ; MoALR, 2015b ; CAPMAS, 2014). • Other sources: • Price series to complement available price information - FAO • Data on potential evapotranspiration, crop-specific evapotranspiration, and effective precipitation to calculate irrigation demand by crop • Collected information on agricultural policy (fertilizer subsidies, government fixed crop prices, etc.). • Data for average daily agriculture labor days and rates in Egypt – Mohamed et al., 2008
  4. 4. Egyptian Labor Market Panel Survey (2012) PROS CONS Household level data on governorate and district levels • 1,161 households in agriculture, out of 12,060 households sampled in the survey Not an agricultural survey • Nationally representative (for labor market but not for the agricultural sector). Data on crop production, areas, agriculture revenue, land and agriculture assets/ownership, livestock No data on input use in agriculture Data on household members employed in agriculture, working in own farms, and number of hired labor for agriculture enterprises • Used to calculate variable cost of agriculture labor hired for some households Some crop production units inconsistent with those reported in other national data sources (CAPMAS/MoALR) Data on employment, education, earnings and assets and other household characteristics Does not capture crop seasonality
  5. 5. CAPMAS/MoALR data (various bulletins) PROS CONS Governorate level data for each crop, per season • To match and enrich household level data Water data is aggregated ; fruit irrigation data aggregated on national level Disaggregated input prices per feddan of crop per season (fertilizer, pesticide, machinery, irrigation, etc.) and disaggregated operational costs per feddan Fertilizer data is in monetary terms for recommended use, not actual quantities and amounts • Does not enable comparison between household fertilizer application
  6. 6. Data cleaning and harmonizing dataset • Converting crop production units (qintar, ardab, qirat,..) • Harmonizing water data • ET data for different governorates • Filling in price data Typology does not include outside of the Nile valley – noteworthy as it is a different system and would have very different types of farm households that should not be ignored if new data is available in the future. Distribution of ELMPS (2012) farm households across districts
  7. 7. A household typology • Should allow us to describe how farm households will behave in response to different changes affecting their resources, opportunities and environment, and how these changes, and their responses to them will affect the welfare of different households • Household types should show similar behavioral characteristics. • A classification based on resource and environmental variables is more likely to meet the requirements of the study than one based primarily on the activities that households engage in (not an agronomic classification) • Groups of variables for the typology: • Agroecology and geography • Importance of agriculture as a source of income • Access to resources and relative resource constraints: land, labor, capital, water, value of assets
  8. 8. Distribution of farm households
  9. 9. Rural Upper EgyptGeography Dependence on agriculture Resource constraints [Clustering households based on similar resources; land, assets and capital composition] Urban Lower Urban Upper 5 4 6 Sole dependence on agriculture (from own farm) Medium dependence on agriculture Low dependence on agriculture Typology structure Rural Lower Egypt 36% of farming households 41% of farming households 24% of farming households 11 clusters clustersclusters
  10. 10. Contrasting types (Upper vs. Lower Egypt; High dependence on agriculture; low resources) Assets and resources Inputs and outputs type Average land (feddan) LW-RU-HAG-1 2.27 UP-RU-HAG-1 0.98 0 2000 4000 6000 8000 10000 12000 14000 16000 18000 mean(Kmachines) mean(Klive) Average value of assets LW-RU-HAG-1 UP-RU-HAG-1 0 20000 40000 60000 80000 mean(vq_crop) mean(v_lvsk) EGP Average revenue LW-RU-HAG-1 UP-RU-HAG-1 145 150 155 160 165 170 175 180 LW-RU-HAG-1 UP-RU-HAG-1 Average fertilized applied per feddan 0 500 1000 1500 2000 2500 3000 3500 4000 4500 LW-RU-HAG-1 UP-RU-HAG-1 m3 Average water used per feddan
  11. 11. Contrasting types (Upper Egypt; Low dependence on agriculture; low vs. high resources) Assets and resources Inputs and outputs 0 20 40 60 80 100 120 140 160 180 UP-RU-LAG-1 UP-RU-LAG-3 Average fertilizer applied per feddan 4100 4200 4300 4400 4500 4600 4700 4800 4900 5000 UP-RU-LAG-1 UP-RU-LAG-3 Average irrigation water used per feddan 0 2000 4000 6000 8000 10000 12000 Crops Livestock EGP Average revenue UP-RU-LAG-1 UP-RU-LAG-3 0 2000 4000 6000 8000 10000 12000 14000 16000 18000 20000 mean(Kmachines) mean(Klive) Average value of assets UP-RU-LAG-1 UP-RU-LAG-3 Type Average land (feddan) UP-RU-LAG-1 0.66 UP-RU-LAG-3 1.93
  12. 12. Way forward • Apply typology to Upper Egypt farmers – to be surveyed as part of FAS-IMCHN evaluation (EIBC Component 1) • Select sample farmers of each “type” from within survey for in-depth qualitative / mixed methods study (Farmer adaptation to climate change and climate smart agriculture (CSA) techniques for different types) • Integrate farm types to a price endogenous model for policy and market evaluations at the sectoral or regional level using individual farm data. • Model will be used to analyze the impact of different climate change scenarios on different household types and the effectiveness of climate smart agriculture (CSA) techniques to mitigate potential negative effects of climate change
  13. 13. Thank you