Hagar El-Didi (IFPRI Egypt) • 2018 IFPRI Egypt Seminar: Unleashing the potential of Egyptian farmers
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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.
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Hagar El-Didi (IFPRI Egypt) • 2018 IFPRI Egypt Seminar: Unleashing the potential of Egyptian farmers
- 1. A typology of farming households in Egypt Alejandro Nin Pratt, Hagar ElDidi and Clemens Breisinger IFPRI March 29, 2018
- 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. 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. 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. 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. 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. 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. Distribution of farm households
- 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. 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. 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. 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. Thank you
Editor's Notes
- In general we know the broad issues faced by farmers in Egypt in terms of productivity, efficiency and poverty, and policies and investments that can help farmers increase productivity and incomes are generally well known from previous research and literature, that includes increasing water use efficiency and soil quality, working on input and output marketing and linking farmers to knowledge and climate smart technologies, etc. But really when we come to implement those policies we will need to consider some factors, including the macroeconomic environment, the structure and functioning of markets, institutions and very important, the heterogeneity of farmers. So not all farmers will benefit or be affected the same way so that is really our starting point. So the contribution of this paper is to try and present a typology of farmers, to identify the main types of farm households in Egypt, not just those with 2 feddan vs those with 10 feddan, but considering other variables as well. From there we try to identify the main constraints faced by these different types of farmers and then show a possible application for this typology by using climate change as an example to see the specific impacts on specific farm types.
- To build a comprehensive dataset of agricultural households, we combine different datasets from different sources together, with some filling in of gaps. The two main datasets we use are the ELMPS, particularly drawing from the agriculture module, and various related bulletins of national data. Disaggregated ag production data from MoALR – ag statistics bulletin, and cost of production and returns bulletin, as well as CAPMAS data from the ministry of irrigation and water resources for irrigation data. We worked on matching information from these two datasets, and sometimes not all of the information we needed was available, so we supplemented the dataset with for example FAO price data for some of the crop prices, especially that not all of the crops reported in ELMPS was available in CAPMAS and vice versa. We used separate crop ET data to capture the difference between crop water requirements and water use for the different seasons and to differentiate between north and south. We got some information on fertilizer prices and subsidies from news articles, particularly AlAhram Agricultural newspaper. Finally, to calculate prices and daily hired labor days, we use a good paper on Economics of Agricultural Employment; ARC and Sohag University
- I just want to quickly highlight pros and cons for using these datasets and why we utilized them together.
- On the other hand, kind of in a complimentary way
- So building and harmonizing the dataset was one of the challenges. We spent some time matching and converting cop production units to tons from various units like qintar, ardab and qirat, which are usually used for specific crops and for these crops there are clear conversion rates, but for others was a bit more tricky. We then worked on harmonizing water data and also using different Crop evapotranspiration rates for the different seasons and regional differences, and filled in any missing price data and made sure the crops the government is setting a price for appear correct. I want to mention that unfortunately the typology does not include any governorates outside of the Nile valley, so basically only “old lands” and behera. But this is important not to be ignored if we have new data because these are different systems, soil, conditions and therefore they would have very different “types” of households.
- Under the same level of efficiency, if irrigation water is increase, that is what would raise production and revenue significantly for farmers, so water seems to be the main constraint, which is consistent with what we hear on the ground from farmers.
- As mentioned, the ELMPS is not primarily and agriculture survey, so farm households are not the major percentage. This is the distribution of farm households (meaning those who cultivate land themselves) Governorates like Beni Suef, Sharkia, Kafr El-Sheikh have a higher percentage of these households. And on the other side here, within those farming households, this is the distribution of farm-only households, or those who depend only on agriculture from those farms. There is a larger percentage of them in Behera, Damietta, Assiut, Menia and Kafr El-Sheikh.
- For the first two cuts, to differentiate households based on geography and based on dependence on agriculture was very important (one of the tree trunk variables, not a leaf) – So if our typology is a tree with branches, these two will be the tree trunk), the rest are leaves. So we have 4 regions but we really concentrate on the two rural regions, and 3 levels of dependence on agriculture. The "leaves" are determined base on factor availability and constraints. So we group households based on their capital composition, assets, land, and resources like irrigation water use. So there are a total of 15 rural farm households types, and 2 urban. Under sole dependence on ag, there are 5 types and these form 36% of rural households in the sample, medium dependence on ag are 4 clusters and constitute 41% and those with low dependence on ag are 6 types and form only 24% The variables used at this level are: gov_agsh= importance of agricultural income relative to other sources of income in the governorate where the hh is located kap_hhs= capital/hhsize vass_hhs=value of assets/hhsize IWR_fdn = irrigation water used per feddan of the farm land= total farm area Kanimal=value of the animal stock
- I’ll quickly give two examples to compare some types. Here it is geography. If we look at 2 similar types, both having high dependence on ag, both having low assets/resources, but one in upper and one in lower Egypt.. -land -Assets -Revenue and input use
- Now we compare based on level of resources. If we look at those two types both in Upper Egypt, both with low agriculture dependece, but one with high and one with low reources... similarly -land -Assets -Revenue and input use
- Our next steps go in two tracks. One is to use this typology and apply it to upper Egypt farmers that will be part of a new survey that IFPRI is about to conduct for another component of this same project. And from there, we would like o select some farmers from each “type” for an in-depth more qualitative, mixed methods study focusing on farmer adaptation to climate change, and the climate smart agriculture options for these different types of farmers. The other is to integrate those types into a sectoral model for policy analysis, a price indigenous model for policy and market evaluations. Then also use this model to analyze the impact of climate change scenarios on the different household types, and integrate effectiveness of the CSA techniques.