Regional borders in Ethiopia Data source: GADM version 1.0, March 2009

Degree of socio-economic marginality in Ethiopia, using the following economic, health and educational positional indicators: 1. Economy 1.1 Access to markets (travel time to next city > 50.000 capitals) 1.2 Transportation infrastructure (average all-weather road density m/km2) 1.3 Information infrastructure (% of households without telephone) 2. Health: 2.1 Access to education (no. of primary schools related to population density) 2.2 Access to information (% of households without radio) 2.3 Access to information (% of households without television) 3. Education: 3.1 Access to education (no. of primary schools related to population density) 3.2 Access to information (% of households without radio) 3.3 Access to information (% of households without television) Data source: 1.1/1.2/1.3/2.2/2.3/3.2/3.3: Central Statistical Agency (CSA) (2007): Population and Housing Census. Atlas of Ethiopia 2007. Washington DC, USA 2.1/3.1 Central Statistical Agency (CSA), Ethiopian Development Research Institute (EDRI), International Food Policy Research Institute (IFPRI) (2006): Atlas of the Ethiopian Rural Economy. Washington DC, USA

Degree of socio-economic marginality in areas with capabilitiy gaps Socio-economic marginality: Socio-economic marginality in Ethiopia was defined by the following economic, health and educational conditional indicators: 1. Economy: 1.1 Regional poverty headcount indices (% of population whose income/consumption is below the poverty line = 3781 birr) 1.2 Food poverty headcount indices (% of population whose income/consumption for food is below the cost of 2.200 kcal/day per adult food consumption) 1.3 Wealth index (% of population being part of the lowest/2.lowest wealth quintile) 2. Health: 2.1 Child mortality rate (no. of deaths out of 1000 live births <5 years) 2.2 Nutritional status of children (% of children <5 years being stunted) 2.3 Nutritional status of adults (% of men/women age 15-49 with BMI <18.5 = acute under nutrition) 3. Education: 3.1 Illiteracy rate (% of population not being able to read/write in their native language) 3.2 Net enrolment ratio primary school 3.3 Net enrolement ratio high school Data source: 1.1/1.2: Ministry of Finance and Economy Development (2012): Ethiopia‘s Progress Towards Eradicating Poverty: An Interim Report on Poverty Analysis Study (2010/11). Addis Ababa, Ethiopia 1.3/2.1/2.2/2.3: Central Statistical Agency(CSA), ICF International (2012): Ethiopia Demographic and Health Survey 2011. Addis Ababa, Ethiopia; Calverton, USA Capability gap: Areas with good agro-ecological suitability, but limited socio-economic capabilities of farmers to make use of this suitability. Agro-ecological suitability in Ethiopia was defined from the raster data set of agro-ecological suitability for rainfed crops (Fischer et al. 2002) Data source: Fischer et al. (2002): Global Agro-ecological Assessment for Agriculture in the 21st Century: Methodology and Results. International Institute for Applied Systems Analysis, Laxenburg, Austria The socio-economic capabilities of farmers were defined by the following indicators: 1. Access to technology (% of holders applying inorganic fertilizer to any crop during Meher season) 2. Access to credit (% of holders utilizing credit services) 3. Access to knowledge (% of holders utilizing advisory services) Data source: Central Statistical Agency (CSA) (2002): Ethiopian Agricultural Sample Enumeration. Addis Ababa, Ethiopia

Using the methodology developed by Graw and Husmann (2014), the map overlays three indicators (high agricultural potential, high poverty mass and high yield gaps) to identify areas with high potential for agricultural development and poverty reduction in Kenya. Data sources and thresholds: Agricultural potential: Suitability of currently available land area for rainfed crops, using maximising crop and technology mix, FGGD map 6.61 (2005), High: top 3 suitability classes (medium high, high and very high) Poverty mass: Number of poor people in Kenya (by district), KIHBS (2005/06), High: >300,000 per district Yield gap: Yield gap for a combination of major crops, FAO/IIASA - GAEZ (2000/05), High: < 0.25 (on a scale from 0-1, with the highest value in Kenya ca. 0.44) District boundaries: Kenya Central Bureau of Statistics (2003)

Capability gap in Ethiopia: difference between conditional and positional marginality classes of the agro-ecological dimension. Classes in blue define capability gaps, where agro-ecological suitability is good while socio-economic conditions of farmers are poor in these areas. Classes in orange/red show areas in which socio-economic possibilities of farmers are good, while agro-ecological suitability is poor. Conditional marginality classes where defined from the raster data set of agro-ecological suitability for rainfed crops (Fischer et al. 2002) Data source: Fischer et al. (2002): Global Agro-ecological Assessment for Agriculture in the 21st Century: Methodology and Results. International Institute for Applied Systems Analysis, Laxenburg, Austria Positional marginality classes where defined by socio-economic capabilities of farmers: 1. Access to technology (% of holders applying inorganic fertilizer to any crop during Meher season) 2. Access to credit (% of holders utilizing credit services) 3. Access to knowledge (% of holders utilizing advisory services) Data source: Central Statistics Agency (CSA) (2002): Ethiopian Agricultural Sample Enumeration. Addis Ababa, Ethiopia

Degree of socio-economic marginality in Ethiopia, using the following conditional indicators of the economic sector: 1. Regional poverty headcount indices (% of population whose income/consumption is below the poverty line = 3781 birr) 2. Food poverty headcount indices (% of population whose income/consumption for food is below the cost of 2.200 kcal/day per adult food consumption) 3. Wealth index (% of population being part of the lowest/2.lowest wealth quintile) Data source: 1.+ 2. Ministry of Finance and Economy Development (2012): Ethiopia‘s Progress Towards Eradicating Poverty: An Interim Report on Poverty Analysis Study (2010/11). Addis Ababa, Ethiopia 3. Central Statistical Agency(CSA), ICF International (2012): Ethiopia Demographic and Health Survey 2011. Addis Ababa, Ethiopia; Calverton, USA

This marginality hotspot map of Ethiopia uses the lowest quartile as thresholds for the dimensions of marginality. Again, this map shows how many dimension of marginality - as defined by Gatzweiler et al. (2011) - overlap.

Potential gap in Ethiopia: Wheat, maize and sorghum play an important role for Ethiopia’s agricultural production, but are grown in different agro-ecological zones. We defined areas of un-/underused agricultural potential from yield and area gaps of these crops. Yield gaps are areas which have good to very high agro-ecological suitability for the specific crop, but yields of this crop type stay below the national mean (z-scores < 0). Area gaps are areas with good to very high agro-ecological suitability for the specific crop, but the % area used for this crop type related to total cereal area is below the national mean (z-scores < 0). Green areas show yield and area gaps of maize, sorghum and wheat. A value >1 signifies the overlap of several gaps. Crop specific agro-ecological suitability was defined from the raster data set of Fischer et al. (2002) Data source: Fischer et al. (2002): Global Agro-ecological Assessment for Agriculture in the 21st Century: Methodology and Results. International Institute for Applied Systems Analysis, Laxenburg, Austria Yield and area information for the specific crops was obtained from the “Crop Production Forecast Sample Survey, 2011/12” (CSA, 2011)

Mohammad Abdul Malek, Md. Amzad Hossain, Ratnajit Saha and Franz W. Gatzweiler. 2013. Mapping marginality hotspots and agricultural potentials in Bangladesh. Data Sources for creating the map have been: - Household Income and Expenditure Survey 2010 data. Bangladesh Bureau of Statistics, Ministry of Finance, People's Republic of Bangladesh - Monitoring the situation of children and women: Multiple Indicator Cluster Survey 2009. Technical report. Available at: http://www.unicef.org/bangladesh/MICS-PP-09v10.pdf - District series of Yearbook of Agricultural Statistics 2010, Dhaka, Bureau of Statistics. Statistics Division, Ministry of Planning, Government of the People's Republic of Bangladesh

Agro-ecological suitability for rainfed crops in Ethiopia based on the raster data set puplished by Fischer et al. (2002) Fischer et al. (2002) produced a worldwide classification raster of agronomic suitability based on crop modeling, including climate, soil and slope data. The mean suitability value of each woreda was calculated from the raster set and mapped on woreda level. Data source: Fischer et al. (2002): Global Agro-ecological Assessment for Agriculture in the 21st Century: Methodology and Results. International Institute for Applied Systems Analysis, Laxenburg, Austria