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

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

This map shows ethnic fractionalization. The Ethnic Fractionalization Index is calculated using data from the 2007 Population and Housing Census. The striped areas show where marginality hotspots are. The map reveals that marginality hotspots are ethnically more homogeneous than non-hotspot areas.

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

Degree of agro-ecological marginality in Ethiopia, using positional indicators (socio-economic possibilities 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 Statistical Agency (CSA) (2002): Ethiopian Agricultural Sample Enumeration. Addis Ababa, Ethiopia

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

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)

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

This map shows the major ethnic groups in Ethiopia. An area is said to be dominated by one ethnic group if more than 50% of the population belong to this group. The data used for this map is from the 2007 Population and Housing Census (CSA, 2008).

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)