ArcGIS layer package (LPK)
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An agricultural potential map of Bangladesh was produced using ArcGIS showing areas where several dimensions of agricultural potential overlap. The map shows that some regions of coastal areas and some areas of the Haor basin and northwestern regions have the highest agricultural potential – unused potential in two to three (out of four) dimensions. Most of these regions are agro-ecologically fragile and have lower productivity due to salinity, submergence and drought. Among them the north-west is affected by droughts and river erosion; the central northern region is subject to serious seasonal flooding that limits crop production; and the southern coastal zones are affected by soil salinity and cyclones. Data sources for creating the map have been: - 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
The mapping of the overlap between the marginality hotspots and agricultural potentials shows that there are eight marginal sub-districts in seven districts with highest unused agricultural potentials. These are Rajibpur (Kurigram), Dowarabazar (Sunamgonj), Porsha (Naogaon), Damurhuda (Chuadanga), Hizla (Barisal), Mehendigonj (Barisal), Bauphal (Patuakhali) and Bhandaria (Pirojpur). These areas are mostly in unfavorable agro-ecological Zones (AEZs). An AEZ in Bangladesh is defined broadly. While most of the areas within an unfavorable AEZ are not suitable for crop agriculture, there may still be some areas which are suitable for agriculture. This will become clear if we compare the map of suitability mapping and the map of unfavorable AEZ which suggests that there are some areas within the unfavorable AEZ which are suitable for agriculture (both agro-climatically and agro-edaphically). Among those marginal areas, Patuakhali, Pirojpur and Barisal are in the coastal region, Kurigram is in the Northern Char region, Sunamgong in the Haor region and Naogaon is in the drought prone areas. Only Chuadanga, among these seven districts, is not in agro-ecologically vulnerable region (Appendix B) but in food in-secured region. Another point to note is that four out of these eight sub-districts are adjacent to the Indian border, whereas the other four sub-districts are located in the coastal region. The concentration of marginality and agricultural potentials overlap in the aforementioned areas may be due to their limited connectivity with the main growth centers and ecological vulnerability. These areas are bypassed due to the general perception of AEZs as uniform entities and therefore receive less attention.
The potential of different business approaches to reduce poverty and marginality depends on the characteristics of different regions and people living in these regions. Here, (a) population density, (b) accessibility, e.g. in terms of mobile phone, internet and road connections, as well as (c) the predominant form of livelihood and/or farming systems may be important factors determining market sizes and transaction costs and thus incentives to invest in these markets. This map is an overlay of these different indicators. Greenish colors show irrigated or perennial areas, brownish colors pastoralist, agro-pastoralist and arid areas and reddish colors indicate areas dominated by different other farming patterns. The lighter the color the lower the population and road density. For the classification of population and connectivity values being ‘high’ or ‘low’ the national mean value is used as threshold. Data: Population density: CIESIN (2011) Connectivity: CSA et al. (2008) Farming systems: HarvestChoice (2001)
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)
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.
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.