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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 data set contains registered land leases obtained by mostly private individuals in the Upper West Region from 1976 to 2013. The data covers the sections or communities and districts where the land transactions took place.
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 survey was conducted in 2013 by interviewing 232 farmers in Tram Kak and Prey Kabas district of Takeo province, Cambodia. The survey provides the information on farmers´perceptions of leguminous green manure.
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
Data is collected from 664 women working at the flower farm and 182 control women who sought employment at the flower time but not successful to get hired in the Oromia region.The survey instrument contains time use module, expenditure module, asset module, welfare related questions and socio-demographic questions.
Firm level data is collected from 196 spontaneously emerging shoe making cluster, 86 government created cluster and 72 firms spread out in the capita out side of the two cluster areas. A detailed firm history, location, investment, production, cost and profit information is collected using a structured questionnaire.
Macro level data (annual) for Indian food grain sector. Based on secondary data