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  • The vector dataset portrays poverty rates at the country level. The data for the poverty dataset comes from: the Tajikistan Living Standard Measurement Survey 2009 (TLSS) collected by the State Statistical Agency of Tajikistan in collaboration with the World Bank, and the 2010 Census of Tajikistan. The TLSS provides information on food and non-food expenditure, labor activities, migration, agriculture, education, dwelling, utilities, and durable goods. The Census of Tajikistan covers approximately 1.6 million households and 8 million individuals. This dataset has been produced based on the data provided in the "Poverty Mapping in Tajikistan: Method and Key Findings" report. This report is the joint product of the World Bank Group (WBG) and the Agency of Statistics under the President of Tajikistan (TajStat).

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    A combination of cattle livestock intensification potential and poverty rates. The resulting dataset enables location analysis for dairy value chain potential investment. - Cattle livestock intensification layer is a combination of the cattle density/distribution and the selected livestock production system (MR Mixed RainfedArid+ MR Mixed Rainfed Humid + MR Mixed Rainfed Temperate + MI Mixed Irrigated Hyperarid + MI Mixed Irrigated Arid + MI Mixed Irrigated Humid+MI Mixed Irrigated Temperate+Urban). Livestock Production System used as 1/0 layer. - Poverty rates vary from 12.7 to 76.2 percent, higher percentage representing areas of higher poverty incidence.

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    Raster representing a potential/suitability score for non-intensive and integrated, small-scale, African Catfish and Nile Tilapia fish farming systems, using ponds and small water bodies (SWB), in consumption-based poverty above the national average regions of Nigeria. Produced under the scope of FAO’s Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis for value chain infrastructure location. Non-intensive aquaculture systems are considered based on natural food supply from SWB or ponds, from integrated systems (crop/livestock byproducts or waste), or with complementary feeding resourcing to on-farm or locally produced feed. The score results from combining sub-model outputs that characterize natural geographical and economical factors: 1. farm-gate sales - based on population density classification 2. Water balance - precipitation/evapotranspiration 3. Soil/slope suitability. 4. Inputs - Crop and livestock byproducts Considered constraints or exclusive criteria are: 1. Urban areas 2. Protected areas 3. Poverty above the national average It consists of an arithmetic weighted sum of normalized grids (0 to 100): ("WaterBalance" X 0.5) + ("Soil/Slope " X 0.25) + (“Byproducts” X 0.125) + (”FarmgateSales” X 0.125)

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    A combination of cattle livestock intensification potential and poverty rates. The resulting dataset enables location analysis for dairy value chain potential investment. - Cattle livestock intensification layer is a combination of the cattle density/distribution and the selected livestock production system (MR Mixed RainfedArid+ MR Mixed Rainfed Humid + MR Mixed Rainfed Temperate + MI Mixed Irrigated Hyperarid + MI Mixed Irrigated Arid + MI Mixed Irrigated Humid+MI Mixed Irrigated Temperate+Urban). Livestock Production System used as 1/0 layer. - The poverty map portrays poverty rates at the country level. Accordingly, the higher percentage of poverty in a particular area, the poorer population is located there.

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    The combination of yield gap and poverty rates into a single map enables to identify best feasible modalities for agricultural development, potential investment, and resource allocation. - Yield gap provides important information for identifying causes of food insecurity and addressing rural poverty. Yield and production gaps have been estimated by comparing at a spatially detailed level of 5 arc-minutes the agro-ecological attainable yield and production of 22 major crops/crop groups, simulated under the historical climate of 1981-2010, with actual yields and production obtained by downscaling for the years 2000 and 2010 statistical data of main food, feed, and fiber crops. - The poverty map portrays poverty rates at the country level. Accordingly, the higher percentage of poverty in a particular area, the poorer population is located there.

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    A combination of goat and sheep dairy intensification potential and poverty rates. The resulting dataset enables location analysis for dairy value chain potential investment. - Goat and sheep livestock intensification layer is a combination of goat and sheep density/distribution and selected livestock production systems(MR Mixed RainfedArid+ MR Mixed Rainfed Humid + MR Mixed Rainfed Temperate + MI Mixed Irrigated Hyperarid + MI Mixed Irrigated Arid + MI Mixed Irrigated Humid+MI Mixed Irrigated Temperate+Urban). Livestock Production System used as 1/0 layer. - Poverty rates vary from 12.7 to 76.2 percent, a higher percentage representing areas of higher poverty incidence.

  • Categories    

    The combination of yield gap and poverty rates into a single map enables to identify best feasible modalities for agricultural development, potential investment, and resource allocation. - Yield gap provides important information for identifying causes of food insecurity and addressing rural poverty. Yield and production gaps have been estimated by comparing at a spatially detailed level of 5 arc-minutes the agro-ecological attainable yield and production of 22 major crops/crop groups, simulated under the historical climate of 1981-2010, with actual yields and production obtained by downscaling for the years 2000 and 2010 statistical data of main food, feed, and fiber crops. - The poverty map portrays poverty rates at the country level. Poverty rates vary from 12.7 to 76.2 percent. Accordingly, the higher percentage of poverty in a particular area, the poorer population is located there.