Kenya agriculture data

The FEWS NET Data Warehouse is **currently unavailable**. Datasets on HDX that refer to the Data Warehouse have been archived. Kenya Near Term Projection FEWS NET Acute Food Insecurity Classifications Geographic data for 2013

The FEWS NET Data Warehouse is **currently unavailable**. Datasets on HDX that refer to the Data Warehouse have been archived. Kenya Near Term Projection FEWS NET Acute Food Insecurity Classifications Geographic data for 2016

Kenya Current Situation FEWS NET Acute Food Insecurity Classifications Data from 2011

This is Kenya , livestock ownership with a resolution of 0.05 , approximately 5000 metres  Source : DHS 2022

Forward-looking analysis representing the most likely food security outcomes for the near term (April 2023 - May 2023) and medium term (June 2023 - September 2023) periods.

The raster dataset consists of a 1km score grid for beans storage sites achieved by processing sub-model outputs that characterize logistical factors for crop warehouse location: • Supply: Crop. • Demand: Human population density, Major cities population (national and bordering countries). • Infrastructure/accessibility: main transportation infrastructure. It consists of an arithmetic weighted sum of normalized grids (0 to 100): ("Crop Production" * 0.4) + ("Human Population Density" * 0.2) + ("Port Accessibility" * 0.2) + (“Major Cities Weighted Accessibility” * 0.1) + (”Regional Cities Weighted Accessibility” * 0.1) This 1km resolution raster dataset is part of FAO’s Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis (GIS-MCDA) aimed at the identification of value chain infrastructure sites (optimal location). **Data publication:** 2021-11-01 **Contact points:** Metadata Contact: [FAO-Data](mailto:fao-data@fao.org) Resource Contact: [Nelson Ribeiro](mailto:Nelson.RosasRibeiroFilho@fao.org) **Data lineage:** lineage statement Major data sources, FAO GIS platform Hand-in-Hand and OpenStreetMap (open data) including the following datasets 1. Human Population Density 2020 – WorldPop2020 - Estimated total number of people per grid-cell 1km. 2. Mapspam Production – IFPRI's Spatial Production Allocation Model (SPAM) estimates of crop distribution within disaggregated units. 3. OpenStreetMap. 4. FAO Data – Rivers of Africa, Inland Waters of Africa, Airports, Ports. **Resource constraints:** This work is made available under the Creative Commons Attribution 4.0 International license (CC-BY 4.0) **Online resources:** Zipped TIF raster file for Kenya beans storage/warehouse location score.

The raster dataset consists of a 1km score grid for cassava storage sites achieved by processing sub-model outputs that characterize logistical factors for crop warehouse location: • Supply: Crop. • Demand: Human population density, Major cities population (national and bordering countries). • Infrastructure/accessibility: main transportation infrastructure. It consists of an arithmetic weighted sum of normalized grids (0 to 100): ("Crop Production" * 0.4) + ("Human Population Density" * 0.2) + ("Port Accessibility" * 0.2) + (“Major Cities Weighted Accessibility” * 0.1) + (”Regional Cities Weighted Accessibility” * 0.1) This 1km resolution raster dataset is part of FAO’s Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis (GIS-MCDA) aimed at the identification of value chain infrastructure sites (optimal location). **Data publication:** 2021-11-01 **Contact points:** Metadata Contact: [FAO-Data](mailto:fao-data@fao.org) Resource Contact: [Nelson Ribeiro](mailto:Nelson.RosasRibeiroFilho@fao.org) **Data lineage:** lineage statement Major data sources, FAO GIS platform Hand-in-Hand and OpenStreetMap (open data) including the following datasets 1. Human Population Density 2020 – WorldPop2020 - Estimated total number of people per grid-cell 1km. 2. Mapspam Production – IFPRI's Spatial Production Allocation Model (SPAM) estimates of crop distribution within disaggregated units. 3. OpenStreetMap. 4. FAO Data – Rivers of Africa, Inland Waters of Africa, Airports, Ports. **Resource constraints:** This work is made available under the Creative Commons Attribution 4.0 International license (CC-BY 4.0) **Online resources:** Zipped TIF raster file for Kenya cassava storage/warehouse location score.

The raster dataset consists of a 1km score grid for sweet potato storage sites achieved by processing sub-model outputs that characterize logistical factors for crop warehouse location: • Supply: Crop. • Demand: Human population density, Major cities population (national and bordering countries). • Infrastructure/accessibility: main transportation infrastructure. It consists of an arithmetic weighted sum of normalized grids (0 to 100): ("Crop Production" * 0.4) + ("Human Population Density" * 0.2) + ("Port Accessibility" * 0.2) + (“Major Cities Weighted Accessibility” * 0.1) + (”Regional Cities Weighted Accessibility” * 0.1) This 1km resolution raster dataset is part of FAO’s Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis (GIS-MCDA) aimed at the identification of value chain infrastructure sites (optimal location). **Data publication:** 2021-11-01 **Contact points:** Metadata Contact: [FAO-Data](mailto:fao-data@fao.org) Resource Contact: [Nelson Ribeiro](mailto:Nelson.RosasRibeiroFilho@fao.org) **Data lineage:** lineage statement Major data sources, FAO GIS platform Hand-in-Hand and OpenStreetMap (open data) including the following datasets 1. Human Population Density 2020 – WorldPop2020 - Estimated total number of people per grid-cell 1km. 2. Mapspam Production – IFPRI's Spatial Production Allocation Model (SPAM) estimates of crop distribution within disaggregated units. 3. OpenStreetMap. 4. FAO Data – Rivers of Africa, Inland Waters of Africa, Airports, Ports. **Resource constraints:** This work is made available under the Creative Commons Attribution 4.0 International license (CC-BY 4.0) **Online resources:** Zipped TIF raster file for Kenya sweet potato storage/warehouse location score.

The raster dataset consists of a 1 km score grid for slaughterhouse industry facilities, produced under the scope of FAO’s Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis for value chain infrastructure location. The analysis is based on livestock production potential using proximity to cities, crop production, and aggregated average weighted density of cattle, goats, sheep and pigs. Animal weight: Goat 30kg (X0.09); Sheep 30kg (X0.09); Cattle 210kg (X0.63), Pig 63kg (X0.19). The score is achieved by processing sub-model outputs that characterize logistical factors: 1. Supply - Feed and livestock distribution. 2. Infrastructure - Transportation network (accessibility). 3. Atlas AI Asset Wealth Index. It consists of an arithmetic weighted sum of normalized grids (0 to 100): (”Livestock Density” * 0.4) + ("Asset Wealth" * 0.3) + (“Major Cities Accessibility” * 0.2) + ("Crop Production" * 0.1). **Contact points:** Metadata Contact: [FAO-Data](mailto:fao-data@fao.org) Resource Contact: [Nelson Ribeiro](mailto:nelson.rosasribeirofilho@fao.org) **Data lineage:** lineage statement Major data sources, FAO GIS platform Hand-in-Hand and OpenStreetMap (open data) including the following datasets 1. Human Population Density 2020 – WorldPop2020 - Estimated total number of people per grid-cell 1km. 2. GLW Gridded Livestock of the World - Gridded Livestock of the World (GLW 3 and GLW 2). 3. IFPRI MapSPAM crop production. 4. OpenStreetMap. **Resource constraints:** This work is made available under the Creative Commons Attribution 4.0 International license (CC-BY 4.0) **Online resources:** Download Kenya location score TIF (zip) dataset

The raster dataset consists of a 1km score grid for potato storage sites achieved by processing sub-model outputs that characterize logistical factors for crop warehouse location: • Supply: Crop. • Demand: Human population density, Major cities population (national and bordering countries). • Infrastructure/accessibility: main transportation infrastructure. It consists of an arithmetic weighted sum of normalized grids (0 to 100): ("Crop Production" * 0.4) + ("Human Population Density" * 0.2) + ("Port Accessibility" * 0.2) + (“Major Cities Weighted Accessibility” * 0.1) + (”Regional Cities Weighted Accessibility” * 0.1) This 1km resolution raster dataset is part of FAO’s Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis (GIS-MCDA) aimed at the identification of value chain infrastructure sites (optimal location). **Data publication:** 2021-11-01 **Contact points:** Metadata Contact: [FAO-Data](mailto:fao-data@fao.org) Resource Contact: [Nelson Ribeiro](mailto:Nelson.RosasRibeiroFilho@fao.org) **Data lineage:** lineage statement Major data sources, FAO GIS platform Hand-in-Hand and OpenStreetMap (open data) including the following datasets 1. Human Population Density 2020 – WorldPop2020 - Estimated total number of people per grid-cell 1km. 2. Mapspam Production – IFPRI's Spatial Production Allocation Model (SPAM) estimates of crop distribution within disaggregated units. 3. OpenStreetMap. 4. FAO Data – Rivers of Africa, Inland Waters of Africa, Airports, Ports. **Resource constraints:** This work is made available under the Creative Commons Attribution 4.0 International license (CC-BY 4.0) **Online resources:** Zipped TIF raster file for Kenya potato storage/warehouse location score.

Point locations of agricultural crops in Kirinyaga, Kenya, with each feature representing a crop recorded by name. Published by Orion Terrametrics.

Point features representing livestock-related locations in Kajiado, Kenya, with a name field for each type. Published by Orion Terrametrics.

This Maps Viewer shows all the Agro-Ecological Zones in Kenya dataset

Point features representing agricultural crop locations in Embu, Kenya, with a crop_name field indicating the crop type. Published by Orion Terrametrics.

Point locations of major crops in Kitui County, Kenya. Each point represents a named crop. Published by Orion Terrametrics.

Point locations related to livestock and fishing in Kitui County, Kenya, with each feature representing a named activity or type such as beekeeping or sheep. Published by Orion Terrametrics.

Point locations of agricultural crops in Kajiado, Kenya, with each feature representing a crop recorded by name. Published by Orion Terrametrics.

Point locations of livestock in Kirinyaga, Kenya, with each feature representing a named type such as dairy cattle. Published by Orion Terrametrics.

Point features representing livestock keeping locations in Embu, Kenya, with a name field for each type. Published by Orion Terrametrics.

Kenya Current Situation FEWS NET Acute Food Insecurity Classifications Geographic data for 2022

Kenya Current Situation FEWS NET Acute Food Insecurity Classifications Geographic data for 2020

Kenya Current Situation FEWS NET Acute Food Insecurity Classifications Geographic data for 2021

The seasonal Gross Biomass Water Productivity expresses the quantity of output (above ground biomass production) in relation to the total volume of water consumed (actual EvapoTranspiration) during the growing cycle of the vegetation. By relating biomass production to total EvapoTranspiration (sum of soil evaporation, canopy transpiration and interception), this indicator provides insights on the impact of vegetation development on consumptive water use and thus on water balance in a given domain. When the focus is on monitoring performance of irrigated agriculture in relation to water consumption, it is more appropriate to use transpiration alone as a denominator, as a measure of water beneficially consumed by the plant. This latter indicator, for which we use the term \"net water productivity\", provides useful information on how effectively vegetation (and particularly crops) uses water to develop its biomass (and thus yield). **Data publication:** 2025-02-06 **Supplemental Information:** No data value: -9999 Unit : kg/m³ Scale Factor : 0.001 Map code : L3-GBWP-S Conversion factor: the pixel value in the downloaded data must be multiplied by 0.001 New dekadal data layers are released approximately 5 days after the end of a dekad. A higher quality version of the same data layer is uploaded after 6 dekads have passed. This final version of the dekadal dataset has a higher quality because gap filling and interpolation processes, where needed, have been based on more data observations. This implies that other temporal aggregations (monthly, seasonal, annual), and layers that depend on those, are updated as well. Practically this means that a final annual aggregation of the most recent full calendar year can only be produced after the end of February. Likewise, the final monthly aggregation of the most recent calendar months can only be produced 2 full months later. **Citation:** FAO 2018. WaPOR Database Methodology: Level 3. Remote Sensing for Water Productivity Technical Report: Methodology Series. Rome, FAO. 72 pages. Licence: CC-BY-4.0 **Contact points:** Resource Contact: [WaPOR](mailto:wapor@fao.org) Metadata Contact: [WaPOR](mailto:wapor@fao.org) **Data lineage:** The calculation of gross biomass water productivity (GBWP) is as follows: GBWP = TBP/ETIa Where TBP is seasonal Total Biomass Production in kg/ha and ETIa is the Actual EvapoTranspiration and Interception in m³/ha cumulated during the course of the growing season, as defined by the phenology information. The following data is used for calculating it: - Seasonal TBP - Dekadal ETIa - Phenology (start of season and end of season) **Resource constraints:** Creative Commons Attribution 4.0 International (CC-BY-4.0) **Online resources:** Download the data from Google Cloud Storage

The Mwea Irrigation Scheme Rice Yield Map (MIS-RYM) is a geospatial vector data layer presenting section-level rice yield estimates for the Mwea Irrigation Scheme in Kenya for the main growing seasons, from 2019 to 2022. The dataset represents five sections (Karaba, Mwea, Tebere, Thiba, and Wamumu) with rice yield estimates stored in the attribute table and spatially linked to section polygons. The rice yield estimates are derived from WaPOR v3 Net Primary Productivity (NPP) layer at 20 m spatial resolution and dekadal (10-day) temporal resolution, aggregated by growing season to support consistent spatial analysis and mapping of yield variability within the scheme. The MIS-RYM is intended for data analysis, visualization, and comparison of seasonal and interannual rice productivity at the irrigation section scale, where detailed spatial assessment is required. **Data publication:** 2025-12-31 **Supplemental Information:** **Attibutes Table** - Id - identity (*Section name*) - Yield_mean - AETI_2019 - AETI_2020 - AETI_2021 - AETI_2022 - Yield_2019 - Yield_2020 - Yield_2021 - Yield_2022 **Unit of measure** - Yield mean: ton/ha/season - AETI: mm/season - Yield: ton/ha/season **Citation:** FAO WaPOR database. [Accessed on [Day/Month/Year]]. [URL] Licence: CC-BY-4.0 **Contact points:** Metadata Contact: [WaPOR](mailto:wapor@fao.org) Resource Contact: [Preeeti Koirala](mailto:preeti.koirala@fao.org) **Data lineage:** MIS-RYM geo-spatial vector dataset was produced by aggregating section-level rice yield estimates derived from WaPOR v3 Net Primary Productivity (NPP) data at 20 m spatial and dekadal (10day) temporal resolution for the main growing seasons (2019–2022). Yield values were spatially summarized using irrigation section boundaries for Karaba, Mwea, Tebere, Thiba, and Wamumu. The dataset is based on remotely sensed, model-derived inputs and is subject to uncertainties from NPP estimation, temporal aggregation, and boundary accuracy; it is intended for comparative spatial analysis rather than field-scale validation. **Resource constraints:** This work is made available under the Creative Commons Attribution 4.0 International licence (CC-BY-4.0) **Online resources:**

The long-term NDVI quality layer is produced as a summary of the quality for the entire time period (2009 to date). Pixel values indicate the percentage of good NDVI observations over the entire time period. The data is provided in near real time from January 2009 to 2023. **Data publication:** 2020-01-01 **Supplemental Information:** No data value: 255 Unit: Percentage (%) Map code: L3-BUS-QUAL-NDVI-LT **Citation:** FAO 2018. WaPOR Database Methodology: Level 3. Remote Sensing for Water Productivity Technical Report: Methodology Series. Rome, FAO. 72 pages. Licence: CC-BY-4.0 **Contact points:** Metadata Contact: [WaPOR](mailto:wapor@fao.org) Resource Contact: [WaPOR](mailto:wapor@fao.org) **Data lineage:** The long-term NDVI quality layer is produced as a summary of the quality for the entire time period (2009-2019). Pixel values indicate the percentage of good NDVI observations over the entire time period. While the dekadal NDVI Quality layer shows the quality of the data for a specific time of year, the long-term NDVI quality layer has been added to give users a quick overview of the areas that tend to suffer more cloud cover. **Resource constraints:** Creative Commons Attribution 4.0 International (CC-BY-4.0) **Online resources:** Download the data from Google Cloud Storage

The layer gives an indication of the quality of the NDVI input data. It shows whether a valid observation was available for that dekad, and if not, the length of the data gap since the nearest valid observation, in dekads. The data is provided in near real time from January 2009 to 2023. **Data publication:** 2020-01-01 **Supplemental Information:** No data value: 255 Unit: Number of Dekads (D) Map code : L3-QUAL-NDVI-D **Citation:** FAO 2018. WaPOR Database Methodology: Level 3. Remote Sensing for Water Productivity Technical Report: Methodology Series. Rome, FAO. 72 pages. Licence: CC-BY-4.0 **Contact points:** Metadata Contact: [WaPOR](mailto:wapor@fao.org) Resource Contact: [WaPOR](mailto:wapor@fao.org) **Data lineage:** The quality layer is produced during the compositing and smoothing of the NDVI. The quality index (QI) for every pixel in each dekad depicts the number of dekads since the last valid observation used for reconstruction. The fundamental idea is that the longer the gap, the less reliable the smoothing is. When a number of consecutive dekads did not have direct observations for calculating the NDVI, the quality layer pixel value of the consecutive dekadal NDVI quality layers will represent the cumulative number of dekads since a valid observation. The user can use consecutive NDVI Quality layers to calculate the length of the gap between valid observations. In the case that the pixel observation is usable and no reconstruction was needed, the QI is set to 0 (ideal situation). For the observations at the profile edges, it is assumed that the (a priori unknown) observations preceding the first valid observation and following the last valid observations are "good".\n\nThis quality layer depicts the quality for the NDVI, fAPAR, albedo and NPP, as all these data components rely on the same input, i.e. the spectral reflectance data. Furthermore, the length of the data gap is the same. Data component developed through collaboration with the FRAME Consortium. More information can be found at: http://www.fao.org/in-action/remote-sensing-for-water-productivity/ **Resource constraints:** Creative Commons Attribution 4.0 International (CC-BY-4.0) **Online resources:** Download the data from Google Cloud Storage

Phenology indicates the cycle or season of a crop and, in this case, is defined by the dekad (D) corresponding to the start, maximum and end of the growing season. This information can be derived from satellite-based vegetation index time series. Phenology is delivered for a maximum of two growing seasons annually. The phenology for one growing season is therefore delivered as three raster files. The first raster indicates the Start of Season (SOS), the second the Maximum of Season (MOS) and the third represents the End of Season (EOS). With a maximum of 2 growing seasons annually, a full year is described by 6 raster files. The pixel values of the phenology data components are expressed in dekad numbers. The data is provided in near real time from January 2009 to 2022. **Data publication:** 2020-01-01 **Supplemental Information:** No data value: 255 Unit: D (dekad number) Map code : L3-PHE-S **Citation:** FAO 2018. WaPOR Database Methodology: Level 3. Remote Sensing for Water Productivity Technical Report: Methodology Series. Rome, FAO. 72 pages. Licence: CC-BY-4.0 **Contact points:** Metadata Contact: [WaPOR](mailto:wapor@fao.org) Resource Contact: [WaPOR](mailto:wapor@fao.org) **Data lineage:** The year that contains the Maximum of season determines the year a growing season is attributed to (i.e. the target year). As the crop calendar is determined from a three-year NDVI time series with the target year in the middle, dekad numbers range between 1 and 108 (3 x 36 = 108 dekads). Since it occurs in the target year, MOS has a value between 37 and 72. Start of season pixel values must be smaller than 72, whilst End of season pixel values must be larger than 36. 254 denotes "no season" (if no growing season can be distinguished), and 253 "out of season". The Start, Maximum and End of up to two seasons for a given calendar year (January - December) is derived by applying the methodology described by Van Hoolst et al. (see methodology document for references). This methodology can derive phenological information from a time series of dekadal vegetation index composites, in this case NDVI. The following data is used to calculate Phenology: - NDVI **Resource constraints:** Creative Commons Attribution 4.0 International (CC-BY-4.0) **Online resources:** Download the data from Google Cloud Storage

The seasonal Net Biomass Water Productivity expresses the quantity of output (total biomass production) in relation to the total volume of water beneficially consumed (through canopy transpiration) during the growing cycle of the vegetation. Contrary to gross water productivity, net water productivity is particularly useful in monitoring how effectively vegetation (and, more importantly, crops) uses water to develop biomass (and thus yield). The data is provided in near real time from January 2010 to 2019. **Data publication:** 2020-01-01 **Supplemental Information:** No data value: -9999 Unit: kg/m³ Scale Factor : 0.001 Map code : L3-NBWP-S Conversion factor: the pixel value in the downloaded data must be multiplied by 0.001 New dekadal data layers are released approximately 5 days after the end of a dekad. A higher quality version of the same data layer is uploaded after 6 dekads have passed. This final version of the dekadal dataset has a higher quality because gap filling and interpolation processes, where needed, have been based on more data observations. This implies that other temporal aggregations (monthly, seasonal, annual), and layers that depend on those, are updated as well. Practically this means that a final annual aggregation of the most recent full calendar year can only be produced after the end of February. Likewise, the final monthly aggregation of the most recent calendar months can only be produced 2 full months later. **Citation:** FAO 2018. WaPOR Database Methodology: Level 3. Remote Sensing for Water Productivity Technical Report: Methodology Series. Rome, FAO. 72 pages. Licence: CC-BY-4.0 **Contact points:** Metadata Contact: [WaPOR](mailto:wapor@fao.org) Resource Contact: [WaPOR](mailto:wapor@fao.org) **Data lineage:** The calculation of net biomass water productivity (NBWP) is as follows: NBWP = TBP/T Where TBP is seasonal Total Biomass Production in kg/ha and T is the Transpiration in m³/ha cumulated during the course of the growing season, as defined by the phenology information. The following data is used for calculating it: - Seasonal TBP - Dekadal T - Phenology (start of season and end of season) **Resource constraints:** Creative Commons Attribution 4.0 International (CC-BY-4.0) **Online resources:** Download the data from Google Cloud Storage

Total Biomass Production (TBP) is defined as the sum of the above-ground dry matter produced during the course of the growing season. The seasonal value represents the total accumulated biomass during one growing season, from start of season (SOS) to end of season (EOS). A pixel value of -9997 denotes "out of season". The data is provided in near real time from January 2009 to 2022. **Data publication:** 2020-01-01 **Supplemental Information:** No data value: -9999 Unit: kg/ha Scale Factor : 10 Map code : L3-TBP-S New dekadal data layers are released approximately 5 days after the end of a dekad. A higher quality version of the same data layer is uploaded after 6 dekads have passed. This final version of the dekadal dataset has a higher quality because gap filling and interpolation processes, where needed, have been based on more data observations. This implies that other temporal aggregations (monthly, seasonal, annual), and layers that depend on those, are updated as well. Practically this means that a final annual aggregation of the most recent full calendar year can only be produced after the end of February. Likewise, the final monthly aggregation of the most recent calendar months can only be produced 2 full months later. **Citation:** FAO 2018. WaPOR Database Methodology: Level 3. Remote Sensing for Water Productivity Technical Report: Methodology Series. Rome, FAO. 72 pages. Licence: CC-BY-4.0 **Contact points:** Metadata Contact: [WaPOR](mailto:wapor@fao.org) Resource Contact: [WaPOR](mailto:wapor@fao.org) **Data lineage:** To derive the accumulation in biomass production over or during a growing season, first the start and the end of the growing season need to be identified using the phenology data component. TBP is then calculated as the sum of NPP, converted into dry matter productivity (DMP) units (kg/ha), between the start of the season (SOS) and the end of the season (EOS). At the end of the season, when the crops for the area can be assessed, the dekadal and seasonal TBP values are adjusted using an additional root-shoot correction factor data layer that allows the user to correct the TBP using the land cover specific root-shoot values. A limitation for the derivation of TBP is the dependency on phenological information, meaning that TBP can only be derived for areas where seasonality is detected. For ecosystems, such as tropical forests or deserts, that experience almost no seasonality, the start of season is theoretically set at January 1st and end of season is set at December 31st. Data component developed through collaboration with the FRAME Consortium. More information can be found at: http://www.fao.org/in-action/remote-sensing-for-water-productivity/en/ **Resource constraints:** Creative Commons Attribution 4.0 International (CC-BY-4.0) **Online resources:** Download the data from Google Cloud Storage

Crop Storage Final Location: Aggregated Production (Kenya - ~1km) consists of a 0.01 decimal degree grid produced under the scope of the Covid-19 sub-Saharan African Corridor project pilot case, Geographical Information Systems - Multicriteria Decision Analysis (GIS-MCDA), for the identification and definition of recommended mobile storage locations (movable warehouses). Top10% aggregated production score grids are computed and filtered by exclusive criteria: - Access to finance - Banks - 10km (0.09 degree) buffer radius; - Distance from major roads - 2km (0.18 degree) buffer radius; - Access to IT - mobile broadband coverage. **Data publication:** 2020-08-03 **Contact points:** Metadata Contact: [FAO-Data](mailto:fao-data@fao.org) Resource Contact: [Nelson Ribeiro](mailto:Nelson.RosasRibeiroFilho@fao.org) **Data lineage:** lineage statement Data sources used, FAO GIS platform Hand-in-Hand, OpenStreetMap (open data), Collins Bartholomew's Mobile Coverage Explorer, including the following datasets 1. Human Population Density 2020 – WorldPop2020 - Estimated total number of people per grid-cell 1km. 2. Mapspam Production – IFPRI's Spatial Production Allocation Model (SPAM) estimates of crop distribution within disaggregated units - Production (mt) for selected major crops (sugc, maiz, pota, swpo, cass, bean, whea, sorg, cowp, rice,acof/rcof). 3. OpenStreetMap - Roads, railways, places, points-of-interest; 4. FAO Data – Rivers of Africa, Inland Waters of Africa, Airports, Ports; 5. World mobile broadband coverage - derived from Collins Bartholomew's mobile coverage explorer. **Resource constraints:** This work is made available under the Creative Commons Attribution 4.0 International license (CC-BY 4.0) **Online resources:**

The 1km raster dataset represents top location score areas suitable for beans storage filtered by exclusive criteria: access to finance, distance to major roads and access to IT (mobile broadband connection). The location score is achieved by processing sub-model outputs characterizing logistical factors for crop warehouse siting: Supply, demand, Infrastructure/accessibility. The location score from 0 to 100 is then obtained through a simple arithmetic weighted sum of the normalized/scaled grids. The top 99th percentile is selected/clipped using the exclusive criteria. Access to finance and roads are defined using a linear distance threshold: • Banks - approx. 10km (0.08 degree) buffer radius. • Major roads - approx. 2km (0.018 degree) buffer radius. Access to IT is characterized applying the mobile broadband coverage map. This 1km resolution raster dataset is part of FAO’s Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis (GIS-MCDA) aimed at the identification of value chain infrastructure sites (optimal location). **Data publication:** 2021-11-01 **Contact points:** Metadata Contact: [FAO-Data](mailto:fao-data@fao.org) Resource Contact: [Nelson Ribeiro](mailto:Nelson.RosasRibeiroFilho@fao.org) **Data lineage:** lineage statement Major data sources, FAO GIS platform Hand-in-Hand and OpenStreetMap (open data) including the following datasets 1. Human Population Density 2020 – WorldPop2020 - Estimated total number of people per grid-cell 1km. 2. Mapspam Production – IFPRI's Spatial Production Allocation Model (SPAM) estimates of crop distribution within disaggregated units. 3. OpenStreetMap. 4. FAO Data – Rivers of Africa, Inland Waters of Africa, Airports, Ports. 5. Mobile Broadband Coverage produced based on: Coverage Data © Collins Bartholomew and GSMA 2020 **Resource constraints:** This work is made available under the Creative Commons Attribution 4.0 International license (CC-BY 4.0) **Online resources:** Zipped TIF raster file for Kenya beans storage/warehouse final location.

The 1km raster dataset represents top location score areas suitable for cassava storage filtered by exclusive criteria: access to finance, distance to major roads and access to IT (mobile broadband connection). The location score is achieved by processing sub-model outputs characterizing logistical factors for crop warehouse siting: Supply, demand, Infrastructure/accessibility. The location score from 0 to 100 is then obtained through a simple arithmetic weighted sum of the normalized/scaled grids. The top 99th percentile is selected/clipped using the exclusive criteria. Access to finance and roads are defined using a linear distance threshold: • Banks - approx. 10km (0.08 degree) buffer radius. • Major roads - approx. 2km (0.018 degree) buffer radius. Access to IT is characterized applying the mobile broadband coverage map. This 1km resolution raster dataset is part of FAO’s Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis (GIS-MCDA) aimed at the identification of value chain infrastructure sites (optimal location). **Data publication:** 2021-11-01 **Contact points:** Metadata Contact: [FAO-Data](mailto:fao-data@fao.org) Resource Contact: [Nelson Ribeiro](mailto:Nelson.RosasRibeiroFilho@fao.org) **Data lineage:** lineage statement Major data sources, FAO GIS platform Hand-in-Hand and OpenStreetMap (open data) including the following datasets 1. Human Population Density 2020 – WorldPop2020 - Estimated total number of people per grid-cell 1km. 2. Mapspam Production – IFPRI's Spatial Production Allocation Model (SPAM) estimates of crop distribution within disaggregated units. 3. OpenStreetMap. 4. FAO Data – Rivers of Africa, Inland Waters of Africa, Airports, Ports. 5. Mobile Broadband Coverage produced based on: Coverage Data © Collins Bartholomew and GSMA 2020 **Resource constraints:** This work is made available under the Creative Commons Attribution 4.0 International license (CC-BY 4.0) **Online resources:** Zipped TIF raster file for cassava storage/warehouse final location.

The 1km raster dataset represents top location score areas suitable for maize storage filtered by exclusive criteria: access to finance, distance to major roads and access to IT (mobile broadband connection). The location score is achieved by processing sub-model outputs characterizing logistical factors for crop warehouse siting: Supply, demand, Infrastructure/accessibility. The location score from 0 to 100 is then obtained through a simple arithmetic weighted sum of the normalized/scaled grids. The top 99th percentile is selected/clipped using the exclusive criteria. Access to finance and roads are defined using a linear distance threshold: • Banks - approx. 10km (0.08 degree) buffer radius. • Major roads - approx. 2km (0.018 degree) buffer radius. • Access to IT is characterized applying the mobile broadband coverage map. This 1km resolution raster dataset is part of FAO’s Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis (GIS-MCDA) aimed at the identification of value chain infrastructure sites (optimal location). **Data publication:** 2021-11-01 **Contact points:** Metadata Contact: [FAO-Data](mailto:fao-data@fao.org) Resource Contact: [Nelson Ribeiro](mailto:Nelson.RosasRibeiroFilho@fao.org) **Data lineage:** lineage statement Major data sources, FAO GIS platform Hand-in-Hand and OpenStreetMap (open data) including the following datasets 1. Human Population Density 2020 – WorldPop2020 - Estimated total number of people per grid-cell 1km. 2. Mapspam Production – IFPRI's Spatial Production Allocation Model (SPAM) estimates of crop distribution within disaggregated units. 3. OpenStreetMap. 4. FAO Data – Rivers of Africa, Inland Waters of Africa, Airports, Ports. 5. Mobile Broadband Coverage produced based on: Coverage Data © Collins Bartholomew and GSMA 2020 **Resource constraints:** This work is made available under the Creative Commons Attribution 4.0 International license (CC-BY 4.0) **Online resources:** Zipped TIF raster file for Kenya maize storage/warehouse final location.

The 1km raster dataset represents top location score areas suitable for potato storage filtered by exclusive criteria: access to finance, distance to major roads and access to IT (mobile broadband connection). The location score is achieved by processing sub-model outputs characterizing logistical factors for crop warehouse siting: Supply, demand, Infrastructure/accessibility. The location score from 0 to 100 is then obtained through a simple arithmetic weighted sum of the normalized/scaled grids. The top 99th percentile is selected/clipped using the exclusive criteria. Access to finance and roads are defined using a linear distance threshold: • Banks - approx. 10km (0.08 degree) buffer radius. • Major roads - approx. 2km (0.018 degree) buffer radius. • Access to IT - mobile broadband coverage map. This 1km resolution raster dataset is part of FAO’s Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis (GIS-MCDA) aimed at the identification of value chain infrastructure sites (optimal location). **Data publication:** 2021-11-01 **Contact points:** Metadata Contact: [FAO-Data](mailto:fao-data@fao.org) Resource Contact: [Nelson Ribeiro](mailto:Nelson.RosasRibeiroFilho@fao.org) **Data lineage:** lineage statement Major data sources, FAO GIS platform Hand-in-Hand and OpenStreetMap (open data) including the following datasets 1. Human Population Density 2020 – WorldPop2020 - Estimated total number of people per grid-cell 1km. 2. Mapspam Production – IFPRI's Spatial Production Allocation Model (SPAM) estimates of crop distribution within disaggregated units. 3. OpenStreetMap. 4. FAO Data – Rivers of Africa, Inland Waters of Africa, Airports, Ports. 5. Mobile Broadband Coverage produced based on: Coverage Data © Collins Bartholomew and GSMA 2020 **Resource constraints:** This work is made available under the Creative Commons Attribution 4.0 International license (CC-BY 4.0) **Online resources:** Zipped TIF raster file for Kenya potato storage/warehouse final location.

The 1km raster dataset represents top location score areas suitable for sweet potato storage filtered by exclusive criteria: access to finance, distance to major roads and access to IT (mobile broadband connection). The location score is achieved by processing sub-model outputs characterizing logistical factors for crop warehouse siting: Supply, demand, Infrastructure/accessibility. The location score from 0 to 100 is then obtained through a simple arithmetic weighted sum of the normalized/scaled grids. The top 99th percentile is selected/clipped using the exclusive criteria. Access to finance and roads are defined using a linear distance threshold: • Banks - approx. 10km (0.08 degree) buffer radius. • Major roads - approx. 2km (0.018 degree) buffer radius. Access to IT is characterized applying the mobile broadband coverage map. This 1km resolution raster dataset is part of FAO’s Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis (GIS-MCDA) aimed at the identification of value chain infrastructure sites (optimal location). **Data publication:** 2021-11-01 **Contact points:** Metadata Contact: [FAO-Data](mailto:fao-data@fao.org) Resource Contact: [Nelson Ribeiro](mailto:Nelson.RosasRibeiroFilho@fao.org) **Data lineage:** lineage statement Major data sources, FAO GIS platform Hand-in-Hand and OpenStreetMap (open data) including the following datasets 1. Human Population Density 2020 – WorldPop2020 - Estimated total number of people per grid-cell 1km. 2. Mapspam Production – IFPRI's Spatial Production Allocation Model (SPAM) estimates of crop distribution within disaggregated units. 3. OpenStreetMap. 4. FAO Data – Rivers of Africa, Inland Waters of Africa, Airports, Ports. 5. Mobile Broadband Coverage produced based on: Coverage Data © Collins Bartholomew and GSMA 2020 **Resource constraints:** This work is made available under the Creative Commons Attribution 4.0 International license (CC-BY 4.0) **Online resources:** Zipped TIF raster file for Kenya sweet potato storage/warehouse final location.

Country raster grid with 0.01 decimal degrees resolution produced under the scope of the sub-Saharan African Corridor project pilot case, using raster-based Geographical Information Systems - Multicriteria Decision Analysis (GIS-MCDA) methodology for the identification and definition of mobile storage locations (movable warehouses). The modeling variables characterize supply, demand and accessibility, main logistical factors for warehousing facilities location. These variables are major crops (supply), human population density (demand) and main transportation network infrastructure (accessibility). The main transportation network infrastructure is the input for the development of raster-based travel time/cost analysis. GIS multicriteria decision analysis GIS-MCDA consists of a method to convert and combine spatial data/geographical information and decision-makers criteria in order to attain evidence for a decision-making process. The location score results from a simple arithmetic weighted sum calculation (cell statistics) of normalized grids, generating a location score varying from 0 to 100. The assumed weight for each of the criteria is as follows: ("Crop Production" * 0.4) + ("Human Population Density" * 0.2) + ("Cities Accessibility" * 0.1) + ("Regional Cities Accessibility" * 0.1) + ("Ports Accessibility" * 0.1) **Data publication:** 2020-08-03 **Contact points:** Metadata Contact: [FAO-Data](mailto:fao-data@fao.org) Resource Contact: [Nelson Ribeiro](mailto:Nelson.RosasRibeiroFilho@fao.org) **Data lineage:** lineage statement Two major sources were used, FAO GIS platform Hand-in-Hand and OpenStreetMap (open data) including the following datasets 1. Human Population Density 2020 – WorldPop2020 - Estimated total number of people per grid-cell 1km. 2. Mapspam Production – IFPRI's Spatial Production Allocation Model (SPAM) estimates of crop distribution within disaggregated units • Production (mt); Crops: sugc, maiz, pota, swpo, cass, bean, whea, sorg, cowp, rice, acof/rcof; 3. OpenStreetMap - Map of the world built by volunteers and released with open-content license. 4. FAO Data – Rivers of Africa, Inland Waters of Africa, Airports, Ports; **Resource constraints:** This work is made available under the Creative Commons Attribution 4.0 International license (CC-BY 4.0) **Online resources:**