Mozambique agriculture data

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

The FEWS NET Data Warehouse is **currently unavailable**. Datasets on HDX that refer to the Data Warehouse have been archived. Mozambique Near Term Projection FEWS NET Acute Food Insecurity Classifications Geographic data for 2011

The FEWS NET Data Warehouse is **currently unavailable**. Datasets on HDX that refer to the Data Warehouse have been archived. Mozambique Current Situation FEWS NET Acute Food Insecurity Classifications Geographic data for 2014

The FEWS NET Data Warehouse is **currently unavailable**. Datasets on HDX that refer to the Data Warehouse have been archived. Mozambique Near Term Projection FEWS NET Acute Food Insecurity Classifications Geographic data for 2015

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 Datasets on HDX that refer to the Data Warehouse have been archived.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

The FEWS NET Data Warehouse is **currently unavailable**. Datasets on HDX that refer to the Data Warehouse have been archived. Mozambique Current Situation FEWS NET Acute Food Insecurity Classifications Geographic data for 2017

The FEWS NET Data Warehouse is **currently unavailable**. Datasets on HDX that refer to the Data Warehouse have been archived. Mozambique Medium Term Projection FEWS NET Acute Food Insecurity Classifications Geographic data for 2017

The FEWS NET Data Warehouse is **currently unavailable**. Datasets on HDX that refer to the Data Warehouse have been archived. Mozambique Medium Term Projection FEWS NET Acute Food Insecurity Classifications Geographic data for 2019

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

The FEWS NET Data Warehouse is **currently unavailable**. Datasets on HDX that refer to the Data Warehouse have been archived. Mozambique Current Situation 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 Datasets on HDX that refer to the Data Warehouse have been archived. Mozambique 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 Datasets on HDX that refer to the Data Warehouse have been archived. Mozambique Near Term Projection FEWS NET Acute Food Insecurity Classifications Geographic data for 2017

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 Datasets on HDX that refer to the Data Warehouse have been archived.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

The FEWS NET Data Warehouse is **currently unavailable**. Datasets on HDX that refer to the Data Warehouse have been archived. Mozambique Current Situation FEWS NET Acute Food Insecurity Classifications Geographic data for 2011

The FEWS NET Data Warehouse is **currently unavailable**. Datasets on HDX that refer to the Data Warehouse have been archived. Mozambique Medium Term Projection FEWS NET Acute Food Insecurity Classifications Geographic data for 2014

The FEWS NET Data Warehouse is **currently unavailable**. Datasets on HDX that refer to the Data Warehouse have been archived. Mozambique Current Situation FEWS NET Acute Food Insecurity Classifications Geographic data for 2012

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

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) Scale Factor : 0.1 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:** 2021-01-01 **Supplemental Information:** No data value: -9999 Unit: kg/m³ Scale Factor : 0.1 Map code : L3-NBWP-S Conversion factor: the pixel value in the downloaded data must be multiplied by 0.001 **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

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). The data is provided in near real time from January 2010 to 2019. **Data publication:** 2021-01-01 **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 **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 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

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:** 2021-01-01 **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

The raster dataset represents top location score areas suitable for bananas storage, filtered by exclusive criteria: access to finance, distance to major roads.. Access to finance and roads are defined using a linear distance threshold: • Banks - 20km buffer radius. • Major roads - 2km buffer radius. The location score is achieved by processing sub-model outputs that characterize production, logistical and socio-economic factors for warehouse/storage locations: • Supply: Production. • Demand: Human population density, Major cities population (national and bordering countries). • Infrastructure/accessibility: main transportation infrastructure. • Poverty. It consists of an arithmetic weighted sum of normalized grids (0 to 100). This 500m 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:** 2022-11-17 **Contact points:** Resource Contact: [FAO-Data](mailto:fao-data@fao.org) Resource Contact: [Dariia Nesterenko](mailto:dariia.nesterenko@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 crop distribution within disaggregated units. 3. OpenStreetMap. 4. Poverty - FAO **Resource constraints:** This work is made available under the Creative Commons Attribution 4.0 International license (CC-BY 4.0) **Online resources:** Zipped raster TIF file for Crop Storage Final Location: Bananas (Mozambique - ~ 500 m)

The raster dataset represents top location score areas suitable for cassava storage, filtered by exclusive criteria: access to finance, and distance to major roads. Access to finance and roads are defined using a linear distance threshold: • Banks - 20km buffer radius. • Major roads - 2km buffer radius. The location score is achieved by processing sub-model outputs that characterize production, logistical and socio-economic factors for warehouse/storage locations: • Supply: Production. • Demand: Human population density, Major cities population (national and bordering countries). • Infrastructure/accessibility: main transportation infrastructure. • Poverty. It consists of an arithmetic weighted sum of normalized grids (0 to 100). This 500m 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:** 2022-11-17 **Contact points:** Resource Contact: [FAO-Data](mailto:fao-data@fao.org) Resource Contact: [Dariia Nesterenko](mailto:dariia.nesterenko@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 crop distribution within disaggregated units. 3. OpenStreetMap. 4. Poverty - FAO **Resource constraints:** This work is made available under the Creative Commons Attribution 4.0 International license (CC-BY 4.0) **Online resources:** Zipped raster TIF file for Crop Storage Final Location: Cassava (Mozambique - ~ 500 m)

The raster dataset represents top location score areas suitable for onion storage, filtered by exclusive criteria: access to finance, and distance to major roads. Access to finance and roads is defined using a linear distance threshold: • Banks - 20km buffer radius. • Major roads - 2km buffer radius. • Access to IT is characterized by applying the mobile broadband coverage map. The location score is achieved by processing sub-model outputs that characterize production, logistical and socio-economic factors for warehouse/storage locations: • Supply: Production. • Demand: Human population density, Major cities population. • Infrastructure/accessibility: main transportation infrastructure. • Poverty. It consists of an arithmetic weighted sum of normalized grids (0 to 100). This 500m 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:** 2023-07-24 **Contact points:** Resource Contact: [FAO-Data](mailto:fao-data@fao.org) Resource Contact: [Dariia Nesterenko](mailto:dariia.nesterenko@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. University of Minnesota. Institute on the Environment. 3. OpenStreetMap. 4. Poverty – Asset Wealth Index 2020. **Resource constraints:** This work is made available under the Creative Commons Attribution 4.0 International license (CC-BY 4.0) **Online resources:** Crop Storage Final Location: Onion (Mozambique - ~ 500 m)

The raster dataset represents top location score areas suitable for potatoes storage, filtered by exclusive criteria: access to finance, and distance to major roads. Access to finance and roads are defined using a linear distance threshold: • Banks - 20km buffer radius. • Major roads - 2km buffer radius. • Access to IT is characterized by applying the mobile broadband coverage map. The location score is achieved by processing sub-model outputs that characterize production, logistical and socio-economic factors for warehouse/storage locations: • Supply: Production. • Demand: Human population density, Major cities population. • Infrastructure/accessibility: main transportation infrastructure. • Poverty. It consists of an arithmetic weighted sum of normalized grids (0 to 100). This 500m 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:** 2023-07-18 **Contact points:** Resource Contact: [FAO-Data](mailto:fao-data@fao.org) Resource Contact: [Dariia Nesterenko](mailto:dariia.nesterenko@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 crop distribution within disaggregated units. 3. OpenStreetMap. 4. Poverty – Asset Wealth Index 2020. **Resource constraints:** This work is made available under the Creative Commons Attribution 4.0 International license (CC-BY 4.0) **Online resources:** Crop Storage Final Location: Potatoes (Mozambique - ~ 500 m)

The raster dataset represents top location score areas suitable for rice storage, filtered by exclusive criteria: access to finance, and distance to major roads. Access to finance and roads is defined using a linear distance threshold: • Banks - 20km buffer radius. • Major roads - 2km buffer radius. • Access to IT is characterized by applying the mobile broadband coverage map. The location score is achieved by processing sub-model outputs that characterize production, logistical and socio-economic factors for warehouse/storage locations: • Supply: Production. • Demand: Human population density, Major cities population. • Infrastructure/accessibility: main transportation infrastructure. • Poverty. It consists of an arithmetic weighted sum of normalized grids (0 to 100). This 500m 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:** 2023-07-18 **Contact points:** Resource Contact: [FAO-Data](mailto:fao-data@fao.org) Resource Contact: [Dariia Nesterenko](mailto:dariia.nesterenko@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 crop distribution within disaggregated units. 3. OpenStreetMap. 4. Poverty – Asset Wealth Index 2020. **Resource constraints:** This work is made available under the Creative Commons Attribution 4.0 International license (CC-BY 4.0) **Online resources:** Download: Crop Storage Final Location: Rice (Mozambique - ~ 500 m)

The raster dataset represents top location score areas suitable for sweet potato storage, filtered by exclusive criteria: access to finance, distance to major roads. Access to finance and roads are defined using a linear distance threshold: • Banks - 20km buffer radius. • Major roads - 2km buffer radius. The location score is achieved by processing sub-model outputs that characterize production, logistical and socio-economic factors for warehouse/storage locations: • Supply: Production. • Demand: Human population density, Major cities population (national and bordering countries). • Infrastructure/accessibility: main transportation infrastructure. • Poverty. It consists of an arithmetic weighted sum of normalized grids (0 to 100). This 500m 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:** 2022-11-17 **Contact points:** Resource Contact: [FAO-Data](mailto:fao-data@fao.org) Resource Contact: [Dariia Nesterenko](mailto:dariia.nesterenko@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 crop distribution within disaggregated units. 3. OpenStreetMap. 4. Poverty - FAO **Resource constraints:** This work is made available under the Creative Commons Attribution 4.0 International license (CC-BY 4.0) **Online resources:** Zipped raster TIF file for Crop Storage Final Location: Sweet Potato (Mozambique - ~ 500 m)

The raster dataset represents top location score areas suitable for tropical fruits storage, filtered by exclusive criteria: access to finance, distance to major roads. Access to finance and roads are defined using a linear distance threshold: • Banks - 20km buffer radius. • Major roads - 2km buffer radius. The location score is achieved by processing sub-model outputs that characterize production, logistical and socio-economic factors for warehouse/storage locations: • Supply: Production. • Demand: Human population density, Major cities population (national and bordering countries). • Infrastructure/accessibility: main transportation infrastructure. • Poverty. It consists of an arithmetic weighted sum of normalized grids (0 to 100). This 500m 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:** 2022-11-17 **Contact points:** Resource Contact: [FAO-Data](mailto:fao-data@fao.org) Resource Contact: [Dariia Nesterenko](mailto:dariia.nesterenko@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 crop distribution within disaggregated units. 3. OpenStreetMap. 4. Poverty - FAO **Resource constraints:** This work is made available under the Creative Commons Attribution 4.0 International license (CC-BY 4.0) **Online resources:** Zipped raster TIF file for Crop Storage Final Location: Tropical Fruits (Mozambique - ~ 500 m)

The raster dataset consists of a 500 m score grid for the crop storage location, produced under the scope of FAO’s Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis for value chain infrastructure location. The location score is achieved by processing sub-model outputs that characterize logistical factors for selected crop warehouse locations: • Supply: Onion. • Demand: Human population density, Major cities population. • Infrastructure/accessibility: main transportation infrastructure. It consists of an arithmetic weighted sum of normalized grids (0 to 100): ("Crop Production" * 0.5) + (“Major Cities Accessibility” * 0.3) + (”Poverty” * 0.1) + ("Human Population Density" * 0.1). **Data publication:** 2023-07-24 **Contact points:** Resource Contact: [FAO-Data](mailto:fao-data@fao.org) Resource Contact: [Dariia Nesterenko](mailto:dariia.nesterenko@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. University of Minnesota. Institute on the Environment. 3. OpenStreetMap. 4. Poverty – Asset Wealth Index 2020. **Resource constraints:** This work is made available under the Creative Commons Attribution 4.0 International license (CC-BY 4.0) **Online resources:** Crop Storage Location Score: Onion (Mozambique - ~ 500 m)

The raster dataset consists of a 500 m score grid for the crop storage location, produced under the scope of FAO’s Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis for value chain infrastructure location. The location score is achieved by processing sub-model outputs that characterize logistical factors for selected crop warehouse locations: • Supply: Potatoes. • Demand: Human population density, Major cities population. • Infrastructure/accessibility: main transportation infrastructure. It consists of an arithmetic weighted sum of normalized grids (0 to 100): ("Crop Production" * 0.5) + (“Major Cities Accessibility” * 0.3) + (”Poverty” * 0.1) + ("Human Population Density" * 0.1). **Data publication:** 2023-07-18 **Contact points:** Resource Contact: [FAO-Data](mailto:fao-data@fao.org) Resource Contact: [Dariia Nesterenko](mailto:dariia.nesterenko@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. Poverty – Asset Wealth Index 2020. **Resource constraints:** This work is made available under the Creative Commons Attribution 4.0 International license (CC-BY 4.0) **Online resources:** Crop Storage Location Score: Potatoes

The raster dataset consists of a 500 m score grid for the crop storage location, produced under the scope of FAO’s Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis for value chain infrastructure location. The location score is achieved by processing sub-model outputs that characterize logistical factors for selected crop warehouse locations: • Supply: Soybean. • Demand: Human population density, Major cities population. • Infrastructure/accessibility: main transportation infrastructure. It consists of an arithmetic weighted sum of normalized grids (0 to 100): ("Crop Production" * 0.5) + (“Major Cities Accessibility” * 0.3) + (”Poverty” * 0.1) + ("Human Population Density" * 0.1). **Data publication:** 2023-07-18 **Contact points:** Resource Contact: [FAO-Data](mailto:fao-data@fao.org) Resource Contact: [Dariia Nesterenko](mailto:dariia.nesterenko@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. Poverty – Asset Wealth Index 2020. **Resource constraints:** This work is made available under the Creative Commons Attribution 4.0 International license (CC-BY 4.0) **Online resources:** Printable map for Crop Storage Location Score: Soybean (Mozambique - ~ 500 m) Crop Storage Location Score: Soybean (Mozambique - ~ 500 m)