Global agriculture data

This dataset contains a list of the countries affected by the El Niño as at April 21, 2016 as reported jointly by FAO, the Global Food Security Cluster and WFP on 21 April 2016 in the 2015-2016 El Niño: WFP and FAO Overview update. According to the World Bank, El Niño is likely to have a negative impact in more isolated local food markets, and many countries are already facing increased food prices. Food Security Cluster partners have implemented preparedness activities and are responding in countries where the effects of El Niño have materialised, such as Ethiopia, Papua New Guinea, Malawi and throughout Central America. In Southern Africa, many areas have seen the driest October-December period since at least 1981, and some 14 million people in the region are already facing hunger, which adds to fears of a spike in the numbers of the food insecure later this year through 2017.

Sustainable Land Management (SLM) Technologies are land management practices that control land degradation and enhance productivity and/ or other ecosystem services. A Technology consists of one or several measures, such as agronomic, vegetative, structural, and management measures. This dataset, derived from the WOCAT Global SLM Database, shows location of documented SLM practices, including technology type and description for each country where this information is available. Definitions of all Technology Groups can be found in the WOCAT Glossary. For more information on the technologies by country, please visit the WOCAT Global SLM Database - technology section **Data publication:** 2026-05-15 **Contact points:** Resource Contact: [WOCAT](mailto: wocat.cde@unibe.ch) Metadata Contact: [FAO-Data](mailto:fao-data@fao.org) **Data lineage:** Data quality information not available **Resource constraints:** This work is made available under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0 ). Please refer to the WOCAT Terms of Agreement for more information on the use of this dataset. **Online resources:** WOCAT website

An approach defines the ways and means used to implement one or several Technologies. It describes technical and material support, involvement, and roles of different stakeholders, etc. An Approach can refer to a project/ programme or to activities initiated by land users themselves. Source: WOCAT Glossary: https://www.wocat.net/en/glossary

This Archive contains shapefiles for FEWS NET Food Security Outlook for **Southern Africa**. It was last updated on September 26, 2014. The classification used is IPC V2.0 Compatible, aimed to address acute food insecurity. The two shapefiles represent the two analytic periods: * southernafrica201307_ML1 Most likely food security outcome for July-September 2014 * southernafrica201407_ML2 Most likely food security outcome for October-December 2014 Within the shapefiles, the food security outlook is contained in a field named as ML1 or ML2 according to the outlook period. The code itself is the IPC phase. Two additional codes are used: * 66 = water * 88 = parks, forests, reserves * 99 = missing data (usually urban centers).

This Archive contains shapefiles for FEWS NET Food Security Outlook for **Southern Africa**. It was last updated on January 19, 2014. The classification used is IPC V2.0 Compatible, aimed to address acute food insecurity. The two shapefiles represent the two analytic periods: * southernafrica201401_ML1 Most likely food security outcome for January-March 2014 * southernafrica201401_ML2 Most likely food security outcome for April-June 2014 Within the shapefiles, the food security outlook is contained in a field named as ML1 or ML2 according to the outlook period. The code itself is the IPC phase. Two additional codes are used: * 66 = water * 88 = parks, forests, reserves * 99 = missing data (usually urban centers)

The Landsat-Derived Global Rainfed and Irrigated-Cropland Product (LGRIP) provides high resolution, global cropland data to assist and address food and water security issues of the twenty-first century. As an extension of the Global Food Security-support Analysis Data (GFSAD) project, LGRIP maps the world’s agricultural lands by dividing them into irrigated and rainfed croplands and calculates irrigated and rainfed areas for every country in the world. LGRIP data are produced using Landsat 8 time-series satellite sensor data for the 2014-2017 time period to create a nominal 2015 product. Each LGRIP 30 meter resolution GeoTIFF file contains a contains a layer that identifies areas of rainfed cropland (cropland areas that are purely dependent on direct precipitation), irrigated cropland (cropland that had at least one irrigation during the crop growing period), non-cropland, and water bodies over a 10° by 10° area, as well as an accuracy assessment of the product. A low-resolution browse image is also available.

This Archive contains shapefiles for FEWS NET Food Security Outlook for **Southern Africa**. It was last updated on January 19, 2014. The classification used is IPC V2.0 Compatible, aimed to address acute food insecurity. The two shapefiles represent the two analytic periods: * southernafrica201401_ML1 Most likely food security outcome for January-March 2014 * southernafrica201401_ML2 Most likely food security outcome for April-June 2014 Within the shapefiles, the food security outlook is contained in a field named as ML1 or ML2 according to the outlook period. The code itself is the IPC phase. Two additional codes are used: * 66 = water * 88 = parks, forests, reserves * 99 = missing data (usually urban centers)

Polygon features representing damaged agricultural areas, published by Orion Terrametrics. Each polygon corresponds to a field, with attributes including area, damage classification, and sensor date. The dataset includes 5593 features.

This Archive contains shapefiles for FEWS NET Food Security Outlook for **East Africa**. It was last updated on April 12, 2016. The classification used is IPC V2.0 Compatible, aimed to address acute food insecurity. The two shapefiles represent the two analytic periods: * EA201304_ML1 Most likely food security outcome for January-March 2016 * EA201304_ML2 Most likely food security outcome for April-June 2016 Within the shapefiles, the food security outlook is contained in a field named as ML1 or ML2 according to the outlook period. The code itself is the IPC phase. Two additional codes are used: * 66 = water * 88 = parks, forests, reserves * 99 = missing data (usually urban centers)

This Archive contains shapefiles for FEWS NET Food Security Outlook for **East Africa**. It was last updated on July 17, 2014. The classification used is IPC V2.0 Compatible, aimed to address acute food insecurity. The two shapefiles represent the two analytic periods: * eastafrica201304_ML1 Most likely food security outcome for April-June 2014 * eastafrica201304_ML2 Most likely food security outcome for July-September 2014 Within the shapefiles, the food security outlook is contained in a field named as ML1 or ML2 according to the outlook period. The code itself is the IPC phase. Two additional codes are used: * 66 = water * 88 = parks, forests, reserves * 99 = missing data (usually urban centers)

This Archive contains shapefiles for FEWS NET Food Security Outlook for **East Africa**. It was last updated on August 07, 2014. The classification used is IPC V2.0 Compatible, aimed to address acute food insecurity. The two shapefiles represent the two analytic periods: * eastafrica201304_ML1 Most likely food security outcome for January-March 2014 * eastafrica201304_ML2 Most likely food security outcome for April-June 2014 Within the shapefiles, the food security outlook is contained in a field named as ML1 or ML2 according to the outlook period. The code itself is the IPC phase. Two additional codes are used: * 66 = water * 88 = parks, forests, reserves * 99 = missing data (usually urban centers)

This Archive contains shapefiles for FEWS NET Food Security Outlook for **East Africa**. It was last updated on September 30, 2015. The classification used is IPC V2.0 Compatible, aimed to address acute food insecurity. The two shapefiles represent the two analytic periods: * easternafrica201507_ML1 Most likely food security outcome for July-September 2015 * easternafrica201507_ML2 Most likely food security outcome for October-December 2015 Within the shapefiles, the food security outlook is contained in a field named as ML1 or ML2 according to the outlook period. The code itself is the IPC phase. Two additional codes are used: * 66 = water * 88 = parks, forests, reserves * 99 = missing data (usually urban centers)

This Archive contains shapefiles for FEWS NET Food Security Outlook for **East Africa**. It was last updated on April 03, 2015. The classification used is IPC V2.0 Compatible, aimed to address acute food insecurity. The two shapefiles represent the two analytic periods: * eastafrica201304_ML1 Most likely food security outcome for January-March 2015 * eastafrica201304_ML2 Most likely food security outcome for April-June 2015 Where xx is one of the region codes listed above. Within the shapefiles, the food security outlook is contained in a field named as ML1 or ML2 according to the outlook period. The code itself is the IPC phase. Two additional codes are used: * 66 = water * 88 = parks, forests, reserves * 99 = missing data (usually urban centers)

This Archive contains shapefiles for FEWS NET Food Security Outlook for **East Africa**. It was last updated on November 14, 2013. The classification used is IPC V2.0 Compatible, aimed to address acute food insecurity. The two shapefiles represent the two analytic periods: * eastafrica201304_ML1 Most likely food security outcome for October-December 2013 * eastafrica201304_ML2 Most likely food security outcome for January-March 2014 Where xx is one of the region codes listed above. Within the shapefiles, the food security outlook is contained in a field named as ML1 or ML2 according to the outlook period. The code itself is the IPC phase. Two additional codes are used: * 66 = water * 88 = parks, forests, reserves * 99 = missing data (usually urban centers)

This Archive contains shapefiles for FEWS NET Food Security Outlook for **East Africa**. It was last updated on June 30, 2015. The classification used is IPC V2.0 Compatible, aimed to address acute food insecurity. The two shapefiles represent the two analytic periods: * eastafrica201304_ML1 Most likely food security outcome for April-June 2015 * eastafrica201304_ML2 Most likely food security outcome for July-September 2015 Within the shapefiles, the food security outlook is contained in a field named as ML1 or ML2 according to the outlook period. The code itself is the IPC phase. Two additional codes are used: * 66 = water * 88 = parks, forests, reserves * 99 = missing data (usually urban centers)

This Archive contains shapefiles for FEWS NET Food Security Outlook for **East Africa**. It was last updated on December 31, 2014. The classification used is IPC V2.0 Compatible, aimed to address acute food insecurity. The two shapefiles represent the two analytic periods: * EA201304_ML1 Most likely food security outcome for October-December 2014 * EA201304_ML2 Most likely food security outcome for January-March 2015 Within the shapefiles, the food security outlook is contained in a field named as ML1 or ML2 according to the outlook period. The code itself is the IPC phase. Two additional codes are used: * 66 = water * 88 = parks, forests, reserves * 99 = missing data (usually urban centers)

This Archive contains shapefiles for FEWS NET Food Security Outlook for **East Africa**. It was last updated on July 14, 2013. The classification used is IPC V2.0 Compatible, aimed to address acute food insecurity. The two shapefiles represent the two analytic periods: * eastafrica201304_ML1 Most likely food security outcome for July-September 2013 * eastafrica201304_ML2 Most likely food security outcome for October-December 2013 Within the shapefiles, the food security outlook is contained in a field named as ML1 or ML2 according to the outlook period. The code itself is the IPC phase. Two additional codes are used: * 66 = water * 88 = parks, forests, reserves * 99 = missing data (usually urban centers)

This Archive contains shapefiles for FEWS NET Food Security Outlook for **East Africa**. It was last updated on January 14, 2013. The classification used is IPC V2.0 Compatible, aimed to address acute food insecurity. The two shapefiles represent the two analytic periods: * eastafrica201304_ML1 Most likely food security outcome for January-March 2013 * eastafrica201304_ML2 Most likely food security outcome for April-June 2013 Within the shapefiles, the food security outlook is contained in a field named as ML1 or ML2 according to the outlook period. The code itself is the IPC phase. Two additional codes are used: * 66 = water * 88 = parks, forests, reserves * 99 = missing data (usually urban centers)

This Archive contains shapefiles for FEWS NET Food Security Outlook for **East Africa**. It was last updated on September 26, 2014. The classification used is IPC V2.0 Compatible, aimed to address acute food insecurity. The two shapefiles represent the two analytic periods: * eastafrica201304_ML1 Most likely food security outcome for July-September 2014 * eastafrica201304_ML2 Most likely food security outcome for October-December 2014 Within the shapefiles, the food security outlook is contained in a field named as ML1 or ML2 according to the outlook period. The code itself is the IPC phase. Two additional codes are used: * 66 = water * 88 = parks, forests, reserves * 99 = missing data (usually urban centers)

The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission measures the temperature of plants to better understand how much water plants need and how they respond to stress. ECOSTRESS is attached to the International Space Station (ISS) and collects data globally between 52° N and 52° S latitudes. The ECOSTRESS Gridded Water Use Efficiency Instantaneous L4 Global 70 m (ECO_L4G_WUE) Version 2 data product provides Water Use Efficiency (WUE) data generated by dividing the Breathing Earth System Simulator (BESS) Gross Primary Production (GPP) by the Priestley-Taylor Jet Propulsion Laboratory Soil Moisture (PT-JPL-SM) transpiration to estimate WUE, the ratio of grams of carbon that plants absorb to kilograms of water that plants release. The product provides a BESS GPP estimate that represents the amount of carbon surrounding the plants. The ECO_L4G_WUE Version 2 data product is available globally and projected to a globally snapped 0.0006° grid with a 70 meter spatial resolution and is distributed in HDF5. Each granule contains layers of Water Use Efficiency (WUE), Water Gross Primary Production (GPP), cloud mask, and water mask. A low-resolution browse is also available showing daily WUE as a stretched image with a color ramp in JPEG format. Known Issues * Data acquisition gap: ECOSTRESS was launched on June 29, 2018, and moved to autonomous science operations on August 20, 2018, following a successful in-orbit checkout period. On September 29, 2018, ECOSTRESS experienced an anomaly with its primary mass storage unit (MSU). ECOSTRESS has a primary and secondary MSU (A and B). On December 5, 2018, the instrument was switched to the secondary MSU, and science operations resumed. On March 14, 2019, the secondary MSU experienced a similar anomaly, temporarily halting science acquisitions. On May 15, 2019, a new data acquisition approach was implemented, and science acquisitions resumed. To optimize the new acquisition approach, only Thermal Infrared (TIR) bands 2, 4, and 5 are being downloaded. The data products are the same as before, but the bands not downloaded contain fill values (L1 radiance and L2 emissivity). This approach was implemented from May 15, 2019, through April 28, 2023. * Data acquisition gap: From February 8 to February 16, 2020, an ECOSTRESS instrument issue resulted in a data anomaly that created striping in band 4 (10.5 micron). These data products have been reprocessed and are available for download. No ECOSTRESS data were acquired on February 17, 2020, due to the instrument being in SAFEHOLD. Data acquired following the anomaly have not been affected. * Data acquisition: ECOSTRESS has now successfully returned to 5-band mode after being in 3-band mode since 2019. This feature was successfully enabled following a Data Processing Unit firmware update (version 4.1) to the payload on April 28, 2023. To better balance contiguous science data scene variables, 3-band collection is currently being interleaved with 5-band acquisitions over the orbital day/night periods. * Solar Array Obstruction: Some ECOSTRESS scenes may be affected by solar array obstructions from the International Space Station (ISS), potentially impacting data quality of obstructed pixels. The 'FieldOfViewObstruction' metadata field is included in all Version 2 products to indicate possible obstructions: * Before October 24, 2024 (orbits prior to 35724): The field is present but was not populated and does not reliably identify affected scenes. * On or after October 24, 2024 (starting with orbit 35724): The field is populated and generally accurate, except for late December 2024, when a temporary processing error may have caused false positives. * A list of scenes confirmed to be affected by obstructions is available and is recommended for verifying historical data (before October 24, 2024) and scenes from late December 2024. * The ISS native pointing information is coarse relative to ECOSTRESS pixels, so ECOSTRESS geolocation is improved through image matching with a basemap. Metadata in the L1B_GEO file shows the success of this geolocation improvement, using categorizations "best", "good", "suspect", and "poor". We recommend that users use only "best" and "good" scenes for evaluations where geolocation is important (e.g., comparison to field sites). For some scenes, this metadata is not reflected in the higher-level products (e.g., land surface temperature, evapotranspiration, etc.). While this metadata is always available in the geolocation product, to save users additional download, we have produced a summary text file that includes the geolocation quality flags for all scenes from launch to present. At a later date, all higher-level products will reflect the geolocation quality flag correctly (the field name is GeolocationAccuracyQA). * During the time period of May 15th, 2025, through July 1st, 2025, ECOSTRESS data was noisier than expected. Cycling the payload resolved the issue, but researchers should use all levels of ECOSTRESS data acquired during this time period with caution. * During the time period of January 1st, 2023, through September 30th, 2023, ECOSTRESS STARS and the resulting ET products are considered lower quality due to the priors used in the STARS model. The bug was corrected, and the lower quality data will be reprocessed. * Coarse Resolution Artifacts: Users may sometimes observe coarse resolution (~500m - 1km) artifacts in the L3 and L4 gridded and tiled products. These artifacts are caused by corresponding artifacts within the ECOSTRESS Tiled Ancillary NDVI and Albedo L2 Global 70 m (ECO_L2T_STARS) Version 2 data product. The ECO_L2T_STARS product is produced through a data fusion of Suomi NPP VIIRS (~500 m - 1 km resolution) and Harmonized Landsat Sentinel-2 (HLS, 30 m) products. When there is significant temporal lag between HLS acquisitions, the VIIRS data dominates, which can produce coarse resolution artifacts in the ECO_L2T_STARS product. This typically corresponds to relatively large uncertainty values.

The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission measures the temperature of plants to better understand how much water plants need and how they respond to stress. ECOSTRESS is attached to the International Space Station (ISS) and collects data globally between 52° N and 52° S latitudes. The ECOSTRESS Gridded Evaporative Stress Index PT-JPL Instantaneous L4 Global 70 m (ECO_L4G_ESI) Version 2 data product uses the Priestley-Taylor Jet Propulsion Laboratory Soil Moisture (PT-JPL-SM) model to generate estimates of both actual and potential instantaneous evapotranspiration (ET). The potential evapotranspiration (PET) estimate represents the maximum expected ET if there were no water stress to plants on the ground. The ratio of the actual ET estimate to the PET estimate forms an index representing the water stress of plants. The ECO_L4G_ESI Version 2 data product is available globally and is projected to a globally snapped 0.0006° grid with a 70 meter spatial resolution and is distributed in HDF5. Each granule contains layers of Evaporative Stress Index (ESI), PET, cloud mask, and water mask. A low-resolution browse is also available showing daily ESI as a stretched image with a color ramp in JPEG format. Known Issues * Data acquisition gap: ECOSTRESS was launched on June 29, 2018, and moved to autonomous science operations on August 20, 2018, following a successful in-orbit checkout period. On September 29, 2018, ECOSTRESS experienced an anomaly with its primary mass storage unit (MSU). ECOSTRESS has a primary and secondary MSU (A and B). On December 5, 2018, the instrument was switched to the secondary MSU, and science operations resumed. On March 14, 2019, the secondary MSU experienced a similar anomaly, temporarily halting science acquisitions. On May 15, 2019, a new data acquisition approach was implemented, and science acquisitions resumed. To optimize the new acquisition approach, only Thermal Infrared (TIR) bands 2, 4, and 5 are being downloaded. The data products are the same as before, but the bands not downloaded contain fill values (L1 radiance and L2 emissivity). This approach was implemented from May 15, 2019, through April 28, 2023. * Data acquisition gap: From February 8 to February 16, 2020, an ECOSTRESS instrument issue resulted in a data anomaly that created striping in band 4 (10.5 micron). These data products have been reprocessed and are available for download. No ECOSTRESS data were acquired on February 17, 2020, due to the instrument being in SAFEHOLD. Data acquired following the anomaly have not been affected. * Data acquisition: ECOSTRESS has now successfully returned to 5-band mode after being in 3-band mode since 2019. This feature was successfully enabled following a Data Processing Unit firmware update (version 4.1) to the payload on April 28, 2023. To better balance contiguous science data scene variables, 3-band collection is currently being interleaved with 5-band acquisitions over the orbital day/night periods. * Solar Array Obstruction: Some ECOSTRESS scenes may be affected by solar array obstructions from the International Space Station (ISS), potentially impacting data quality of obstructed pixels. The 'FieldOfViewObstruction' metadata field is included in all Version 2 products to indicate possible obstructions: * Before October 24, 2024 (orbits prior to 35724): The field is present but was not populated and does not reliably identify affected scenes. * On or after October 24, 2024 (starting with orbit 35724): The field is populated and generally accurate, except for late December 2024, when a temporary processing error may have caused false positives. * A list of scenes confirmed to be affected by obstructions is available and is recommended for verifying historical data (before October 24, 2024) and scenes from late December 2024. * The ISS native pointing information is coarse relative to ECOSTRESS pixels, so ECOSTRESS geolocation is improved through image matching with a basemap. Metadata in the L1B_GEO file shows the success of this geolocation improvement, using categorizations "best", "good", "suspect", and "poor". We recommend that users use only "best" and "good" scenes for evaluations where geolocation is important (e.g., comparison to field sites). For some scenes, this metadata is not reflected in the higher-level products (e.g., land surface temperature, evapotranspiration, etc.). While this metadata is always available in the geolocation product, to save users additional download, we have produced a summary text file that includes the geolocation quality flags for all scenes from launch to present. At a later date, all higher-level products will reflect the geolocation quality flag correctly (the field name is GeolocationAccuracyQA). *During the time period of May 15th, 2025, through July 1st, 2025, ECOSTRESS data was noisier than expected. Cycling the payload resolved the issue, but researchers should use all levels of ECOSTRESS data acquired during this time period with caution. * During the time period of January 1st, 2023, through September 30th, 2023, ECOSTRESS STARS and the resulting ET products are considered lower quality due to the priors used in the STARS model. The bug was corrected, and the lower quality data will be reprocessed. * Coarse Resolution Artifacts: Users may sometimes observe coarse resolution (~500m - 1km) artifacts in the L3 and L4 gridded and tiled products. These artifacts are caused by corresponding artifacts within the ECOSTRESS Tiled Ancillary NDVI and Albedo L2 Global 70 m (ECO_L2T_STARS) Version 2 data product. The ECO_L2T_STARS product is produced through a data fusion of Suomi NPP VIIRS (~500 m - 1 km resolution) and Harmonized Landsat Sentinel-2 (HLS, 30 m) products. When there is significant temporal lag between HLS acquisitions, the VIIRS data dominates, which can produce coarse resolution artifacts in the ECO_L2T_STARS product. This typically corresponds to relatively large uncertainty values.

The yield value is one of the key indicators of the Global Spatially-Disaggregated Crop Production Statistics Data for 2010, which includes harvest area, physical area and production, for 42 crops — disaggregated at the input-levels (e.g., irrigated/rainfed and high/low-input) on a 10 km grid globally. This new version of MapSPAM, available to download from the Harvard Dataverse Website, marks the third generation of the SPAM data series, following those of 2000 and 2005. More information on the production systems and selected crops is available in the Global Spatially-Disaggregated Crop Production Statistics Data (MapSPAM) full metadata at https://data.apps.fao.org/catalog/iso/59f7a5ef-2be4-43ee-9600-a6a9e9ff562a **Data publication:** 2019-10-09 **Supplemental Information:** No data value: -1 Unit : ha Scale Factor : 1.0 Map code : GYLD **Citation:** Citation: International Food Policy Research Institute, 2019, “Global Spatially-Disaggregated Crop Production Statistics Data for 2010 Version 1.1”, https://doi.org/10.7910/DVN/PRFF8V, Harvard Dataverse, V3. **Contact points:** Metadata Contact: Koo, Jawoo International Food Policy Research Institute (IFPRI) Resource Contact: You, Liangzhi International Food Policy Research Institute (IFPRI) Resource Contact: Harvard Dataverse **Data lineage:** Differences compared to SPAM 2010 V1r0 (Uploaded December 2018): - No more rounding errors of 0.1 ha or mt, ie areas and production in each pixel satisfy conditions: R=A-I and R=H+L+S. - CSV files do not have 'strange' entries for Yemen - admin names with "," and ";" were corrected. - Value of production only has one set of entries for Sudan - in previous version it had 2. - Missing values for maize in Nigeria/Osun (fisp1=NI31) now included. **Resource constraints:** IFPRI DATAVERSE TERMS OF USE This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). **Online resources:** Download the data from Google Cloud Storage ReadMe_V1r1_GeoTiff.txt Map SPAM Data Center Download the latest version from Harvard Dataverse Download - MapSpam Dimensions:Technology and Crops (CVS)

This dataset is one of the key indicators of the Global Spatially-Disaggregated Crop Production Statistics Data (MapSPAM) for 2010, which includes physical area, harvest area and yield, for 42 crops — disaggregated at the input-levels (e.g., irrigated/rainfed and high/low-input) on a 10 km grid globally. This new version of MapSPAM, available to download from the Harvard Dataverse Website, marks the third generation of the SPAM data series, following those of 2000 and 2005. More information on the production systems and selected crops is available in the Global Spatially-Disaggregated Crop Production Statistics Data (MapSPAM) full metadata at https://data.apps.fao.org/catalog/iso/59f7a5ef-2be4-43ee-9600-a6a9e9ff562a **Data publication:** 2019-10-09 **Supplemental Information:** No data value: -1 Unit : mt Scale Factor : 1.0 Map code : GPROD **Citation:** Citation: International Food Policy Research Institute, 2019, “Global Spatially-Disaggregated Crop Production Statistics Data for 2010 Version 1.1”, https://doi.org/10.7910/DVN/PRFF8V, Harvard Dataverse, V3. **Contact points:** Metadata Contact: Koo, Jawoo International Food Policy Research Institute (IFPRI) Resource Contact: You, Liangzhi International Food Policy Research Institute (IFPRI) Resource Contact: Harvard Dataverse **Data lineage:** Differences compared to SPAM 2010 V1r0 (Uploaded December 2018): - No more rounding errors of 0.1 ha or mt, ie areas and production in each pixel satisfy conditions: R=A-I and R=H+L+S. - CSV files do not have 'strange' entries for Yemen - admin names with "," and ";" were corrected. - Value of production only has one set of entries for Sudan - in previous version it had 2. - Missing values for maize in Nigeria/Osun (fisp1=NI31) now included. **Resource constraints:** IFPRI DATAVERSE TERMS OF USE This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). **Online resources:** Download the data from Google Cloud Storage Download the latest version from Harvard Dataverse Download - MapSpam Dimensions:Technology and Crops (CVS) ReadMe_V1r1_GeoTiff.txt Map SPAM Data Center

The harvested area value is one of the key indicators of the Global Spatially-Disaggregated Crop Production Statistics Data (MapSPAM) for 2010, along with physical area, production and yield, for 42 crops — disaggregated at the input-levels (e.g., irrigated/rainfed and high/low-input) on 10 km grids globally. This new version of MapSPAM, available to download from the Harvard Dataverse Website, marks the third generation of the SPAM data series, following those of 2000 and 2005. More information on the production systems and selected crops is available in the Global Spatially-Disaggregated Crop Production Statistics Data (MapSPAM) full metadata at https://data.apps.fao.org/catalog/iso/59f7a5ef-2be4-43ee-9600-a6a9e9ff562a **Data publication:** 2019-10-09 **Supplemental Information:** No data value: -1 Unit : ha Scale Factor : 1.0 Map code : GHA **Citation:** Citation: International Food Policy Research Institute, 2019, “Global Spatially-Disaggregated Crop Production Statistics Data for 2010 Version 1.1”, https://doi.org/10.7910/DVN/PRFF8V, Harvard Dataverse, V3. **Contact points:** Metadata Contact: Koo, Jawoo International Food Policy Research Institute (IFPRI) Resource Contact: You, Liangzhi International Food Policy Research Institute (IFPRI) Resource Contact: Harvard Dataverse **Data lineage:** Differences compared to SPAM 2010 V1r0 (Uploaded December 2018): - No more rounding errors of 0.1 ha or mt, ie areas and production in each pixel satisfy conditions: R=A-I and R=H+L+S. - CSV files do not have 'strange' entries for Yemen - admin names with "," and ";" were corrected. - Value of production only has one set of entries for Sudan - in previous version it had 2. - Missing values for maize in Nigeria/Osun (fisp1=NI31) now included. **Resource constraints:** IFPRI DATAVERSE TERMS OF USE This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). **Online resources:** Download the data from Google Cloud Storage Map SPAM Data Center ReadMe_V1r1_GeoTiff.txt Download the latest version from Harvard Dataverse Download - MapSpam Dimensions:Technology and Crops (CVS)

This dataset is one of the key indicators of the Global Spatially-Disaggregated Crop Production Statistics Data (MapSPAM) for 2010, which includes harvested area, production and yield, for 42 crops — disaggregated at the input-levels (e.g., irrigated/rainfed and high/low-input) on a 10 km grid globally. This new version of MapSPAM, available to download from the Harvard Dataverse Website, marks the third generation of the SPAM data series, following those of 2000 and 2005. More information on the production systems and selected crops is available in the Global Spatially-Disaggregated Crop Production Statistics Data (MapSPAM) full metadata at https://data.apps.fao.org/catalog/iso/59f7a5ef-2be4-43ee-9600-a6a9e9ff562a **Data publication:** 2019-10-09 **Supplemental Information:** No data value: -1 Unit : ha Scale Factor : 1.0 Map code : GPA **Citation:** Citation: International Food Policy Research Institute, 2019, “Global Spatially-Disaggregated Crop Production Statistics Data for 2010 Version 1.1”, https://doi.org/10.7910/DVN/PRFF8V, Harvard Dataverse, V3. **Contact points:** Metadata Contact: Koo, Jawoo International Food Policy Research Institute (IFPRI) Resource Contact: You, Liangzhi International Food Policy Research Institute (IFPRI) Resource Contact: Harvard Dataverse **Data lineage:** Differences compared to SPAM 2010 V1r0 (Uploaded December 2018): - No more rounding errors of 0.1 ha or mt, ie areas and production in each pixel satisfy conditions: R=A-I and R=H+L+S. - CSV files do not have 'strange' entries for Yemen - admin names with "," and ";" were corrected. - Value of production only has one set of entries for Sudan - in previous version it had 2. - Missing values for maize in Nigeria/Osun (fisp1=NI31) now included. **Resource constraints:** IFPRI DATAVERSE TERMS OF USE This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). **Online resources:** Download the data from Google Cloud Storage Download - MapSpam Dimensions:Technology and Crops (CVS) ReadMe_V1r1_GeoTiff.txt Map SPAM Data Center Download from Harvard Dataverse

This dataset is one of the outputs of the Global Spatially-Disaggregated Crop Production Statistics Data (MapSPAM) for 2010, which includes physical area, harvest area, production and yield, for 42 crops, disaggregated at the input-levels (e.g., irrigated/rainfed and high/low-input) on a 10 km grid globally. Harvested area values in this dataset are given for each technology aggregated by categories – crops/food/non-food - with no information on individual crops. This new version of MapSPAM, available to download from the Harvard Dataverse Website, marks the third generation of the SPAM data series, following those of 2000 and 2005. More information on the production systems and selected crops is available in the Global Spatially-Disaggregated Crop Production Statistics Data (MapSPAM) full metadata at https://data.apps.fao.org/catalog/iso/59f7a5ef-2be4-43ee-9600-a6a9e9ff562a **Data publication:** 2019-10-09 **Supplemental Information:** No data value: -1 Unit : ha Scale Factor : 1.0 Map code : GVPAH **Citation:** Citation: International Food Policy Research Institute, 2019, “Global Spatially-Disaggregated Crop Production Statistics Data for 2010 Version 1.1”, https://doi.org/10.7910/DVN/PRFF8V, Harvard Dataverse, V3. **Contact points:** Metadata Contact: Koo, Jawoo International Food Policy Research Institute (IFPRI) Resource Contact: You, Liangzhi International Food Policy Research Institute (IFPRI) Resource Contact: Harvard Dataverse **Data lineage:** Differences compared to SPAM 2010 V1r0 (Uploaded December 2018): - No more rounding errors of 0.1 ha or mt, ie areas and production in each pixel satisfy conditions: R=A-I and R=H+L+S. - CSV files do not have 'strange' entries for Yemen - admin names with "," and ";" were corrected. - Value of production only has one set of entries for Sudan - in previous version it had 2. - Missing values for maize in Nigeria/Osun (fisp1=NI31) now included. **Resource constraints:** IFPRI DATAVERSE TERMS OF USE This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). **Online resources:** Download the data from Google Cloud Storage Download the latest version from Harvard Dataverse ReadMe_V1r1_GeoTiff.txt Download - MapSpam Dimensions:Technology and Crops (CVS) Map SPAM Data Center

This dataset is one of the outputs of the Global Spatially-Disaggregated Crop Production Statistics Data (MapSPAM) for 2010, which includes physical area, harvest area, production and yield, for 42 crops, disaggregated at the input-levels (e.g., irrigated/rainfed and high/low-input) on a 10 km grid globally. Production values in this dataset are given for each technology aggregated by categories - crops/food/non-food - with no information on individual crops. This new version of MapSPAM, available to download from the Harvard Dataverse Website, marks the third generation of the SPAM data series, following those of 2000 and 2005. More information on the production systems and selected crops is available in the Global Spatially-Disaggregated Crop Production Statistics Data (MapSPAM) full metadata at https://data.apps.fao.org/catalog/iso/59f7a5ef-2be4-43ee-9600-a6a9e9ff562a **Data publication:** 2019-10-09 **Citation:** Citation: International Food Policy Research Institute, 2019, “Global Spatially-Disaggregated Crop Production Statistics Data for 2010 Version 1.1”, https://doi.org/10.7910/DVN/PRFF8V, Harvard Dataverse, V3. **Contact points:** Metadata Contact: Koo, Jawoo International Food Policy Research Institute (IFPRI) Resource Contact: You, Liangzhi International Food Policy Research Institute (IFPRI) Resource Contact: Harvard Dataverse **Data lineage:** Differences compared to SPAM 2010 V1r0 (Uploaded December 2018): - No more rounding errors of 0.1 ha or mt, ie areas and production in each pixel satisfy conditions: R=A-I and R=H+L+S. - CSV files do not have 'strange' entries for Yemen - admin names with "," and ";" were corrected. - Value of production only has one set of entries for Sudan - in previous version it had 2. - Missing values for maize in Nigeria/Osun (fisp1=NI31) now included. **Resource constraints:** IFPRI DATAVERSE TERMS OF USE This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). **Online resources:** Download the data from Google Cloud Storage Map SPAM Data Center ReadMe_V1r1_GeoTiff.txt Download the latest version from Harvard Dataverse Download - MapSpam Dimensions:Technology and Crops (CVS)

This dataset is one of the outputs of the Global Spatially-Disaggregated Crop Production Statistics Data (MapSPAM) for 2010, which includes physical area, harvest area, production and yield, for 42 crops, disaggregated at the input-levels (e.g., irrigated/rainfed and high/low-input) on a 10 km grid globally. Crop production values in this dataset are given per ha for each technology aggregated by categories - crops/food/non-food - with no information on individual crops. This new version of MapSPAM, available to download from the Harvard Dataverse Website, marks the third generation of the SPAM data series, following those of 2000 and 2005. More information on the production systems and selected crops is available in the Global Spatially-Disaggregated Crop Production Statistics Data (MapSPAM) full metadata at https://data.apps.fao.org/catalog/iso/59f7a5ef-2be4-43ee-9600-a6a9e9ff562a **Data publication:** 2019-10-09 **Supplemental Information:** No data value: -1 Unit : Scale Factor : 1.0 Map code : GVPAPH **Citation:** Citation: International Food Policy Research Institute, 2019, “Global Spatially-Disaggregated Crop Production Statistics Data for 2010 Version 1.1”, https://doi.org/10.7910/DVN/PRFF8V, Harvard Dataverse, V3. **Contact points:** Metadata Contact: Koo, Jawoo International Food Policy Research Institute (IFPRI) Resource Contact: You, Liangzhi International Food Policy Research Institute (IFPRI) Resource Contact: Harvard Dataverse **Data lineage:** Differences compared to SPAM 2010 V1r0 (Uploaded December 2018): - No more rounding errors of 0.1 ha or mt, ie areas and production in each pixel satisfy conditions: R=A-I and R=H+L+S. - CSV files do not have 'strange' entries for Yemen - admin names with "," and ";" were corrected. - Value of production only has one set of entries for Sudan - in previous version it had 2. - Missing values for maize in Nigeria/Osun (fisp1=NI31) now included. **Resource constraints:** IFPRI DATAVERSE TERMS OF USE This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). **Online resources:** Download the data from Google Cloud Storage Download the latest version from Harvard Dataverse Download - MapSpam Dimensions:Technology and Crops (CVS) ReadMe_V1r1_GeoTiff.txt Map SPAM Data Center

Simplified AEZ classification (33 classes) at about 1 km resolution at the equator, using different climate data source and based on different Representative Concentration Pathways (RCPs) according to the time period as follows: - climate data source CRUTS32 based on historical data for the time period 1981-2010; - climate data source ENSEMBLE based on the Representative Concentration Pathway RCP8.5 for time periods 2041-2070 and 2071-2100. The Simplified AEZ classification dataset is part of the GAEZ v4 Theme 1 Land and Water Resources, Agro-Ecological Zones sub-theme. The agro-ecological zones classification provides a characterization of bio-physical resources relevant to agricultural production systems. AEZ definitions and map classes follow a rigorous methodology and an explicit set of principles. The inventory combines spatial layers of thermal and moisture regimes with broad categories of soil/terrain qualities. It also indicates locations of areas with irrigated soils and shows land with severely limiting bio-physical constraints including very cold and very dry (desert) areas as well as areas with very steep terrain or very poor soil/terrain conditions. For further details, please refer to the GAEZ v4 Model Documentation. **Data publication:** 2021-05-01 **Supplemental Information:** No data value: 0 Unit : class Scale Factor : 1.0 Map code : LR-AEZ-V9V2RED **Contact points:** Metadata Contact: [GAEZ@fao.org](mailto:gaez@fao.org) Resource Contact: [Fischer Gunther](mailto:fisher@iiasa.ac.at) Resource Contact: [UNFAO - NSL Geospatial Unit](mailto:gaez@fao.org) **Data lineage:** Class - Caption 0.0 - 0 1.0 - Tropics, lowland; semi-arid 2.0 - Tropics, lowland; sub-humid 3.0 - Tropics, lowland; humid 4.0 - Tropics, highland; semi-arid 5.0 - Tropics, highland; sub-humid 6.0 - Tropics, highland; humid 7.0 - Sub-tropics, warm; semi-arid 8.0 - Sub-tropics, warm; sub-humid 9.0 - Sub-tropics, warm; humid 10.0 - Sub-tropics, moderately cool; semi-arid 11.0 - Sub-tropics, moderately cool; sub-humid 12.0 - Sub-tropics, moderately cool; humid 13.0 - Sub-tropics, cool; semi-arid 14.0 - Sub-tropics, cool; sub-humid 15.0 - Sub-tropics, cool; humid 16.0 - Temperate, moderate; dry 17.0 - Temperate, moderate; moist 18.0 - Temperate, moderate; wet 19.0 - Temperate, cool; dry 20.0 - Temperate, cool; moist 21.0 - Temperate, cool; wet 22.0 - Cold, no permafrost; dry 23.0 - Cold, no permafrost; moist 24.0 - Cold, no permafrost; wet 25.0 - Dominantly very steep terrain 26.0 - Land with severe soil/terrain limitations 27.0 - Land with ample irrigated soils 28.0 - Dominantly hydromorphic soils 29.0 - Desert/Arid climate 30.0 - Boreal/Cold climate 31.0 - Arctic/Very cold climate 32.0 - Dominantly built-up land 33.0 - Dominantly water **Resource constraints:** GAEZ v4 Disclaimer at https://gaez.fao.org/pages/disclaimer **Online resources:** Translation of dimensions filters with label Download the data from Google Cloud Storage GAEZ v4 Model Documentation GAEZ v4 Data Portal SLD Data for download: Simplified AEZ classification (33 classes) for the time period 1981-2010 using climate data source CRUTS32 based on historical data Data for download: Simplified AEZ classification (33 classes) for the time period 2041-2070 using climate data source ENSEMBLE based on RCP8.5 Data for download: Simplified AEZ classification (33 classes) for the time period 2071-2100 using climate data source ENSEMBLE based on RCP8.5

Spatial distribution of potential yield (crops/y) of tambaqui fed at 75% satiation and harvested at 600g. Yield estimations for tambaqui were based on water temperature which in turn were predicted from air temperature. Mean maximum and minimum monthly air temperatures grids were provide by FAO's Agrometeorology group. In order to explore a range of commercial aquaculture possibilities by the use of the growth model, simulations were conducted for tambaqui, assuming two different feeding levels and two harvesting weights (600 and 1000 g). Feed application rates corresponding to 50% and 75% satiation were assumed to represent commercial aquaculture operations with low and high feeding rates, respectively. **Data publication:** 1997-01-01 **Supplemental Information:** This map derives from a wider study that evaluates inland fish farming potential at a continental-level, published by J.M. Kapetsky and S. S. Nath with the title "A strategic assessment of the potential for freshwater fish farming in Latin America " . COPESCAL Technical Paper. No. 10. Rome, FAO. 1997. 128p. Resolution 9 km x 9 km. **Contact points:** Metadata Contact: [Jose Aguilar-Manjarrez](mailto:Jose.AguilarManjarrez@fao.org) **Data lineage:** Conversion from ASCII files to Arc/Info GRID **Online resources:** Online resource Online resource Potential yield (crops/y) of tambaqui fed at 75% satiation and harvested at 600 g (GRID format)

Map derived from the combined suitability of areas apt for commercial fish farming development and operation with areas with potential yield (crops/y) of Nile tilapia, African catfish and Common carp for small-scale farming. Areas with suitability for commercial fish farming were derived from the combined suitability of five land-quality factors important for fish farming development and operation; net annual water requirement for shallow ponds, soil and terrain suitability for the construction of fish ponds, potential livestock wastes and agriculture by-products as feed and fertilizer inputs, potential for farm-gate sales and potential market demand. Predictions of growth were based on water temperature, in turn predicted from air temperature and wind velocity data. Gridded values of mean monthly daily minimum and maximum temperatures were obtained from the Centre for Resource and Environmental Studies (CRES) at the Australian National University. Mean annual wind velocity was obtained from UNEP/DEIA/GRID-Geneva. **Data publication:** 1998-01-01 **Supplemental Information:** This map derives from a wider study that evaluates inland fish farming potential at a continental-level, published by J.Aguilar-Manjarrez and S. S. Nath with the title "A strategic reassessment of fish farming potential in Africa". CIFA Technical Paper. No. 32. Rome, FAO. 1998. 170p. This study is a follow-up of an earlier assessment of warm-water fish farming potential in Africa by Kapetsky (1994) with the objective to assess locations and areal expanses that have potential for warm-water and temperate-water fish farming in continental Africa. Resolution 5 km x 5 km. **Contact points:** Metadata Contact: [Jose Aguilar-Manjarrez](mailto:Jose.AguilarManjarrez@fao.org) **Data lineage:** Conversion from ASCII files to Arc/Info GRID **Online resources:** Online resource Suitability for commercial farming and potential yield (crops/y) of Nile tilapia, African catfish and Common carp (GRID format)

Spatial distribution of potential yield (crops/y) of Nile tilapia with a CFB of 0.075 kg/m3 and harvested at 150g. Yield estimations for carp were based on water temperature which in turn were predicted from air temperature. Mean maximum and minimum monthly air temperatures grids were provide by FAO's Agrometeorology group. In order to explore a range of small-scale aquaculture possibilities by the use of the growth model, simulations were conducted for Nile tilapia, assuming a harvesting weight of 150g. Natural food availability was modelled as a function of fish biomass and approach that requires definition of the critical standing crop (in kg/ha) or critical fish biomass (CFB; in kg/m3). For tilapia, ponds that are not heavily fertilized or fed, a CFB of about 0.075 kg/m3 (equivalent to a fish biomass of 750 kg/ha) was considered reasonable. **Data publication:** 1997-01-01 **Supplemental Information:** This map derives from a wider study that evaluates inland fish farming potential at a continental-level, published by J.M. Kapetsky and S. S. Nath with the title "A strategic assessment of the potential for freshwater fish farming in Latin America " . COPESCAL Technical Paper. No. 10. Rome, FAO. 1997. 128p. Resolution 9 km x 9 km. **Contact points:** Metadata Contact: [Jose Aguilar-Manjarrez](mailto:Jose.AguilarManjarrez@fao.org) **Data lineage:** Conversion from ASCII files to Arc/Info GRID **Online resources:** Online resource Online resource Potential yield of Nile Tilapia (GRID format)

Map derived from the combined suitability of areas apt for commercial fish farming development and operation with areas with potential yield (crops/y) of African catfish for commercial farming. Areas with suitability for commercial fish farming were derived from the combined suitability of five land-quality factors important for fish farming development and operation; net annual water requirement for shallow ponds, soil and terrain suitability for the construction of fish ponds, potential livestock wastes and agriculture by-products as feed and fertilizer inputs, potential for farm-gate sales and potential market demand. Predictions of growth were based on water temperature, in turn predicted from air temperature and wind velocity data. Gridded values of mean monthly daily minimum and maximum temperatures were obtained from the Centre for Resource and Environmental Studies (CRES) at the Australian National University. Mean annual wind velocity was obtained from UNEP/DEIA/GRID-Geneva. **Data publication:** 1998-01-01 **Supplemental Information:** This map derives from a wider study that evaluates inland fish farming potential at a continental-level, published by J.Aguilar-Manjarrez and S. S. Nath with the title "A strategic reassessment of fish farming potential in Africa" . CIFA Technical Paper. No. 32. Rome, FAO. 1998. 170p. This study is a follow-up of an earlier assessment of warm-water fish farming potential in Africa by Kapetsky (1994) with the objective to assess locations and areal expanses that have potential for warm-water and temperate-water fish farming in continental Africa. Resolution 5 km x 5 km. **Contact points:** Metadata Contact: [Jose Aguilar-Manjarrez](mailto:Jose.AguilarManjarrez@fao.org) **Data lineage:** Conversion from ASCII files to Arc/Info GRID **Online resources:** Online resource Suitability for commercial farming and potential yield (crops/y) of African catfish (GRID format)

Areas in which livestock wastes could contribute to the establishment of pond culture systems such as integrated agriculture-aquaculture farming. Livestock population was used as a surrogate for manure. Livestock populations for cattle, goats, sheep and pigs in the form of animal/km2 were obtained from U.S. Army CERL and CRSSA, Cook College, Rutgers University. **Data publication:** 1998-01-01 **Supplemental Information:** This map derives from a wider study that evaluates inland fish farming potential at a continental-level, published by J.Aguilar-Manjarrez and S. S. Nath with the title "A strategic reassessment of fish farming potential in Africa" . CIFA Technical Paper. No. 32. Rome, FAO. 1998. 170p. This study is a follow-up of an earlier assessment of warm-water fish farming potential in Africa by Kapetsky (1994) with the objective to assess locations and areal expanses that have potential for warm-water and temperate-water fish farming in continental Africa. Resolution 5 km x 5 km. **Contact points:** Metadata Contact: [Jose Aguilar-Manjarrez](mailto:Jose.AguilarManjarrez@fao.org) **Data lineage:** Conversion from ASCII files to Arc/Info GRID **Online resources:** Online resource Total manure available (GRID format)

Spatial distribution of potential yield (crops/y) of pacu fed at 75% satiation and harvested at 600g. Yield estimations for pacu were based on water temperature which in turn were predicted from air temperature. Mean maximum and minimum monthly air temperatures grids were provide by FAO's Agrometeorology group. In order to explore a range of commercial aquaculture possibilities by the use of the growth model, simulations were conducted for pacu, assuming two different feeding levels and two harvesting weights (600 and 1,000 g). Feed application rates corresponding to 50% and 75% satiation were assumed to represent commercial aquaculture operations with low and high feeding rates, respectively. **Data publication:** 1997-01-01 **Supplemental Information:** This map derives from a wider study that evaluates inland fish farming potential at a continental-level, published by J.M. Kapetsky and S. S. Nath with the title "A strategic assessment of the potential for freshwater fish farming in Latin America " . COPESCAL Technical Paper. No. 10. Rome, FAO. 1997. 128p. Resolution 9 km x 9 km. **Contact points:** Metadata Contact: [Jose Aguilar-Manjarrez](mailto:Jose.AguilarManjarrez@fao.org) **Data lineage:** Conversion from ASCII files to Arc/Info GRID **Online resources:** Online resource Online resource Potential yield (crops/y) of pacu fed at 75% satiation and harvested at 600 g (GRID format)

Areas with potential availability of agricultural by products for use as feeds or fertilizer inputs. It is based on soil productivity and length of growing period (LGP). Two geo-referenced datasets provided the basic material for the evaluation: the FAO/UNESCO Soil Map of the World and an inventory of climate regimes to characterize length of the growing period (LGP). **Data publication:** 1997-01-01 **Supplemental Information:** This map derives from a wider study that evaluates inland fish farming potential at a continental-level, published by J.M. Kapetsky and S. S. Nath with the title "A strategic assessment of the potential for freshwater fish farming in Latin America " . COPESCAL Technical Paper. No. 10. Rome, FAO. 1997. 128p. Resolution 9 km x 9 km. **Contact points:** Metadata Contact: [Jose Aguilar-Manjarrez](mailto:Jose.AguilarManjarrez@fao.org) **Data lineage:** Conversion from ASCII files to Arc/Info GRID **Online resources:** Online resource Online resource Potential for agricolture by-products as feed and fertiliser inputs (GRID format) iiasa