Americas agriculture data

The Hand-in-Hand Initiative (HiH) agricultural typologies Regional Initiative for the Amazon, was prepared for the Amazon Cooperation Treaty Organization (ACTO), which includes 8 countries: Bolivia, Brazil, Colombia, Ecuador, Peru, Guyana, Suriname, and Venezuela. The HiH Agricultural Typology analysis targets the identification of micro-regional level innovation opportunities, bottlenecks, and investment gaps. This concept is based on the production possibilities frontier applied to farm activities. It draws on household-level surveys and geospatial data on agroecological conditions, accessibility, and poverty. The final Agricultural Typology consists of a classification representing a combination of agricultural potential, agricultural efficiency, and priority (poverty) components in each region. The 7 classes are as follows: • Critical with moderate agricultural opportunities. • Medium priority with moderate agricultural opportunities. • Low priority. • High priority. • Medium priority with high agricultural opportunities. • Low priority with high agricultural opportunities. • High-performance. **Data Components** The Agricultural Potential component provides the maximum agricultural income smallholder farmers can attain if performing at maximum capacity (their own, as well as of the markets, productive infrastructure, and basic services surrounding them). It is determined by both the biophysical and economic factors. The interaction of these two sets of elements establishes the maximum income a farmer can earn from agricultural activities. The Agricultural Efficiency component defines how much of the potential is attained by farmers in a region under current conditions. To increase their efficiency, farmers need to reduce transaction costs in agricultural production and marketing through improved infrastructure (such as roads) and services (such as market information), overcome market failures (access to credit, insurance, land markets, etc.), and receive better access to basic services (such as education and extension services). The Priority component reveals a region’s degree of urgency for investments in development, measured in terms of the wellbeing of the local population, the target beneficiaries of agricultural innovation efforts. **Supplemental Information** The Agricultural Typology analysis is a dataset developed in the framework of the HiH, which is an evidence-based, country-led, and country-owned initiative of the Food and Agriculture Organization of the United Nations (FAO) aiming to accelerate the agricultural transformation and sustainable rural development to eradicate poverty, and end hunger and all forms of malnutrition.

The Hand-in-Hand Initiative (HiH) agricultural typologies Regional Initiative for the Amazon, was prepared for the Amazon Cooperation Treaty Organization (ACTO), which includes 8 countries: Bolivia, Brazil, Colombia, Ecuador, Peru, Guyana, Suriname, and Venezuela. The HiH Agricultural Typology analysis targets the identification of micro-regional level innovation opportunities, bottlenecks, and investment gaps. This concept is based on the production possibilities frontier applied to farm activities. It draws on household-level surveys and geospatial data on agroecological conditions, accessibility, and poverty. The final Agricultural Typology consists of a classification representing a combination of agricultural potential, agricultural efficiency, and priority (poverty) components in each region. The 7 classes are as follows: • Critical with moderate agricultural opportunities. • Medium priority with moderate agricultural opportunities. • Low priority. • High priority. • Medium priority with high agricultural opportunities. • Low priority with high agricultural opportunities. • High-performance. **Data Components** The Agricultural Potential component provides the maximum agricultural income smallholder farmers can attain if performing at maximum capacity (their own, as well as of the markets, productive infrastructure, and basic services surrounding them). It is determined by both the biophysical and economic factors. The interaction of these two sets of elements establishes the maximum income a farmer can earn from agricultural activities. The Agricultural Efficiency component defines how much of the potential is attained by farmers in a region under current conditions. To increase their efficiency, farmers need to reduce transaction costs in agricultural production and marketing through improved infrastructure (such as roads) and services (such as market information), overcome market failures (access to credit, insurance, land markets, etc.), and receive better access to basic services (such as education and extension services). The Priority component reveals a region’s degree of urgency for investments in development, measured in terms of the wellbeing of the local population, the target beneficiaries of agricultural innovation efforts. **Supplemental Information** The Agricultural Typology analysis is a dataset developed in the framework of the HiH, which is an evidence-based, country-led, and country-owned initiative of the Food and Agriculture Organization of the United Nations (FAO) aiming to accelerate the agricultural transformation and sustainable rural development to eradicate poverty, and end hunger and all forms of malnutrition.

The Hand in Hand Initiative (HiH) - Building Resilience in the Dry Corridor and Arid Zones of the Central American Economic Integration System (SICA) regional initiative, is being developed since 2022, involves eight countries: Belize, Costa Rica, El Salvador, Honduras, Guatemala, Nicaragua, Panama and the Dominican Republic. The HiH Agricultural Typology analysis targets the identification of micro-regional level innovation opportunities, bottlenecks, and investment gaps. This concept is based on the production possibilities frontier applied to farm activities. It draws on household-level surveys and geospatial data on agroecological conditions, accessibility, and poverty. The final Agricultural Typology consists of a classification representing a combination of agricultural potential, agricultural efficiency, and priority (poverty) components in each region. The 7 classes are as follows: • Critical with moderate agricultural opportunities. • Medium priority with moderate agricultural opportunities. • Low priority. • High priority. • Medium priority with high agricultural opportunities. • Low priority with high agricultural opportunities. • High-performance. **Data Components** The Agricultural Potential component provides the maximum agricultural income smallholder farmers can attain if performing at maximum capacity (their own, as well as of the markets, productive infrastructure, and basic services surrounding them). It is determined by both the biophysical and economic factors. The interaction of these two sets of elements establishes the maximum income a farmer can earn from agricultural activities. The Agricultural Efficiency component defines how much of the potential is attained by farmers in a region under current conditions. To increase their efficiency, farmers need to reduce transaction costs in agricultural production and marketing through improved infrastructure (such as roads) and services (such as market information), overcome market failures (access to credit, insurance, land markets, etc.), and receive better access to basic services (such as education and extension services). The Priority component reveals a region’s degree of urgency for investments in development, measured in terms of the wellbeing of the local population, the target beneficiaries of agricultural innovation efforts. **Supplemental Information** The Agricultural Typology analysis is a dataset developed in the framework of the HiH, which is an evidence-based, country-led, and country-owned initiative of the Food and Agriculture Organization of the United Nations (FAO) aiming to accelerate the agricultural transformation and sustainable rural development to eradicate poverty, and end hunger and all forms of malnutrition.

This Archive contains shapefiles for FEWS NET Food Security Outlook for **Central America and The Caribbean**. 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: * xx201304_ML1 Most likely food security outcome for January-March 2013 * xx201304_ML2 Most likely food security outcome for April-June 2013 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 **Central America and The Caribbean**. It was last updated on October 08, 2015. The classification used is IPC V2.0 Compatible, aimed to address acute food insecurity. The two shapefiles represent the two analytic periods: * LAC201304_ML1 Most likely food security outcome for July-September 2015 * LAC201304_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 **Central America and The Caribbean**. 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: * xx201401_ML1 Most likely food security outcome for January-March 2014 * xx201401_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 **Central America and The Caribbean**. It was last updated on May 15, 2014. The classification used is IPC V2.0 Compatible, aimed to address acute food insecurity. The two shapefiles represent the two analytic periods: * LAC201304_ML1 Most likely food security outcome for April-June 2013 * LAC201304_ML2 Most likely food security outcome for July-September 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 **Central America and The Caribbean**. It was last updated on November 13, 2014. The classification used is IPC V2.0 Compatible, aimed to address acute food insecurity. The two shapefiles represent the two analytic periods: * LAC201304_ML1 Most likely food security outcome for October-December 2014 * LAC201304_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: * 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 **Central America and The Caribbean**. It was last updated on April 2014. The classification used is IPC V2.0 Compatible, aimed to address acute food insecurity. The two shapefiles represent the two analytic periods: * xx201404_ML1 Most likely food security outcome for April-June 2014 * xx201404_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 **Central America and The Caribbean**. It was last updated on March 31, 2015. The classification used is IPC V2.0 Compatible, aimed to address acute food insecurity. The two shapefiles represent the two analytic periods: * LAC201304_ML1 Most likely food security outcome for January-March 2015 * LAC201304_ML2 Most likely food security outcome for April-June 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 **Central America and The Caribbean**. 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: * xx201304_ML1 Most likely food security outcome for October-December 2013 * xx201304_ML2 Most likely food security outcome for January-March 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 **Central America and The Caribbean**. It was last updated on July 01, 2015. The classification used is IPC V2.0 Compatible, aimed to address acute food insecurity. The two shapefiles represent the two analytic periods: * LAC201304_ML1 Most likely food security outcome for April-June 2015 * LAC201304_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 **Central America and The Caribbean**. It was last updated on September 30, 2014. The classification used is IPC V2.0 Compatible, aimed to address acute food insecurity. The two shapefiles represent the two analytic periods: * LAC201304_ML1 Most likely food security outcome for July-September 2014 * LAC201304_ML2 Most likely food security outcome for October-December 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)

The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Global Food Security-support Analysis Data (GFSAD) data product provides cropland extent data over South America for nominal year 2015 at 30 meter resolution (GFSAD30SACE). The monitoring of global cropland extent is critical for policymaking and provides important baseline data that are used in many agricultural cropland studies pertaining to water sustainability and food security. The GFSAD30SACE data product uses the pixel-based supervised classifier, Random Forest (RF), to retrieve cropland extent from a combination of Landsat 5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper Plus (ETM+), Landsat 8 Operational Land Imager (OLI) data, and elevation derived from the Shuttle Radar Topography Mission (SRTM) Version 3 data products. Each GFSAD30SACE GeoTIFF file contains a cropland extent layer that defines areas of cropland, non-cropland, and water bodies over a 10° by 10° area. Known Issues * Known issues, including constraints and limitations, are provided on page 18 of the ATBD.