Agriculture data

Napa Valley American Viticultural Area (AVA) This layer is composed of a polygon that depicts the land in Napa VALLEY that is part of the Napa Valley Viticultural Area

The Pasture feature class designates boundaries to establish extent of livestock distribution and management within pastures. This is a published layer created by combining GIS data managed by each National Forest and attribute data stored in the Forest Service Infra database application. This dataset is designed for reporting and analysis and is not used to enter or edit data. The data will be used to create permit maps, field working maps and any other logical uses that the internal and external public may determine this information is helpful for. Pasture is a feature class in the Rangeland Management data set. It represents the area boundaries of livestock grazing pastures. The area corresponds to tabular data in the RIMS (Rangeland Information Management System). See all details of this USFS Enterprise Content: https://gbp-blm-egis.hub.arcgis.com/datasets/BLM-EGIS::blm-colorado-grazing-allotments/about

The Kenya Crop Land layer provides information on the extent of cropland, area specific major crop and other crops being grown in the same location. This layer was generated from the Landsat 8 - 30 meters resolution imagery data for September/October 2000 and validated using location points collected from subsequent field visits.

Layer showing changes in Kenya crop land areas from 2000 to 2010

Layer showing changes in Kenya maize land areas from 2000 to 2010.

Polygon zoning areas designated for agricultural development and the protection of rural character, published by Galway City Council as part of its Development Plan 2017. Each feature records a zone identifier, zoning code and description, and its area in square kilometres, hectares and acres.

Polygon zoning areas designated for agriculture and the protection of areas of visual importance, published by Galway City Council as part of its Development Plan 2017. Each feature carries a zone identifier, zoning code and description, and its area in square kilometres, hectares and acres.

A central focus for governing bodies in Africa is the need to secure the necessary food sources to support their populations. It has been estimated that the current production of crops will need to double by 2050 to meet future needs for food production. Higher level crop-based products that can assist with managing food insecurity, such as cropping watering intensities, crop types, or crop productivity, require as a starting point precise and accurate cropland extent maps indicating where cropland occurs. Current cropland extent maps are either inaccurate, have coarse spatial resolutions, or are not updated regularly. An accurate, high-resolution, and regularly updated cropland area map for the African continent is therefore recognised as a gap in the current crop monitoring services.  Key Properties Geographic Coverage: Continental Africa - approximately 37° North to 35 South Temporal Coverage: 2019 Spatial Resolution: 10 x 10 meter Update Frequency: TBD Number of Bands: 3 Bands Parent Dataset: Digital Earth Africa's Sentinel-2 Semiannual GeoMAD Source Data Coordinate System: WGS 84 / NSIDC EASE-Grid 2.0 Global (EPSG:6933) Service Coordinate System: WGS 84 / NSIDC EASE-Grid 2.0 Global (EPSG:6933) Digital Earth Africa’s cropland extent maps for Eastern, Western, and Northern Africa show the estimated location of croplands in these countries for the period of January to December 2019:  Eastern: Tanzania, Kenya, Uganda, Ethiopia, Rwanda and Burundi Western: Nigeria, Benin, Togo, Ghana, Cote d'Ivoire, Liberia, Sierra Leone, Guinea and Guinea-Bissau Northern: Morocco, Algeria, Tunisia, Libya and Egypt Sahel: Mauritania, Senegal, Gambia, Mali, Burkina Faso, Niger, Chad, Sudan, South Sudan, Somalia and Djibouti Southern: South Africa, Namibia, Botswana, Lesotho and Eswatini   Cropland is defined as: "a piece of land of minimum 0.01 ha (a single 10m x 10m pixel) that is sowed/planted and harvestable at least once within the 12 months after the sowing/planting date." This definition will exclude non-planted grazing lands and perennial crops which can be difficult for satellite imagery to differentiate from natural vegetation. The provisional cropland extent maps have a resolution of 10 metres and were built using Copernicus Sentinel-2 satellite images from 2019. The cropland extent maps were built separately using extensive training data from Eastern, Western, and Northern Africa, coupled with a Random Forest machine learning model. A detailed exploration of the methods used to produce the cropland extent map can be found in the Jupyter Notebooks in DE Africa’s crop-mask GitHub repository. Independent validation datasets suggest the following accuracies: The Eastern Africa cropland extent map has an overall accuracy of 90.3 %, and an f-score of 0.85 The Western Africa cropland extent map has an overall accuracy of 83.6 %, and an f-score of 0.75 The Northern Africa cropland extent map has an overall accuracy of 94.0 %, and an f-score of 0.91 The Sahel Africa cropland extent map has an overall accuracy of 87.9 %, and an f-score of 0.78 The Southern Africa cropland extent map has an overall accuracy of 86.4 %, and an f-score of 0.75 The algorithms for all regions tend to report more omission errors (labelling actual crops as non-crops) than commission errors (labelling non-crops as crops). Where commission errors occur, they tend to be focussed around wetlands and seasonal grasslands which spectrally resemble some kinds of cropping. Available Bands Band ID Description Value range Data type NoData/Fill value mask crop extent (pixel) 0 - 1 uint8 0 prob crop probability (pixel) 0 - 100 uint8 0 filtered crop extent (object-based) 0 - 1 uint8 0 mask: This band displays cropped regions as a binary map. Values of 1 indicate the presence of crops, while a value of 0 indicates the absence of cropping. This band is a pixel-based cropland extent map, meaning the map displays the raw output of the pixel-based Random Forest classification. prob: This band displays the prediction probabilities for the ‘crop’ class. As this service uses a random forest classifier, the prediction probabilities refer to the percentage of trees that voted for the random forest classification. For example, if the model had 200 decision trees in the random forest, and 150 of the trees voted ‘crop’, the prediction probability is 150 / 200 x 100 = 75 %. Thresholding this band at > 50 % will produce a map identical to mask. filtered: This band displays cropped regions as a binary map. Values of 1 indicate the presence of crops, while a value of 0 indicates the absence of cropping. This band is an object-based cropland extent map where the mask band has been filtered using an image segmentation algorithm (see this paper for details on the algorithm used). During this process, segments smaller than 1 Ha (100 10m x 10m pixels) are merged with neighbouring segments, resulting in a map where the smallest classified region is 1 Ha in size. The filtered dataset is provided as a complement to the mask band; small commission errors are removed by object-based filtering, and the ‘salt and pepper’ effect typical of classifying pixels is diminished. More details on this dataset can be found here.

Overview This layer, prepared by the Commission for Environmental Cooperation (CEC), shows the percentage of the total land area that was equipped for irrigation, around the year 2000. Irrigation is the practice of applying water to land to promote the growth of vegetation such as crops and pastures. The land area actually irrigated is unknown but was smaller than the amount equipped for irrigation. The data are derived from the Global Map of Irrigation Areas, version 4.0.1, produced by the Johann Wolfgang Goethe University, Frankfurt am Main, Germany, and the Food and Agriculture Organization of the United Nations (FAO), Rome, Italy. Geographic Coverage The coverage spans North America, including Canada, Mexico, and the United States. Data Sources and Licensing Commission for Environmental Cooperation (CEC). Siebert, S., P. Döll, S. Feick, J. Hoogeveen and K. Frenken. 2007. Global map of irrigation areas, version 4.0.1. Johann Wolfgang Goethe University, Frankfurt am Main, Germany / Food and Agriculture Organization of the United Nations, Rome, Italy. The data is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0), allowing redistribution and adaptation with appropriate attribution. Update frequency: Not Planned Limitations of use: None. Acknowledgement of the Commission for Environmental Cooperation and (or) the Food and Agriculture Organization of the United Nations (FAO) – Global Information System on Water and Agriculture would be appreciated in products derived from these data. Vintage: 2000 Applications (Intended Use) Assessing the extent and distribution of irrigated agriculture across North America. Water resource and agricultural planning. Supporting research and education on land and water use for agriculture. Cross-border environmental cooperation and reporting among North American nations. Files Download

The Kenya Crop Land layer provides information on the extent of cropland, area specific major crop and other crops being grown in the same location. This layer was generated from the Landsat 8 - 30 meters resolution imagery data for September/October 2015 and validated using location points collected from subsequent field visits.

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.

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

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

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

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

El Salvador Near Term Projection FEWS NET Acute Food Insecurity Classifications Geographic data for 2022

Mauritania Medium Term Projection 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. Zimbabwe 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. Zimbabwe Near Term Projection FEWS NET Acute Food Insecurity Classifications Geographic data for 2018

The FEWS NET Data Warehouse is **currently unavailable**. Datasets on HDX that refer to the Data Warehouse have been archived. Zambia 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. Yemen Near 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. Chad 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. South Sudan Near 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. Rwanda Medium 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. Malawi 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. Chad Current Situation FEWS NET Acute Food Insecurity Classifications Geographic data for 2018

The FEWS NET Data Warehouse is **currently unavailable**. Datasets on HDX that refer to the Data Warehouse have been archived. Madagascar 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. Rwanda 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. Niger 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. Yemen 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. Afghanistan Near Term Projection FEWS NET Acute Food Insecurity Classifications Geographic data for 2018

Burkina Faso Medium Term Projection 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. Afghanistan Current Situation FEWS NET Acute Food Insecurity Classifications Geographic data for 2017