Land Use and Productivity Data
Data describing the cultivated area, the crops grown and yield in the different regions.
Most governments make estimates of the cultivated area, crops grown and yield in different regions to monitor food security and economic development. These figures have a strategic value for all value-chain actors in better planning and adapting their businesses.
- Land use data
- Cultivated areas
- Current crop in the fields
- Harvested crop
- Crop types
Expected impact: High
- A farmer or their advisor may use the data to plan the crops to be planted next year in relation to the cropping pattern in the region in the previous year(s).
Use by other actors
- Processors, storage facilities and traders need to plan and anticipate the next harvest. By having data on the success of previous harvests or data on the (condition of) the standing crop they can plan better.
- Having data on the land use in different regions allows input suppliers to expand or adopt their business strategically.
- Information on previous harvest successes allows financial service providers to make better estimates of the risk they take when providing loans or insuring farmers in a certain area and in making strategic decisions on how to develop their business
- When used with caution, the data can help civil society to evaluate the success of agricultural policies.
- Data only needs to be shared once, being accessible for other government bodies, researchers or for reporting indicators under different international treaties, e.g. UN Sustainable Development Goals (SDG 2.3 or SDG 2.4).
Most governments collect information about their agricultural productivity. Traditionally this information is collected by a survey as part of the duties of an extension service, using a paper- based system. Nowadays, satellite information is used to complement traditional modes of data collection. Collecting accurate agricultural productivity data is often challenging. A survey takes a high degree of effort because of the extent of many agricultural areas, and even with good satellite interpretations many observations in the field are needed to validate the results. Records are often inaccurate and incomplete depending on the amount of effort and dedication required to collect them. Once collected and processed, the data is generally stored in statistical records and tables and is easily published as such as open data. Satellite information is already in a digital format.
Examples of implementation
- The US Department of Agriculture provides a crop-specific land cover data layer annually through its statistical service using moderate resolution satellite imagery and extensive agricultural ground truth. All historical crop data layer products are available for use and free for download through CropScape. https://nassgeodata.gmu.edu/CropScape/. The Agency also provides periodical updates on the crop progress and condition through the year in the different states. https://www.nass.usda.gov/Charts_and_Maps/Crop_Progress_&_Condition/
- Land Use Statistics India (LUS) The Directorate of Economics and Statistics in the Ministry of Agriculture has been collecting data on the nine-fold classification of land, irrigated area (source-wise and crop-wise) and total area under crops from States and union territories in the country. Indian Land Use statistics. https://data.gov.in/catalog/land-use-statistics-lus https://data.gov.in/catalog/land-use-pattern
- Knoema World Data Atlas harvests national and international open data sources to visualize agricultural productivity worldwide. https://knoema.com/atlas/topics/Agriculture
Initiatives that support interoperability
- Spatial data standards are maintained by the Open GIS Consortium http://www.opengeospatial.org/
- In the EU the INSPIRE directive is regulating the exchange of spatial government data in data infrastructures. http://inspire.ec.europa.eu/
- https://sdmx.org/ A global initiative to improve Statistical Data and Metadata eXchange