Value chain data

Description

Data describing the companies and organisations involved in the agricultural value chain and the quality of their activities. For example data on farmers, cooperatives, processors, retailers and input suppliers.

Key datasets

• Profiles of different value chain actors and organisations
• Farm data, e.g., farming system, crops, land area, farm income, household composition, farm employment, farm holder’s age, fertilizer use etc.
• Cooperatives
• Trade
• Processors, e.g. type, size, turnover, capital, investments, environmental transparency indicators etc.
• Retail
• (Food) product data, e.g. food nutritional value, food composition, origin of produce, environmental factors, time and location of production, etc
• (Safety) inspection results
• Certification

Rationale

Most governments collect a lot of information on the individual value-chain actors, e.g. farmers are monitored on a regular basis, provide data on nutrient management to match legal requirements or submit information to get subsidies in order to comply with regulations. Sharing this information will allow value-chain actors to increases their insights, facilitating the functioning of the value chain.

Expected impact: High

Farmer use

• Farmers can use the data to benchmark their farm against the results of others to understand the competitiveness of their farm and see what should be improved.

Use by other actors

• Input suppliers, processors and traders can forecast their business better and meet local and regional needs by knowing the type of farms, their characteristics and competitiveness in a region.
• Other value-chain actors can also benchmark their company against similar companies to understand their competitiveness and see what aspects should be improved.
• Financial service providers can use value-chain data to profile new or existing clients before lending or insuring their clients, being better able to make a risk estimates facilitating financial inclusion.
• NGOs can design better, evidence-based rural development programmes.
• Data only needs to be shared once, being accessible thereafter by other government bodies, researchers or for reporting indicators according to the terms of international agreements and treaties, e.g. SDG.
• When used with care, the data can be used by civil society to evaluate the success of government policies

Readiness

Many governments collect data from value-chain actors using a census or survey to monitor, evaluate or make new policies. Other governments have extension agents noting farmer characteristics and recommendations to support their advice work and governmental inputs distribution. The quality of this data varies and this also depends on whether the system is paper based or online and digital. When available centrally, digitally and with high quality, it is simple and useful to publish the data. However, privacy and other sensitivities need to be taken into account, especially when vulnerable farmers are concerned. One way to address this challenge is by aggregating the data at a higher level, so that an individual farmer can no longer be tracked in the data (see the example of Agrimati.nl below).

Examples of implementation

• www.Agrimati.nl Dutch webportal on the agricultural sector. This portal provides insights into the “people, planet and profit” performance of the Dutch agricultural sector. It combines the best available data sources and presents long-term developments on hundreds of indicators on themes like agricultural trade, farm income, environmental impacts, employment and prices. Because of the sensitivity of the raw data, the portal provides interactive charts to navigate, and aggregated data download options. Online the data is mainly available at national level, but can be disaggregated for the farming types in the Netherlands.
• The US National Statistical Agency undertakes an agricultural census every five years. The 2012 Census of Agriculture collected more than six million data items directly from farmers. The Ag Census Web Maps application makes this information available at the county level. The maps and accompanying data help users visualize, download, and analyze Census of Agriculture data in a geospatial context. https://www.agcensus.usda.gov/Publications/2012/Online_Resources/Ag_Census_Web_Maps/Overview/
• The Packers and Stockyards Programme (PSP) Annual Report (2008 – Present) of the United States provides an overview of the PSP, its unit level activities, and management. In particular, it analyzes the economic state of the livestock and poultry industries. http://www.gipsa.usda.gov/psp/publications.aspx.
• There are several examples of UK (food) safety inspection data sets shared by the Animal and Plant Health Agency, including:
  • Mad cow disease https://data.gov.uk/data/search?q=tse+surveillance and
  • Food and Mouth disease https://data.gov.uk/data/search?q=food+and+mouth

Initiatives that support interoperability

• https://sdmx.org/ A global initiative to improve Statistical Data and Metadata eXchange
• Global Product Classification (GPC) classifies products by grouping them into categories based on their essential properties as well as their relationships to other products. Including the Global Location Number (GLN) and Global Trade Item Number (GTIN). http://www.gs1.org/gpc