Overview of Key Data and Metrics available through the Foresight Portal
Using foresight analysis to inform policy and investment decision making regarding the transformation of food, land, and water systems requires access to actionable, transparent and interoperable data and metrics.
Below is a list of data and metric types covered in the Foresight portal including both external links as well as data generated and compiled by the partners of the Foresight Portal:
- • Mappings between classifications for enhanced interoperability.
- • Key input data for selected quantitative foresight models.
- • Some standard scenarios and their underlying data.
- • Spatial data.
- • Key quantitative foresight model results.
- • Foresight output metrics dashboards and visualizations.
- • Foresight input data dashboards and visualizations.
- • Underlying data related to global drivers of change.
- • Selected global foresight relevant datasets.
- • Selected national and sub-national foresight relevant datasets.
- • Links to other data platforms relevant for foresight.
For data to be accessible, transparent and reusable it is essential that it comes with rich metadata. Metadata provides context, such as how and when the data was collected, its accuracy, its format, and any limitations or assumptions made during data collection or analysis. This information is vital for ensuring that data users can assess the relevance, reliability, and applicability of the data. Transparent metadata supports the verification of results and facilitates the reuse of data, enhancing the overall credibility and utility of the foresight analysis.
Mappings between classifications for enhanced interoperability
By creating mappings between different classifications or controlled vocabularies, we enhance the interoperability of data across various sources and systems. This is particularly valuable in foresight analysis, where integrating and comparing data across different organizations and datasets is essential. For example, by mapping the geographic location names used by the Food and Agriculture Organization (FAO) and the World Bank to ISO geographic codes, you enable a more seamless integration of data related to these locations, regardless of the source. Similarly, creating mappings between the food items listed in the FAO’s Food Balance Sheets and those in the USDA’s nutrition tables allows for a comprehensive analysis that leverages both sets of data, providing more nuanced insights into food security, nutrition, and agricultural economics.
This kind of work facilitates not only the comparison and aggregation of data from different sources but also supports more informed policy-making, research, and investment decisions by providing a unified view of the data. It’s a crucial aspect of data management and analysis in any field that relies on diverse data sources, especially in global contexts where standardization varies widely
For convenience we provide our mappings in different formats. They are downloadable as CSV file, or in GDX format for incorporation into GAMS-based modeling frameworks.
Key input data for selected quantitative foresight models
This includes all the baseline data that these models need to run their simulations and projections. It could range from historical data on weather patterns, crop yields, water usage, to economic indicators. The accuracy and granularity of this input data directly impact the reliability of the foresight models.
More about: ADAM input data
The Agri-food Data Analysis Modeling framework (ADAM) uses data from various sources to create new metrics. One of the Key innovations of ADAM is to create interoperable data from different sources. The mappings mentioned earlier on this page are part and parcel of the approach in ADAM. The data presented here is in GDX format for use with GAMS models.
Some of the external data sources that have been made interoperable include:
FAO production data
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Getting the underlying data
Interoperable production data
GAMS Code to do it yourself
- FAO food balance sheets:
- Key World Bank Indicators:
More about: ADAM
More about: Social Accounting Matrices
For instance computable general equilibrium (CGE) models use social accounting matrices (SAMs) as their key input data. SAMs are also used for multiplier analysis.
Some standard scenarios and their underlying data
These scenarios provide frameworks within which the foresight analysis can be conducted, often based on different assumptions about future developments (e.g., economic growth, climate change impacts). The data underpinning these scenarios needs to be robust and comprehensive.
Spatial data
Geographic information is critical for understanding and visualizing how different factors interact across space. This can include data on land use, water availability, infrastructure, and population distribution, which is essential for any analysis that has a geographic or locational component.
Key quantitative foresight model results and projections
These results, including explorations of plausible and probable futures and potential future trends, are what policymakers and investors will use to make informed decisions. Ensuring that these results are presented in a clear, understandable, and transparent manner is crucial.
Foresight output metrics dashboards and visualizations
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Foresight input data dashboards and visualizations
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Underlying data related to global drivers of change
The major global drivers of change related to food, land, and water systems are multifaceted and interconnected, reflecting the complexity of these systems. Here are some of the critical drivers and the data sources that can be used in analyses.
There are three major drivers of global change:
- • Climate change
- • Population growth
- • Economic development
There are secondary drivers that are linked to the above major drivers that are equally important:
- • Trends in technological advancement
- • Trends in policy and governance
- • Changes in societal attitudes and behaviors
- • Trends in global trade
- • Natural resource depletion
- • Trends in environmental degradation
- • Trends in biodiversity loss
- • Dietary change
Find underlying data
Population Growth and Urbanization: The growing global population and increasing urbanization rates exert pressure on food, land, and water resources. Urban sprawl consumes arable land, and higher population densities increase the demand for food and water, impacting resource availability and sustainability
See data sources: historic data and projections
The data portal of the United Nations Population Division is a key resource.
For data on urbanization the 2018 world urbanization prospects data portal of the United Nations Division of Social Affairsis a good resource.
Climate Change: Climate change affects precipitation patterns, temperature regimes, and the frequency and intensity of extreme weather events, all of which have profound impacts on agricultural productivity, water availability, and land integrity. These changes necessitate adaptations in farming practices, water management, and land use planning.
See data sources: parameters, historic data and projections
The EU Copernicus climate datasets
If one is interested in looking into the effects of land use change on climate, data that is needed in the analysis concerns land conversion emission factors. Here are some sources of information on those conversion factors
Land conversion emission factors…
Economic Development: Economic growth and industrialization can lead to increased consumption and waste, more intensive agricultural practices, and greater demand for land and water resources. However, economic development can also provide resources for investment in sustainable technologies and practices.
See data sources: historic data
The world bank indicators subset on global economic monitoring is a key resource.
Technological Advancement: Innovations in agriculture, water management, and land use can significantly influence the sustainability and productivity of these systems. Technologies like precision agriculture, advanced irrigation, and sustainable land management practices can improve efficiency and reduce environmental impacts.
See data sources
Policy and Governance: Decisions made at the local, national, and international levels regarding regulations, subsidies, investments, and planning can greatly influence food, land, and water systems. Effective governance is crucial for coordinating responses to challenges and for promoting sustainable practices.
See data sources
Societal Attitudes and Behaviors: Consumer preferences, cultural norms, and societal values can drive changes in food consumption patterns, land use practices, and water management. Raising awareness and fostering sustainable habits are key to managing the demand side of these systems.
See data sources
Global Trade: International trade in food and agricultural products links local and global food, land, and water systems, with trade policies and global market dynamics playing influential roles. While trade can enhance food security and economic development, it can also lead to resource depletion and environmental degradation if not managed sustainably.
See data sources: historic data and parameters
The United Nations trade data is a rich resource
The World Trade Organization (WTO) provides quantitative information in relation to economic and trade policy issues. Its data-bases and publications provide access to data on trade flows, tariffs, non-tariff measures (NTMs) and trade in value added.
Resource Depletion and Environmental Degradation: Overexploitation of land and water resources and environmental degradation, including soil erosion, deforestation, and water pollution, undermine the long-term sustainability of food, land, and water systems.
See data sources
Biodiversity Loss: The loss of biodiversity affects ecosystem services that are critical for supporting food production, maintaining water quality, and ensuring the resilience of land and water systems.
See data sources
Understanding these drivers is crucial for developing strategies to manage food, land, and water systems sustainably and to anticipate and mitigate the impacts of future changes.
Overview of selected data sources of global data at regional and nation al levels of aggregation
Production datasets
United States Department of Agriculture: Foreign Agricultural Service: production data
Price datasets
Gender and social inclusion datasets
Consumption and health datasets
Trade datasets
Detailed national and sub-national level data for foresight analysis
Besides more global level data there is also a wealth of national and subnational data that can be used in foresight analysis.
Links to other foresight related platforms
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Adding more data to the Foresight Portal
We strive to add more data to the Foresight Portal. However, we cannot do this on our own and need the support of the entire foresight community.
Read more about our collaborative data approach
The Foresight Portal is actively seeking to broaden its data repository and enhance its effectiveness in guiding policy and investment decisions. To this end, it is crucial to cultivate collaborative relationships within the foresight community. Below are outlined strategies aimed at fostering such collaboration and support:
- Engagement with the Community: The portal invites engagement from the foresight community via various platforms, including forums, conferences, and digital channels. By sharing the portal’s goals, its significance, and the necessity for diverse, high-quality data, the initiative aims to garner feedback, ideas, and data contributions, fostering a sense of shared purpose and collaboration.
- Campaigns for Data Contribution: The portal initiates campaigns to motivate both organizations and individuals to share their data, highlighting the advantages of contribution like enhanced data visibility and the chance to support a crucial global resource.
- Formation of Strategic Partnerships: The portal seeks to establish alliances with entities such as academic institutions, governmental bodies, NGOs, and research centers, leveraging their data, expertise, and other resources to enrich the portal.
- Incentives for Contributions: Contributors are encouraged through various incentives, including recognition initiatives or certification programs, providing them opportunities to showcase their research and insights on the portal.
- Support and Guidance for Data Submission: The portal offers comprehensive guidelines and technical assistance to ensure submitted data is consistent, high-quality, and compatible, possibly including standardized templates and metadata protocols.
- Commitment to Transparency: Upholding a transparent approach in data usage and attribution, the portal endorses open access where feasible, encouraging broader data contribution and engagement.
- Effective Communication: Through regular updates and stories of impact, the portal communicates its value, encouraging sustained interaction and backing from the community.
- Pursuit of Excellence: Continuous feedback is solicited to enhance the portal’s functionality, relevance, and effectiveness, ensuring it remains a pivotal tool for the foresight community.
- These efforts aim to cultivate a robust and participatory community around the Foresight Portal, leveraging collective expertise and data to shape a sustainable future for food, land, and water systems globally.