Know Where Graph Logo
Back to All Prototypes

Food Supply Chain Resilience

Background Imagery - County Map of the USA with a vectored web of lines and nodes wrapping across the country, signifying connections

It is critically important to understand and improve the robustness and adaptability of our Food Supply Chain, making it more resilient to disturbances in food supply and demand networks. There is inherently a risk of network fracturing and delayed recovery during extreme weather events, wildfires, floods, and other natural hazards. In the face of uncertain natural hazards of increasing frequency and severity, it is vital that the implications of these disruptions are evaluated for the source nodes of our supply chains, such that resiliency in the whole supply chain can be promoted. Ensuring this requires bridging across topics and domains, but data are siloed, distributed, variously described, and have regional variations that create different needs and solutions. To solve this challenge, we are partnering with the Food Industry Association (FMI), which has identified food safety and food quality issues rising from environmental disasters or disturbances as a high-priority industry concern. Together we will utilize the graph and enrichment services to link across data silos from multiple domains with rich public data to address one or more of the following pilot use cases:

We will focus initially on leafy greens tainted in the field by ash falling from wildfires. FMI members want to understand better when and where a tainted product is and how to supplement similar products (i.e., from alternate sources) in the supply chain. Example graph queries include: (1) What percentage of product from a specific smoke-affected area is potentially tainted? (2) Can we identify and notify key stakeholders in the supply chain about a product that may be (or may become) tainted? (3) How can we best anticipate and react to shortages of a product that the current disaster may taint? (4) Are the current events comparable to previous situations both in the short term and in relation to long-term trends or are they outliers? For instance, beyond the dynamic short-term impacts of wildfires, some wine regions are having to adjust the grape varieties grown due to the effects of climate change on local temperatures, which also can also taint the wine; what does this mean for California? FMI members currently carry out these analyses internally, with limited data inputs, dependent on available internal knowledge, and often only in retrospect.

Our goal is to deliver graph-based tools that substantially enhance current capabilities for assessment and strategic planning during real-time events/issues by providing online analysis, forecasting and alerts, enriched with location and context-specific intelligence, to ensure key stakeholders throughout the supply chain are ready with backup strategies to keep products moving. Such a system also has large implications for the farmers/growers. By providing informative analytics and predictive and informative capabilities, it allows them to identify how they can be better prepared to mitigate and/or build resilience in the face of such events. Interestingly, one of the key challenges from a graph perspective is that data is widely available but difficult to interlink, e.g., data layers for air quality, smoke plume simulations and observations, wildfires and their burn intensity exist but cannot be easily combined to answer questions about the impacts of one specific fire. Within our graph, we perform parts of the analysis and processing and then serve the pre-integrated data together with topological information about the affected areas. While each of these data is sourced from public sources, such as the United States Environmental Protection Agency, information about individual fields and farmers is not. While this information will be linked to the graph to enable our use case and provide further services such as link prediction, the KnowWhereGraph will also support authenticated access to privileged data that will not be accessible as part of the publicly available graph.

Agricultural (Farm) Credit

Agricultural (Farm) Credit risk assessment and land valuation is an activity essential to the seventy farm credit associations and four banks in the U.S. that annually provide more than $2 billion USD in farm loans. Together with our partners, we will be developing a use case focused on enhancing the quality and speed of evaluations of the key credit factors in the farming operations collateral valuation process to estimate the level of risk associated with a particular loan application. Five key factors include: (1) appraisals to calibrate the system, (2) loss given default (i.e., how much can be recovered upon default), (3) prepayment (i.e., forecast to deal with cash flow and tax laws), (4) crop yield projections, and (5) modeling for probability of default. Their assessment must consider a multitude of different data, such as the specific farm equipment and farming practices used, local weather conditions, prospective crop type, and predicted crop prices. We will first focus on graph-based assessment of current and future land value, wherein agricultural operations that employ soil-health farming practices experience less financial risk than conventional operations. Farming operations using these practices can be identified, mapped, and quantified using remote surveillance and data analysis, and lifted to the KnowWhereGraph. While most of the data and services will be part of a bespoke solution, all data about soil health, soil management practices, and crops (e.g., vector data sources such as the Soil Survey Geographic database SSURGO and raster data sources like the USDA’s Cropland Data Layer) will become part of the public graph, accessible to a multitude of other applications.