The heterogenous character of nature complicates the measuring of nature-related dependencies, impacts, risks and opportunities in a supply chain. Knowing where commodities in your supply chain have been produced or extracted is essential when measuring nature.

Current supply chain analysis frequently uses aggregated data summarised at country or regional scales. While these help prioritise issues, the insights are too coarse for companies looking to drive nature positive actions in their supply chain. 

So we’re working hard to bridge the gap! We believe that companies shouldn’t have to choose between inaction and impossible data demands. We’re building a robust and scalable method to give companies insights they can act on, even with limited data.

Nature is Incredibly Location-Dependent

The condition of ecosystems, the severity of water stress and the presence of biodiversity can vary dramatically, even within the same country or region. A river basin under severe water stress might lie next to one in relative abundance. A biodiversity hotspot can sit just miles from an intensively farmed landscape. Nature doesn’t follow national borders, and its variation can’t be averaged away.

Current Approaches are Limited Because They Don’t Account for Site-Level Detail

Example 1: The country-level blind spot
Imagine a global company trying to reduce water-related risks across its supply chain. It sources from tens of thousands of farms worldwide. To prioritize efforts, it ranks countries by their average water stress. France scores as “Medium”.

But this misses a crucial detail: the farms supplying the company are located in Western France — an area experiencing Very High water stress.

Because the insights are coarse, the company can’t confidently say whether or not its farms in France face water risks. As a result, it may choose not to engage with those farms, which would expose it to risks such as disrupted supply or product shortfalls.

Example 2: The regional mismatch

Regional data is better than country data. But it also can be limited.

For example, consider a company sourcing soybeans from the USA. It wants to understand whether to put in place measures to protect biodiversity. So it creates average biodiversity intactness scores for all the states it sources from. The results suggest Arkansas has good biodiversity intactness. 

However, almost all soy farming in Arkansas happens in the eastern part of the state, an area with lower biodiversity than the state average. Meanwhile, the more biodiverse western region isn’t involved in soybean production at all. 

Once again, the company cannot confidently act. In this case, it cannot state whether it should, or should not engage with soybean suppliers in Arkansas, so it may be incorrectly de-prioritised.

What Natcap is doing about it

At Natcap, we’re focused on building that missing middle ground: solutions that go beyond country- or region-level generalizations but don’t require full site-level data for every supplier.

We sample likely production sites within regions, for example, using crop maps. This allows us to run our site level metrics such as biodiversity intactness or ecosystem fragmentation. Using site metrics allows us to:

• Understand the distribution of potential impacts, which can vary widely within a region
• Highlight potential worst case scenarios (e.g. sourcing from a protected area)
• Directly compare the supply chain to direct operations, by using the same site metrics