Policy Experts & Technologists gather 'round a table...

This article was co-authored with Elliott Grant, Google X's Sr. Director and Project Lead for Mineral, Computational Agriculture Project. It also appears within his LinkedIn feed. 

Last month, The Nature Conservancy and X, the moonshot factory, convened a roundtable discussion about the role of innovation in climate-friendly agriculture. For many of us, this was one of the first moments in a long time that we could come together (in-person! over a meal!) with people we don’t get to work with everyday to discuss challenges with our food systems. The roundtable was made up of folks thinking about building a sustainable food system from different angles – consumer behavior to sourcing, policy engagement to farm inputs, venture capital and more. X and TNC went in hoping to better understand the right role for technology in the agriculture policy space, and came away with an appreciation for how much room there is to find opportunities for technology in agriculture.

Bringing together this broad range of voices sparked unexpected conversation, and breaking down silos allowed us to think about climate and agricultural issues in new ways. Of course, there were some shared and foundational agreements: that we must keep farm enterprises and farm profitability at the center of the conversation, that a more robust measurement of climate outcomes (above and below ground) are needed in order to drive incentives in carbon markets, and further discussion is needed about who is bearing the cost of the ‘green premium’ and how we can move towards a food system where that premium no longer exists. Our conversation was a fantastic reminder of how important it is for technologists to listen to and learn from others as we build. 

The most enlightening discussions, however, came in the translation across the industries and sectors represented. We found that, even though everyone around the table was working on the same issues and often towards the shared outcomes, the terminology used varied greatly. Coming together proved useful in clarifying these different languages, and some insights were actually found rather than lost in translation – it was in this cross-pollination that some of the most interesting and new ideas were sparked.

10 people gathered together who use different terminologies to speak to the same topic, what comes out?

We wanted to share two examples of thinking that emerged during our conversation in the hope that it can inspire a broader vision for what a future system might look like. 

Optimizing land use, beyond maximizing output 

Just as we’re thinking about getting to zero and eventually net negative carbon emissions, we should start applying that kind of thinking to land use – how might we support the shift of managed land towards zero and eventually a positive-carbon sequestration? 

To do this, we have to get to a place where the value of conserving land is greater than the value of the net energy we get from that land, and will require new ways of creating value. It starts with thinking more broadly about the role productive land has and could have on our ecosystem.

Through the implementation of nature-based solutions, we can move closer to achieving our goal of a more sustainable system for climate, water, biodiversity and food, feed, and fuel production. There is an incredible opportunity to think creatively about how to get there. One example of this is maximizing land use through solution stacking. The new area of ‘agrovoltaics’ is an interesting experiment here - implementing solar energy on farms experiencing heat stress. This is obviously early days, and there are many examples like this, but gives a sense of how we might think across sectors to creatively ensure we’re harnessing the opportunity of land.  

Farm enterprise privacy and trust is paramount, and we have technical solutions to ensure it doesn’t limit progress

We are optimistic that there are technology solutions to address the very real concerns about data privacy as the agricultural system becomes more and more digitized. New advances in machine learning, such as Federated Learning, can help us train robust models without sharing data. This is an area where we can learn much from other industries which are facing similar challenges.  The medical industry, for example,  has found ways to discover scientific breakthroughs while preserving the privacy and security of individual patients’ medical records and sensitive information. 

Like any good gathering, we left with more questions than answers:

How do our respective sectors measure success?

Where do we have alignment on end goals and outcomes?

Who are policymakers and technologists held accountable to and building for?

How can we make quick and also long-term progress? 

This was the start of a long learning journey. Look out for more engagements between The Nature Conservancy and X, the moonshot factory – and please reach out if you’re interested in these topics or joining a future discussion!