US Data Investments to Support the Next Generation of Ag Innovation

While we all know about the US government’s big investments in agriculture research, their role as a big data and analytics provider is less well appreciated.

Seeing the phrase ‘big data and analytics’ we think of machine learning, artificial intelligence, cloud computing, and other popu;ar 21st century concepts, but the US government has been providing important data and analytics to researchers, farmers and their advisors, and ag tech companies for over a century. The US has been the innovation leader of global agriculture for most of this period, and weather and soil data, nutrient models, geospatial information and crop yield data have all beem important components.

Soils data is a great example, from formal soil surveys starting in 1899, to today’s USDA soils resources wealth of data and models. If you want to dive in, check out the amazing SSURGO dataset, which you can access (in a kind of clunky way) through the Web Soil Survey.

These data and analytics assets are an amazing resource and a huge, national competitive advantage. Importantly, they are all available free of charge to anyone, which is not the case in many other places around the world.

The US strategy for innovation-enabling ag data and analytics warrants a longer discussion, but let me kick it off with a list of 5 opportunities for the the USDA to have an even bigger impact:

1. Farm Efficiency. An array of new practices have emerged since the turn of the century, including new tillage strategies and cover crops. On the surface these seem sensible, but do they achieve their goals? Is there a solid cost-benefit win, or are they a mixed bag? Helping to inform grower decisions with real data and analysis has been a key role of the USDA, and it is an ideal time for a deeper look at the impact of these emerging practices.
2. Soils, Topography. The US soils data is great, and it is a critical tool for managing water usage and resources. Small changes in the sand content of soil or shifts in the shape of the terrain can make dramatic changes to how much irrigation is required in a given season. As we need to manage water more carefully in the future, any improvement in this data, especially in high irrigation regions, will be invaluable.
3. Weather. Weather data is the single most important factor in nearly every aspect of crop modeling, including fertility, irrigation, yield, disease and pests. The US gets big points for making all weather data free for all uses, but there are opportunities to improve the spatial density of precipitation-related forecasts and readings, and to improve inputs and calculation of key derived values, such as evapotranspiration.
4. Satellite Imagery. I predict we will look back on the Twenty-Teens as the start of the golden age of satellite data in ag. Fly-bys will increase from every 10+ days to every day (cloud cover permitting), and our ability to leverage what we’re seeing will increase radically with more data. Most of these new capabilities are private (namely PlanetLabs), which is , but there is an important role for the government to play in providing freely available data of increasing quality and scope.
5. Genetic Data. When we think of agricultural genetics we naturally think of GMO corn, but that’s a tiny fraction of what will matter in the future. Other crops, the soil microbiome, and the genetics of disease and pests will all be impactful areas for research and applications. Historically the big ag companies have taken the lead in genetics, but between with their capture of intellectual property and international ownership, there is a strong argument that the USDA should secure the core genetics data to enable a broader base of research and private sector innovation.

Conclusion

Data is increasingly viewed as an asset, and that has been true in agriculture for decades. The US government deserves credit for recognizing this before most, and now its time to look forward, continue to invest, and modernize their approach to match today’s technology and priorities.