While policy leaders around the world are pushing to reduce greenhouse gas (GHG) emissions at an unprecedented speed, the science is still working to catch up, at least when it comes to measuring carbon sequestration rates and emissions at the farm level.
The conventional way of measuring carbon is to look at what is currently stored in the soil, but Dr. Kaiyu Guan of the University of Illinois and his team, with funding provided by the Foundation for Food & Agriculture Research (FFAR), FoodShot Global, the U.S. Department of Energy and the U.S. National Science Foundation, are working on a project to approach carbon movement from a whole system approach.
It’s a matter of calculating what is coming into the system through photosynthesis of the plants, as well as measuring what is going back into the atmosphere by way of agricultural production, says Guan. It’s like your bank account: you deposit some money, you use some money, you should be able to calculate the net change of your account.
While taking soil samples can work to calculate the movement of carbon, sampling can be expensive and getting answers can take a long time. Satellite data and artificial intelligence (AI) may be the solution to that.
In this interview with RealAgriculture’s Amber Bell, Guan explains that a lot of this data can be gathered with satellites that can measure the amount of photosynthesis happening on the ground. Where his work differs from a lot of other ongoing projects is in the process of integrating this satellite data with historical data and weather patterns and then using it to train AI.
Not only would this AI be able to calculate the national agricultural GHG emissions, but it will be able to calculate individual farm emissions which could allow producers access to funding or carbon credits down the road, he says.
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