Researchers led by the University of Minnesota have significantly improved the performance of numerical predictions for agricultural nitrous oxide emissions using a knowledge-guided machine learning model.
Known as KGML-ag, it is a form of artificial intelligence that is 1,000 times faster than current systems and could significantly reduce greenhouse gas emissions from agriculture.
The framework presents significant opportunities for quantifying the agricultural greenhouse gas emissions, helping to verify carbon credits, and optimizing farming management practices and policymaking.
Read the story at European Geosciences Union
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The ability to quantify GHG emissions along the entire value chain from farm to shelf for a specific product is the key to enhancing the transparency of the origin of emissions and for reducing emissions. This is especially the case for emissions that are incurred once the paddy rice leaves the farm and enters the phase of processing where emissions from food product losses are often not accounted for and the methods that do exist for quantification are complex and out of reach for non-experts.
Mitigating greenhouse gas emissions from irrigated rice cultivation through improved fertilizer and water management
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