Artificial Intelligence Approach for Estimating Dairy Methane Emissions.

California's dairy sector accounts for ∼50% of anthropogenic CH 4 emissions in the state's greenhouse gas (GHG) emission inventory. Although California dairy facilities' location and herd size vary over time, atmospheric inverse modeling studies rely on decade-old facility-scale geospatial information. For the first time, we apply artificial intelligence (AI) to aerial imagery to estimate dairy CH 4 emissions from California's San Joaquin Valley (SJV), a region with ∼90% of the state's dairy population. Using an AI method, we process 316,882 images to estimate the facility-scale herd size across the SJV. The AI approach predicts herd size that strongly (>95%) correlates with that made by human visual inspection, providing a low-cost alternative to the labor-intensive inventory development process. We estimate SJV's dairy enteric and manure CH 4 emissions for 2018 to be 496-763 Gg/yr (mean = 624; 95% confidence) using the predicted herd size. We also apply our AI approach to estimate CH 4 emission reduction from anaerobic digester deployment. We identify 162 large (90th percentile) farms and estimate a CH 4 reduction potential of 83 Gg CH 4 /yr for these large facilities from anaerobic digester adoption. The results indicate that our AI approach can be applied to characterize the manure system ( e.g. , use of an anaerobic lagoon) and estimate GHG emissions for other sectors.