Artificial Intelligence Approach for Estimating Dairy Methane Emissions

Abstract

California's dairy sector accounts for ∼50% of anthropogenic CH₄ 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 CH₄ 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 CH₄ 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 CH₄ emission reduction from anaerobic digester deployment. We identify 162 large (90th percentile) farms and estimate a CH₄ reduction potential of 83 Gg CH₄/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.

Keywords: aerial image; artificial intelligence; dairy; emission; greenhouse gas; methane.