Artificial Intelligence driven Benchmarking Tool for Emission Reduction in Canadian Dairy Farms

Abstract

This study develops an Artificial Intelligence-driven benchmarking tool to reduce methane emissions in Canadian dairy farms, responding to the urgent need to mitigate environmental impacts from agriculture. Utilizing a comprehensive dataset from over 1000 dairy farms and processors across Canada, combined with satellite-driven methane emission data, we apply advanced machine learning technologies and data analytics, including geospatial analysis and time series forecasting. This approach identifies critical emission hotspots and temporal trends. We tested several predictive models--ARIMA, LSTM, GBR, and PROPHET--with the LSTM model showing the greatest accuracy in forecasting emissions, demonstrated by the lowest Root Mean Squared Error (RMSE) of 15.40. Our results highlight the transformative potential of AI tools in agricultural environmental management by providing dairy farmers and policymakers with precise, real-time emission insights. This facilitates informed decision-making and the implementation of effective emission reduction strategies. This study not only advances understanding of emission dynamics in dairy farming but also underscores the role of technology in sustainable agricultural practices and achieving environmental targets consistent with global agreements.