AI-Driven Carbon Footprint Assessment in Agricultural Production
DOI:
https://doi.org/10.64137/3108088X/IJAES-V2I2P104Keywords:
Artificial Intelligence, Carbon Footprint, Agricultural Production, Precision Agriculture, Deep Learning, Climate Change Mitigation, Greenhouse Gas AccountingAbstract
Agricultural production is simultaneously a major victim of climate change and a significant contributor to global greenhouse gas (GHG) emissions. Traditional carbon accounting frameworks in agriculture rely on static, empirical emissions factors that fail to capture the highly dynamic, spatial-temporal variations inherent in biological systems. This study proposes an integrated Artificial Intelligence (AI) and Machine Learning (ML) framework for real-time, high-resolution carbon footprint assessment across diversified cropping systems. By coupling deep learning architectures specifically Long Short-Term Memory (LSTM) networks and Gradient Boosted Decision Trees (GBDT) with multi-source remote sensing data, Internet of Things (IoT) soil sensors, and historical management logs, we predict nitrous oxide (N2O}$), methane ($\{CH}_4$), and carbon dioxide (CO}_2$) fluxes at a sub-field scale. Methods: The model was trained and validated on a large national dataset over multiple years of observations from all major agro-ecological zones. We present an AI-driven approach that predicts total GHG emissions with $R^2$ = 0.89, outperforming traditional Intergovernmental Panel on Climate Change (IPCC) Tier 1 and Tier 2 empirical models which have respective $R^2$s of 0.54 - 0.68 from the best-fit regression lines to each study's mean bulk density, carbon content data points across different studies within human-dominated ecosystems considered here. The analysis Prances and P4amongms that focused on how soil moisture behaved in one place over time, real-time estimates of the microbial pool nitrogen at random dates throughout 2015 with our infrared canopy temperature anomalies was: Steve Papazian3.nrted single emissions spikes. Moreover, scenario analysis demonstrates that AI-assisted precision nitrogen application and optimized irrigation scheduling can reduce the total carbon footprint of cereal production by as much as 34% without impacting crop yields. This work lays out a scalable, automated digital twin infrastructure for agricultural carbon accounting. It provides an accessibly robust technology foundation for validating carbon credits (attribution), tracking supply chain insets, and providing localized climate-smart decision support systems.
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