Leveraging satellite data for greenhouse gas mitigation in Canadian poultry farming
Bibliographic record
Abstract
■ Satellite data and machine learning predict methane and CO₂ emissions in poultry farming. ■ ARIMA, LSTM, and XGBoost models reveal emission trends across 1300 Canadian poultry farms. ■ Regional and seasonal variability in emissions is driven by climate and farm practices. ■ Ontario leads methane emissions in Canadian poultry farms, driven by large-scale operations. ■ Methane emissions peak in summer, with farms emitting 67% more than processors annually. Accurate monitoring of greenhouse gas (GHG) emissions from poultry farms is essential for effective climate change mitigation. This study integrates satellite imagery with advanced machine learning techniques to analyze methane (CH₄) and carbon dioxide (CO₂) emissions from over 1,300 poultry farms and processors across Canada from 2019 to 2023. Utilizing high-resolution atmospheric data from Sentinel-5P and NASA's OCO-2 satellites, emissions were systematically mapped both temporally and spatially. We employed Autoregressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM), and Extreme Gradient Boosting (XGBoost) models to forecast emission trends and identify primary emission drivers. The LSTM model demonstrated superior predictive accuracy, achieving the lowest Root Mean Square Error (RMSE) values of 10 for CH₄ and 362 for CO₂. Our analysis reveals significant regional and seasonal variability in emissions, influenced by climatic conditions and operational practices. Additionally, benchmarking of emissions data was conducted to establish performance standards and monitor progress towards reduction targets. These findings provide valuable insights for policymakers and industry stakeholders, facilitating the development of targeted emission reduction strategies that align with regulatory standards and promote environmental sustainability. By combining state-of-the-art data analytics with satellite-based monitoring, this research enhances the precision and efficiency of GHG tracking in the Canadian poultry sector. Furthermore, it establishes a robust framework for formulating effective climate change mitigation strategies, thereby supporting Canada's broader environmental objectives and advancing sustainable agricultural practices.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".