Tracing the life cycle carbon footprint of global staple crops: an integrated approach combining machine learning and life cycle assessment
Bibliographic record
Abstract
Crop production is a major source of agricultural carbon emissions, yet the life cycle carbon footprints (LCCFs) of key global staple crops remain underexplored. This study quantifies the LCCFs of three major grain crops—maize, rice, and wheat—using a hybrid approach that integrates machine learning (ML) models and life cycle assessment (LCA) for the period from 2006 to 2019. We systematically calculated the cradle-to-farm-gate carbon footprint (CF), accounting for emissions from upstream inputs, transportation, and field operations. Emission factors (EFs) and CF compositions were assessed over different time periods. Additionally, we developed a novel Supply-Demand Balanced Carbon Allocation Model (SD-CAM) to trace the sources and flows of upstream CF. Our results reveal a steady increase in the CF of these crops over time, with significant regional variations in both EFs and CF composition. The primary carbon footprint of global rice production is mainly attributed to field carbon emissions, with nitrogen fertilizers as the secondary carbon source. In contrast, nitrogen fertilizers are the dominant carbon source for maize and wheat. Interestingly, while maize's total field emissions are a net carbon source, wheat production acts as a carbon sink. The majority of the CF is concentrated in a few key grain-producing countries, such as China, India, and the United States. Regarding the upstream carbon footprint (IUCCF), major producing countries like China and Canada have consistently been the primary sources of upstream carbon inputs throughout the study period. However, with the rise of global economic initiatives like the Belt and Road, emerging upstream contributors such as Morocco and Vietnam have increasingly become significant contributors in upstream carbon emissions. This study provides valuable insights into the environmental impacts of agricultural production over time, offering guidance for sustainable agricultural policies, carbon responsibility allocation, and international low-carbon cooperation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".