Carbon Flux Estimation for Potato Production: A Literature-Based Study
Bibliographic record
Abstract
This study reviews and synthesizes published data to estimate the net carbon flux associated with the complete potato production process. It identifies the key components that contribute to this flux and explores potential mitigation strategies, including both cultivation and post-harvest storage. Data were compiled from field-scale studies (primarily using eddy covariance) and life cycle assessment studies. The results indicate that potato production can act as a carbon sink or a carbon source, depending on the production scenario. In Scenario 1, which represents the worst-case scenario, potato production acts as a carbon source, with a carbon flux of 13,874.816 kg CO2 eq ha−1 season−1. In contrast, in Scenario 2, the best-case scenario, potato production acts a carbon sink with a carbon flux of −12,830.567 kg CO2 eq ha−1 season−1. Similarly, in Scenario 3, which is the average scenario, potato production acts as a carbon sink, though a minor one, with a carbon flux of −90.703 kg CO2 eq ha−1 season−1. Notably, the growing phase has the most significant impact on potato production’s overall carbon flux, as it is the period in which the highest levels of carbon sequestration and emissions occur. Fertilization is the primary carbon source among all potato production operations, averaging 1219.225 kg CO2 eq ha−1 season−1. Optimizing farming practices, including fertilization, irrigation, tillage methods, and cultivar selection, are essential to enhance carbon sequestration and reduce greenhouse gas emissions. Additionally, further research through controlled experiments is recommended to deepen the understanding of the relationships between various farming factors and carbon flux, ultimately supporting more sustainable potato production practices.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.016 | 0.019 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".