Improving farming practices reduces the carbon footprint of spring wheat production
Bibliographic record
Abstract
Wheat is one of the world's most favoured food sources, reaching millions of people on a daily basis. However, its production has climatic consequences. Fuel, inorganic fertilizers and pesticides used in wheat production emit greenhouse gases that can contribute negatively to climate change. It is unknown whether adopting alternative farming practices will increase crop yield while reducing carbon emissions. Here we quantify the carbon footprint of alternative wheat production systems suited to semiarid environments. We find that integrating improved farming practices (that is, fertilizing crops based on soil tests, reducing summerfallow frequencies and rotating cereals with grain legumes) lowers wheat carbon footprint effectively, averaging -256 kg CO2 eq ha(-1) per year. For each kg of wheat grain produced, a net 0.027-0.377 kg CO2 eq is sequestered into the soil. With the suite of improved farming practices, wheat takes up more CO2 from the atmosphere than is actually emitted during its production.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".