Climate change and crop production: contributions, impacts, and adaptations
Bibliographic record
Abstract
Crop production and climate change affect each other because crop production (1) produces greenhouse gases (GHGs), (2) is affected by climate change, (3) will have to adapt to changed climatic regimes, and (4) has a potential role in mitigating the production of GHGs. Agriculture is not a major producer of GHGs, at less than 10% of Canada's total. Agriculture is a major producer of methane and nitrous oxide (21 and 310 times more effective at heat trapping than CO2, respectively), but a minor producer of CO2. The impacts on agriculture will come through increased CO2 effects on plant growth, warmer and drier conditions, changes in wind speed, insect and disease pressures, and many more subtle changes resulting from altered interactions among components of crop agro-ecosystems. Predictions are for net increases in world food production as temperature increases become larger. Potential adaptations are (1) management and genetic alterations to crops, (2) legislative changes, (3) policy and economic changes, and (4) adoption of mitigation practices. Mitigation of GHG effects can be through new cropping systems and crops that reduce net GHG production by emitting less nitrous oxide, increasing soil organic matter content, and allowing production of bio-products such as bio-fuels.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".