Agriculture and climate change: implications for environmental sustainability indicators
Bibliographic record
Abstract
Agricultural activities have many effects on the environment, including effects on soil and air quality, water quality and quantity, contamination, and wildlife habitat.Agriculture and Agri-Food Canada has calculated a set of science-based agri-environmental indicators (AEIs) to provide information on environmental conditions in agriculture and their trends.The indicators are based on models developed from scientific understanding of the interactions between agriculture and the environment and are calculated using mathematical models to integrate information on soils, climate, and landscape with agricultural activity or practice information from the Canadian Census of Agriculture.Several AEIs were selected to explore the effects of climate change on agri-environmental sustainability.Based on the analysis in this study, those indicators that would likely decrease in their performance in the future include: risks of soil erosion from wind and water, soil salinization, water quality, nitrogen contamination, phosphorous contamination, pesticide contamination, and particulate matter emission rate.The one indicator found to likely improve with climate change was the greenhouse gas budget.In addition, the impacts of climate change were difficult to assess for several indicators as their values are more affected by both agricultural and non-agricultural factors, as well as lack of knowledge.AEIs are important tools to measure climate impacts and to indicate the success of adaptation to those impacts.This research is only emerging, however, and many knowledge gaps exist.
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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".