Environmental impacts of organic agriculture in temperate regions.
Bibliographic record
Abstract
Abstract Can organic agriculture elaborate a scientifically based, resource-efficient and agroecological approach to low-input farm management? This review examines the literature from temperate regions, with a particular emphasis on Canadian and US studies that relate to environmental and ecological impacts of organic agriculture with respect to (i) soil organic matter storage, (ii) soil quality/soil health, (iii) nutrient loading and risks of off-farm nutrient and agrochemical losses, (iv) biodiversity and (v) energy use and global warming potential. The context and implications of semi-arid conditions and low soil P levels, common to many organic farms in North America, and widespread adoption of genetically engineered crops in conventional production, is also considered. The consensus of the data available to date indicates the distinctiveness of cropping, floral and habitat diversity, soil management regime, nutrient intensity and use efficiency, and energy, and pesticide use in organic farming confer important environmental and ecological benefits. These include maintenance of soil organic matter and added return of carbon to soil, improved soil health, reduced off-farm nitrogen and phosphorus losses, enhanced vegetative and wildlife (bird) biological diversity, extended sometimes to other taxa depending on landscape context, improved support for pollinators and pollination and reduced energy use and improved energy efficiency. The continued evolution of organic agriculture to a more outcomes-based, agroecological production system will require an expanded multi-disciplinary research effort, linked ideally to support from consumers and policy-makers on the basis of renewed understanding of its potential contribution to global environmental sustainability.
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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".