Ecological intensification and diversification approaches to maintain biodiversity, ecosystem services and food production in a changing world
Bibliographic record
Abstract
How do we redesign agricultural landscapes to maintain their productivity and profitability, while promoting rather than eradicating biodiversity, and regenerating rather than undermining the ecological processes that sustain food production and are vital for a liveable planet? Ecological intensification harnesses ecological processes to increase food production per area through management processes that often diversify croplands to support beneficial organisms supplying these services. By adding more diverse vegetation back into landscapes, the agricultural matrix can also become both more habitable and more permeable to biodiversity, aiding in conserving biodiversity over time. By reducing the need for costly inputs while maintaining productivity, ecological intensification methods can maintain or even enhance profitability. As shown with several examples, ecological intensification and diversification can assist in creating multifunctional landscapes that are more environmentally and economically sustainable. While single methods of ecological intensification can be incorporated into large-scale industrial farms and reduce negative impacts, complete redesign of such systems using multiple methods of ecological intensification and diversification can create truly regenerative systems with strong potential to promote food production and biodiversity. However, the broad adoption of these methods will require transformative socio-economic changes because many structural barriers continue to maintain the current agrichemical model of agriculture.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".