Eco-functional Intensification and Food Security: Synergy or Compromise?
Bibliographic record
Abstract
There is an increased understanding that the challenges of producing enough food and biomass while preserving soil, water and biodiversity necessary for ecosystem services can not be solved by prevalent types of conventional agriculture and that agro-ecological approaches and ecological intensification is fundamental for our future food production. FAO has stated that “Ecosystem services sustain agricultural productivity and resilience” and advocates production intensification through ecosystem management. Terminologies such as agro-ecology and ecological/ eco-functional/sustainable intensification are being proposed for agricultural development, which builds on higher input of knowledge, observation skills and management and improved use of agro-ecological methods. Contrary, increased global demand for food, and non-food biomass has increased the pressure for intensifying land use and increasing crop yields based on conventional inputs, while still aiming at reducing environmental impact. There is a battle of discourse between these approaches in competition for – among others – research and development funding. The examples of improved local food security from introducing agro-ecological and low external input agriculture practices among smallholder farmers are many. However, upscaling remains a challenge and the ability of such eco-functional intensification to feed the increased urban populations in emerging economies remains an open question. A broader view of what is organic and conventional farming is necessary and the use of new understandings from ecology and molecular biology will be needed to create and profit from synergies between preserving and building on eco-systems services and providing increased food and biomass.
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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 | 0.010 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.014 | 0.020 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.026 | 0.003 |
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".