Un‐yielding: Evidence for the agriculture transformation we need
Bibliographic record
Abstract
There has been a seismic shift in the center of gravity of scientific writing and thinking about agriculture over the past decades, from a prevailing focus on maximizing yields toward a goal of balancing trade-offs and ensuring the delivery of multiple ecosystem services. Maximizing crop yields often results in a system where most benefits accrue to very few (in the form of profits), alongside irreparable environmental harm to agricultural ecosystems, landscapes, and people. Here, we present evidence that an un-yielding, which we define as de-emphasizing the importance of yields alone, is necessary to achieve the goal of a more Food secure, Agrobiodiverse, Regenerative, Equitable and just (FARE) agriculture. Focusing on yields places the emphasis on one particular outcome of agriculture, which is only an intermediate means to the true endpoint of human well-being. Using yields as a placeholder for this outcome ignores the many other benefits of agriculture that people also care about, like health, livelihoods, and a sense of place. Shifting the emphasis to these multiple benefits rather than merely yields, and to their equitable delivery to all people, we find clear scientific evidence of win-wins for people and nature through four strategies that foster FARE agriculture: reduced disturbance, systems reintegration, diversity, and justice (in the form of securing rights to land and other resources). Through a broad review of the current state of agriculture, desired futures, and the possible pathways to reach them, we argue that while trade-offs between some ecosystem services in agriculture are unavoidable, the same need not be true of the end benefits we desire from them.
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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 | 0.041 | 0.108 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.003 | 0.016 |
| Scholarly communication | 0.017 | 0.021 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.029 | 0.004 |
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".