Rising farm costs, marginal land cropping, and ecosystem service markets
Bibliographic record
Abstract
Abstract A growing challenge with industrialized agriculture is compensating farmers for devoting land towards producing ecosystem services, at a time when global food demands are accelerating. Here, we explore revenue thresholds that Payment for Ecosystem Service programs (PES) must approach to be competitive in present-day crop markets, amalgamating long-term North American data especially from Canada on input costs, crop yields, crop revenues after expenses, government subsidies, and land use. Two trends suggest that PES markets with stable revenues can be increasingly competitive, with inflation-adjusted farm input costs now 50x higher than a century earlier and increasingly high revenue instability including net losses for some crops in some years. Since 1994, crop revenues in some regions have averaged $39 acre− 1 US, peaking at $412 but losing money 25.3% of time. Importantly, these data show how government subsidies have been a major stabilizing force, increasing revenues by 37.6% while reducing the frequency of losses by 50% - societal compensation to North American farmers is already the norm. PES programs could be most feasible on marginal lands, which are often targeted for retirement due to higher input requirements. However, trends in Canada reveal that marginal land cropping has increased by 5.2 million acres since 1990 and now constitutes 28.8% of all cropland. Our work reinforces how revenue instability simultaneously creates and constrains opportunities for PES markets, favoring market competitiveness because of shrinking crop revenues but pressuring farmers to expand production, including on marginal lands, as they struggle to offset revenue shortfalls while attempting to capitalize on growing global food demands.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".