Landscape diversity and the resilience of agricultural returns: a portfolio analysis of land-use patterns and economic returns from lowland agriculture
Bibliographic record
Abstract
Conventional agriculture is increasingly based on highly specialized, highly productive farms. It has been suggested that 1) this specialization leads to farms that lack resilience to changing market and environmental conditions; and 2) that by decreasing agricultural diversity, the resilience of the farming system also decreases. We used agricultural gross margin (GM) forecasts from 1966 to 2010 and remote sensing data from agricultural landscapes in the lowland UK, in conjunction with modern portfolio theory, to test the hypothesis that decreasing land-use diversity results in landscapes that provide higher, but more volatile, economic returns. We considered the role of spatial scale on the expected levels of volatility and resilience of agricultural returns. We found that: 1) there was a strong linear trade-off between expected GMs and the expected volatility of those GMs in real lowland agricultural landscapes in the UK; 2) land-use diversification was negatively correlated with expected GMs from agriculture, and positively correlated with decreasing expected volatility in GMs; 3) the resilience of agricultural returns was positively correlated with the diversity of agricultural land use, and the resilience of agricultural returns rose quickly with increased land-holding size at small spatial extents, but this effect diminished after landholdings reached 12,000 hectares. Land-use diversity may have an important role in ensuring resilient agricultural returns in the face of uncertain market and environmental conditions, and land-holding size plays a pivotal role in determining the relationships between resilience and returns at a landscape scale. Creating finer-grained land-use patterns based on pre-existing local land uses may increase the resilience of individual farms, while maintaining aggregate yield across landscapes.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".