People prefer arable fields and flower strips with continuous soil cover and diversified vegetation
Bibliographic record
Abstract
Agriculture is a key driver of the dynamics and transformation of rural landscapes in Western Europe. However, little is known about the influence of cropping techniques on visual perceptions of the aesthetic and ecological value of agricultural fields. We used an online survey to investigate the preferences of French residents for fields of contrasting appearances due to different cropping systems. Participants were shown photographs taken at four periods of field management (i.e., fallow period, seedbed preparation, recommencement of plant growth after winter, and end of the cycle crop), either alone or in combination with temporal sequences typical of organic, conservation, and conventional cropping systems. The perception of flower strips across the seasons was also evaluated according to three levels of diversity and two management options (with and without summer mowing). Agricultural fields with high degrees of soil cover (e.g., presence of a cover crop, narrow inter-rows) and diversified vegetation (e.g., intercrop, weeds) were perceived as more aesthetic and favorable for biodiversity. The temporal sequences reflecting visual appearances of cropped fields under conservation agriculture were considered more aesthetically appealing and favorable to biodiversity than those for organic or conventional agriculture. Participants ranked wildflower strip sequences in descending order of plant species diversity. Within diversity levels, strips not mown in summer were preferred over those mown in summer for both aesthetic and biodiversity preservation value. These results could provide the basis of a design for payments for environmental services, including the socio-cultural services provided by agroecological cropping systems.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".