The effects of farmland heterogeneity on ecosystem service provision
Bibliographic record
Abstract
Landscape simplification and the intensification of farming practices have negative effects on the provision of many non-food ecosystem services (ES). Incorporating greater landscape heterogeneity in cropped areas (i.e. greater farmland heterogeneity) may be a key lever to increase ES provision. To date, studies have mostly focused on the effects of farmland heterogeneity on biodiversity or a single ES, and at a single scale. We need to understand how farmland heterogeneity, specifically mean field size and crop diversity, affect multiple ES across scales, since it is unclear whether a certain level or scale of farmland heterogeneity can maximize ES provision. I determined the effects of farmland heterogeneity on the provision of six key ES (food production, soil fertility, water quality regulation, carbon storage, pollination and pest control) in the Montérégie, an important agricultural region in Quebec, Canada. I sampled 32 soybean fields embedded within landscapes that fell along independent gradients of mean field size and crop diversity. I evaluated how heterogeneity effects change at two different scales, 500 m and 1000 m, while controlling for the effects of in-field management practices. I found that the mean size of agricultural fields had a stronger influence on ES provision than crop diversity, especially at the 500 m scale. I observed key trade-offs between ES at different levels of farmland heterogeneity. Fields in landscapes with higher farmland heterogeneity benefited from greater bee species richness, water quality regulation and defoliator regulation, but had less food production, aphid regulation and syrphid species richness. To further explore these trade-offs, I calculated the multiple ecosystem service landscape index (MESLI). Below a field size of four hectares, multifunctionality decreased due to trade-offs. Based on these results, I suggest that four to six hectares may be a target field size to maintain ES and biodiversity in the Montérégie. Understanding the trade-offs that occur at different levels and scales of farmland heterogeneity will help farmers and policymakers to make more informed decisions regarding agricultural 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".