Biometeorological feedbacks on peatlands: Raising the water table to reduce meteorologically-related stress on cattle
Bibliographic record
Abstract
Peatland restoration is an important mitigation action in the fight against climate change. Researchers encourage farmers to rewet deep-drained lands on organic soil to a shallow water table depth (WTD) to reduce carbon emissions. Raising WTD under grasslands will likely affect local air temperature (TA) and increase relative humidity (RH), with uncertain consequences during heat waves on cattle welfare. We used WTD, TA and RH data (both measured between 1.25 and 2 m above ground) from 22 peatland sites globally to evaluate peatlands’ overall Temperature Humidity Index (THI), an indicator correlated to cattle welfare used in dairy farms (THI>68 increases heart rate, breathing rate and reduces milk yield). We compared them with THI at state weather stations located on neighbouring lands with short grass on non-organic soil, and assessed the impact of WTD. At most sites, peatlands with shallow WTD had lower TA, higher RH, and an overall lower THI than surrounding lands, compared to those with deep WTD. In most cases, THI decreased with increasing WTD, especially at night in the temperate region, except for coastal peatlands. Shallow and submerged sites had 20 % less hours with stressful meteorologic conditions (high THI) than surrounding areas. In contrast, the number of hours with high THI did not change significantly on peatlands with WTD under 20 cm below ground level compared to control sites. Our results confirm the influence of WTD on local temperature and THI, and suggest that raising WTD on drained peatlands will slightly improve cattle welfare with reduced THI during heat waves, but also acknowledge that local geographic characteristics add complexity to this relationship. Our research indicates that raising WTD to ground level in sections of grasslands could provide “heat wave shelters” and increase cattle resilience to climate change while contributing to the global reduction of carbon emissions.
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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 | 0.000 | 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) | 0.001 | 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".