Nutrient Release from Living and Terminated Cover Crops Under Variable Freeze–Thaw Cycles
Bibliographic record
Abstract
Core Ideas Light frosts did not increase phosphorus release from cover crops. Heavy frosts released more water‐extractable phosphorus than light frosts. Herbicide induced termination increased phosphorus and ammonium losses. Frost tolerant species released less phosphorus than frost‐intolerant species. Cover crops remain a suitable management practice in temperate regions. Cover crops (CC) are planted into fields during the non‐growing season as a best management practice (BMP) for agronomic and environmental benefits. However, freeze–thaw cycles (FTC) may increase the availability of water extractable P (WEP) from damaged plant tissues, leading some to question their efficacy as a nutrient BMP due to their potential to release P during snowmelt. The objectives of this study were to experimentally determine the influence of: (1) FTC magnitude (4°C, −4 to 4°C, –18 to 4°C, and –18 to 10°C), (2) CC species [cereal rye (Secale cereale L.), oilseed radish (Raphanus sativus L. var. oleoferus Metzg Stokes), red clover (Trifolium pratense L.), oat (Avena sativa L.), and hairy vetch (Vicia villosa Roth)], and (3) termination using herbicide on the magnitude of WEP, NH4+, and NO3− release. Shoot tissue clippings underwent five FTC followed by extraction. Large magnitude FTC from –18 to 4 and –18 to 10°C (heavy frost) elevated WEP release, whereas the −4 to 4°C (light frost) treatment did not. Responses varied with plant type, where frost‐intolerant species released more WEP than frost‐tolerant species. In contrast, NH4+, and NO3− release did not increase following FTC. Termination elevated WEP and NH4+ release across all temperature treatments. The use of CC as a nutrient BMP should be used with caution in some regions, but in areas with mild winter climates, growing frost tolerant species without termination may reduce the risk of P leaching from vegetation in winter and early spring.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".