Conventional and industrial by-product fertilization do not induce greenhouse gas emissions in sandy soils under wild lowbush blueberry cropping in eastern Canada
Why this work is in the frame
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Bibliographic record
Abstract
Greenhouse gas (GHG) emissions from wild lowbush blueberry (WLB) production after fertilization with conventional mineral (MIN) and organic (ORG) or industrial pulp and paper mill sludge (PPMS) and synthetic anhydrite (SA; calcic amendment; CaSO 4 ) remain unknown. We assessed nitrous oxide (N 2 O) and methane (CH 4 ) emissions following application of combined PPMS and SA under WLB production compared to MIN and ORG fertilizers during a two-year cropping cycle. A 50 kg nitrogen (N) ha −1 recommended input was broadcasted before stem emergence during the pruning phase with MIN, ORG, and PPMS treatments alongside an unfertilized control (0 N; CTL). The PPMS treatment was also combined with SA as 6 Mg ha −1 (1SA) and 12 Mg ha −1 (2SA) inputs, which were also applied alone for a total of eight treatments. The GHG emissions were monitored using non-flow-through, non-steady-state chambers during two growing seasons. The N 2 O and CH 4 emissions were unaffected by fertilizer applications. The N 2 O emissions were significantly higher during the pruning phase (0.06 ± 0.009 kg N 2 O-N ha −1 yr −1 ) than during the harvesting phase (0.03 ± 0.005 kg N 2 O-N ha −1 yr −1 ). The fertilizer-induced emission factor (FIEF) values (−0.01 ± 0.02 %) were much lower than the default 1 % used for GHG inventories. A CH 4 uptake was observed during both growing seasons, with higher uptake during the pruning phase (−2.1 ± 0.1 kg CH 4 -C ha −1 yr −1 ) than in the harvesting phase (−1.6 ± 0.1 kg CH 4 -C ha −1 yr −1 ). High aeration of sandy soils combined with low soil NO 3 contents (0.9 μg NO 3 -N cm −2 yr −1 during the pruning phase) might constrain N 2 O emissions. Proposed WLB-specific FIEF should be used in future GHG inventories to prevent emission overestimates. Further research is needed on the agronomic benefits and yield effects of combining PPMS and SA for WLB productivity.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it