Short-term effects of grassland set-asides on soil properties in the Fraser River delta of British Columbia
Bibliographic record
Abstract
Grassland set-asides (GLSA) in the Fraser River delta are fields that are taken out of crop production and seeded with a mixture of grasses and legumes for 1–4 yr. During this time, the farmer is compensated with a cost-share payment to recover a portion of the financial returns that could have been earned from cash crops. The objectives of this study were to (i) evaluate the effects of GLSA on soil properties during the initial two seasons of enrollment, (ii) determine how GLSA effects differ between fields that were considered productive and unproductive, and (iii) identify soil baseline indicators and preliminary soil thresholds for predicting GLSA vegetation responses. Out of eight fields entering the program, two were considered to be unproductive and exchangeable sodium had the strongest negative relationship to GLSA aboveground biomass (r = −0.61, P = 0.0002). During the second season of GLSA establishment, the mean weight diameter of water-stable soil aggregates was consistently higher in productive GLSAs than paired annual crop rotation (ACR) fields, being 21% higher in April, 14% in July, and 19% in September after crop harvest. After two seasons of GLSA enrollment, both aeration porosity and bulk density were improved by GLSA relative to ACR fields with aeration porosity being 24% greater and bulk density 7% lower in GLSA. The results suggest that GLSA rotations in productive agricultural fields within the Fraser River delta provide an alternative to continued ACR that can improve soil structure and reduce compaction after only two seasons of establishment.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".