Nitrogen Dynamics Following Incorporation of 3-Year Old Grassland Set-Asides in the Fraser River Delta of British Columbia
Bibliographic record
Abstract
Short-term grassland set-asides (GLSA) have been incorporated into intensive annual crop rotations to improve soil quality. The legacy of the GLSA to subsequent annual crops, however, is not well understood. The objective of this study was to determine the impacts of 3-year-old GLSA on nitrogen (N) dynamics and the yield of the subsequent cash crop. A regional analysis was conducted over two years, utilizing eight production fields transitioning from GLSA, paired with fields in continuous annual crop rotation (ACR) with matching management. A controlled plot-scale experiment was also conducted on a single 3-year-old GLSA, comparing fertilizer types, rates, and timing of incorporation. In each experiment, soils were sampled every 10–14 days for ammonium (NH4+-N) and nitrate (NO3−-N), along with ion probes, installed near the rooting zone to track plant available nitrogen (PAN) throughout the season. The results from the regional analysis are confounding, in 2015 showing that GLSA supplied an additional 18 kg PAN ha−1 compared to ACR but showed no PAN benefits in 2016. The controlled plot-scale experiment highlighted the importance of fertilizer type to subsequent PAN, showing synthetic treatments consistently supplied more PAN than organic. The results from this study suggest that 3-year-old GLSAs can potentially improve PAN to subsequent crops depending on how they are managed.
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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 | 0.001 |
| 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".