The effect of rejuvenation of Aspen Parkland ecoregion grass–legume pastures on dry matter yield and forage quality
Bibliographic record
Abstract
A 3-yr study was conducted on Black and Gray Wooded soils at five different sites in the Aspen Parkland of Saskatchewan to determine the effect of spiking, burning, mowing, deep-banding (Trt) and applications of N, P, K and S liquid and granular fertilizers (Fert) on dry matter yield (DMY) and forage quality of primarily smooth bromegrass (Bromus inermis Leyss.) and alfalfa (Medicago sativa L.) pastures. Fertilizer application was a liquid form blended to provide 100 kg N ha–1, 45 kg P2O5 ha–1, 23 kg K2O ha–1 and 12 kg S ha–1 in 350 kg of fertilizer ha–1. The experimental design at each site was a randomized complete block in a split-plot arrangement. Main plots were spike, burn, mow, deep-band, deep-band liquid fertilizer and control. The split-plot treatment was granular fertilizer broadcast at 0 and 350 kg ha−1 (providing 100 kg N ha−1, 45 kg P2O5 ha−1, 23 kg K2O ha–1 and 12 kg S ha−1). All treatments were applied in the spring of 1994. Interaction effects of Trt × Yr and Fert × Yr were significant (P < 0.05) indicating a wide range of response to the rejuvenation methods among years. Spiking reduced (P < 0.05) DMY in year 1 at two sites. Deep-banding and mowing increased (P < 0.05) DMY at one site in year 3. Burning increased (P < 0.05) DMY in years 1 and 2 only at the Gray Wooded soil site. In year 1, liquid plus granular fertilizer (200 kg N ha–1) [deep-banded liquid fertilizer (DBLIQ at 100 kg N ha−1) + broadcast fertilizer (+F at 100 kg N ha−1] increased DMY at all sites by 84 to 185% over control plots. This effect carried over (P < 0.05) into year 2 at four sites but not the third and final year. The high rate of N (200 kg N ha−1) of the DBLIQ + F almost doubled (P < 0.05) crude protein content of year 1 forage, 170.3 g kg−1 compared with 96.4 g kg–1 for control. It was concluded that an application of broadcast or liquid fertilizer alone or combined with mechanical treatments will produce a significant effect on herbage yield and quality but only in the short term. Key words: Rejuvenation, fertilizer, spike, burn, deep-band, quality
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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 | 0.000 | 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".