PSXII-14 Impact of Nitrogen Application and Intercrop Forage Species on Chemical Composition and Yield of Standing Corn and Intercropped Forages
Bibliographic record
Abstract
Abstract This study evaluated the impact of nitrogen (N) application rate and forage species of intercropped corn for potential grazing of beef cattle in late fall/early winter. A split plot design (4 replicates/treatment) was used at 2 experimental sites in Manitoba, Canada with 2 treatment factors: 1) N application rate as the main plot and 2) forage intercrop species as the sub plot. Nitrogen application rates were 45 kg N/ha and 112 kg N/ha. Forage species were: italian ryegrass (Lolium multiflorum), hairy vetch (Vicia villosa), graza forage radish (Raphanus sativus), red clover (Trifolium incarnatum) and a mix of all 4 forages. Intercropped treatments were compared with corn only control treatments with no intercrop at both N rates. Plots were seeded in 2019 with corn from May 8-10 and intercrops seeded from June 17-25. Chemical composition of intercropped forages and corn were determined in early October. Averaged over sites and intercrop treatment, increasing N application increased (P< 0.004) CP of the intercrops from 20.5% (low N) to 22.3% (high N), with CP similar at both sites. On average, radish had the greatest CP (29%), clover least (13.2%), with the remaining crops intermediate (mean 21.7%). Corn CP increased (P< 0.01) with N application (6.6 to 7.5% averaged over treatments), with no effect of intercrop species. Intercrop TDN was not affected by N rate, with greatest concentrations observed in radish (68.2%), least in hairy vetch and red clover (53.6%) and intermediate in the mix and italian ryegrass (mean 60.2%). Dry matter yield for all intercrop and corn treatments were less than expected at both locations due to exceptional drought conditions. Intercrop treatment and its interaction with N rate did not impact corn yield. In conclusion, intercrop CP ranged from 12-29%, thus offering the potential to increase the feeding value of corn for overwintering cattle.
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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 | 0.001 | 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".