Spring Nitrogen Management is Important for Triticale Forage Yield and Quality
Bibliographic record
Abstract
Double cropping of forages can increase yield per hectare and reduce nutrient loss. Early planting and N addition can impact triticale forage if no spring N is applied. Spring N management is critical for optimal triticale yield and forage quality. Including triticale (x Triticosecale Wittmack) in forage rotations can provide economic and environmental benefits if optimally managed. We determined the impact of planting date, fall N availability, and spring N application on triticale dry matter (DM) forage yield and crude protein (CP) content. Three trials were conducted in New York from 2012 to 2014, each with two planting dates, five fall N rates (0, 34, 67, 101, 135 kg N ha−1), and five spring N rates (0, 34, 67, 101, 135 kg N ha−1) using a randomized complete block split‐split‐plot design in four replications. Plants were sampled for biomass in November before frost and harvested in May at flag‐leaf stage. Across sites, a small amount of fall N (34 kg N ha−1) increased spring yield in the zero‐N plots from 1.9 to 3.7 Mg DM ha−1 when seeded by 20 September. For later seedings, fall N did not benefit yield (2.7 Mg DM ha−1 average yield). Forage CP was 10.7% of DM when 135 kg N ha−1 was fall‐applied to sites planted by 20 September, versus 9.4% averaged across all other N rates and planting dates. While earlier planting increased spring yields, the most economic rate of N (MERN) and yield at the MERN in the spring were not impacted by fall N or planting date. Planting after 20 September increased CP at the MERN by about 1%. While fall management had some influence on spring performance, spring N management was most critical for achieving optimal yield and 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".