Yield response of forage grasses to N fertilizer as related to spring soil nitrate sorbed on anionic exchange membranes
Bibliographic record
Abstract
Soil N availability is an important factor in forage grass production. Maximising N fertilizer efficiency is essential to improve profitability and to reduce the environmental risk associated with residual excess soil N. The objectives of this study were (i): to determine the effects of N fertilizer on yield, N uptake and NO3–N concentration of forage grasses produced in Western Quebec; and (ii) to compare spring soil NO3−measured by anionic exchange membranes (NO3AEMs) and by water extraction (NO3w) as a criterion to predict fertilizer N requirements of forage grasses. The yield response of grasses, especially timothy (Phleum pratense L.), to different rates of NH4NO3 (0 to 240 kg N ha−1) on heavy clay soils (Humic Gleysols) was studied from 1994 to 1996 at four sites in the Abitibi-Temiscamingue area, Quebec (Canada). Nitrogen significantly (P < 0.001) increased forage yield, N uptake, and NO3–N concentration. The economically optimum N fertilizer rate (Nop) for forage yield varied from 25 to 240 kg N ha−1 depending on sites and years, and averaged 125 kg N ha−1. The Nop can be predicted more adequately by NO3AEMs (R2 = 0.45) than by NO3w (R2 = 0.09). Based only on the relationship between the relative yield and spring soil nitrate, NO3AEMs could be used as a criterion for fertilizer N recommendation of forage grasses in this cool continental climate. Key words: N fertilizer, nitrate, grass, economically optimum N fertilizer rate
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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 | 0.000 | 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".