Response of Soybean and Lentil to a Seed-Row Placed Starter Nitrogen-Phosphorus Fertilizer Blend in a Brown Chernozem in South-Central Saskatchewan
Bibliographic record
Abstract
Soybean and lentil are important legume crops in southern Saskatchewan (SK) that can supply the majority of their nitrogen (N) requirement through biological N fixation (BNF). However, the onset of BNF can be slow in cold; dry prairie soils and a small amount of seed-row placed fertilizer containing both N and phosphorus (P) may benefit the crop. Nevertheless, high rates of fertilizer in close proximity to the seed can also cause injury. This study was conducted to determine the response of lentil and soybean to a starter N-P fertilizer blend applied in the seed-row. A farm field located at the boundary of the Brown and Dark Brown soil zones in south-central Saskatchewan was selected to evaluate the effect of seed-row placed N-P fertilizer blend: 50% Urea + 50% mono-ammonium phosphate (MAP) applied at 0, 10, 20 and 30 kg N and P2O5 ha-1 on emergence, yield, and nutrient uptake. The proportion of nitrogen derived from fixation (ndff) was determined in the soybean using N-15 dilution technique. The rate of 10 kg N and P2O5 ha-1 was found to be the rate that did not significantly reduce emergence, stand count or proportion of N derived from fixation, and was sufficient to maximize yield, N and P uptake for both soybean and lentil under field conditions. Rates higher than 10 kg N ha-1 in the seed row as starter 28-26-0 blend reduced emergence and decreased the proportion of ndff.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".