Effect of forage legumes on phosphorus availability to the following wheat crop in a Black Chernozem
Bibliographic record
Abstract
Including forage legumes in rotation with annual crops could increase phosphorus (P) availability to the annual crop due to their deep roots and intensive mycorrhizal infection. However, the benefit of forage legumes to increase soil P availability to the subsequent crop has gained less attention in the western Canada. The aim of this study was to investigate the impact of short rotation forage legumes on P availability to the following crop. Forage legumes evaluated in this study were red clover and alfalfa in comparison to an annual legume (pea) and non-legume (flax). The study was conducted at four sites across Saskatchewan. This poster reports on the results at one site near Melfort, SK. In this experiment, two-year alfalfa rotation produced the highest (P = 0.015) biomass yield. Inclusion of short rotation forage legumes had positive impact on wheat grain yield (P < 0.001) , but it did not affect wheat straw P uptake (P = 0.76). The amounts of soil P fractions extracted from all treatments were generally similar. The results suggested that a two year rotation of legumes may be too short a time period to significantly enhance in soil P availability to the following crop.
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 | 0.001 | 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".