Mineral phosphorus fertilization for silage corn in manured soils in the Fraser Valley, Canada
Bibliographic record
Abstract
Abstract Efficient management of fertilizer phosphorus (P) is crucial for enhanced resilience of agro‐ecosystems. We assessed five rates (0, 5, 10, 15, and 20 kg P ha−1) of starter P fertilizer on silage corn (Zea mays L) yield in high‐P manured soils at eight sites in 2020 and 2021 in the Fraser Valley, Canada, monitoring soil phosphate concentrations using anion exchange membranes (AEM‐P). At the V3 and V6 (3‐ and 6‐leaf) stages, corn dry matter (DM) weight response to starter P was not significant, except at one site where the critical rate was 5 kg P ha−1. At maturity, corn DM yields were in the optimum provincial range (20–25 Mg ha−1), with the exception of two sites, one with low initial soil P concentrations and the other with waterlogged soils. These results indicate that during the growing season, phosphate supply from manure application alone was sufficient for silage corn growth. Root length and diameter were not affected by starter fertilizer, while root surface area, volume, and root DM weight decreased with increasing starter fertilizer at the V3 stage in 2020. In addition, AEM‐P increased with starter P only during the first week after application. We conclude that starter fertilizer P, at any application rate, in high‐P manured soils does not improve silage corn yield; farmers applying manure at plowing in soils with high P concentrations can reduce or eliminate starter fertilizer P without impacting silage corn yields, decreasing reliance on off‐farm P inputs, and reducing potential P loss to the environment.
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 | 0.001 | 0.000 |
| Open science | 0.001 | 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".