Nitrogen and Phosphorous Fertilizer Timing, Source, and Placement in Sugarbeet
Bibliographic record
Abstract
Core Ideas Evaluated 4R strategies of fertilizer N source, placement, timing in sugarbeet. No benefit of N with P fertilizer in 5‐ by 5‐cm band based on root and sucrose yield. No need to modify fertilizer N and P based on anticipated sugarbeet harvest date. Late harvest may mitigate N losses due to lower soil mineral N at late than early. An industry‐led renaissance of nutrient management is occurring in North America to apply the correct rate, source, placement, and timing. Crop consultants have recently recommended including fertilizer N in a 5‐ by 5‐cm band during sugarbeet (Beta vulgaris L.) planting, but this practice has not been rigorously evaluated regarding its influence on N dynamics and/or yield. In 2013 to 2015 at two fields in southwestern Ontario, an experiment evaluated the impact of fertilizer application on sugarbeet (Beta vulgaris L.) productivity at two harvest timings (mid‐September vs. late October–early November). Treatments were a negative control: no fertilizer (N0P0), positive controls: pre‐plant broadcast incorporated N (NpreP0) and in‐season, injected N (NseasonP0) and six fertilizer application method and timing combinations, all at an equal N application rate (112 kg N ha−1) to determine the need to include both N and P applied in a 5‐ by 5‐cm band (N5x5P5x5). The lack of treatment × harvest date interaction on all parameters suggested no need to adjust fertilizer based on harvest date. Average root yield in N5x5P5x5 treatment was 82 Mg ha−1, which was 15 to 20 Mg ha−1 greater than N0P0 but not different than other treatments. Sucrose yield and soil mineral nitrogen (SMN) did not differ among the fertilized treatments. From an agronomic and environment perspective, there was little evidence to suggest the need to band fertilizer N with the seed.
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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".