Agroeconomics of Phosphate Fertilizer in Manitoba
Bibliographic record
Abstract
The recent increase in P fertilizer prices have resulted in farmers reconsidering their fertilization practices in order to maximize their profits and if there is a need to consider reducing or completely eliminating phosphorus (P) application. Soil testing has been a pillar in deriving fertilizer recommendations. However, one has to consider that existing soil testing databases were developed based on the Law of Minimum and for that only soils that were not previously fertilized were used. Nowadays it is virtually impossible to find such soils and the behaviour of P in a soil is quite different once the soil has been fertilized for a prolonged period of time. A compilation of yield data from 155 experiments showed that when soil test levels were less than 10 lb P/acre (deficient), not all crops responded to P fertilization in all cases. At the same time when soil test levels were greater than 30 lb P/acre (sufficient) still a number of crops were responding to high P levels. Although the frequency of responses was higher at lower soil test P levels, there were no clear trends, which would suggest that response to phosphate fertilizer is indeed affected by factors other than the soil test level and in any event application of P would be necessary almost at any soil test level. A long-term (23-year) showed that elimination of P fertilization results in immediate yield losses. The maintenance portion of P fertilization can be forgone in years of high fertilizer and low commodity prices on the understanding that prolonged removal of that portion from the fertilizer plan will inadvertently results in increasing soil P deficiency.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".