Bibliographic record
Abstract
Yield data collected by Statistics Canada show that annual hay yields have been declining in several Canadian provinces including Saskatchewan for the past 31 yr. Data from Saskatchewan provincial statistics on Beef Cow number, Area Harvested for Hay, Value of Land and Buildings, and Fertilizer Price Index were used to examine the relationships between hay yield and these variables. Weather from 16 sites across Saskatchewan’s agricultural region for the April, May, and June (AMJ) period was also examined from 1973 to 2003. Precipitation during AMJ was positively correlated to hay yield, but the difference between maximum and minimum temperatures (Δtemperature) was negatively correlated. Fertilizer Price Index was negatively correlated to hay yield, suggesting that producers are reducing fertilizer input to this crop resulting in lower yields. Step-wise regression analysis suggested that fertilizer cost was the most important variable, which explained 38% of the variation in hay yield. When fertilizer price was combined with Δtemperature, the Value of Land and Buildings and AMJ precipitation, 65% of the variation in Saskatchewan hay yield could be explained. Examination of census data on tame forage and tame pasture acres by district did not support the hypothes is that more hay has been seeded in lower yield regions of the Province. We speculate that climate change is negatively affecting Saskatchewan’s ability to produce hay, particularly since low inputs of fertilizer and infrequent stand rejuvenation are commonly practiced on forage crops. This problem needs more in-depth analysis to confirm the cause(s) of this decline and avoid a future scenario where low hay yields make ruminant livestock production economically unsustainable in Western Canada. Key words: Forage yield, precipitation, temperature, fertilizer
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".