Seasonal Yield and Nitrogen Content of Three Grasses Grown Alone and in Association with Each Other and with White Dutch Clover
Bibliographic record
Abstract
The chief aim of pasture research is to discover means of providing the largest possible amount of highly nutritious forage at the time the animal requires it. Throughout the grazing season animal herbage requirements are relatively constant, while herbage production from pastures usually is quite variable. Several practices have been employed in an attempt to obtain more uniform seasonal production, and overcome periods of feed surplus and feed shortage during the grazing season. One such practice has been to seed pastures comprised of several species. This is based on the observation that forage species differ in their requirements, and make maximum growth at different times of the year. Thus, it has been suggested, if the correct combination of species is obtained, production will be uniform throughout the season. However, uniform production has not generally been realized, and most mixtures follow similar patterns of production, determined by factors other than species composition. This observation, brings up the question of whether it has been correct to assume that each species would maintain its individual characteristics in a mixture. It seems reasonable that one species would affect the growth habit of others with which it is associated. In the work reported herein, a comparison was made of the seasonal frowth and yield of 3 grass species when grown alone and when grown in mixtures. The mixtures included all combinations of the three species with each other, and with white Dutch clover. The total yields of the mixtures were also compared for seasonal yield fluctuations. Nitrogen determinations were made on most of the separated species. Some of the literature pertaining to problems involved in obtaining uniform seasonal production was reviewed. The work was done at the Dominion Experimental Station, Lethbridge, Alberta, Canada, in 1952 and 1953.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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".