Response of forage yield and quality to thinning and fertilization of young forests: implications for silvopasture management
Bibliographic record
Abstract
Integration of trees with forage and livestock production (silvopastoralism) could increase productivity of forest and range resources in western North America. Pre-commercial thinning (PCT) and fertilization are two silvicultural practices that could enhance silvopasture. We tested two hypotheses (H): that yield and quality of forage would be enhanced by (H1) heavy thinning (PCT) to ≤1000 stems·ha−1 and by (H2) repeated fertilization in lodgepole pine (Pinus contorta Dougl. ex Loud. var. latifolia Engelm.) stands. Study areas were located near Summerland and Kelowna in south-central British Columbia, Canada. Each study area had six treatments: three pairs of stands thinned to densities of ∼500 (low), ∼1000 (medium), and ∼2000 (high) stems·ha−1 with one stand of each pair fertilized five times at 2 year intervals. Forage yield was enhanced by PCT, but only within fertilized stands. Forage quality was generally not affected by PCT, except for crude protein of herbs that was poorer in heavily thinned stands. Fertilization tended to enhance forage yield and quality in the heavily thinned stands. Significantly improved quality of pinegrass (Calamagrostis rubescens Buckley) indicated that repeated fertilization, coupled with heavy thinning, may extend the period when high-quality forage is available, thereby allowing for increased stocking densities of cattle (Bos taurus L.) and perhaps extending the grazing season into the fall.
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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 | 0.000 | 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) | 0.001 | 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".