Farmer experience with tree fodder
Bibliographic record
Abstract
Use of trees for drought feed on New Zealand farms has been practised sporadically for many years, after farmers found that tree prunings were useful as supplementary feed during summer droughts. The Ministry of Agriculture & Forestry (MAF) Sustainable Farming Fund recently funded a farmer-led team to develop the concept of tree fodder use on livestock farms in the southern North Island. Livestock farmers in Hawke's Bay, Rangitikei and Wairarapa who are already using tree fodder were interviewed to generate practical guidelines from their experience. Additional experience from Otago has been included here. Farmers obtain tree fodder by pruning and pollarding soil conservation trees, and by coppicing or grazing livestock on fodder blocks, or by taking advantage of natural leaf fall from poplar trees. The most common practice was pruning willows and poplars originally planted for soil conservation, during summer using a chainsaw. Most farmers found tree fodder feeding a valuable practice and well worthwhile. Over a three-tofour week period, two farmers reported taking 1.5-2 hours per day to feed 1,000 sheep, or cutting five or six trees per day to feed approximately 1,000 ewes. Keywords: tree fodder; poplars; willows; coppicing; pruning.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 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".