Assessment of Wool Waste and Hair Waste as Soil Amendment and Nutrient Source
Bibliographic record
Abstract
A field and two container experiments were conducted to assess uncomposted wool and hair wastes as a nutrient source for crops and to evaluate their potential to improve soil biological and chemical properties. Overall, addition of wool or hair waste to soil increased yields of basil (Ocimum basilicum L. 'Trakia'), thorn apple (Datura innoxia Mill. 'Inka'), peppermint (Mentha x piperita L. 'Black Mitchum'), and garden sage (Salvia officinalis L. 'Desislava'), increased NH(4)-N and NO(3)-N in soil, increased total N (and protein) content in plant tissue, stimulated soil microbial biomass, and decreased mycorrhizae colonization of plant roots of thorn apple but not in basil. Wool and hair waste additions to soil altered slightly the content and composition of plant secondary metabolites (essential oils or alkaloids); however, overall the constituents remained within the "typical" range for the respective crops. Scanning electron microscopy (SEM) and energy dispersive X-ray (EDX) analysis demonstrated that wool and hair wastes decompose slowly under field or greenhouse conditions, and act as a slow release S, N, P, and K fertilizer. These results, along with the measured concentrations of NO(3)-N in soil at harvest, suggest that the addition of wool or hair waste of only 3.3 g kg(-1) of soil may support two to five harvests or crops under greenhouse conditions and two to four field seasons in field production systems, and would improve soil biological and chemical characteristics. Further research is needed to optimize the rate of application of these waste materials to the nutrient requirements of specific crops to avoid nitrate leaching into the ground water. In addition, the effect of wool and hair waste on other environmental end points should also be further investigated before specific recommendations for growers are provided.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".