Uncomposted Wool and Hair‐Wastes as Soil Amendments for High‐Value Crops
Bibliographic record
Abstract
The hypothesis of this work was that uncomposted sheep wool and human hair could be used as nutrient source for nonedible high‐value plants. Pot and field experiments were conducted to assess uncomposted sheep wool‐wastes and human hair‐wastes as a nutrient source for high‐value crops and to evaluate the effect of these waste materials on soil microbial community and mycorrhizae. In the pot experiments, addition of uncomposted wool‐ or hair‐waste to soil increased yields from pot marigold (Calendula officinalis L.) and valerian (Valeriana officinalis L.). In the field experiment, wool‐waste was added to purple foxglove (Digitalis purpurea L.) at rates of 0, 15.8, and 31.7 t ha−1. Wool additions to soil increased foxglove yields over the next two seasons by 1.7 to 3.5 times relative to the control. Overall, addition of wool‐ or hair‐waste to soil increased NH4–N and NO3–N in soil, increased total N (and protein) concentration in plant tissue, and stimulated soil microbial biomass. Scanning electron microscopy (SEM) and energy dispersive x‐ray (EDX) analyses indicated that some of wool and hair in soil from the pot and field experiments, after two seasons and several harvests, retained their original structure, a significant concentration of S, some N, and were not fully decomposed. High rates of wool addition to soil in field experiments resulted in shifts in the microbial community composition, while a low rate of wool‐waste addition did not affect the microbial community relative to the unamended control. Our results suggest that the addition of uncomposted wool‐waste or hair‐waste of only 0.33% by weight to soil would support at least 2 to 3 harvests of crops, without the addition of other fertilizers. Uncomposted wool and hair‐wastes can be used as a nutrient source for high‐value crops.
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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 | 0.000 | 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".