Use of crop residues as substrates for the cultivation of king stropharia (Stropharia rugosoannulata) mushrooms
Bibliographic record
Abstract
,King stropharia (Stropharia rugosoannulata) is a white rot fungus that produces nutritious edible mushrooms. The species is prized among backyard mushroom cultivators due to its ability to grow on various lignocellulosic substrates in a range of environmental conditions and compete with contaminant microorganisms. These characteristics make king stropharia a great potential tool for enhancing crop residue decomposition in northern environments while producing a valuable crop of mushrooms. However, the species is understudied and underutilized since its production parameters have never been optimized. This study sought to determine 1) which readily available substrate (of alder chips, barley straw and hemp straw) produces the best yield of king stropharia mushrooms, 2) whether substrate impacts the nutritional content of king stropharia mushrooms, 3) how king stropharia chemically alters substrates, 4) how king stropharia alters substrate microbial communities, and 5) which spent substrate makes the best soil amendment for crop production. A cultivation trial was conducted at a farm in Prince George, British Columbia, Canada, from June to October 2022. Eight 1 m by 1 m wooden frames of each substrate were prepared. Five frames each of alder chips and barley straw were inoculated with king stropharia spawn and the three remaining frames served as uninoculated controls. Six frames of hemp straw were inoculated, leaving two uninoculated controls. Substrate samples were collected prior to inoculation and again after the cultivation period. The date, mass and count of mushrooms produced from each frame was recorded. Mushrooms samples were also collected for analysis. Mushroom and substrate samples were analysed for content of carbon, nitrogen and a suite of other elements. Mushrooms were analysed for protein and lipid content. Substrate sample lignocellulosic biomass fractions (lignin, cellulose and hemicellulose) were quantified, and substrate pH and electrical conductivity were tested. Substrate fungal and bacterial DNA were extracted, amplified, sequenced and analyzed. Hemp straw tended to be the fastest and highest yielding substrate in the cultivation trial. Hemp straw appears to have been the best performing substrate due to its water content, nutrient profile, lignin content and surface area to volume ratio compared to the other substrates. There also seemed to be a distinctive bacterial consortium associated with the successful cultivation of king stropharia in barley straw and hemp straw, with high relative abundance of the genera Bacillus and Paenibacillus in these samples. Further cultivation experiments using fresh spawn are necessary to properly assess king stropharia’s yield and effect on substrates since the results of this study were impacted by spawn contamination. All post-cultivation mushroom substrate types had beneficial properties for agricultural soil amendment, even though the substrates were not completely spent at the end of the cultivation trial. King stropharia presents a potential win-win scenario whereby farmers can produce nutritious mushrooms with their crop residues while enhancing crop residue decomposition and nutrient cycling with minimal technology and labour. Although further research is required to fully realize the potential of this species, this study demonstrates how king stropharia can contribute to sustainable agricultural practices.
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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 | 0.001 | 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".