Sawdust and Bark-Based Substrates for Soilless Strawberry Production: Irrigation and Electrical Conductivity Management
Bibliographic record
Abstract
The objective of this work was to optimize a soilless growing system for producing bare-root strawberry transplants in three organic substrates. Three trials were conducted in the Quebec City area to determine the productivity potential of a peat-sawdust mixture (PS25) and an aged bark (AB) material compared to conventional coconut fiber (CF) substrate. A first experiment was carried out to define appropriate irrigation set points for each substrate that allowed optimal plant growth and fruit yields. For all substrates, wetter conditions (irrigation started at -1.0 kPa for CF; -1.5 kPa for AB and PS25, relative to -1.5 kPa for CF; -2.5 kPa for AB and PS25) enhanced plant growth and fruit production. The second trial was carried out to test the productivity potential for commercial production of the three substrates using high-tunnels. After the addition of an initial fertilizer application to PS25, we successfully established bare-root plants that gave similar fruit yields than those in CF and AB. The productivity potential of PS25 and AB were further confirmed during a third trial under greenhouse conditions. The critical factor for plant establishment in PS25 was attributed to consistent N, P and S immobilization by microorganisms, as well as the retention of other elements (Mg2+, K+) in the growth media. Taken together, our results showed that PS25 and AB are promising alternative substrates to coconut coir dust for strawberry cultivation. This paper also provides a useful guide for strawberry cultivation in Quebec, and suggests future research that might be conducted to optimize soilless systems for cold-climate strawberry production in Northern America.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".