Surveying North American Specialty Crop Growers’ Current Use of Soilless Substrates and Future Research and Education Needs
Bibliographic record
Abstract
Many specialty crop growers are transitioning high-value crops from in-ground production to soilless culture due to the diminishing availability of fumigants, increasing pest pressure, extreme weather, and the need for flexible production practices. The objective of this study was to determine the research and educational needs of specialty crop growers who are transitioning to soilless substrates. North American growers were surveyed using an online instrument that incorporated Likert-type statement matrices, open-ended questions, and demographic questions. Additionally, two virtually led focus groups were conducted to further expand upon the quantitative findings with descriptive data. Respondents indicated the most important factors in considering whether to adopt soilless substrates were improving, managing, and reducing overall plant quality, disease management, and crop loss, respectively. The most important research needs were understanding the effects of substrates on crop quality and uniformity, fertilizer management, and economic costs and benefits/return on investment. In both the grower survey and focus groups, crop quality and uniformity were among the highest-scored responses. Food safety, disease and pest management, consumer perception, substrate disposal-related issues, transportation, and return-on-investment were also identified as important factors when considering soilless substrates.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".