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Record W4386324796 · doi:10.3390/agriculture13091727

Surveying North American Specialty Crop Growers’ Current Use of Soilless Substrates and Future Research and Education Needs

2023· article· en· W4386324796 on OpenAlex
Jeb S. Fields, James S. Owen, Alexa J. Lamm, James E. Altland, Brian E. Jackson, Lorence R. Oki, Jayesh B. Samtani, Youbin Zheng, Kristopher S. Criscione

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAgriculture · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Disease Management Techniques
Canadian institutionsUniversity of Guelph
FundersNational Institute of Food and AgricultureU.S. Department of Agriculture
KeywordsReturn on investmentInvestment (military)Agricultural scienceBusinessCropIntegrated pest managementQuality (philosophy)SpecialtyProduction (economics)AgroforestryAgricultural engineeringGeographyEnvironmental scienceEngineeringAgronomyForestryMedicineEconomicsBiology

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.333
Threshold uncertainty score0.300

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.072
GPT teacher head0.300
Teacher spread0.228 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it