Factors Influencing the Intention to Adopt Precision Agriculture Technology Among Agronomist Firms in Saskatchewan
Bibliographic record
Abstract
Abstract\nPrecision agricultural technologies relating to soil quality and land management may be useful\ntools to improve the level of agricultural productivity and profitability. Agronomists often help\nproducers become aware of and integrate new technologies that can improve performance.\nHowever, the rate of adoption of technology among agronomists varies and can be shaped by a\nwide array of influencing determinants.\nPredictive Soil Mapping Systems (PSMS) are web-based platforms that generates high-resolution\npredictive soil characteristics maps for users. PSMS help users make informed decisions and use\nthe right amount and type of inputs in the right place and right time.\nUsing a survey of agronomist firms in Saskatchewan, I examined factors relating to the intention\nto adopt PSMS. I focused on strategy-oriented factors at the firm level (e.g., market orientation,\ncannibalization, innovativeness, and performance), and used Partial Least Square Structural\nEquation Modeling (PLS-SEM) for analyzing my conceptual model based on the theoretical\nframework of the TOE (Technology-Organization-Environment) model.\nResults show the firm's subjective perception of past performance relative to others in the industry\nhas a significant positive relationship with intention to adopt the PSMS technology and other\nstrategic factors of the company are insignificant
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".