ADOPTION OF PRECISION AGRICULTURE TO REDUCE INPUTS, ENHANCE SUSTAINABILTIY AND INCREASE FOOD PRODUCTION: A STUDY OF SOUTHERN ALBERTA, CANADA
Bibliographic record
Abstract
Precision agriculture (PA) identifies variability in fields.This allows greater accuracy in targeting the correct amount of inputs, at the correct time, and in the correct location compared to conventional agricultural methods.As such, precision agriculture has significant potential to reduce agricultural inputs, enhance agricultural sustainability, and increase production in order to meet the growing worldwide demand for food.This study focuses on southern Alberta, the largest, most fertile and productive agricultural region in Canada.Given the high concentration of agriculture in this region, the potential benefits of precision agriculture could be significant.A greater understanding of the adoption of precision agriculture is therefore warranted.Based on a survey of farmers, the study finds the region is actively advancing PA technologies and the findings indicate PA technologies tend to be spread across all land and crop types; the technologies are applied to both dryland and irrigated farms and across cereals, oilseeds and speciality crops.Farmers are highly satisfied with precision agriculture and intend to continue adoption of precision agriculture technologies.Further, some non-adopters intend to become adopters.If there are limits to adoption it is because some farms are too small to warrant the adoption of precision agriculture and the attendant high investment costs.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".