Factors that Influence the Rate and Intensity of Adoption of Improved Irrigation Technologies in Alberta, Canada
Bibliographic record
Abstract
Despite the importance of adopting improved irrigation technologies to increase on-farm irrigation efficiency, our understanding of what determines farmers’ adoption decisions in southern Alberta remains relatively poor. The overall goals of this study are to examine the extent of adoption (proportion of all irrigators that have started the adoption process), how far along they are in the adoption process, and the intensity of adoption (percentage of irrigated land on which the technology is adopted) of improved irrigation technologies in southern Alberta, and to assess the major factors that influenced farmers’ adoption decisions. The data were collected in a farm-household survey conducted in the 12 largest irrigation districts (IDs) as well as among private irrigators in southern Alberta. Results show that adoption of improved irrigation technologies is widespread at various levels of intensity. By 2011, 81.3% of farmers had started the adoption process, are now using some kind of improved technology to apply water to their crops, and used it on 76.8% of all irrigated land. The most commonly used irrigation technology is a low pressure center pivot system. Receiving support services following the adoption decision played an important role in increasing the intensity of adoption. Obtaining information on irrigation technologies from individual farmers or farmers’ associations, and extension agencies significantly influenced farmers’ decisions to adopt. Farmers who increased their social capital through attending meetings related to agricultural production practices were more likely to adopt while farmers who participated in recreational or social organizations were less likely to adopt. Finally, the extent and intensity of adoption are higher for those with corporate farm structure, larger families, more generations of ownership and higher education.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".