A Composite Index Measuring Adoption of Conservation Agriculture among Maize and Soybean Farmers in Québec
Bibliographic record
Abstract
Conservation agriculture (CA) has appeared in America since 1970 as an alternative practice to conventional tillage to limit soil degradation. Despite its expansion around the world, socioeconomic analyses of its adoption, as well as its impact on agricultural yields, still suffer from imperfect identification of CA adopters. The present study therefore proposes a new composite index for measuring the adoption of CA among maize and soybean farmers in the province of Québec, Canada. A model of partial adoption of CA both at parcel and farm levels is developed to build the composite index; and experts’ judgements and the Analytical Hierarchy Process are used for weight elicitation of principles of CA. Data from 144 maize and soybean farmers are also used to assess the level of adoption of CA in Québec. The new composite index improves on the measure of adoption of conservation agriculture, as it can be used to discriminate among farmers according to the level of adoption of principles of CA. Indeed, the new composite index shows that 77.08%, 21.53% and 1.39% of maize and soybean farmers, respectively, are partial adopters, full adopters and non-adopters of CA, whereas the traditional binary indicator indicates that 83.33% and 16.67% of maize and soybean farmers, respectively, are adopters and non-adopters of CA. The results also show that many maize and soybean farmers (38.89%) have shown a certain flexibility in the adoption of CA.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".