The influence of the seasons: how the agricultural calendar impacts farmer perceptions of cover crops
Bibliographic record
Abstract
Abstract Across the Midwest, substantial funding and personnel time have been allocated to encourage farmers to adopt a wide range of conservation practices but adoption rates for many of these practices remain low. Prior research focuses largely on the influence of individual-level factors (e.g., beliefs, attitudes) on conservation practice adoption rather than on contextual factors (e.g., seasons) that might also play a role. In the present study, we considered seasonal variation and its potential influence on farmer cover crop decision-making. We first established how farmer temporal and financial resources fluctuate across the year and then compared the annual agricultural decision and cover crop decision calendars. We also considered farmer cover crop perceptions and likely behaviors. To study this, we surveyed the same Midwestern farmers in the spring, summer and winter within a 12-month period. Results indicated that farmers were generally the least busy and the most financially comfortable in the winter months. Moreover, farmers perceived the benefits of cover crops differently throughout the year. These results indicate that seasonality can be a confounding factor which should be considered when designing and conducting research and farmer engagement. As researchers, it is our responsibility to understand the specific calendar experienced by our sample and how that may influence responses so we can examine theory-supported factors of interest rather than seasonality as a driver of farmer responses. As practitioners, it is important to use research findings to engage with farmers about conservation in a way that prioritizes communicating about the most salient aspects of the practice at the time of year when farmers will be most receptive.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".