Manure use benefits and barriers according to agricultural stakeholders
Bibliographic record
Abstract
Abstract Using manure as a crop fertilizer promotes recycling of locally available organic nutrients and reduces needs for manufactured inorganic fertilizers. However, the factors that motivate and constrain manure use are unclear. To explore stakeholder perceptions, we designed a quantitative survey assessing potential benefits and barriers to manure use, knowledge of manure impacts, and preferred information resources. Using mailing lists and mass media, we distributed the online survey to a broad sample of crop farmers, animal feeding operation managers, and public and private sector advisors in the United States and Canada ( n = 709 responses). In addition to computing descriptive statistics, we examined associations between participant role and years of experience with response choices using cumulative logistic and log‐binomial models. Overall, respondents rated manure as highly beneficial to crop yields, soil fertility, soil physical properties, and soil biological properties, but shared mixed perceptions regarding the impacts of manure on environmental quality. The most frequently identified barriers to manure use were (1) the cost of manure transportation and land application, (2) odors and air quality impairment, and (3) the timeliness of manure application. Respondents reported they were likely to use scientific information sources and their professional networks in making manure nutrient management decisions. Additionally, we found that role and years of professional experience were often associated with response choices, illustrating distinct extension and education needs of different stakeholder segments. Our results indicated wide recognition of manure benefits to crop yields and soil properties and suggested that practical barriers may often limit manure use.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".