Validation of Feed and Manure Data Collected on Wisconsin Dairy Farms
Bibliographic record
Abstract
An on-farm study of 54 representative Wisconsin dairy farms was conducted to evaluate the influence of biophysical and socioeconomic factors on overall feed, fertilizer, and manure nutrient use. This report validates 1) how well data on cow diets, feed analyses, and milk production reflected established feed-milk-manure relationships; and 2) how well farmer-recorded data on manure land application reflected literature values of manure N and P excretion, collection, and loss. Calculated feed N and P use efficiencies (18 to 33% and 18 to 35%, respectively) fell within ranges expected for dairy farms. This suggested that our on-farm methods of data collection provided reliable information on relationships among feed N and P intake, secretions in milk, and excretion in manure. On stanchion farms, there were no differences between farmer estimates (kg/farm) of manure P collected (1,140) and land-applied (1,210) and what would be calculated from the literature (1,340). On freestall farms, there were no differences in amounts (kg/farm) of manure P collected (2,889), land-applied (2,350), or literature estimates (2,675). Manure P applications (kg/ha) to tilled cropland would be similar using either farmer estimates of manure collected and land-applied, or literature estimates. The data provided a snapshot of Wisconsin industry practices, as well as information on the range of feed and manure management practices on individual dairy farms. Improvements to data collection methods would require increased skill and training of both farmers and those responsible for assisting farmers in on-farm data collection and analyses.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".