Systematic Nutrient (im) Balances in Dairy Farm Systems of the Northeast and Mid-Atlantic Regions of the United States
Bibliographic record
Abstract
Many governmental programs that address non-point source pollution from animal feeding operations havefocussed on promoting land-based best management practices (BMPs). Our objectives were to illustrate and quantify nitrogen (N) and phosphorus (P) balances of Northeast and Mid-Atlantic dairy farms using (1) a hypothetical and representative Northeastern and Mid-Atlantic dairy farm, and (2) three case study dairy farms with animal densities of 1.6 to 2.4 milking cows ha-1. Analyses of N and P balances for the representative farm showed an annual surplus of 258 kg N and 31 kg P2O5 ha-1. For the three case study farms, 65-73% of the N and 41-62% of the P that entered the farm through feed, fertilizer, fixation, animal purchases and/or bedding were not exported in the form of milk, animals or crops, resulting in excesses of 114-248 kg N ha-1 and 37-42 kg P2O5 ha-1. These quantifications suggest that land-based BMPs to address non-point source pollution will fall short of expectations over the long-term because they do not recognize the strategic issues faced by many of today’s dairy farmers in the Northeast and Mid-Atlantic regions. We conclude that for the long-term sustainability of the dairy industry, a land-based BMP approach should be complimented with whole farm nutrient mass balance assessments and address nutrient source reduction and/or manure treatment and export. The latter requires a change in cropping systems and/or innovative systems to treat the manure to decrease transport costs and/or add economic value.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".