Economic effect of reducing nitrogen and phosphorus mass balance on Wisconsin and Québec dairy farms
Bibliographic record
Abstract
Our objective was to explore the trade-offs between economic performance (farm net income, FNI) and environmental outcomes (whole-farm P and N balances) of dairy farms in Wisconsin (WI; United States) and Québec (QC; Canada). An Excel-based linear program model (N-CyCLES; nutrient cycling: crops, livestock, environment, and soil) was developed to optimize feeding, cropping, and manure management as a single unit of management. In addition to FNI, P and N balances model outputs included (1) the mix of up to 9 home-grown and 17 purchased feeds for up to 5 animal groups, (2) the mix of up to 5 crop rotations in up to 5 land units and c) the mix of up to 7 fertilizers (solid and liquid manure and 5 commercial fertilizers) to allocate in each land unit. The model was parameterized with NRC nutritional guidelines and regional nutrient management planning rules. Simulations were conducted on a typical WI farm of 107 cows and 151 ha of cropland and, a Southern QC farm of 87 cows and 142 ha of cropland and all results were expressed per kg of fat- and protein-corrected milk (FPCM). In absence of constraints on P and N balances, maximum FNI was 0.12 and 0.11 $/kg of FPCM for WI and QC, respectively, with P and N balances of 1.05 and 14.29 g/kg of FPCM in WI but 0.60 and 15.70 g/kg of FPCM in QC. The achievable reduction (balance at maximum FNI minus balance when the simulation objective was to minimize P or N balance) was 0.31 and 0.54 g of P/kg of FPCM (29 and 89% reduction), but 2.37 and 3.31 g of N/kg of FPCM (17 and 24% reduction) in WI and QC, respectively. Among other factors, differences in animal unit per hectare and reliance on biological N fixation may have contributed to lower achievable reductions of whole-farm balances in WI compared with QC. Subsequent simulations to maximize FNI under increasing constraints on nutrient balances revealed that it was possible to reduce P balance, N balance, and both together by up to 33% without a substantial effect on FNI. Partial reduction in P balance reduced N balance (synergetic effect) in WI, but increased N balance (antagonistic effect) in QC. In contrast, reducing N balance increased P balance in both regions, albeit in different magnitudes. The regional comparison highlighted the importance of site-specific conditions on modeling outcomes. This study demonstrated that even when recommended guidelines are followed for herd nutrition and crop fertilization, the optimization of herd feeding, cropping, and manure spreading as a single unit of management may help identify management options that preserve FNI, while substantially reducing whole-farm nutrient balance.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".