Modeling whole farm profitability and environmental performance of four alfalfa‐grass binary mixtures in eastern Canadian dairy farms
Bibliographic record
Abstract
Abstract Forages are a major source of nutrients for milk production, but little is known about the effects of forage species on whole‐farm profitability and environmental performance. Using a whole‐farm approach, the N‐CyCLES (Nutrient‐Cycling Crops‐Livestock‐Environment‐Soil) optimization model, and experimentally measured data, this study aimed to compare the profitability and environmental performance of dairy farms when four alfalfa ( Medicago sativa L.)‐grass binary mixtures harvested and stored as silage and hay are used in ration formulation of heifers and lactating and dry cows: (a) alfalfa‐timothy ( Phleum pratense L.) (Al+Tim), (b) alfalfa‐tall fescue [ Schedonorus arundinaceus (Schreb.) Dumort.] (Al+TF), (c) alfalfa‐meadow fescue fescue [ Schedonorus pratensis (Huds.) P. Beauv.] (Al+MF), and (d) alfalfa‐meadow bromegrass ( Bromus biebersteinii Roem. & Schult.) (Al+Bro). Simulations were conducted on two virtual eastern Canadian dairy farms: southwestern (SWQ) and eastern Quebec (EQ). Comparisons were based on net income, greenhouse gas emissions, and nutrient balances. In SWQ, Al+Bro and Al+Tim led to the highest net income, but only slight differences were calculated between the four mixtures (maximum difference of 1.4%). In EQ, results suggested that Al+TF is the most viable alternative to Al+Tim mixtures as it allowed the farm to reach the highest profit (maximum difference of 8%). The choice of grass in alfalfa‐grass binary mixture had little impact on the farm's total greenhouse gas emissions: the lowest total greenhouse gas emissions were with Al+ MF in SWQ and with Al+TF in EQ. In SWQ, phosphorus balance was low for the four mixtures and the lowest nitrogen balance was reached with Al+MF and Al+Tim. Finally, in EQ, the lowest phosphorus balance was reached with Al+MF, whereas the lowest nitrogen balance was reached with Al+Bro. In conclusion, despite slight differences, all grass species tested allowed the farm to reach similar profits and environmental performance.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".