The effect of feed demand on greenhouse gas emissions and farm profitability for organic and conventional dairy farms
Bibliographic record
Abstract
The reduction of product-related greenhouse gas (GHG) emissions in milk production appears to be necessary. The reduction of emissions on an individual farm might be highly accepted by farm owners if it were accompanied by an increase in profitability. Using life cycle assessments to determine the product carbon footprints (PCF) and farm-level evaluations to record profitability, we explored opportunities for optimization based on analysis of 81 organic and conventional pasture-based dairy farms in southern Germany. The objective of the present study was to detect common determining factors for low PCF and high management incomes (MI) to achieve GHG reductions at the lowest possible operational cost. In our sample, organic farms, which performed economically better than conventional farms, produced PCF that were significantly higher than those produced by conventional farms [1.61 ± 0.29 vs. 1.45 ± 0.28 kg of CO₂ equivalents (CO₂eq) per kg of milk; means ± SD)]. A multiple linear regression analysis of the sample demonstrated that low feed demand per kilogram of milk, high grassland yield, and low forage area requirements per cow are the main factors that decrease PCF. These factors are also useful for improving a farm's profitability in principle. For organic farms, a reduction of feed demand of 100 g/kg of milk resulted in a PCF reduction of 105 g of CO₂eq/kg of milk and an increase in MI of approximately 2.1 euro cents (c)/kg of milk. For conventional farms, a decrease of feed demand of 100 g/kg of milk corresponded to a reduction in PCF of 117 g of CO₂eq/kg of milk and an increase in MI of approximately 3.1 c/kg of milk. Accordingly, farmers could achieve higher profits while reducing GHG emissions. Improved education and training of farmers and consultants regarding GHG mitigation and farm profitability appear to be the best methods of improving efficiency under traditional and organic farming practices.
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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 | 0.001 |
| 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 | 0.000 |
| 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".