Invited review: Sustainability of the US dairy industry
Bibliographic record
Abstract
The US dairy industry has realized tremendous improvements in efficiencies and milk production since the 1940s. During this time, farm and total cow numbers have decreased and average herd size has increased. This intensification, combined with the shift to a largely urban public, has resulted in increased scrutiny of the dairy industry by social and environmental movements and increased concern regarding the dairy industry's sustainability. In response to these concerns, a group of scientists specializing in animal welfare, nutrient management, greenhouse gas emissions, animal science, agronomy, agricultural engineering, microbiology, and economics undertook a critical review of the US dairy industry. Although the US dairy system was identified as having significant strengths, the consensus was that the current structure of the industry lacks the resilience to adapt to changing social and environmental landscapes. We identified several factors affecting the sustainability of the US dairy industry, including climate change, rapid scientific and technological innovation, globalization, integration of societal values, and multidisciplinary research initiatives. Specific challenges include the westward migration of milk production in the United States (which is at odds with projected reductions in precipitation and associated limitations in water availability for cattle and crops), and the growing divide between industry practices and public perceptions, resulting in less public trust. Addressing these issues will require improved alignment between industry practices and societal values, based upon leadership from within the industry and sustained engagement with other interested participants, including researchers, consumers, and the general public.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".