Nutrient management for intensive animal agriculture: policies and practices for sustainability
Bibliographic record
Abstract
Abstract. The intensity of animal production around the world has increased substantially during the last half‐century, which has led to large problems with the disposal of manures and waste waters. The focus of this paper is on the development of national policies to improve the nutrient management of concentrated animal feeding operations (CAFOs), where nutrients are invariably in surplus. To create proper nutrient management strategies for CAFOs, and to avoid environmental problems when surplus nutrients enter air, soil and water, we need to know the number of animals/birds in the unit, the quantity of manure/slurry produced, how this material is stored and handled and how much land is available for manure spreading. In this paper, we discuss the development of nutrient management strategies for CAFOs in Europe and North America, and the voluntary measures and environmental regulations related to this. For the planning of nutrient management to be comprehensive and efficient, we need expertise from several disciplines. This planning includes development of: animal diets that reduce the amounts of excreted nutrients; efficient storage and land application technologies; land application programmes to optimize yields and reduce nutrient losses; and strategies for use of excess manure outside the farm. Also, large‐scale efforts involving many stakeholders (farmers, governments and private industry) are needed to solve problems with nutrient imbalances over the long term. Efforts along these lines include manure relocation, alternative uses of manures, nutrient trading, and a general extensification of animal agriculture. The overall guiding principle for policies and planning should be a balance of nutrients, on farms as well as at larger scales.
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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 | 0.016 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".