Impacts of Alternative Manure Application Rates on Texas Animal Feeding Operations: A Macro Level Analysis<sup>1</sup>
Bibliographic record
Abstract
Abstract: An integrated economic and environmental modeling system was developed for evaluating agro‐environmental policies and practices implemented on large scales. The modeling system, the Comprehensive Economic and Environmental Optimization Tool‐Macro Modeling System (CEEOT‐MMS), integrates the Farm‐level Economic Model (FEM) and the Agricultural Policy Environmental eXtender (APEX) model, as well as national databases and clustering and aggregation algorithms. Using micro simulations of statistically derived representative farms and subsequent aggregation of farm‐level results, a wide range of agricultural best management practices can be investigated within CEEOT‐MMS. In the present study, CEEOT‐MMS was used to evaluate the economic and water quality impacts of nitrogen (N) and phosphorus (P) based manure application rates when implemented on all animal feeding operations in the State of Texas. Results of the study indicate that edge‐of‐field total P losses can be reduced by about 0.8 kg/ha/year or 14% when manure applications are calibrated to supply all of the recommended crop P requirements from manure total P sources only, when compared to manure applications at the recommended crop N agronomic rate. Corresponding economic impacts are projected to average a US$4,800 annual cost increase per farm. Results are also presented by ecological subregion, farm type, and farm size categories.
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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 | 0.001 | 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".