Benefit-cost analysis of near-infrared spectroscopy technology adoption by Alberta hog producers
Bibliographic record
Abstract
Feed cost is a significant component of livestock production costs, accounting for over half of total operating costs for hog producers. This provides an incentive to minimize feed costs while meeting dietary requirements. However, producers may not know the nutritional content of their feed grains with certainty. Near-infrared reflectance spectroscopy (NIRS) imaging technology can quickly and accurately estimate the nutritional content of different types of feed grain. Although the technology has been available for almost five decades, producer adoption has been slow due to issues with cost and usability. The objectives of this study are to estimate feed cost savings resulting from the adoption of NIRS on a representative Alberta hog farm and to conduct a benefit-cost analysis to investigate the potential viability of NIRS adoption. A joint mathematical programming-simulation approach is used to estimate the cost savings generated by adoption of NIRS technology. Results suggest mean annual savings of up to $4 per hog and benefit-cost results suggest that adopting NIRS technology may be viable, particularly for larger Alberta hog operations. However, initial investment requirements, uncertainty in the magnitude of benefits, and access to the technology from feed mills will likely continue to limit adoption.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".