Rapid Analysis of Hog Manure and Manure‐amended Soils Using Near‐infrared Spectroscopy
Bibliographic record
Abstract
Application of hog ( Sus domesticus ) manure to agricultural land converts waste to fertilizer. Nevertheless, matching nutrients in highly variable manure to soil or crop needs requires analytical capability that is ideally field portable and cost‐effective. This study explored using rapid nondestructive near‐infrared spectroscopy (NIRS) to analyze nutrients in hog manure and receiving soil. Spectral data in the visible and near‐infrared (NIR) region (400–2500 nm) from manure samples were correlated with chemical analytical data from the same samples using multiple linear regression statistics to develop calibrations for the prediction of future unknown samples. For 64 manure samples from seven manure storage facilities, r 2 between NIR‐predicted values and chemically measured values was 0.93 to 0.99 for NH 4 –N, total dissolved N (TDN), suspended N, soluble reactive P (SRP), total dissolved P (TDP), suspended P, suspended C, Na, and Mg. For K, Ca, conductivity, and pH, r 2 was >0.80. Subsequent analysis of 75 samples from 25 facilities gave similar or slightly less successful results. Soil samples collected before and following application of manure were scanned in a field‐moist state and after drying. For field‐moist soil, r 2 for N, organic matter, Mg, and moisture was >0.84; for SO 4 –S was 0.7. For dry soil, results were similar for N and better for Mg SO 4 –S, Ca, and K. Near‐infrared spectroscopy has potential to predict some nutrient and salt concentrations in manure rapidly and without sample preparation. It can determine moisture, organic matter, total N, and Mg in field‐moist or dry soil and SO 4 –S, Ca, and possibly K in dry soil.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".