Compositional Analysis of Cattle Manure During Composting Using a Field‐Portable Near‐Infrared Spectrometer
Bibliographic record
Abstract
Composting of livestock manure is an effective method for managing the nutrients for agronomic purposes and reducing environmental and human health risks. Capability to analyze the biowastes on‐site at the start of, periodically during, and at the end of composting could facilitate managing the composting process and increase the value of the end products. Near‐infrared spectroscopy (NIRS) is well known for its capability to analyze organic substances rapidly and cost‐effectively. This study was conducted to explore the capability of a field‐portable NIR spectrometer to determine nutrient composition of beef feedlot manure when raw, stockpiled (not turned), and composted (windrowed and turned). Over a 2‐yr period, beef feedlot manure mixed with bedding (wheat straw) was sampled annually at cleanout, after storage for some months in a large stockpile, and from windrows subjected to active thermophilic composting. Samples were dried and ground and scanned with the field‐portable Corona 45 VIS NIR (visible/near‐infrared) spectrometer (Carl Zeiss, Germany) from 360 to 1690 nm. NIRS was found useful in two ways. Classification analysis (Soft Independent Modeling of Class Analogy [SIMCA]) using the spectral data alone showed that stockpiling the manure did not change in composition significantly whereas compost was significantly, different from and less variable than raw or stockpiled manure. Second, by combining spectral and compositional data representing raw, stockpiled, and composted manure for both years, useful calibrations were developed for total C, organic C, total N, C:N, S, K, and pH. These calibrations can be used to rapidly predict these constituents in new samples. The calibration for P may be useful for screening, but those for nitrate+nitrite, available P, and Na were not successful. On the basis of analysis of dried samples, the field‐portable NIR spectrometer was found successful for the rapid determination of C, N, and several other parameters in raw, stockpiled, and composted manure. For application of the technology on‐site, these results need to be confirmed in further studies using moist, “as is” manure and compost samples.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".