Effect of Slow-Release Non-Protein Nitrogen Produced from Agro-Industrial Byproducts on Bio Gas Production, Feed Digestibility and Inoculum Parameters
Bibliographic record
Abstract
Abstract In the recent decades, air and surface water pollution by nitrogen from agro-industrial discards has become a global environmental concern. Generally, these byproducts and or discards are nutrient rich and could inexpensively be utilized for various purposes marginally helping with mitigation strategies. In this context, our study was conducted in two phases: producing lactosylurea from agro-industrial by-products and subsequently evaluating the possibility of using lactosylurea as a non-protein nitrogen source in the ruminant’s diet and its effect on feed digestibility as well as ruminal parameters. Gas production test and in-vitro disappearance method were used to describe the kinetics of digestion in both dry matter (DM) and crude protein (CP) of the four experimental treatments. Protozoa count and total volatile fatty acids concentration were utilized to evaluate the ruminal parameters. The treatments were 1) basal diet + urea (BDU), 2) basal diet+ lactosylurea (BDL), 3) basal diet+ concentrated lactosylurea (BDCL), 4) basal diet+ Optigen (slow release NPN) (BDO). According to our findings, produced gas, DM, and CP disappearance in were significantly higher in concentrated lactosylurea and positive control groups than the other treatments (P<0.05). Moreover, estimated metabolizable energy, digestible organic matter and short chain fatty Acid were significantly higher for the same treatments (P<0.05). The values for protozoa count (2.66×106 organism/ml) and total volatile fatty acids concentration (30.96 mmol/L) were significantly lower and for urea treatment compared with others (P<0.05). In conclusion, lactosylurea as agro-industrial by-products can be a good alternative for urea or Optigen to reduce environmental contamination.
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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) | 0.001 | 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) | 0.001 | 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".