Phosphorus Composition of Manure from Swine Fed Low‐Phytate Grains
Bibliographic record
Abstract
Including low-phytic-acid grains in swine diets can reduce P concentrations in manure, but the influence on manure P composition is relatively unknown. To address this we analyzed manure from swine fed one of four barley (Hordeum vulgare L.) varieties. The barley types consisted of wild-type barley (CDC bold, normal barley diet) and three low-phytic-acid mutant barleys that contained similar amounts of total P but less phytic acid. The phytic acid concentrations in the mutant barleys were reduced by 32% (M422), 59% (M635), and 97% (M955) compared with that in the wild-type barley, respectively. Phosphorus concentrations were approximately one-third less in manures from animals fed low-phytic-acid barleys compared with those fed the wild-type variety. Phytic acid constituted up to 55% of the P in feed, but only trace concentrations were detected in NaOH-EDTA extracts of all manures by solution (31)P nuclear magnetic resonance (NMR) spectroscopy. Phosphate was the major P fraction in the manures (86-94% extracted P), with small concentrations of pyrophosphate and simple phosphate monoesters also present. The latter originated mainly from the hydrolysis of phospholipids during extraction and analysis. These results suggest that phytic acid is hydrolyzed in swine, possibly in the hind gut by intestinal microflora before being excreted in feces, even though the animals have little phytase activity in the gut and derive little nutritional benefit from phytate P. We conclude that feeding low-phytic-acid grains reduces total manure P concentrations and the manure P is no more soluble than P generated from normal barley diets.
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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".